Podcasts about rest apis

Here comes SQL Server 2025! While at Build, Richard chatted with Bob Ward about releasing a preview version of SQL Server 2025. Bob discusses SQL Server 2025 as an AI-ready enterprise database with numerous capabilities specifically tailored to your organization's AI needs, including a new vector data type. This includes making REST API calls to Azure OpenAI, Ollama, or OpenAI. This is also the version of SQL Server designed to integrate with Microsoft Fabric through mirroring. There are many more features, even a new icon!LinksSQL Server 2025 AnnouncementJSON Data TypeOllamaRecorded May 20, 2025

Join Dan Vega and DaShaun Carter for the latest updates from the Spring Ecosystem. In this episode, Dan and DaShaun are joined by Spring team member Dave Syer to explore the official Spring gRPC project. At the end of this episode, you will understand how Spring gRPC provides a Spring-friendly API for developing high-performance gRPC applications, including server and client autoconfiguration, Spring Boot integration, and streamlined Protocol Buffer handling. The hosts will demonstrate how to build gRPC services with Spring's familiar dependency injection patterns and discuss the benefits of gRPC's HTTP/2-based communication over traditional REST APIs. You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform.Show Notes: Dave Syer on GitHubDave Syer on BlueskySpring gRPC

"We're here to take the complexity out of unified communications — and turn it into simplicity." — Todd Remely, Unimax At Cisco Live 2025 in San Diego, Technology Reseller News publisher Doug Green caught up with Todd Remely of Unimax to explore how the company is streamlining unified communications (UC) management for enterprises and partners alike. With over 30 years in business, Unimax is a veteran in the telecom software space. Their tools help organizations manage Cisco, Microsoft Teams, Zoom, and Avaya UC systems more efficiently — and that value was on full display across two booths at Cisco Live: one in the Collaboration Village (highlighting Webex integration) and another on the main show floor. Three Ways Unimax Delivers Simplicity in Complex UC Environments: Second Nature A power-user interface that layers over Cisco and other major UC platforms, enabling administrators to perform complex MACDs (moves, adds, changes, deletes) and configuration tasks like provisioning, routing, and device pool management — all from one streamlined dashboard. Automation Platform Unimax enables full automation of provisioning and deprovisioning processes. Their platform integrates with Active Directory, ServiceNow, Remedy, HRIS systems, and any REST API-enabled tool, drastically reducing time and human error in user onboarding and offboarding. HelpOne A lightweight interface that empowers Tier 1 help desk agents to complete routine MACDs — such as password resets — without telecom expertise, freeing up UC teams to focus on higher-priority work. Remely noted strong engagement from MSPs and resellers, many of whom use Unimax's multi-tenant solutions to manage customer UC systems at scale. "We're solution-focused," he said. "And we love working with end users — because that's where the real problems are, and that's where we can help most." For more information or to request a demo, visit unimax.com or contact the team at tellmemore@unimax.com.

An airhacks.fm conversation with RichardBair (@RichardBair) about: the relaxed nature of JavaOne keynote presentations with James Gosling, the experience of delivering live demos versus pre-recorded content, impressions of the recent JavaOne conference with 70% new attendees, the Hashgraph team including former Sun/Oracle employees like Josh Marinacci and Jasper Potts, explanation of Hedera Hashgraph's consensus service as a message bus system, discussion of a practical enterprise use case for Hashgraph to create immutable release pipelines, storing release stages as messages in a topic, capturing build metadata including dependencies and test results on the blockchain, the ability to run your own mirror node to query data for free, the potential to create a release pipeline listener that triggers actions based on blockchain messages, the advantage of having an immutable audit trail for compliance purposes, the possibility of creating an enterprise gateway that handles payment and provides REST APIs, the difference between consensus nodes and mirror nodes, the benefits of using blockchain for software supply chain verification, the performance capabilities of the system for reading thousands of messages per second RichardBair on twitter: @RichardBair

In this episode, Lois Houston and Nikita Abraham, along with Nick Wagner, focus on GoldenGate's terminology and architectural evolution.   Nick defines source and target systems, which are crucial for data replication, and then moves on to explain the data extraction and replication processes.   He also talks about the new microservices architecture, which replaces the classic architecture, offering benefits like simplified management, enhanced security, and a user-friendly interface. Nick highlights how this architecture facilitates easy upgrades and provides a streamlined experience for administrators.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston: Director of Innovation Programs. Lois: Hi there! Thanks for joining us again as we make our way through Oracle GoldenGate 23ai. Last week, we discussed all the new features introduced in 23ai and today, we'll move on to the terminology, the different processes and what they do, and the architecture of the product at a high level. 00:56 Nikita: Back with us is Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Let's get into some of the terminology. What do we actually call stuff in GoldenGate? Nick: Within GoldenGate, we have our source systems and our target systems. The source is where we're going to be capturing data from, the targets, where we're going to be applying data into. And when we start talking about things like active-active or setting up GoldenGate for high availability, where your source can also be your target, it does become a little bit more complex. And so in some of those cases, we might refer to things as East and West, or America and Europe, or different versions of that. We also have a couple of different things within the product itself. We have what we call our Extract and our Replicat. The Extract is going to be the process that pulls the data out of the database, our capture technology. Our Replicat's going to be the one that applies the data into the target system, or you can also look at it as a push technology. We have what we call our Distribution Path. Our Distribution Path is going to be how we're sending the data across the network. A lot of times when customers run GoldenGate, they don't have the luxury of just having a single server of GoldenGate that can pull data from one database and push data into another one. They need to set up multiple hops of that data. And so in that case, we would use what we call a Distribution Path to send that data from one system to the next. We also have what we call a Target Initiated Path. It's kind of a subset of your Distribution Path, but it allows you to communicate from a less secure environment into a more secure environment.  02:33 Lois: Nick, what about parameter names. I've seen them in uppercase…title case…does that matter?  Nick: GoldenGate has a lot of parameters. This is something you'll see all over the place within GoldenGate itself.  These parameters are in your Extract and Replicat parameter files during your distribution path parameter files. Parameters for GoldenGate are case insensitive.  Within your own environments, you can set it up to have lowercase, mixed case, whatever you want, but just be aware that they are case insensitive. GoldenGate doesn't care, it's just for readability. And then we also have something called trail files. Trail files is where GoldenGate stores all the data before we're able to apply it into that target system. Think about it as our queuing mechanism, and we're queuing everything outside the database so that we're not overloading those database environments. And that's some of the terminology for the product itself. We also have microservices within GoldenGate. 03:31 Nikita: And at the heart of everything is the Service Manager, right? Talk to us about what it is and what it does. Nick: The service manager is responsible for making sure that everything else is up and running. If you are familiar with GoldenGate classic architecture, this is kind of similar to a GoldenGate manager where that process was there to make sure that processes were running the trail files, or excuse me, that certain error logs were getting written out. If a process went down, the manager would restart that process. The service manager is performing a lot of those same functions. Now attached to the service manager, we have our configuration service. This is new in GoldenGate 23ai. This configuration service is going to allow you to set up GoldenGate for highly available environments. So you can build HA into GoldenGate itself using the configuration service. 04:22 Lois: And what does this configuration service do? Nick: This configuration service essentially moves the checkpoint files that used to be on disk into a database so that everything can be stored inside of a database.  Also attached to the service manager, we have the performance metric service. This is a service that is going to be gathering all the performance metrics of GoldenGate. So it's going to tell you how fast things are going, what the latencies are, how many bytes per second we're reading from, the transaction logs or writing to our trail files. How quickly a distribution path is sending data across a network. If you want to know any of your lag information, you'll get it from the performance metrics server. We also have the receiver service and the distribution service. These two work hand in hand to establish network communication between two GoldenGate environments. So on what we call our source system, we have a distribution service that's going to send the data to our target system. On the target system, a receiver service is going to receive that data and then rewrite the trail files. We also have the administration service that's responsible for authentication and authorization of the users, as well as making sure that people have access to the right information. 05:33 Nikita: Ok. Moving on the deployment, how is GoldenGate actually deployed, Nick? Nick: GoldenGate is kinda nice. So the way that the product is installed is you install the GoldenGate environment and that's what we call our service manager deployment under a specific GoldenGate home. So the software binaries themselves get installed under a home, we'll say U01/OGG23AI. Now once I've installed GoldenGate once, that's my OGG home. I can now have any number of service managers and deployments tied to that same home.  06:11 Lois: Ok, let's work with an example to make this simpler. Let's say I've got a service manager that's going be responsible for three different deployments: Accounting, Finance, and Sales. Nick: Each of these deployments is going to reside in its own directory. Each of these deployments is going to have its own set of microservices. And so this also means that each of these deployments can have their own set of users. So the people that access the GoldenGate accounting deployment can be different than the ones that access the sales deployment. This means with this distribution of roles that I can have somebody come in and administer the sales database, but they wouldn't have any information or any access to accounting or finance. And this is very important, it allows you to really pull that information apart and separate it. Each of these environments also has their own set of parameter files, Extract process, Replicat process, distribution services, and everything. So it's a very nice way of splitting things up, but all having them tied to the same GoldenGate home system. And this home is very important. So I can take a deployment, let's say my finance deployment, and if I want to move it to a new GoldenGate home and that GoldenGate home is a different version, like let's say that my original home is 23.4, my new GoldenGate home is 23.7, I simply stop that GoldenGate deployment. I stopped at a finance deployment. I changed its OGG home from 23.4 to 23.7. I restart the deployment, that deployment is automatically upgraded to the new environment and attached to the new system. So it makes upgrading very, very simple, very easy, very elegant. 07:53 Nikita: Ok. So, we've spoken about the services…some of the terminology. Let's get into the architecture next. Nick: So when we talk about the architecture for GoldenGate, we used to have two different architectures. We had a classic architecture and a microservices architecture. Classic architecture was something that's been around since the very beginning of GoldenGate in the late '90s. We announced that, that architecture was deprecated in 19c. And Oracle deprecated means that feature is no longer going to be enhanced and it'll be patched selectively. And at some point in the future, it'll be entirely desupported. Well, GoldenGate 23ai is that future. And so in 23ai, the classic architecture is desupported, that means that it's no longer in the build at all. And so it's just microservices architecture. 08:41 Lois: Is there a tool to assist with this migration?  Nick: We do have a migration utility that will convert an old classic architecture into the new microservices architecture. But there is quite a bit of learning curve to the new microservices architecture. So it's important that we go through how it works in the changes. 09:04 Are you looking to optimize your implementation strategies and improve efficiency? We have a solution for you! Our new Oracle Fusion Cloud Applications Foundations training and certification program. You'll learn to leverage Oracle Modern Best Practice (OMBP) to re-imagine business processes using advanced technologies in Oracle Fusion Cloud Applications such as AI, mobile, analytics, and more. Visit mylearn.oracle.com to get started today.  09:37 Nikita: Welcome back! Nick, what are the benefits of this microservices architecture?  Nick: It's got that simplified lifecycle for patching and upgrading. A lot of the GoldenGate patches that you get, especially these bundle patches, are complete installs as well. So you can go into My Oracle Support and download a complete install of a patch and that way, you don't have to use old patch to apply them. The only time you'll be using old patch is for one-off patches or smaller patches that need to be applied to your GoldenGate system. The microservices product has the same trusted Capture and Apply process that Classic did. There's almost no changes between the two except on how they communicate with their parent processes. And so the same logic that you use to pull data from Oracle or to apply data into Oracle is all the same. 10:25 Lois: And has the interface been upgraded as well? Nick: We've added a really nice, easy to use web interface for the microservices version of GoldenGate. Not only is this web interface work with all your standard browsers, but it's also mobile friendly too. So I can actually control and administer GoldenGate right through my mobile device. It also has new secure remote administration. This is something that the classic architecture was really missing. And so in the classic architecture, to use the command line interface, you had to log into the database server where GoldenGate was installed. Now, the command line interface, as well as the web interface and the REST API, all use remote administration and authentication. So that means that I can install the new command line interface or what we call admin client on my laptop locally and I can connect to any GoldenGate deployment as long as I have the username and password for that deployment. It's also more secure. GoldenGate microservices can also be deployed on premise or in OCI as a service and now also on these third-party clouds like Azure and Google Cloud. And it's also easier for developers to integrate in with the APIs themselves. Everything that GoldenGate does through the admin client as well as the web UI can all be traced. The REST API calls for GoldenGate are all fully published so you can get them right directly from the documentation, you can build your own web interface if you want to. So it makes it very easy. The REST APIs are also streamlined. With a single REST API call, I can do something like add an Extract process, create it, set up my parameter file, and set up the trail files all with a single API command. Whereas in the past, it would require multiple command line interface commands to do that same thing. So it's extremely elegant, very advanced. 12:16 Nikita: What does the microservices architecture look like? I know it's a bit complicated when we're not actually looking at a diagram of it, but just a high level, can you explain the different parts of it? Nick: It's pretty straightforward. But essentially what you've got on each system is a service manager. That service manager is then going to have a number of processes or services beneath it. It'll have the configuration service that stores the checkpoint information for GoldenGate. It'll have the administrative service for the authentication and users, the distribution service to send the data across a network, a receiver service to receive that information, performance metrics to get the performance statistics out of GoldenGate. And then of course, you also have your Extracts and Replicats that capture and apply technology. Each of those Extracts and Replicats will then connect to a database on the Extract side of things. That Extract is going to write to trail files. Those trail files are then going to be sent across the network where they're rebuilt on the target system and the Replicat's going to consume them and apply them into the target database. So the Replicat behaves almost like an end user. So it's taking that trail file data and simply converting it to DML operations, insert, update, delete, or a DDL operation in the case of Oracle, alter table, create table, et cetera, to go into that target database.   13:39 Lois: To look at a diagram of this architecture and learn about it in more detail, check out the Oracle GoldenGate 23ai Fundamentals course on mylearn.oracle.com. So, Nick, if I'm looking to deploy GoldenGate, what should I primarily keep in mind? Nick: So as you go to install GoldenGate and you look at a deployment, there's a couple of important environment variables that you want to make sure you're aware of. So one of the first ones is your OGG_Home. This environment variable is extremely important. This is the location of the GoldenGate software itself. And I want to stress how important it is to always use version numbers when you're setting up your GoldenGate home. When you go to install the software, if you're installing GoldenGate 23.5, use 23.5 within the home directory structure. If you're installing GoldenGate 23.7, use 23.7 inside that directory structure. 14:33 Nikita: Right… that way I'll always know which versions are which, and it'll make it really easy to upgrade and move from one version to the next. Ok, got it. What else, Nick? Nick: There's a couple other important directories. You have your OGG_ETC_HOME. This is where things like the configuration files are going to reside, parameter files, all your certificates for security, including the wallets where we store the credentials for not only the database accounts, but also for the GoldenGate user accounts as well. We have our GoldenGate variable home directory or VAR home. This is where all the GoldenGate log files are residing. And these are the log files that allow you to see what's going on in GoldenGate for auditing purposes. Anytime anybody makes a change to GoldenGate, you're going to see information go into the log files on what was happening and how it was working and what they did, what time they did, what command they issued. Another big important feature about these log files is it also gives you error information and troubleshooting details. So if you ever need to find out what happened in GoldenGate, what went wrong, you would look at these log files to find out that information. And then you also have your OGG_DATA_HOME. This is where those trail files are going to go. Essentially, this is kind of the queuing or overflow for GoldenGate. There's a couple of other additional components. We've got the admin client. This is our command line utility. If you don't want to use a web browser or prefer a command line utility, you can use the admin client. The admin client is also fully scriptable. So if you wanted to write scripts that would go off and automate things in GoldenGate, you can do that. A lot of customers did that with GGSCI in the classic architecture. You can do the same thing now with the admin client. The other component is the microservices security authentication and authorization services. These handle communication security, especially making sure that any passwords or usernames and everything like that is all encrypted. And instead of using an actual username and password, everything through the product is going to be done through an alias. And then it also handles all the authorization authentication, permissions, user accountability, and roles within GoldenGate. 16:39 Lois: Anything else you'd like to talk about before we wrap up for today, Nick? Nick: I also wanted to take a minute to talk about the REST API. All the microservices provide REST APIs to administer them and all of these are fully documented. They can be used by any client that can make REST API calls. So if you wanted to use Python, cURL, a web browser, you can do that as well. They're all just HTTP or HTTPS calls, get, put, patch, the standard REST API standards. And then GoldenGate does provide our admin client as well as a WebUI that use these REST APIs under the covers if you ever wanted to get a more advanced look at how it works. 17:18 Nikita: Well, that's all the time we have for today. Thanks for joining us, Nick.  Lois: Yes, thanks Nick. We look forward to having you back next week to talk with us about security strategies and data recovery. Nikita: And if you want to learn more about the topics we discussed today, head over to mylearn.oracle.com and take a look at the Oracle GoldenGate 23ai Fundamentals course. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off!  17:43 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

React Core team member Dan Abramov joins us to explore "JSX over the wire" and the evolving architecture of React Server Components. We dive into the shift from traditional REST APIs to screen-specific data shaping, the concept of Backend for Frontend (BFF), and why centering UI around the user experience—not server/client boundaries—matters more than ever. Links https://danabra.mov https://github.com/gaearon https://bsky.app/profile/danabra.mov https://overreacted.io https://www.youtube.com/@danabramov Resources JSX Over The Wire: https://overreacted.io/jsx-over-the-wire/ Impossible Components: https://overreacted.io/impossible-components/ What Does "use client" Do?: https://overreacted.io/what-does-use-client-do/ Our Journey With Caching: https://nextjs.org/blog/our-journey-with-caching https://parceljs.org https://nextjs.org/docs/app We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Dan Abramov.

www.iotusecase.com#ENERGIEMANAGEMENT #SMARTCITY #NetZeroIn Episode 167 des IoT Use Case Podcasts spricht Gastgeberin Ing. Madeleine Mickeleit mit Benjamin Schminke, Abteilungsleiter Energieautomation bei der GELSENWASSER AG, und Daniel Will, Channel Manager Smart Building Solutions bei CREM SOLUTIONS, über skalierbares Energiemanagement für kommunale Infrastrukturen. Im Fokus: LoRaWAN-Wasserzähler, Leckageerkennung und KI-gestützte Analysen mit der Plattform Energy.Sie zeigen, wie durch smarte Sensorik, offene Systeme und standardisierte Daten CO₂, Energie und Kosten eingespart werden – praxisnah und regelkonform.Folge 167 auf einen Blick (und Klick):(13:32) Herausforderungen, Potenziale und Status quo – So sieht der Use Case in der Praxis aus(19:44) Lösungen, Angebote und Services – Ein Blick auf die eingesetzten Technologien (30:44) Übertragbarkeit, Skalierung und nächste Schritte – So könnt ihr diesen Use Case nutzen Podcast ZusammenfassungEnergieversorger befinden sich im Wandel – weg vom reinen Lieferanten, hin zum digitalen Enabler für Effizienz, Nachhaltigkeit und neue Geschäftsmodelle.Viele Städte und kommunale Versorger stehen dabei vor der Aufgabe, ihre Gebäude, Liegenschaften und Infrastrukturen datengestützt und klimafreundlich zu betreiben.Manuelle Zählerablesung, fehlende Transparenz über Verbräuche und hoher Aufwand bei der Datenauswertung bremsen Fortschritt und Zielerreichung – vor allem im Hinblick auf die Klimaziele 2035 bzw. 2045.In dieser Podcastfolge zeigen Benjamin Schminke (GELSENWASSER AG) und Daniel Will (CREM SOLUTIONS), wie eine Partnerschaft zwischen Infrastrukturbetreiber und Technologieanbieter genau diese Lücke schließt. Die cloudbasierte Plattform Energy ermöglicht es, Verbrauchsdaten im 15-Minuten-Takt zu erfassen, zu visualisieren und mithilfe von KI gezielt zu analysieren – etwa um ineffiziente Betriebszustände wie dauerhaft laufende Klimaanlagen oder nicht erkannte Leckagen aufzudecken.Die Lösung ist vollständig hardwareagnostisch, nutzt offene Schnittstellen (z. B. REST-API) und lässt sich in bestehende IoT-Infrastrukturen wie LoRaWAN oder NB-IoT integrieren. Über 200.000 digitale Wasserzähler werden aktuell ausgerollt – dazu kommen smarte Sensoren für Wärme, Strom, Gas – sowie erste Projekte zur Grundwasserüberwachung.Kernthesen der Folge:Energiemanagement funktioniert nur mit kontinuierlicher, automatisierter Datenerfassung.KI hilft, relevante Muster zu erkennen und Kapazitäten gezielt einzusetzen.Skalierbare Plattformen ermöglichen Kommunen, regulatorische Vorgaben effizient umzusetzen.Nachhaltigkeit entsteht durch die Umsetzung datenbasierter Erkenntnisse – nicht durch Datensammlung allein.Jetzt reinhören und mehr über skalierbare IoT-Lösungen für den kommunalen Sektor erfahren!-----Relevante Folgenlinks:Madeleine (https://www.linkedin.com/in/madeleine-mickeleit/)Daniel (https://www.linkedin.com/in/daniel-will-b188b2a3/)Benjamin (https://www.linkedin.com/in/benjamin-schminke/)Download Use Case CREM SOLUTIONS + GELSENWASSER AG (https://dialog.crem-solutions.de/gelsenwasser_energy)CREM SOLUTIONS (https://www.crem-solutions.de/unternehmen/)Jetzt IoT Use Case auf LinkedIn folgen

In this episode, Lois Houston and Nikita Abraham continue their deep dive into Oracle GoldenGate 23ai, focusing on its evolution and the extensive features it offers. They are joined once again by Nick Wagner, who provides valuable insights into the product's journey.   Nick talks about the various iterations of Oracle GoldenGate, highlighting the significant advancements from version 12c to the latest 23ai release. The discussion then shifts to the extensive new features in 23ai, including AI-related capabilities, UI enhancements, and database function integration.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   -----------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! Last week, we introduced Oracle GoldenGate and its capabilities, and also spoke about GoldenGate 23ai. In today's episode, we'll talk about the various iterations of Oracle GoldenGate since its inception. And we'll also take a look at some new features and the Oracle GoldenGate product family. 00:57 Lois: And we have Nick Wagner back with us. Nick is a Senior Director of Product Management for GoldenGate at Oracle. Hi Nick! I think the last time we had an Oracle University course was when Oracle GoldenGate 12c was out. I'm sure there's been a lot of advancements since then. Can you walk us through those? Nick: GoldenGate 12.3 introduced the microservices architecture. GoldenGate 18c introduced support for Oracle Autonomous Data Warehouse and Autonomous Transaction Processing Databases. In GoldenGate 19c, we added the ability to do cross endian remote capture for Oracle, making it easier to set up the GoldenGate OCI service to capture from environments like Solaris, Spark, and HP-UX and replicate into the Cloud. Also, GoldenGate 19c introduced a simpler process for upgrades and installation of GoldenGate where we released something called a unified build. This means that when you install GoldenGate for a particular database, you don't need to worry about the database version when you install GoldenGate. Prior to this, you would have to install a version-specific and database-specific version of GoldenGate. So this really simplified that whole process. In GoldenGate 23ai, which is where we are now, this really is a huge release.  02:16 Nikita: Yeah, we covered some of the distributed AI features and high availability environments in our last episode. But can you give us an overview of everything that's in the 23ai release? I know there's a lot to get into but maybe you could highlight just the major ones? Nick: Within the AI and streaming environments, we've got interoperability for database vector types, heterogeneous capture and apply as well. Again, this is not just replication between Oracle-to-Oracle vector or Postgres to Postgres vector, it is heterogeneous just like the rest of GoldenGate. The entire UI has been redesigned and optimized for high speed. And so we have a lot of customers that have dozens and dozens of extracts and replicats and processes running and it was taking a long time for the UI to refresh those and to show what's going on within those systems. So the UI has been optimized to be able to handle those environments much better. We now have the ability to call database functions directly from call map. And so when you do transformation with GoldenGate, we have about 50 or 60 built-in transformation routines for string conversion, arithmetic operation, date manipulation. But we never had the ability to directly call a database function. 03:28 Lois: And now we do? Nick: So now you can actually call that database function, database stored procedure, database package, return a value and that can be used for transformation within GoldenGate. We have integration with identity providers, being able to use token-based authentication and integrate in with things like Azure Active Directory and your other single sign-on for the GoldenGate product itself. Within Oracle 23ai, there's a number of new features. One of those cool features is something called lock-free reservation columns. So this allows you to have a row, a single row within a table and you can identify a column within that row that's like an inventory column. And you can have multiple different users and multiple different transactions all updating that column within that same exact row at that same time. So you no longer have row-level locking for these reservation columns. And it allows you to do things like shopping carts very easily. If I have 500 widgets to sell, I'm going to let any number of transactions come in and subtract from that inventory column. And then once it gets below a certain point, then I'll start enforcing that row-level locking. 04:43 Lois: That's really cool… Nick: The one key thing that I wanted to mention here is that because of the way that the lock-free reservations work, you can have multiple transactions open on the same row. This is only supported for Oracle to Oracle. You need to have that same lock-free reservation data type and availability on that target system if GoldenGate is going to replicate into it. 05:05 Nikita: Are there any new features related to the diagnosability and observability of GoldenGate?  Nick: We've improved the AWR reports in Oracle 23ai. There's now seven sections that are specific to Oracle GoldenGate to allow you to really go in and see exactly what the GoldenGate processes are doing and how they're behaving inside the database itself. And there's a Replication Performance Advisor package inside that database, and that's been integrated into the Web UI as well. So now you can actually get information out of the replication advisor package in Oracle directly from the UI without having to log into the database and try to run any database procedures to get it. We've also added the ability to support a per-PDB Extract.  So in the past, when GoldenGate would run on a multitenant database, a multitenant database in Oracle, all the redo data from any pluggable database gets sent to that one redo stream. And so you would have to configure GoldenGate at the container or root level and it would be able to access anything at any PDB. Now, there's better security and better performance by doing what we call per-PDB Extract. And this means that for a single pluggable database, I can have an extract that runs at that database level that's going to capture information just from that pluggable database. 06:22 Lois And what about non-Oracle environments, Nick? Nick: We've also enhanced the non-Oracle environments as well. For example, in Postgres, we've added support for precise instantiation using Postgres snapshots. This eliminates the need to handle collisions when you're doing Postgres to Postgres replication and initial instantiation. On the GoldenGate for big data side, we've renamed that product more aptly to distributed applications in analytics, which is really what it does, and we've added a whole bunch of new features here too. The ability to move data into Databricks, doing Google Pub/Sub delivery. We now have support for XAG within the GoldenGate for distributed applications and analytics. What that means is that now you can follow all of our MAA best practices for GoldenGate for Oracle, but it also works for the DAA product as well, meaning that if it's running on one node of a cluster and that node fails, it'll restart itself on another node in the cluster. We've also added the ability to deliver data to Redis, Google BigQuery, stage and merge functionality for better performance into the BigQuery product. And then we've added a completely new feature, and this is something called streaming data and apps and we're calling it AsyncAPI and CloudEvent data streaming. It's a long name, but what that means is that we now have the ability to publish changes from a GoldenGate trail file out to end users. And so this allows through the Web UI or through the REST API, you can now come into GoldenGate and through the distributed applications and analytics product, actually set up a subscription to a GoldenGate trail file. And so this allows us to push data into messaging environments, or you can simply subscribe to changes and it doesn't have to be the whole trail file, it can just be a subset. You can specify exactly which tables and you can put filters on that. You can also set up your topologies as well. So, it's a really cool feature that we've added here. 08:26 Nikita: Ok, you've given us a lot of updates about what GoldenGate can support. But can we also get some specifics? Nick: So as far as what we have, on the Oracle Database side, there's a ton of different Oracle databases we support, including the Autonomous Databases and all the different flavors of them, your Oracle Database Appliance, your Base Database Service within OCI, your of course, Standard and Enterprise Edition, as well as all the different flavors of Exadata, are all supported with GoldenGate. This is all for capture and delivery. And this is all versions as well. GoldenGate supports Oracle 23ai and below. We also have a ton of non-Oracle databases in different Cloud stores. On an non-Oracle side, we support everything from application-specific databases like FairCom DB, all the way to more advanced applications like Snowflake, which there's a vast user base for that. We also support a lot of different cloud stores and these again, are non-Oracle, nonrelational systems, or they can be relational databases. We also support a lot of big data platforms and this is part of the distributed applications and analytics side of things where you have the ability to replicate to different Apache environments, different Cloudera environments. We also support a number of open-source systems, including things like Apache Cassandra, MySQL Community Edition, a lot of different Postgres open source databases along with MariaDB. And then we have a bunch of streaming event products, NoSQL data stores, and even Oracle applications that we support. So there's absolutely a ton of different environments that GoldenGate supports. There are additional Oracle databases that we support and this includes the Oracle Metadata Service, as well as Oracle MySQL, including MySQL HeatWave. Oracle also has Oracle NoSQL Spatial and Graph and times 10 products, which again are all supported by GoldenGate. 10:23 Lois: Wow, that's a lot of information! Nick: One of the things that we didn't really cover was the different SaaS applications, which we've got like Cerner, Fusion Cloud, Hospitality, Retail, MICROS, Oracle Transportation, JD Edwards, Siebel, and on and on and on.  And again, because of the nature of GoldenGate, it's heterogeneous. Any source can talk to any target. And so it doesn't have to be, oh, I'm pulling from Oracle Fusion Cloud, that means I have to go to an Oracle Database on the target, not necessarily.  10:51 Lois: So, there's really a massive amount of flexibility built into the system.  11:00 Unlock the power of AI Vector Search with our new course and certification. Get more accurate search results, handle complex datasets easily, and supercharge your data-driven decisions. From now through May 15, 2025, we are waiving the certification exam fee (valued at $245). Visit mylearn.oracle.com to enroll. 11:26 Nikita: Welcome back! Now that we've gone through the base product, what other features or products are in the GoldenGate family itself, Nick? Nick: So we have quite a few. We've kind of touched already on GoldenGate for Oracle databases and non-Oracle databases. We also have something called GoldenGate for Mainframe, which right now is covered under the GoldenGate for non-Oracle, but there is a licensing difference there. So that's something to be aware of. We also have the OCI GoldenGate product. We are announcing and we have announced that OCI GoldenGate will also be made available as part of the Oracle Database@Azure and Oracle Database@ Google Cloud partnerships.  And then you'll be able to use that vendor's cloud credits to actually pay for the OCI GoldenGate product. One of the cool things about this is it will have full feature parity with OCI GoldenGate running in OCI. So all the same features, all the same sources and targets, all the same topologies be able to migrate data in and out of those clouds at will, just like you do with OCI GoldenGate today running in OCI.  We have Oracle GoldenGate Free.  This is a completely free edition of GoldenGate to use. It is limited on the number of platforms that it supports as far as sources and targets and the size of the database.  12:45 Lois: But it's a great way for developers to really experience GoldenGate without worrying about a license, right? What's next, Nick? Nick: We have GoldenGate for Distributed Applications and Analytics, which was formerly called GoldenGate for big data, and that allows us to do all the streaming. That's also where the GoldenGate AsyncAPI integration is done. So in order to publish the GoldenGate trail files or allow people to subscribe to them, it would be covered under the Oracle GoldenGate Distributed Applications and Analytics license. We also have OCI GoldenGate Marketplace, which allows you to run essentially the on-premises version of GoldenGate but within OCI. So a little bit more flexibility there. It also has a hub architecture. So if you need that 99.99% availability, you can get it within the OCI Marketplace environment. We have GoldenGate for Oracle Enterprise Manager Cloud Control, which used to be called Oracle Enterprise Manager. And this allows you to use Enterprise Manager Cloud Control to get all the statistics and details about GoldenGate. So all the reporting information, all the analytics, all the statistics, how fast GoldenGate is replicating, what's the lag, what's the performance of each of the processes, how much data am I sending across a network. All that's available within the plug-in. We also have Oracle GoldenGate Veridata. This is a nice utility and tool that allows you to compare two databases, whether or not GoldenGate is running between them and actually tell you, hey, these two systems are out of sync. And if they are out of sync, it actually allows you to repair the data too. 14:25 Nikita: That's really valuable…. Nick: And it does this comparison without locking the source or the target tables. The other really cool thing about Veridata is it does this while there's data in flight. So let's say that the GoldenGate lag is 15 or 20 seconds and I want to compare this table that has 10 million rows in it. The Veridata product will go out, run its comparison once. Once that comparison is done the first time, it's then going to have a list of rows that are potentially out of sync. Well, some of those rows could have been moved over or could have been modified during that 10 to 15 second window. And so the next time you run Veridata, it's actually going to go through. It's going to check just those rows that were potentially out of sync to see if they're really out of sync or not. And if it comes back and says, hey, out of those potential rows, there's two out of sync, it'll actually produce a script that allows you to resynchronize those systems and repair them. So it's a very cool product.  15:19 Nikita: What about GoldenGate Stream Analytics? I know you mentioned it in the last episode, but in the context of this discussion, can you tell us a little more about it?  Nick: This is the ability to essentially stream data from a GoldenGate trail file, and they do a real time analytics on it. And also things like geofencing or real-time series analysis of it.  15:40 Lois: Could you give us an example of this? Nick: If I'm working in tracking stock market information and stocks, it's not really that important on how much or how far down a stock goes. What's really important is how quickly did that stock rise or how quickly did that stock fall. And that's something that GoldenGate Stream Analytics product can do. Another thing that it's very valuable for is the geofencing. I can have an application on my phone and I can track where the user is based on that application and all that information goes into a database. I can then use the geofencing tool to say that, hey, if one of those users on that app gets within a certain distance of one of my brick-and-mortar stores, I can actually send them a push notification to say, hey, come on in and you can order your favorite drink just by clicking Yes, and we'll have it ready for you. And so there's a lot of things that you can do there to help upsell your customers and to get more revenue just through GoldenGate itself. And then we also have a GoldenGate Migration Utility, which allows customers to migrate from the classic architecture into the microservices architecture. 16:44 Nikita: Thanks Nick for that comprehensive overview.  Lois: In our next episode, we'll have Nick back with us to talk about commonly used terminology and the GoldenGate architecture. And if you want to learn more about what we discussed today, visit mylearn.oracle.com and take a look at the Oracle GoldenGate 23ai Fundamentals course. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 17:10 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Conosciamo Patrick Arminio, Founding Engineer presso FastAPI Labs, creatore di Strawberry e presidente di Python Italia. Iniziamo con una panoramica di GraphQL e REST API. Chiudiamo poi con la nuovissima FastAPI Labs, avventura imprenditoriale di Patrick Arminio e Sebastián Ramírez (tiangolo).

I'm transitioning from SPAs with REST APIs to SSR applications using React Router Framework. While I've used layout routes and tools like SWR/React Query for route protection in React Router DOM, I just found out that actions in React Router Framework are still vulnerable to unauthorized POST requests. I use JWT auth with tokens stored in cookies—do I need to verify the JWT in every action on each route, or is there a global solution like Fastify's onRequest hook? React Router v7.3.0 changelog which introduces middleware support Securing Routes in React Router Framework

Discover how Oracle APEX leverages OCI AI services to build smarter, more efficient applications. Hosts Lois Houston and Nikita Abraham interview APEX experts Chaitanya Koratamaddi, Apoorva Srinivas, and Toufiq Mohammed about how key services like OCI Vision, Oracle Digital Assistant, and Document Understanding integrate with Oracle APEX.   Packed with real-world examples, this episode highlights all the ways you can enhance your APEX apps.   Oracle APEX: Empowering Low Code Apps with AI: https://mylearn.oracle.com/ou/course/oracle-apex-empowering-low-code-apps-with-ai/146047/ Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Hello and welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hi everyone! Last week, we looked at how generative AI powers Oracle APEX and in today's episode, we're going to focus on integrating APEX with OCI AI Services. Lois: That's right, Niki. We're going to look at how you can use Oracle AI services like OCI Vision, Oracle Digital Assistant, Document Understanding, OCI Generative AI, and more to enhance your APEX apps. 01:03 Nikita: And to help us with it all, we've got three amazing experts with us, Chaitanya Koratamaddi, Director of Product Management at Oracle, and senior product managers, Apoorva Srinivas and Toufiq Mohammed. In today's episode, we'll go through each Oracle AI service and look at how it interacts with APEX. Apoorva, let's start with you. Can you explain what the OCI Vision service is? Apoorva: Oracle Cloud Infrastructure Vision is a serverless multi-tenant service accessible using the console or REST APIs. You can upload images to detect and classify objects in them. With prebuilt models available, developers can quickly build image recognition into their applications without machine learning expertise. OCI Vision service provides a fully managed model infrastructure. With complete integration with OCI Data Labeling, you can build custom models easily. OCI Vision service provides pretrained models-- Image Classification, Object Detection, Face Detection, and Text Recognition. You can build custom models for Image Classification and Object Detection. 02:24 Lois: Ok. What about its use cases? How can OCI Vision make APEX apps more powerful? Apoorva: Using OCI Vision, you can make images and videos discoverable and searchable in your APEX app.  You can use OCI Vision to detect and classify objects in the images. OCI Vision also highlights the objects using a red rectangular box. This comes in handy in use cases such as detecting vehicles that have violated the rules in traffic images. You can use OCI Vision to identify visual anomalies in your data. This is a very popular use case where you can detect anomalies in cancer X-ray images to detect cancer. These are some of the most popular use cases of using OCI Vision with your APEX app. But the possibilities are endless and you can use OCI Vision for any of your image analysis. 03:29 Nikita: Let's shift gears to Oracle Digital Assistant. Chaitanya, can you tell us what it's all about? Chaitanya: Oracle Digital Assistant is a low-code conversational AI platform that allows businesses to build and deploy AI assistants. It provides natural language understanding, automatic speech recognition, and text-to-speech capabilities to enable human-like interactions with customers and employees. Oracle Digital Assistant comes with prebuilt templates for you to get started.  04:00 Lois: What are its key features and benefits, Chaitanya? How does it enhance the user experience? Chaitanya: Oracle Digital Assistant provides conversational AI capabilities that include generative AI features, natural language understanding and ML, AI-powered voice, and analytics and insights. Integration with enterprise applications become easier with unified conversational experience, prebuilt chatbots for Oracle Cloud applications, and chatbot architecture frameworks. Oracle Digital Assistant provides advanced conversational design tools, conversational designer, dialogue and domain trainer, and native multilingual support. Oracle Digital Assistant is open, scalable, and secure. It provides multi-channel support, automated bot-to-agent transfer, and integrated authentication profile. 04:56 Nikita: And what about the architecture? What happens at the back end? Chaitanya: Developers assemble digital assistants from one or more skills. Skills can be based on prebuilt skills provided by Oracle or third parties, custom developed, or based on one of the many skill templates available. 05:16 Lois: Chaitanya, what exactly are “skills” within the Oracle Digital Assistant framework?  Chaitanya: Skills are individual chatbots that are designed to interact with users and fulfill specific type of tasks. Each skill helps a user complete a task through a combination of text messages and simple UI elements like select list. When a user request is submitted through a channel, the Digital Assistant routes the user's request to the most appropriate skill to satisfy the user's request. Skills can combine multilingual NLP deep learning engine, a powerful dialogflow engine, and integration components to connect to back-end systems.  Skills provide a modular way to build your chatbot functionality. Now users connect with a chatbot through channels such as Facebook, Microsoft Teams, or in our case, Oracle APEX chatbot, which is embedded into an APEX application. 06:21 Nikita: That's fascinating. So, what are some use cases of Oracle Digital Assistant in APEX apps? Chaitanya: Digital assistants streamline approval processes by collecting information, routing requests, and providing status updates. Digital assistants offer instant access to information and documentation, answering common questions and guiding users. Digital assistants assist sales teams by automating tasks, responding to inquiries, and guiding prospects through the sales funnel. Digital assistants facilitate procurement by managing orders, tracking deliveries, and handling supplier communication. Digital assistants simplify expense approvals by collecting reports, validating receipts, and routing them for managerial approval. Digital assistants manage inventory by tracking stock levels, reordering supplies, and providing real-time inventory updates. Digital assistants have become a common UX feature in any enterprise application. 07:28 Want to learn how to design stunning, responsive enterprise applications directly from your browser with minimal coding? The new Oracle APEX Developer Professional learning path and certification enables you to leverage AI-assisted development, including generative AI and Database 23ai, to build secure, scalable web and mobile applications with advanced AI-powered features. From now through May 15, 2025, we're waiving the certification exam fee (valued at $245). So, what are you waiting for? Visit mylearn.oracle.com to get started today. 08:09 Nikita: Welcome back! Thanks for that, Chaitanya. Toufiq, let's talk about the OCI Document Understanding service. What is it? Toufiq: Using this service, you can upload documents to extract text, tables, and other key data. This means the service can automatically identify and extract relevant information from various types of documents, such as invoices, receipts, contracts, etc. The service is serverless and multitenant, which means you don't need to manage any servers or infrastructure. You can access this service using the console, REST APIs, SDK, or CLI, giving you multiple ways to integrate. 08:55 Nikita: What do we use for APEX apps?  Toufiq: For APEX applications, we will be using REST APIs to integrate the service. Additionally, you can process individual files or batches of documents using the ProcessorJob API endpoint. This flexibility allows you to handle different volumes of documents efficiently, whether you need to process a single document or thousands at once. With these capabilities, the OCI Document Understanding service can significantly streamline your document processing tasks, saving time and reducing the potential for manual errors. 09:36 Lois: Ok.  What are the different types of models available? How do they cater to various business needs? Toufiq: Let us start with pre-trained models. These are ready-to-use models that come right out of the box, offering a range of functionalities. The available models are Optical Character Recognition (OCR) enables the service to extract text from documents, allowing you to digitize, scan the documents effortlessly. You can precisely extract text content from documents. Key-value extraction, useful in streamlining tasks like invoice processing. Table extraction can intelligently extract tabular data from documents. Document classification automatically categorizes documents based on their content. OCR PDF enables seamless extraction of text from PDF files. Now, what if your business needs go beyond these pre-trained models. That's where custom models come into play. You have the flexibility to train and build your own models on top of these foundational pre-trained models. Models available for training are key value extraction and document classification. 10:50 Nikita: What does the architecture look like for OCI Document Understanding? Toufiq: You can ingest or supply the input file in two different ways. You can upload the file to an OCI Object Storage location. And in your request, you can point the Document Understanding service to pick the file from this Object Storage location.  Alternatively, you can upload a file directly from your computer. Once the file is uploaded, the Document Understanding service can process the file and extract key information using the pre-trained models. You can also customize models to tailor the extraction to your data or use case. After processing the file, the Document Understanding service stores the results in JSON format in the Object Storage output bucket. Your Oracle APEX application can then read the JSON file from the Object Storage output location, parse the JSON, and store useful information at local table or display it on the screen to the end user. 11:52 Lois: And what about use cases? How are various industries using this service? Toufiq: In financial services, you can utilize Document Understanding to extract data from financial statements, classify and categorize transactions, identify and extract payment details, streamline tax document management. Under manufacturing, you can perform text extraction from shipping labels and bill of lading documents, extract data from production reports, identify and extract vendor details. In the healthcare industry, you can automatically process medical claims, extract patient information from forms, classify and categorize medical records, identify and extract diagnostic codes. This is not an exhaustive list, but provides insights into some industry-specific use cases for Document Understanding. 12:50 Nikita: Toufiq, let's switch to the big topic everyone's excited about—the OCI Generative AI Service. What exactly is it? Toufiq: OCI Generative AI is a fully managed service that provides a set of state of the art, customizable large language models that cover a wide range of use cases. It provides enterprise grade generative AI with data governance and security, which means only you have access to your data and custom-trained models. OCI Generative AI provides pre-trained out-of-the-box LLMs for text generation, summarization, and text embedding. OCI Generative AI also provides necessary tools and infrastructure to define models with your own business knowledge. 13:37 Lois: Generally speaking, how is OCI Generative AI useful?  Toufiq: It supports various large language models. New models available from Meta and Cohere include Llama2 developed by Meta, and Cohere's Command model, their flagship text generation model. Additionally, Cohere offers the Summarize model, which provides high-quality summaries, accurately capturing essential information from documents, and the Embed model, converting text to vector embeddings representation. OCI Generative AI also offers dedicated AI clusters, enabling you to host foundational models on private GPUs. It integrates LangChain and open-source framework for developing new interfaces for generative AI applications powered by language models. Moreover, OCI Generative AI facilitates generative AI operations, providing content moderation controls, zero downtime endpoint model swaps, and endpoint deactivation and activation capabilities. For each model endpoint, OCI Generative AI captures a series of analytics, including call statistics, tokens processed, and error counts. 14:58 Nikita: What about the architecture? How does it handle user input? Toufiq: Users can input natural language, input/output examples, and instructions. The LLM analyzes the text and can generate, summarize, transform, extract information, or classify text according to the user's request. The response is sent back to the user in the specified format, which can include raw text or formatting like bullets and numbering, etc. 15:30 Lois: Can you share some practical use cases for generative AI in APEX apps?  Toufiq: Some of the OCI generative AI use cases for your Oracle APEX apps include text summarization. Generative AI can quickly summarize lengthy documents such as articles, transcripts, doctor's notes, and internal documents. Businesses can utilize generative AI to draft marketing copy, emails, blog posts, and product descriptions efficiently. Generative AI-powered chatbots are capable of brainstorming, problem solving, and answering questions. With generative AI, content can be rewritten in different styles or languages. This is particularly useful for localization efforts and catering to diverse audience. Generative AI can classify intent in customer chat logs, support tickets, and more. This helps businesses understand customer needs better and provide tailored responses and solutions. By searching call transcripts, internal knowledge sources, Generative AI enables businesses to efficiently answer user queries. This enhances information retrieval and decision-making processes. 16:47 Lois: Before we let you go, can you explain what Select AI is? How is it different from the other AI services? Toufiq: Select AI is a feature of Autonomous Database. This is where Select AI differs from the other AI services. Be it OCI Vision, Document Understanding, or OCI Generative AI, these are all freely managed standalone services on Oracle Cloud, accessible via REST APIs. Whereas Select AI is a feature available in Autonomous Database. That means to use Select AI, you need Autonomous Database.  17:26 Nikita: And what can developers do with Select AI? Toufiq: Traditionally, SQL is the language used to query the data in the database. With Select AI, you can talk to the database and get insights from the data in the database using human language. At the very basic, what Select AI does is it generates SQL queries using natural language, like an NL2SQL capability.  17:52 Nikita: How does it actually do that? Toufiq: When a user asks a question, the first step Select AI does is look into the AI profile, which you, as a developer, define. The AI profile holds crucial information, such as table names, the LLM provider, and the credentials needed to authenticate with the LLM service. Next, Select AI constructs a prompt. This prompt includes information from the AI profile and the user's question.  Essentially, it's a packet of information containing everything the LLM service needs to generate SQL. The next step is generating SQL using LLM. The prompt prepared by Select AI is sent to the available LLM services via REST. Which LLM to use is configured in the AI profile. The supported providers are OpenAI, Cohere, Azure OpenAI, and OCI Generative AI. Once the SQL is generated by the LLM service, it is returned to the application. The app can then handle the SQL query in various ways, such as displaying the SQL results in a report format or as charts, etc.  19:05 Lois: This has been an incredible discussion! Thank you, Chaitanya, Apoorva, and Toufiq, for walking us through all of these amazing AI tools. If you're ready to dive deeper, visit mylearn.oracle.com and search for the Oracle APEX: Empowering Low Code Apps with AI course. You'll find step-by-step guides and demos for everything we covered today.  Nikita: Until next week, this is Nikita Abraham… Lois: And Lois Houston signing off! 19:31 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.  

Hey everyone, Alex here

Episode 36 "Telum Like It Is"What's Newz/OS dependencies moved from Semeru 17 to 21. EOS for Semeru 11 is 30 Nov 2025.Manual of the Moment: z/OS 3.1 Planning for Installation refresh, Software Dependencies sectionMainframe - Position z/OS for IBM z17z/OS V2.4 and higher is supported on IBM z17. Use SMP/E FIXCATs to find the necessary service.z/OS Sysplex requirements for coexistence is z15, z16, and z17.Exploitation for z17 in z/OS includes: Workload level sustainability and power consumption, Workfload classification pricing, new CL6 link technology, BCPii and HMC hardened security, CF Level 26, and more.Mainframe Also - Preview of z/OS 3.2z/OS 3.2 will IPL on z15 and higher servers. Planned GA is 3Q2025.Some new functions previewed, but stay tuned for further announcements with more complete functions.Highlights: new REST APIs for doing installation of service updates, support for direct encryption to tape, new z/OSMF Storage Management interface, RACF certificate support for multiple altnames, and more.Performance - IBM z17 highlightsTelum 2 processor has the 2nd generation on-chip AI accelerator, with 8-bit integer (INT8) and 16-bit floating point (FP16) datatypes for more thruput.5nm (from 7nm), with clock speeed of 5.5 GHz (from 5.2).DPU has 4 port cards (from 2), with energy consumption reduction. DPU accessible from other chips and DCMs.1 to 4 processor drawers. 4-drawer models are Max183 and Max 208.Memory 64 TB (from 40).Measurements with new Channel Measurement Groups 4 and 5, with SMF 73 support.TopicsMartin and Marna discuss how they prepared materials for IBM z17, with the Upgrade Workflow and Redbooks.On the blogMarna's blogMartin's blogsSo It Goes

If you're in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!If you're not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!We are SO excited to share our conversation with Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai.A particularly compelling concept we discussed is the idea of "hybrid teams" - the next evolution in workplace organization where human workers collaborate with AI agents as team members. Just as we previously saw hybrid teams emerge in terms of full-time vs. contract workers, or in-office vs. remote workers, Dharmesh predicts that the next frontier will be teams composed of both human and AI members. This raises interesting questions about team dynamics, trust, and how to effectively delegate tasks between human and AI team members.The discussion of business models in AI reveals an important distinction between Work as a Service (WaaS) and Results as a Service (RaaS), something Dharmesh has written extensively about. While RaaS has gained popularity, particularly in customer support applications where outcomes are easily measurable, Dharmesh argues that this model may be over-indexed. Not all AI applications have clearly definable outcomes or consistent economic value per transaction, making WaaS more appropriate in many cases. This insight is particularly relevant for businesses considering how to monetize AI capabilities.The technical challenges of implementing effective agent systems are also explored, particularly around memory and authentication. Shah emphasizes the importance of cross-agent memory sharing and the need for more granular control over data access. He envisions a future where users can selectively share parts of their data with different agents, similar to how OAuth works but with much finer control. This points to significant opportunities in developing infrastructure for secure and efficient agent-to-agent communication and data sharing.Other highlights from our conversation* The Evolution of AI-Powered Agents – Exploring how AI agents have evolved from simple chatbots to sophisticated multi-agent systems, and the role of MCPs in enabling that.* Hybrid Digital Teams and the Future of Work – How AI agents are becoming teammates rather than just tools, and what this means for business operations and knowledge work.* Memory in AI Agents – The importance of persistent memory in AI systems and how shared memory across agents could enhance collaboration and efficiency.* Business Models for AI Agents – Exploring the shift from software as a service (SaaS) to work as a service (WaaS) and results as a service (RaaS), and what this means for monetization.* The Role of Standards Like MCP – Why MCP has been widely adopted and how it enables agent collaboration, tool use, and discovery.* The Future of AI Code Generation and Software Engineering – How AI-assisted coding is changing the role of software engineers and what skills will matter most in the future.* Domain Investing and Efficient Markets – Dharmesh's approach to domain investing and how inefficiencies in digital asset markets create business opportunities.* The Philosophy of Saying No – Lessons from "Sorry, You Must Pass" and how prioritization leads to greater productivity and focus.Timestamps* 00:00 Introduction and Guest Welcome* 02:29 Dharmesh Shah's Journey into AI* 05:22 Defining AI Agents* 06:45 The Evolution and Future of AI Agents* 13:53 Graph Theory and Knowledge Representation* 20:02 Engineering Practices and Overengineering* 25:57 The Role of Junior Engineers in the AI Era* 28:20 Multi-Agent Systems and MCP Standards* 35:55 LinkedIn's Legal Battles and Data Scraping* 37:32 The Future of AI and Hybrid Teams* 39:19 Building Agent AI: A Professional Network for Agents* 40:43 Challenges and Innovations in Agent AI* 45:02 The Evolution of UI in AI Systems* 01:00:25 Business Models: Work as a Service vs. Results as a Service* 01:09:17 The Future Value of Engineers* 01:09:51 Exploring the Role of Agents* 01:10:28 The Importance of Memory in AI* 01:11:02 Challenges and Opportunities in AI Memory* 01:12:41 Selective Memory and Privacy Concerns* 01:13:27 The Evolution of AI Tools and Platforms* 01:18:23 Domain Names and AI Projects* 01:32:08 Balancing Work and Personal Life* 01:35:52 Final Thoughts and ReflectionsTranscriptAlessio [00:00:04]: Hey everyone, welcome back to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Small AI.swyx [00:00:12]: Hello, and today we're super excited to have Dharmesh Shah to join us. I guess your relevant title here is founder of Agent AI.Dharmesh [00:00:20]: Yeah, that's true for this. Yeah, creator of Agent.ai and co-founder of HubSpot.swyx [00:00:25]: Co-founder of HubSpot, which I followed for many years, I think 18 years now, gonna be 19 soon. And you caught, you know, people can catch up on your HubSpot story elsewhere. I should also thank Sean Puri, who I've chatted with back and forth, who's been, I guess, getting me in touch with your people. But also, I think like, just giving us a lot of context, because obviously, My First Million joined you guys, and they've been chatting with you guys a lot. So for the business side, we can talk about that, but I kind of wanted to engage your CTO, agent, engineer side of things. So how did you get agent religion?Dharmesh [00:01:00]: Let's see. So I've been working, I'll take like a half step back, a decade or so ago, even though actually more than that. So even before HubSpot, the company I was contemplating that I had named for was called Ingenisoft. And the idea behind Ingenisoft was a natural language interface to business software. Now realize this is 20 years ago, so that was a hard thing to do. But the actual use case that I had in mind was, you know, we had data sitting in business systems like a CRM or something like that. And my kind of what I thought clever at the time. Oh, what if we used email as the kind of interface to get to business software? And the motivation for using email is that it automatically works when you're offline. So imagine I'm getting on a plane or I'm on a plane. There was no internet on planes back then. It's like, oh, I'm going through business cards from an event I went to. I can just type things into an email just to have them all in the backlog. When it reconnects, it sends those emails to a processor that basically kind of parses effectively the commands and updates the software, sends you the file, whatever it is. And there was a handful of commands. I was a little bit ahead of the times in terms of what was actually possible. And I reattempted this natural language thing with a product called ChatSpot that I did back 20...swyx [00:02:12]: Yeah, this is your first post-ChatGPT project.Dharmesh [00:02:14]: I saw it come out. Yeah. And so I've always been kind of fascinated by this natural language interface to software. Because, you know, as software developers, myself included, we've always said, oh, we build intuitive, easy-to-use applications. And it's not intuitive at all, right? Because what we're doing is... We're taking the mental model that's in our head of what we're trying to accomplish with said piece of software and translating that into a series of touches and swipes and clicks and things like that. And there's nothing natural or intuitive about it. And so natural language interfaces, for the first time, you know, whatever the thought is you have in your head and expressed in whatever language that you normally use to talk to yourself in your head, you can just sort of emit that and have software do something. And I thought that was kind of a breakthrough, which it has been. And it's gone. So that's where I first started getting into the journey. I started because now it actually works, right? So once we got ChatGPT and you can take, even with a few-shot example, convert something into structured, even back in the ChatGP 3.5 days, it did a decent job in a few-shot example, convert something to structured text if you knew what kinds of intents you were going to have. And so that happened. And that ultimately became a HubSpot project. But then agents intrigued me because I'm like, okay, well, that's the next step here. So chat's great. Love Chat UX. But if we want to do something even more meaningful, it felt like the next kind of advancement is not this kind of, I'm chatting with some software in a kind of a synchronous back and forth model, is that software is going to do things for me in kind of a multi-step way to try and accomplish some goals. So, yeah, that's when I first got started. It's like, okay, what would that look like? Yeah. And I've been obsessed ever since, by the way.Alessio [00:03:55]: Which goes back to your first experience with it, which is like you're offline. Yeah. And you want to do a task. You don't need to do it right now. You just want to queue it up for somebody to do it for you. Yes. As you think about agents, like, let's start at the easy question, which is like, how do you define an agent? Maybe. You mean the hardest question in the universe? Is that what you mean?Dharmesh [00:04:12]: You said you have an irritating take. I do have an irritating take. I think, well, some number of people have been irritated, including within my own team. So I have a very broad definition for agents, which is it's AI-powered software that accomplishes a goal. Period. That's it. And what irritates people about it is like, well, that's so broad as to be completely non-useful. And I understand that. I understand the criticism. But in my mind, if you kind of fast forward months, I guess, in AI years, the implementation of it, and we're already starting to see this, and we'll talk about this, different kinds of agents, right? So I think in addition to having a usable definition, and I like yours, by the way, and we should talk more about that, that you just came out with, the classification of agents actually is also useful, which is, is it autonomous or non-autonomous? Does it have a deterministic workflow? Does it have a non-deterministic workflow? Is it working synchronously? Is it working asynchronously? Then you have the different kind of interaction modes. Is it a chat agent, kind of like a customer support agent would be? You're having this kind of back and forth. Is it a workflow agent that just does a discrete number of steps? So there's all these different flavors of agents. So if I were to draw it in a Venn diagram, I would draw a big circle that says, this is agents, and then I have a bunch of circles, some overlapping, because they're not mutually exclusive. And so I think that's what's interesting, and we're seeing development along a bunch of different paths, right? So if you look at the first implementation of agent frameworks, you look at Baby AGI and AutoGBT, I think it was, not Autogen, that's the Microsoft one. They were way ahead of their time because they assumed this level of reasoning and execution and planning capability that just did not exist, right? So it was an interesting thought experiment, which is what it was. Even the guy that, I'm an investor in Yohei's fund that did Baby AGI. It wasn't ready, but it was a sign of what was to come. And so the question then is, when is it ready? And so lots of people talk about the state of the art when it comes to agents. I'm a pragmatist, so I think of the state of the practical. It's like, okay, well, what can I actually build that has commercial value or solves actually some discrete problem with some baseline of repeatability or verifiability?swyx [00:06:22]: There was a lot, and very, very interesting. I'm not irritated by it at all. Okay. As you know, I take a... There's a lot of anthropological view or linguistics view. And in linguistics, you don't want to be prescriptive. You want to be descriptive. Yeah. So you're a goals guy. That's the key word in your thing. And other people have other definitions that might involve like delegated trust or non-deterministic work, LLM in the loop, all that stuff. The other thing I was thinking about, just the comment on Baby AGI, LGBT. Yeah. In that piece that you just read, I was able to go through our backlog and just kind of track the winter of agents and then the summer now. Yeah. And it's... We can tell the whole story as an oral history, just following that thread. And it's really just like, I think, I tried to explain the why now, right? Like I had, there's better models, of course. There's better tool use with like, they're just more reliable. Yep. Better tools with MCP and all that stuff. And I'm sure you have opinions on that too. Business model shift, which you like a lot. I just heard you talk about RAS with MFM guys. Yep. Cost is dropping a lot. Yep. Inference is getting faster. There's more model diversity. Yep. Yep. I think it's a subtle point. It means that like, you have different models with different perspectives. You don't get stuck in the basin of performance of a single model. Sure. You can just get out of it by just switching models. Yep. Multi-agent research and RL fine tuning. So I just wanted to let you respond to like any of that.Dharmesh [00:07:44]: Yeah. A couple of things. Connecting the dots on the kind of the definition side of it. So we'll get the irritation out of the way completely. I have one more, even more irritating leap on the agent definition thing. So here's the way I think about it. By the way, the kind of word agent, I looked it up, like the English dictionary definition. The old school agent, yeah. Is when you have someone or something that does something on your behalf, like a travel agent or a real estate agent acts on your behalf. It's like proxy, which is a nice kind of general definition. So the other direction I'm sort of headed, and it's going to tie back to tool calling and MCP and things like that, is if you, and I'm not a biologist by any stretch of the imagination, but we have these single-celled organisms, right? Like the simplest possible form of what one would call life. But it's still life. It just happens to be single-celled. And then you can combine cells and then cells become specialized over time. And you have much more sophisticated organisms, you know, kind of further down the spectrum. In my mind, at the most fundamental level, you can almost think of having atomic agents. What is the simplest possible thing that's an agent that can still be called an agent? What is the equivalent of a kind of single-celled organism? And the reason I think that's useful is right now we're headed down the road, which I think is very exciting around tool use, right? That says, okay, the LLMs now can be provided a set of tools that it calls to accomplish whatever it needs to accomplish in the kind of furtherance of whatever goal it's trying to get done. And I'm not overly bothered by it, but if you think about it, if you just squint a little bit and say, well, what if everything was an agent? And what if tools were actually just atomic agents? Because then it's turtles all the way down, right? Then it's like, oh, well, all that's really happening with tool use is that we have a network of agents that know about each other through something like an MMCP and can kind of decompose a particular problem and say, oh, I'm going to delegate this to this set of agents. And why do we need to draw this distinction between tools, which are functions most of the time? And an actual agent. And so I'm going to write this irritating LinkedIn post, you know, proposing this. It's like, okay. And I'm not suggesting we should call even functions, you know, call them agents. But there is a certain amount of elegance that happens when you say, oh, we can just reduce it down to one primitive, which is an agent that you can combine in complicated ways to kind of raise the level of abstraction and accomplish higher order goals. Anyway, that's my answer. I'd say that's a success. Thank you for coming to my TED Talk on agent definitions.Alessio [00:09:54]: How do you define the minimum viable agent? Do you already have a definition for, like, where you draw the line between a cell and an atom? Yeah.Dharmesh [00:10:02]: So in my mind, it has to, at some level, use AI in order for it to—otherwise, it's just software. It's like, you know, we don't need another word for that. And so that's probably where I draw the line. So then the question, you know, the counterargument would be, well, if that's true, then lots of tools themselves are actually not agents because they're just doing a database call or a REST API call or whatever it is they're doing. And that does not necessarily qualify them, which is a fair counterargument. And I accept that. It's like a good argument. I still like to think about—because we'll talk about multi-agent systems, because I think—so we've accepted, which I think is true, lots of people have said it, and you've hopefully combined some of those clips of really smart people saying this is the year of agents, and I completely agree, it is the year of agents. But then shortly after that, it's going to be the year of multi-agent systems or multi-agent networks. I think that's where it's going to be headed next year. Yeah.swyx [00:10:54]: Opening eyes already on that. Yeah. My quick philosophical engagement with you on this. I often think about kind of the other spectrum, the other end of the cell spectrum. So single cell is life, multi-cell is life, and you clump a bunch of cells together in a more complex organism, they become organs, like an eye and a liver or whatever. And then obviously we consider ourselves one life form. There's not like a lot of lives within me. I'm just one life. And now, obviously, I don't think people don't really like to anthropomorphize agents and AI. Yeah. But we are extending our consciousness and our brain and our functionality out into machines. I just saw you were a Bee. Yeah. Which is, you know, it's nice. I have a limitless pendant in my pocket.Dharmesh [00:11:37]: I got one of these boys. Yeah.swyx [00:11:39]: I'm testing it all out. You know, got to be early adopters. But like, we want to extend our personal memory into these things so that we can be good at the things that we're good at. And, you know, machines are good at it. Machines are there. So like, my definition of life is kind of like going outside of my own body now. I don't know if you've ever had like reflections on that. Like how yours. How our self is like actually being distributed outside of you. Yeah.Dharmesh [00:12:01]: I don't fancy myself a philosopher. But you went there. So yeah, I did go there. I'm fascinated by kind of graphs and graph theory and networks and have been for a long, long time. And to me, we're sort of all nodes in this kind of larger thing. It just so happens that we're looking at individual kind of life forms as they exist right now. But so the idea is when you put a podcast out there, there's these little kind of nodes you're putting out there of like, you know, conceptual ideas. Once again, you have varying kind of forms of those little nodes that are up there and are connected in varying and sundry ways. And so I just think of myself as being a node in a massive, massive network. And I'm producing more nodes as I put content or ideas. And, you know, you spend some portion of your life collecting dots, experiences, people, and some portion of your life then connecting dots from the ones that you've collected over time. And I found that really interesting things happen and you really can't know in advance how those dots are necessarily going to connect in the future. And that's, yeah. So that's my philosophical take. That's the, yes, exactly. Coming back.Alessio [00:13:04]: Yep. Do you like graph as an agent? Abstraction? That's been one of the hot topics with LandGraph and Pydantic and all that.Dharmesh [00:13:11]: I do. The thing I'm more interested in terms of use of graphs, and there's lots of work happening on that now, is graph data stores as an alternative in terms of knowledge stores and knowledge graphs. Yeah. Because, you know, so I've been in software now 30 plus years, right? So it's not 10,000 hours. It's like 100,000 hours that I've spent doing this stuff. And so I've grew up with, so back in the day, you know, I started on mainframes. There was a product called IMS from IBM, which is basically an index database, what we'd call like a key value store today. Then we've had relational databases, right? We have tables and columns and foreign key relationships. We all know that. We have document databases like MongoDB, which is sort of a nested structure keyed by a specific index. We have vector stores, vector embedding database. And graphs are interesting for a couple of reasons. One is, so it's not classically structured in a relational way. When you say structured database, to most people, they're thinking tables and columns and in relational database and set theory and all that. Graphs still have structure, but it's not the tables and columns structure. And you could wonder, and people have made this case, that they are a better representation of knowledge for LLMs and for AI generally than other things. So that's kind of thing number one conceptually, and that might be true, I think is possibly true. And the other thing that I really like about that in the context of, you know, I've been in the context of data stores for RAG is, you know, RAG, you say, oh, I have a million documents, I'm going to build the vector embeddings, I'm going to come back with the top X based on the semantic match, and that's fine. All that's very, very useful. But the reality is something gets lost in the chunking process and the, okay, well, those tend, you know, like, you don't really get the whole picture, so to speak, and maybe not even the right set of dimensions on the kind of broader picture. And it makes intuitive sense to me that if we did capture it properly in a graph form, that maybe that feeding into a RAG pipeline will actually yield better results for some use cases, I don't know, but yeah.Alessio [00:15:03]: And do you feel like at the core of it, there's this difference between imperative and declarative programs? Because if you think about HubSpot, it's like, you know, people and graph kind of goes hand in hand, you know, but I think maybe the software before was more like primary foreign key based relationship, versus now the models can traverse through the graph more easily.Dharmesh [00:15:22]: Yes. So I like that representation. There's something. It's just conceptually elegant about graphs and just from the representation of it, they're much more discoverable, you can kind of see it, there's observability to it, versus kind of embeddings, which you can't really do much with as a human. You know, once they're in there, you can't pull stuff back out. But yeah, I like that kind of idea of it. And the other thing that's kind of, because I love graphs, I've been long obsessed with PageRank from back in the early days. And, you know, one of the kind of simplest algorithms in terms of coming up, you know, with a phone, everyone's been exposed to PageRank. And the idea is that, and so I had this other idea for a project, not a company, and I have hundreds of these, called NodeRank, is to be able to take the idea of PageRank and apply it to an arbitrary graph that says, okay, I'm going to define what authority looks like and say, okay, well, that's interesting to me, because then if you say, I'm going to take my knowledge store, and maybe this person that contributed some number of chunks to the graph data store has more authority on this particular use case or prompt that's being submitted than this other one that may, or maybe this one was more. popular, or maybe this one has, whatever it is, there should be a way for us to kind of rank nodes in a graph and sort them in some, some useful way. Yeah.swyx [00:16:34]: So I think that's generally useful for, for anything. I think the, the problem, like, so even though at my conferences, GraphRag is super popular and people are getting knowledge, graph religion, and I will say like, it's getting space, getting traction in two areas, conversation memory, and then also just rag in general, like the, the, the document data. Yeah. It's like a source. Most ML practitioners would say that knowledge graph is kind of like a dirty word. The graph database, people get graph religion, everything's a graph, and then they, they go really hard into it and then they get a, they get a graph that is too complex to navigate. Yes. And so like the, the, the simple way to put it is like you at running HubSpot, you know, the power of graphs, the way that Google has pitched them for many years, but I don't suspect that HubSpot itself uses a knowledge graph. No. Yeah.Dharmesh [00:17:26]: So when is it over engineering? Basically? It's a great question. I don't know. So the question now, like in AI land, right, is the, do we necessarily need to understand? So right now, LLMs for, for the most part are somewhat black boxes, right? We sort of understand how the, you know, the algorithm itself works, but we really don't know what's going on in there and, and how things come out. So if a graph data store is able to produce the outcomes we want, it's like, here's a set of queries I want to be able to submit and then it comes out with useful content. Maybe the underlying data store is as opaque as a vector embeddings or something like that, but maybe it's fine. Maybe we don't necessarily need to understand it to get utility out of it. And so maybe if it's messy, that's okay. Um, that's, it's just another form of lossy compression. Uh, it's just lossy in a way that we just don't completely understand in terms of, because it's going to grow organically. Uh, and it's not structured. It's like, ah, we're just gonna throw a bunch of stuff in there. Let the, the equivalent of the embedding algorithm, whatever they called in graph land. Um, so the one with the best results wins. I think so. Yeah.swyx [00:18:26]: Or is this the practical side of me is like, yeah, it's, if it's useful, we don't necessarilyDharmesh [00:18:30]: need to understand it.swyx [00:18:30]: I have, I mean, I'm happy to push back as long as you want. Uh, it's not practical to evaluate like the 10 different options out there because it takes time. It takes people, it takes, you know, resources, right? Set. That's the first thing. Second thing is your evals are typically on small things and some things only work at scale. Yup. Like graphs. Yup.Dharmesh [00:18:46]: Yup. That's, yeah, no, that's fair. And I think this is one of the challenges in terms of implementation of graph databases is that the most common approach that I've seen developers do, I've done it myself, is that, oh, I've got a Postgres database or a MySQL or whatever. I can represent a graph with a very set of tables with a parent child thing or whatever. And that sort of gives me the ability, uh, why would I need anything more than that? And the answer is, well, if you don't need anything more than that, you don't need anything more than that. But there's a high chance that you're sort of missing out on the actual value that, uh, the graph representation gives you. Which is the ability to traverse the graph, uh, efficiently in ways that kind of going through the, uh, traversal in a relational database form, even though structurally you have the data, practically you're not gonna be able to pull it out in, in useful ways. Uh, so you wouldn't like represent a social graph, uh, in, in using that kind of relational table model. It just wouldn't scale. It wouldn't work.swyx [00:19:36]: Uh, yeah. Uh, I think we want to move on to MCP. Yeah. But I just want to, like, just engineering advice. Yeah. Uh, obviously you've, you've, you've run, uh, you've, you've had to do a lot of projects and run a lot of teams. Do you have a general rule for over-engineering or, you know, engineering ahead of time? You know, like, because people, we know premature engineering is the root of all evil. Yep. But also sometimes you just have to. Yep. When do you do it? Yes.Dharmesh [00:19:59]: It's a great question. This is, uh, a question as old as time almost, which is what's the right and wrong levels of abstraction. That's effectively what, uh, we're answering when we're trying to do engineering. I tend to be a pragmatist, right? So here's the thing. Um, lots of times doing something the right way. Yeah. It's like a marginal increased cost in those cases. Just do it the right way. And this is what makes a, uh, a great engineer or a good engineer better than, uh, a not so great one. It's like, okay, all things being equal. If it's going to take you, you know, roughly close to constant time anyway, might as well do it the right way. Like, so do things well, then the question is, okay, well, am I building a framework as the reusable library? To what degree, uh, what am I anticipating in terms of what's going to need to change in this thing? Uh, you know, along what dimension? And then I think like a business person in some ways, like what's the return on calories, right? So, uh, and you look at, um, energy, the expected value of it's like, okay, here are the five possible things that could happen, uh, try to assign probabilities like, okay, well, if there's a 50% chance that we're going to go down this particular path at some day, like, or one of these five things is going to happen and it costs you 10% more to engineer for that. It's basically, it's something that yields a kind of interest compounding value. Um, as you get closer to the time of, of needing that versus having to take on debt, which is when you under engineer it, you're taking on debt. You're going to have to pay off when you do get to that eventuality where something happens. One thing as a pragmatist, uh, so I would rather under engineer something than over engineer it. If I were going to err on the side of something, and here's the reason is that when you under engineer it, uh, yes, you take on tech debt, uh, but the interest rate is relatively known and payoff is very, very possible, right? Which is, oh, I took a shortcut here as a result of which now this thing that should have taken me a week is now going to take me four weeks. Fine. But if that particular thing that you thought might happen, never actually, you never have that use case transpire or just doesn't, it's like, well, you just save yourself time, right? And that has value because you were able to do other things instead of, uh, kind of slightly over-engineering it away, over-engineering it. But there's no perfect answers in art form in terms of, uh, and yeah, we'll, we'll bring kind of this layers of abstraction back on the code generation conversation, which we'll, uh, I think I have later on, butAlessio [00:22:05]: I was going to ask, we can just jump ahead quickly. Yeah. Like, as you think about vibe coding and all that, how does the. Yeah. Percentage of potential usefulness change when I feel like we over-engineering a lot of times it's like the investment in syntax, it's less about the investment in like arc exacting. Yep. Yeah. How does that change your calculus?Dharmesh [00:22:22]: A couple of things, right? One is, um, so, you know, going back to that kind of ROI or a return on calories, kind of calculus or heuristic you think through, it's like, okay, well, what is it going to cost me to put this layer of abstraction above the code that I'm writing now, uh, in anticipating kind of future needs. If the cost of fixing, uh, or doing under engineering right now. Uh, we'll trend towards zero that says, okay, well, I don't have to get it right right now because even if I get it wrong, I'll run the thing for six hours instead of 60 minutes or whatever. It doesn't really matter, right? Like, because that's going to trend towards zero to be able, the ability to refactor a code. Um, and because we're going to not that long from now, we're going to have, you know, large code bases be able to exist, uh, you know, as, as context, uh, for a code generation or a code refactoring, uh, model. So I think it's going to make it, uh, make the case for under engineering, uh, even stronger. Which is why I take on that cost. You just pay the interest when you get there, it's not, um, just go on with your life vibe coded and, uh, come back when you need to. Yeah.Alessio [00:23:18]: Sometimes I feel like there's no decision-making in some things like, uh, today I built a autosave for like our internal notes platform and I literally just ask them cursor. Can you add autosave? Yeah. I don't know if it's over under engineer. Yep. I just vibe coded it. Yep. And I feel like at some point we're going to get to the point where the models kindDharmesh [00:23:36]: of decide where the right line is, but this is where the, like the, in my mind, the danger is, right? So there's two sides to this. One is the cost of kind of development and coding and things like that stuff that, you know, we talk about. But then like in your example, you know, one of the risks that we have is that because adding a feature, uh, like a save or whatever the feature might be to a product as that price tends towards zero, are we going to be less discriminant about what features we add as a result of making more product products more complicated, which has a negative impact on the user and navigate negative impact on the business. Um, and so that's the thing I worry about if it starts to become too easy, are we going to be. Too promiscuous in our, uh, kind of extension, adding product extensions and things like that. It's like, ah, why not add X, Y, Z or whatever back then it was like, oh, we only have so many engineering hours or story points or however you measure things. Uh, that least kept us in check a little bit. Yeah.Alessio [00:24:22]: And then over engineering, you're like, yeah, it's kind of like you're putting that on yourself. Yeah. Like now it's like the models don't understand that if they add too much complexity, it's going to come back to bite them later. Yep. So they just do whatever they want to do. Yeah. And I'm curious where in the workflow that's going to be, where it's like, Hey, this is like the amount of complexity and over-engineering you can do before you got to ask me if we should actually do it versus like do something else.Dharmesh [00:24:45]: So you know, we've already, let's like, we're leaving this, uh, in the code generation world, this kind of compressed, um, cycle time. Right. It's like, okay, we went from auto-complete, uh, in the GitHub co-pilot to like, oh, finish this particular thing and hit tab to a, oh, I sort of know your file or whatever. I can write out a full function to you to now I can like hold a bunch of the context in my head. Uh, so we can do app generation, which we have now with lovable and bolt and repletage. Yeah. Association and other things. So then the question is, okay, well, where does it naturally go from here? So we're going to generate products. Make sense. We might be able to generate platforms as though I want a platform for ERP that does this, whatever. And that includes the API's includes the product and the UI, and all the things that make for a platform. There's no nothing that says we would stop like, okay, can you generate an entire software company someday? Right. Uh, with the platform and the monetization and the go-to-market and the whatever. And you know, that that's interesting to me in terms of, uh, you know, what, when you take it to almost ludicrous levels. of abstract.swyx [00:25:39]: It's like, okay, turn it to 11. You mentioned vibe coding, so I have to, this is a blog post I haven't written, but I'm kind of exploring it. Is the junior engineer dead?Dharmesh [00:25:49]: I don't think so. I think what will happen is that the junior engineer will be able to, if all they're bringing to the table is the fact that they are a junior engineer, then yes, they're likely dead. But hopefully if they can communicate with carbon-based life forms, they can interact with product, if they're willing to talk to customers, they can take their kind of basic understanding of engineering and how kind of software works. I think that has value. So I have a 14-year-old right now who's taking Python programming class, and some people ask me, it's like, why is he learning coding? And my answer is, is because it's not about the syntax, it's not about the coding. What he's learning is like the fundamental thing of like how things work. And there's value in that. I think there's going to be timeless value in systems thinking and abstractions and what that means. And whether functions manifested as math, which he's going to get exposed to regardless, or there are some core primitives to the universe, I think, that the more you understand them, those are what I would kind of think of as like really large dots in your life that will have a higher gravitational pull and value to them that you'll then be able to. So I want him to collect those dots, and he's not resisting. So it's like, okay, while he's still listening to me, I'm going to have him do things that I think will be useful.swyx [00:26:59]: You know, part of one of the pitches that I evaluated for AI engineer is a term. And the term is that maybe the traditional interview path or career path of software engineer goes away, which is because what's the point of lead code? Yeah. And, you know, it actually matters more that you know how to work with AI and to implement the things that you want. Yep.Dharmesh [00:27:16]: That's one of the like interesting things that's happened with generative AI. You know, you go from machine learning and the models and just that underlying form, which is like true engineering, right? Like the actual, what I call real engineering. I don't think of myself as a real engineer, actually. I'm a developer. But now with generative AI. We call it AI and it's obviously got its roots in machine learning, but it just feels like fundamentally different to me. Like you have the vibe. It's like, okay, well, this is just a whole different approach to software development to so many different things. And so I'm wondering now, it's like an AI engineer is like, if you were like to draw the Venn diagram, it's interesting because the cross between like AI things, generative AI and what the tools are capable of, what the models do, and this whole new kind of body of knowledge that we're still building out, it's still very young, intersected with kind of classic engineering, software engineering. Yeah.swyx [00:28:04]: I just described the overlap as it separates out eventually until it's its own thing, but it's starting out as a software. Yeah.Alessio [00:28:11]: That makes sense. So to close the vibe coding loop, the other big hype now is MCPs. Obviously, I would say Cloud Desktop and Cursor are like the two main drivers of MCP usage. I would say my favorite is the Sentry MCP. I can pull in errors and then you can just put the context in Cursor. How do you think about that abstraction layer? Does it feel... Does it feel almost too magical in a way? Do you think it's like you get enough? Because you don't really see how the server itself is then kind of like repackaging theDharmesh [00:28:41]: information for you? I think MCP as a standard is one of the better things that's happened in the world of AI because a standard needed to exist and absent a standard, there was a set of things that just weren't possible. Now, we can argue whether it's the best possible manifestation of a standard or not. Does it do too much? Does it do too little? I get that, but it's just simple enough to both be useful and unobtrusive. It's understandable and adoptable by mere mortals, right? It's not overly complicated. You know, a reasonable engineer can put a stand up an MCP server relatively easily. The thing that has me excited about it is like, so I'm a big believer in multi-agent systems. And so that's going back to our kind of this idea of an atomic agent. So imagine the MCP server, like obviously it calls tools, but the way I think about it, so I'm working on my current passion project is agent.ai. And we'll talk more about that in a little bit. More about the, I think we should, because I think it's interesting not to promote the project at all, but there's some interesting ideas in there. One of which is around, we're going to need a mechanism for, if agents are going to collaborate and be able to delegate, there's going to need to be some form of discovery and we're going to need some standard way. It's like, okay, well, I just need to know what this thing over here is capable of. We're going to need a registry, which Anthropic's working on. I'm sure others will and have been doing directories of, and there's going to be a standard around that too. How do you build out a directory of MCP servers? I think that's going to unlock so many things just because, and we're already starting to see it. So I think MCP or something like it is going to be the next major unlock because it allows systems that don't know about each other, don't need to, it's that kind of decoupling of like Sentry and whatever tools someone else was building. And it's not just about, you know, Cloud Desktop or things like, even on the client side, I think we're going to see very interesting consumers of MCP, MCP clients versus just the chat body kind of things. Like, you know, Cloud Desktop and Cursor and things like that. But yeah, I'm very excited about MCP in that general direction.swyx [00:30:39]: I think the typical cynical developer take, it's like, we have OpenAPI. Yeah. What's the new thing? I don't know if you have a, do you have a quick MCP versus everything else? Yeah.Dharmesh [00:30:49]: So it's, so I like OpenAPI, right? So just a descriptive thing. It's OpenAPI. OpenAPI. Yes, that's what I meant. So it's basically a self-documenting thing. We can do machine-generated, lots of things from that output. It's a structured definition of an API. I get that, love it. But MCPs sort of are kind of use case specific. They're perfect for exactly what we're trying to use them for around LLMs in terms of discovery. It's like, okay, I don't necessarily need to know kind of all this detail. And so right now we have, we'll talk more about like MCP server implementations, but We will? I think, I don't know. Maybe we won't. At least it's in my head. It's like a back processor. But I do think MCP adds value above OpenAPI. It's, yeah, just because it solves this particular thing. And if we had come to the world, which we have, like, it's like, hey, we already have OpenAPI. It's like, if that were good enough for the universe, the universe would have adopted it already. There's a reason why MCP is taking office because marginally adds something that was missing before and doesn't go too far. And so that's why the kind of rate of adoption, you folks have written about this and talked about it. Yeah, why MCP won. Yeah. And it won because the universe decided that this was useful and maybe it gets supplanted by something else. Yeah. And maybe we discover, oh, maybe OpenAPI was good enough the whole time. I doubt that.swyx [00:32:09]: The meta lesson, this is, I mean, he's an investor in DevTools companies. I work in developer experience at DevRel in DevTools companies. Yep. Everyone wants to own the standard. Yeah. I'm sure you guys have tried to launch your own standards. Actually, it's Houseplant known for a standard, you know, obviously inbound marketing. But is there a standard or protocol that you ever tried to push? No.Dharmesh [00:32:30]: And there's a reason for this. Yeah. Is that? And I don't mean, need to mean, speak for the people of HubSpot, but I personally. You kind of do. I'm not smart enough. That's not the, like, I think I have a. You're smart. Not enough for that. I'm much better off understanding the standards that are out there. And I'm more on the composability side. Let's, like, take the pieces of technology that exist out there, combine them in creative, unique ways. And I like to consume standards. I don't like to, and that's not that I don't like to create them. I just don't think I have the, both the raw wattage or the credibility. It's like, okay, well, who the heck is Dharmesh, and why should we adopt a standard he created?swyx [00:33:07]: Yeah, I mean, there are people who don't monetize standards, like OpenTelemetry is a big standard, and LightStep never capitalized on that.Dharmesh [00:33:15]: So, okay, so if I were to do a standard, there's two things that have been in my head in the past. I was one around, a very, very basic one around, I don't even have the domain, I have a domain for everything, for open marketing. Because the issue we had in HubSpot grew up in the marketing space. There we go. There was no standard around data formats and things like that. It doesn't go anywhere. But the other one, and I did not mean to go here, but I'm going to go here. It's called OpenGraph. I know the term was already taken, but it hasn't been used for like 15 years now for its original purpose. But what I think should exist in the world is right now, our information, all of us, nodes are in the social graph at Meta or the professional graph at LinkedIn. Both of which are actually relatively closed in actually very annoying ways. Like very, very closed, right? Especially LinkedIn. Especially LinkedIn. I personally believe that if it's my data, and if I would get utility out of it being open, I should be able to make my data open or publish it in whatever forms that I choose, as long as I have control over it as opt-in. So the idea is around OpenGraph that says, here's a standard, here's a way to publish it. I should be able to go to OpenGraph.org slash Dharmesh dot JSON and get it back. And it's like, here's your stuff, right? And I can choose along the way and people can write to it and I can prove. And there can be an entire system. And if I were to do that, I would do it as a... Like a public benefit, non-profit-y kind of thing, as this is a contribution to society. I wouldn't try to commercialize that. Have you looked at AdProto? What's that? AdProto.swyx [00:34:43]: It's the protocol behind Blue Sky. Okay. My good friend, Dan Abramov, who was the face of React for many, many years, now works there. And he actually did a talk that I can send you, which basically kind of tries to articulate what you just said. But he does, he loves doing these like really great analogies, which I think you'll like. Like, you know, a lot of our data is behind a handle, behind a domain. Yep. So he's like, all right, what if we flip that? What if it was like our handle and then the domain? Yep. So, and that's really like your data should belong to you. Yep. And I should not have to wait 30 days for my Twitter data to export. Yep.Dharmesh [00:35:19]: you should be able to at least be able to automate it or do like, yes, I should be able to plug it into an agentic thing. Yeah. Yes. I think we're... Because so much of our data is... Locked up. I think the trick here isn't that standard. It is getting the normies to care.swyx [00:35:37]: Yeah. Because normies don't care.Dharmesh [00:35:38]: That's true. But building on that, normies don't care. So, you know, privacy is a really hot topic and an easy word to use, but it's not a binary thing. Like there are use cases where, and we make these choices all the time, that I will trade, not all privacy, but I will trade some privacy for some productivity gain or some benefit to me that says, oh, I don't care about that particular data being online if it gives me this in return, or I don't mind sharing this information with this company.Alessio [00:36:02]: If I'm getting, you know, this in return, but that sort of should be my option. I think now with computer use, you can actually automate some of the exports. Yes. Like something we've been doing internally is like everybody exports their LinkedIn connections. Yep. And then internally, we kind of merge them together to see how we can connect our companies to customers or things like that.Dharmesh [00:36:21]: And not to pick on LinkedIn, but since we're talking about it, but they feel strongly enough on the, you know, do not take LinkedIn data that they will block even browser use kind of things or whatever. They go to great, great lengths, even to see patterns of usage. And it says, oh, there's no way you could have, you know, gotten that particular thing or whatever without, and it's, so it's, there's...swyx [00:36:42]: Wasn't there a Supreme Court case that they lost? Yeah.Dharmesh [00:36:45]: So the one they lost was around someone that was scraping public data that was on the public internet. And that particular company had not signed any terms of service or whatever. It's like, oh, I'm just taking data that's on, there was no, and so that's why they won. But now, you know, the question is around, can LinkedIn... I think they can. Like, when you use, as a user, you use LinkedIn, you are signing up for their terms of service. And if they say, well, this kind of use of your LinkedIn account that violates our terms of service, they can shut your account down, right? They can. And they, yeah, so, you know, we don't need to make this a discussion. By the way, I love the company, don't get me wrong. I'm an avid user of the product. You know, I've got... Yeah, I mean, you've got over a million followers on LinkedIn, I think. Yeah, I do. And I've known people there for a long, long time, right? And I have lots of respect. And I understand even where the mindset originally came from of this kind of members-first approach to, you know, a privacy-first. I sort of get that. But sometimes you sort of have to wonder, it's like, okay, well, that was 15, 20 years ago. There's likely some controlled ways to expose some data on some member's behalf and not just completely be a binary. It's like, no, thou shalt not have the data.swyx [00:37:54]: Well, just pay for sales navigator.Alessio [00:37:57]: Before we move to the next layer of instruction, anything else on MCP you mentioned? Let's move back and then I'll tie it back to MCPs.Dharmesh [00:38:05]: So I think the... Open this with agent. Okay, so I'll start with... Here's my kind of running thesis, is that as AI and agents evolve, which they're doing very, very quickly, we're going to look at them more and more. I don't like to anthropomorphize. We'll talk about why this is not that. Less as just like raw tools and more like teammates. They'll still be software. They should self-disclose as being software. I'm totally cool with that. But I think what's going to happen is that in the same way you might collaborate with a team member on Slack or Teams or whatever you use, you can imagine a series of agents that do specific things just like a team member might do, that you can delegate things to. You can collaborate. You can say, hey, can you take a look at this? Can you proofread that? Can you try this? You can... Whatever it happens to be. So I think it is... I will go so far as to say it's inevitable that we're going to have hybrid teams someday. And what I mean by hybrid teams... So back in the day, hybrid teams were, oh, well, you have some full-time employees and some contractors. Then it was like hybrid teams are some people that are in the office and some that are remote. That's the kind of form of hybrid. The next form of hybrid is like the carbon-based life forms and agents and AI and some form of software. So let's say we temporarily stipulate that I'm right about that over some time horizon that eventually we're going to have these kind of digitally hybrid teams. So if that's true, then the question you sort of ask yourself is that then what needs to exist in order for us to get the full value of that new model? It's like, okay, well... You sort of need to... It's like, okay, well, how do I... If I'm building a digital team, like, how do I... Just in the same way, if I'm interviewing for an engineer or a designer or a PM, whatever, it's like, well, that's why we have professional networks, right? It's like, oh, they have a presence on likely LinkedIn. I can go through that semi-structured, structured form, and I can see the experience of whatever, you know, self-disclosed. But, okay, well, agents are going to need that someday. And so I'm like, okay, well, this seems like a thread that's worth pulling on. That says, okay. So I... So agent.ai is out there. And it's LinkedIn for agents. It's LinkedIn for agents. It's a professional network for agents. And the more I pull on that thread, it's like, okay, well, if that's true, like, what happens, right? It's like, oh, well, they have a profile just like anyone else, just like a human would. It's going to be a graph underneath, just like a professional network would be. It's just that... And you can have its, you know, connections and follows, and agents should be able to post. That's maybe how they do release notes. Like, oh, I have this new version. Whatever they decide to post, it should just be able to... Behave as a node on the network of a professional network. As it turns out, the more I think about that and pull on that thread, the more and more things, like, start to make sense to me. So it may be more than just a pure professional network. So my original thought was, okay, well, it's a professional network and agents as they exist out there, which I think there's going to be more and more of, will kind of exist on this network and have the profile. But then, and this is always dangerous, I'm like, okay, I want to see a world where thousands of agents are out there in order for the... Because those digital employees, the digital workers don't exist yet in any meaningful way. And so then I'm like, oh, can I make that easier for, like... And so I have, as one does, it's like, oh, I'll build a low-code platform for building agents. How hard could that be, right? Like, very hard, as it turns out. But it's been fun. So now, agent.ai has 1.3 million users. 3,000 people have actually, you know, built some variation of an agent, sometimes just for their own personal productivity. About 1,000 of which have been published. And the reason this comes back to MCP for me, so imagine that and other networks, since I know agent.ai. So right now, we have an MCP server for agent.ai that exposes all the internally built agents that we have that do, like, super useful things. Like, you know, I have access to a Twitter API that I can subsidize the cost. And I can say, you know, if you're looking to build something for social media, these kinds of things, with a single API key, and it's all completely free right now, I'm funding it. That's a useful way for it to work. And then we have a developer to say, oh, I have this idea. I don't have to worry about open AI. I don't have to worry about, now, you know, this particular model is better. It has access to all the models with one key. And we proxy it kind of behind the scenes. And then expose it. So then we get this kind of community effect, right? That says, oh, well, someone else may have built an agent to do X. Like, I have an agent right now that I built for myself to do domain valuation for website domains because I'm obsessed with domains, right? And, like, there's no efficient market for domains. There's no Zillow for domains right now that tells you, oh, here are what houses in your neighborhood sold for. It's like, well, why doesn't that exist? We should be able to solve that problem. And, yes, you're still guessing. Fine. There should be some simple heuristic. So I built that. It's like, okay, well, let me go look for past transactions. You say, okay, I'm going to type in agent.ai, agent.com, whatever domain. What's it actually worth? I'm looking at buying it. It can go and say, oh, which is what it does. It's like, I'm going to go look at are there any published domain transactions recently that are similar, either use the same word, same top-level domain, whatever it is. And it comes back with an approximate value, and it comes back with its kind of rationale for why it picked the value and comparable transactions. Oh, by the way, this domain sold for published. Okay. So that agent now, let's say, existed on the web, on agent.ai. Then imagine someone else says, oh, you know, I want to build a brand-building agent for startups and entrepreneurs to come up with names for their startup. Like a common problem, every startup is like, ah, I don't know what to call it. And so they type in five random words that kind of define whatever their startup is. And you can do all manner of things, one of which is like, oh, well, I need to find the domain for it. What are possible choices? Now it's like, okay, well, it would be nice to know if there's an aftermarket price for it, if it's listed for sale. Awesome. Then imagine calling this valuation agent. It's like, okay, well, I want to find where the arbitrage is, where the agent valuation tool says this thing is worth $25,000. It's listed on GoDaddy for $5,000. It's close enough. Let's go do that. Right? And that's a kind of composition use case that in my future state. Thousands of agents on the network, all discoverable through something like MCP. And then you as a developer of agents have access to all these kind of Lego building blocks based on what you're trying to solve. Then you blend in orchestration, which is getting better and better with the reasoning models now. Just describe the problem that you have. Now, the next layer that we're all contending with is that how many tools can you actually give an LLM before the LLM breaks? That number used to be like 15 or 20 before you kind of started to vary dramatically. And so that's the thing I'm thinking about now. It's like, okay, if I want to... If I want to expose 1,000 of these agents to a given LLM, obviously I can't give it all 1,000. Is there some intermediate layer that says, based on your prompt, I'm going to make a best guess at which agents might be able to be helpful for this particular thing? Yeah.Alessio [00:44:37]: Yeah, like RAG for tools. Yep. I did build the Latent Space Researcher on agent.ai. Okay. Nice. Yeah, that seems like, you know, then there's going to be a Latent Space Scheduler. And then once I schedule a research, you know, and you build all of these things. By the way, my apologies for the user experience. You realize I'm an engineer. It's pretty good.swyx [00:44:56]: I think it's a normie-friendly thing. Yeah. That's your magic. HubSpot does the same thing.Alessio [00:45:01]: Yeah, just to like quickly run through it. You can basically create all these different steps. And these steps are like, you know, static versus like variable-driven things. How did you decide between this kind of like low-code-ish versus doing, you know, low-code with code backend versus like not exposing that at all? Any fun design decisions? Yeah. And this is, I think...Dharmesh [00:45:22]: I think lots of people are likely sitting in exactly my position right now, coming through the choosing between deterministic. Like if you're like in a business or building, you know, some sort of agentic thing, do you decide to do a deterministic thing? Or do you go non-deterministic and just let the alum handle it, right, with the reasoning models? The original idea and the reason I took the low-code stepwise, a very deterministic approach. A, the reasoning models did not exist at that time. That's thing number one. Thing number two is if you can get... If you know in your head... If you know in your head what the actual steps are to accomplish whatever goal, why would you leave that to chance? There's no upside. There's literally no upside. Just tell me, like, what steps do you need executed? So right now what I'm playing with... So one thing we haven't talked about yet, and people don't talk about UI and agents. Right now, the primary interaction model... Or they don't talk enough about it. I know some people have. But it's like, okay, so we're used to the chatbot back and forth. Fine. I get that. But I think we're going to move to a blend of... Some of those things are going to be synchronous as they are now. But some are going to be... Some are going to be async. It's just going to put it in a queue, just like... And this goes back to my... Man, I talk fast. But I have this... I only have one other speed. It's even faster. So imagine it's like if you're working... So back to my, oh, we're going to have these hybrid digital teams. Like, you would not go to a co-worker and say, I'm going to ask you to do this thing, and then sit there and wait for them to go do it. Like, that's not how the world works. So it's nice to be able to just, like, hand something off to someone. It's like, okay, well, maybe I expect a response in an hour or a day or something like that.Dharmesh [00:46:52]: In terms of when things need to happen. So the UI around agents. So if you look at the output of agent.ai agents right now, they are the simplest possible manifestation of a UI, right? That says, oh, we have inputs of, like, four different types. Like, we've got a dropdown, we've got multi-select, all the things. It's like back in HTML, the original HTML 1.0 days, right? Like, you're the smallest possible set of primitives for a UI. And it just says, okay, because we need to collect some information from the user, and then we go do steps and do things. And generate some output in HTML or markup are the two primary examples. So the thing I've been asking myself, if I keep going down that path. So people ask me, I get requests all the time. It's like, oh, can you make the UI sort of boring? I need to be able to do this, right? And if I keep pulling on that, it's like, okay, well, now I've built an entire UI builder thing. Where does this end? And so I think the right answer, and this is what I'm going to be backcoding once I get done here, is around injecting a code generation UI generation into, the agent.ai flow, right? As a builder, you're like, okay, I'm going to describe the thing that I want, much like you would do in a vibe coding world. But instead of generating the entire app, it's going to generate the UI that exists at some point in either that deterministic flow or something like that. It says, oh, here's the thing I'm trying to do. Go generate the UI for me. And I can go through some iterations. And what I think of it as a, so it's like, I'm going to generate the code, generate the code, tweak it, go through this kind of prompt style, like we do with vibe coding now. And at some point, I'm going to be happy with it. And I'm going to hit save. And that's going to become the action in that particular step. It's like a caching of the generated code that I can then, like incur any inference time costs. It's just the actual code at that point.Alessio [00:48:29]: Yeah, I invested in a company called E2B, which does code sandbox. And they powered the LM arena web arena. So it's basically the, just like you do LMS, like text to text, they do the same for like UI generation. So if you're asking a model, how do you do it? But yeah, I think that's kind of where.Dharmesh [00:48:45]: That's the thing I'm really fascinated by. So the early LLM, you know, we're understandably, but laughably bad at simple arithmetic, right? That's the thing like my wife, Normies would ask us, like, you call this AI, like it can't, my son would be like, it's just stupid. It can't even do like simple arithmetic. And then like we've discovered over time that, and there's a reason for this, right? It's like, it's a large, there's, you know, the word language is in there for a reason in terms of what it's been trained on. It's not meant to do math, but now it's like, okay, well, the fact that it has access to a Python interpreter that I can actually call at runtime, that solves an entire body of problems that it wasn't trained to do. And it's basically a form of delegation. And so the thought that's kind of rattling around in my head is that that's great. So it's, it's like took the arithmetic problem and took it first. Now, like anything that's solvable through a relatively concrete Python program, it's able to do a bunch of things that I couldn't do before. Can we get to the same place with UI? I don't know what the future of UI looks like in a agentic AI world, but maybe let the LLM handle it, but not in the classic sense. Maybe it generates it on the fly, or maybe we go through some iterations and hit cache or something like that. So it's a little bit more predictable. Uh, I don't know, but yeah.Alessio [00:49:48]: And especially when is the human supposed to intervene? So, especially if you're composing them, most of them should not have a UI because then they're just web hooking to somewhere else. I just want to touch back. I don't know if you have more comments on this.swyx [00:50:01]: I was just going to ask when you, you said you got, you're going to go back to code. What

Check out The Link.AI Consulting at https://agentic.constructionConnect with Hugh on LinkedinHere's a shorter briefing based on the same informationExecutive Summary:Anthropic's Model Context Protocol (MCP), announced in late November 2024, is an open protocol designed to standardize how AI systems interact with external data sources and tools. It aims to overcome the current fragmented landscape of AI integration, where bespoke solutions are often required for each new connection. MCP establishes a universal framework for communication, simplifying development, enhancing AI agent effectiveness through improved context and tool access, and fostering a vibrant ecosystem of AI capabilities. By utilizing a client-server architecture and defining key primitives for data and action exchange, MCP offers a more dynamic and context-aware approach compared to traditional REST APIs. The emergence of MCP registries and marketplaces like smithery.ai further signifies its potential to transform the future of AI by enabling more interconnected, adaptable, and powerful AI systems.Key Themes and Important Ideas/Facts:1. Addressing the Challenges of AI Integration:The current method of integrating AI models with external resources is often complex and requires custom solutions for each connection. "When building AI applications today, each project frequently requires unique, bespoke solutions for how AI processes are constructed and how they connect with necessary data resources." (Introduction)This leads to significant development and maintenance burdens.MCP aims to solve this by providing a universal, open standard for connecting AI systems with data sources and tools. "MCP offers a unified solution to this problem by providing a universal, open standard for connecting AI systems with data sources, effectively replacing these fragmented integrations with a single, consistent protocol." (Introduction)The motivation is to overcome the limitations of isolated AI models "trapped behind information silos and legacy systems." (Introduction, citing source 2)MCP addresses the "MxN problem" by transforming it into an "N plus M setup," where each model and tool only needs to conform to the standard once. "Without a standardized protocol, this results in a complex web of M multiplied by N individual integrations... MCP's approach transforms this into a much simpler N plus M setup, where each tool and each model only needs to conform to the MCP standard once..." (Introduction, citing source 3)By open-sourcing MCP, Anthropic intends to foster collaboration and a shared ecosystem.2. Core Concepts of MCP:Client-Server Architecture: MCP is built on this established pattern. "At its core, the Model Context Protocol (MCP) is built upon a client-server architecture, a well-established design pattern in computing, to facilitate the connection between AI models and external resources." (Core Concepts)Host: The AI-powered application or agent environment the user interacts with (e.g., Claude desktop app, IDE plugin). "The Host is the AI-powered application or agent environment that the end-user directly interacts with." (Core Concepts) It can connect to multiple MCP servers and manages client permissions.Client: An intermediary within the Host that manages the connection to a single MCP server, maintaining a one-to-one link. "The Client acts as an intermediary within the Host, responsible for managing the connection to a single MCP server." (Core Concepts) It handles communication lifecycle and maintains stateful sessions.Server: An external program that implements MCP and provides capabilities (tools, data, prompts) for a specific domain (e.g., databases, cloud services). "The Server is a program, typically external to the AI model itself, that implements the MCP standard and provides a specific set of capabilities." (Core Concepts) Anthropic and the community have released servers for Google Drive, Slack, GitHub, Postgres, SQLite, and web browsing.This architecture is likened to a "USB port" for AI. "This client-server architecture, often likened to a 'USB port' for AI applications, provides a standardized way for AI assistants to 'plug into' any data source or service without requiring custom code for each connection." (Core Concepts, citing source 3)3. MCP vs. REST APIs for AI Agents:Limitations of REST APIs: Require significant manual effort, lack standardized context management, often stateless, static API definitions. "Integrating AI agents with external services via REST APIs often requires significant manual effort and lacks a standardized way to manage the evolving context of agent interactions." (MCP vs. REST APIs for AI Agents)Advantages of MCP:Standardized Communication: Based on JSON-RPC, simplifying integration.Dynamic Tool Discovery: AI can query servers to understand available tools. "AI models equipped with an MCP client can query connected servers to understand the tools and resources they offer." (MCP vs. REST APIs for AI Agents)Two-Way Real-Time Interaction: Supports persistent connections for context updates.Superior Approach Scenarios: Complex workflows with multiple tools, real-time data integration, frequently changing toolsets, intelligent assistants, automated coding tools, dynamic data analytics.4. Enhancing AI Agent Effectiveness:Improved Contextual Awareness and Management: MCP allows agents to access and retain relevant context from multiple sources, overcoming context window limitations. "One of the most significant ways in which the Model Context Protocol enhances the effectiveness of AI agents is by enabling improved contextual awareness and management." (Enhancing AI Agent Effectiveness)The ability to connect to multiple servers simultaneously supports complex workflows.The "Resources" primitive provides just-in-time, modular context, leading to more efficient processing and accurate responses.Facilitating Seamless Integration: MCP eliminates the need for custom code for each new data source or tool. "By providing a standardized interface, MCP eliminates the need for developers to write custom code for each new data source or tool that an AI agent needs to interact with." (Enhancing AI Agent Effectiveness)Pre-built servers for popular systems (Google Drive, Slack, GitHub, databases) streamline integration.Supporting Advanced Reasoning and Decision-Making: The "Tools" primitive allows agents to invoke functions and access real-time data.The "Sampling" primitive enables complex, multi-step reasoning processes (with recommended human approval).Real-World Examples:Corporate chatbots querying multiple internal systems.AI-powered coding assistants (Sourcegraph Cody, Zed Editor) accessing codebases.Anthropic's Claude Desktop accessing local files. "By integrating MCP, Claude can securely access local files, applications, and services on the user's computer." (Enhancing AI Agent Effectiveness)AI2SQL generating SQL from natural language.Apify allowing AI agents to access Apify Actors for automation.5. Driving Adoption for AI Tool Providers:Standardized Integration: Reduces the complexity and costs of developing and maintaining multiple custom integrations. "By providing a single, open standard for connecting AI models with tools, MCP reduces the need for tool providers to develop and maintain multiple custom integrations tailored to different AI platforms." (Driving Adoption for AI Tool Providers)Increased Interoperability: Tools can work with any MCP-compatible AI model, broadening the potential user base and reducing vendor lock-in. "Tools built using the MCP standard can seamlessly work with any AI model that has implemented an MCP client, regardless of the AI provider (e.g., Anthropic, OpenAI) or whether it's an open-source model." (Driving Adoption for AI Tool Providers)Opportunities for Innovation and Specialization: Enables developers to create specialized servers that can be accessed by any MCP client, fostering a division of labor.Benefits for Scalability and Future-Proofing: Ensures integrations remain compatible with future AI models adhering to the standard.6. Real-World Use Cases and Examples of MCP Implementation (Detailed):Coding Assistants: Sourcegraph Cody and Zed Editor.Enterprise Integrations: Block and Apollo. "Companies like Block and Apollo have adopted MCP to securely connect their AI systems with internal data repositories and customer relationship management (CRM) systems." (Real-World Use Cases and Examples of MCP Implementation)Desktop AI Applications: Anthropic's Claude Desktop.Data Querying Tools: AI2SQL.Automation Platforms: Apify.Community-Built Servers: Numerous servers on platforms like Smithery.ai and mcp-get.com for databases, cloud services, etc.7. Future Implications and the Evolving AI Ecosystem:Fostering Interoperability and Standardization: MCP has the potential to become a universal standard for AI integration. "By establishing a universal standard for AI integration, MCP could become the equivalent of HTTP for the web or USB-C for device connectivity in the AI world." (Future Implications and the Evolving AI Ecosystem)Could decouple AI model choice from underlying integrations.Potential Impact on AI R&D and Deployment: May shift focus towards effective utilization of external information over solely increasing model size. Could lead to more modular AI system designs.Addressing Potential Challenges: Requires buy-in from AI providers and tool developers. Security is paramount. Ensuring user trust and human oversight are crucial. "Security is another paramount concern. Allowing AI agents to access and interact with external systems, especially sensitive enterprise data, necessitates robust security measures to prevent unauthorized access or data leaks." (Future Implications and the Evolving AI Ecosystem)Conclusion:MCP offers a promising path towards a more interconnected, context-aware, and effective AI ecosystem. Its standardized framework addresses critical integration challenges, enhances AI agent capabilities, and provides new opportunities for tool providers and the broader AI community. While adoption challenges exist, the potential transformative impact of MCP on the future of AI is significant.

An airhacks.fm conversation with Alvaro Hernandez (@ahachete) about: discussion about stackgres as a complete database cloud solution for PostgreSQL, kubernetes as an abstraction layer over infrastructure providing a programmable API, Stackgres offering high availability with primary and replica nodes using patroni, integrated connection pooling with PgBouncer, kubernetes operators and Custom Resource Definitions (CRDs) as a powerful way to extend Kubernetes, day two operations automated through CRDs including benchmarks and version upgrades, Stackgres supporting sharding with Citus for horizontal scaling similar to DynamoDB, Change Data Capture capabilities using embedded debezium, failover mechanisms taking typically 30 seconds with DNS updates, synchronous vs asynchronous replication options affecting data loss during failover, Stackgres being implemented in Java using quarkus, ContainerD as a programmable container runtime that can be used without Kubernetes, Stackgres offering multiple interfaces including CRDs, REST API, and a web console, considerations for running databases on Kubernetes vs cloud-managed services, the advantages of containerization for infrastructure, the challenges of multi-leader setups in PostgreSQL requiring conflict resolution, the value of Kubernetes for on-premises deployments vs cloud environments Alvaro Hernandez on twitter: @ahachete

In this episode of Quality Matters, host Andy Reynolds is joined by Ed Yurcisin, Chief Technology Officer at NCQA, to break down the complexities of digital transformation in health care quality. Ed explains how NCQA's push for digital measurement cuts through inefficiencies and inconsistencies in assessing quality. Traditionally, HEDIS® quality measures have existed as large, text-heavy PDFs, leaving room for misinterpretation. By digitalizing these measures into computer code—Clinical Quality Language (CQL)—NCQA removes ambiguity and standardizes interpretation. That makes it easier for health care organizations to implement and use quality measures. This shift reduces administrative burden and helps ensure that quality assessments are more accurate and actionable.The conversation then shifts to FHIR® (Fast Healthcare Interoperability Resources), a standard designed to streamline health care data exchange. Ed explains that while FHIR might sound intimidating, it's built on the basic web technologies that power everyday internet browsing. FHIR brings five essential components to the table—JSON files, Rest APIs, standardized value sets, a common data model and government-mandated data exchange. While the government requires organizations to “pitch” data (make data available), there's no mandate to “catch” data (actually use the data). That means organizations that choose to use the data gain a competitive advantage.The discussion ends by focusing on data quality, an issue that looms large over digital transformation efforts. Ed introduces the Bulk FHIR Quality Coalition, a collaborative initiative aimed at improving the reliability of data exchanged between health care providers and insurers. Using the analogy of water through pipes, Ed explains that current data-sharing efforts help ensure flow, but don't always guarantee that data are “clean” enough to be useful. The coalition enhances existing provider–insurer relationships to test and improve large-scale data exchange methods. Ultimately, Ed underscores that digital transformation in health care is only as strong as the quality of the data being exchanged. Standardization, accessibility and interoperability are the foundations of progress, ensuring that technology-driven solutions improve enhance outcomes. Digital HEDIS, FHIR and the Bulk FHIR Quality Coalition are examples of how NCQA is reducing measurement burden to streamline measurement and improve quality. Key Quote:“ The digital transformation of health care is necessary to deliver higher quality care. But that is dependent on high-quality data and the ability to exchange this data.  It starts with high-quality data–making it accessible, interoperable,  exchangeable. That is the foundation for being able to deliver digital health care transformation.  Nothing in digital transformation in health care makes sense without high-quality data exchange.”-Ed YurcisinTime Stamps:(1:03) The How and Why of Digital Measurement(04:14) Understanding FHIR(08:32) From Data Exchange to Competitive Advantage(10:42) The Bulk FHIR Quality CoalitionLinks:Connect with Edward YurcisinNCQA Digital Hub Bulk FHIR Quality Coalition 

In this episode, we discuss using AWS Lambda Powertools for Python to build serverless REST APIs with AWS Lambda. We cover the benefits of using Powertools for routing, validation, OpenAPI support, and more. Powertools provides an excellent framework for building APIs while maintaining Lambda best practices.In this episode, we mentioned the following resources: AWS Bites 41. How can Middy make writing Lambda functions easier? - ⁠https://awsbites.com/41-how-can-middy-make-writing-lambda-functions-easier⁠ AWS Bites 120. Lambda Best Practices - ⁠https://awsbites.com/120-lambda-best-practices/⁠ REST API - Powertools for AWS Lambda (Python) - ⁠https://docs.powertools.aws.dev/lambda/python/latest/core/event_handler/api_gateway/⁠ Hono - ⁠https://hono.dev/⁠ Fastify - ⁠https://fastify.dev/⁠ Axum - ⁠https://github.com/tokio-rs/axum⁠ FastAPI - ⁠https://fastapi.tiangolo.com/⁠Do you have any AWS questions you would like us to address?Leave a comment here or connect with us on BlueSky or LinkedIn: https://bsky.app/profile/eoin.sh | https://www.linkedin.com/in/eoins/ https://bsky.app/profile/loige.co | https://www.linkedin.com/in/lucianomammino/

Trustfall is a library based on a simple question - what happens if we can query absolutely anything? If you could join REST APIs and databases with filesystems and dockerfiles? It's possible in theory because those are all just datasources. Predrag Gruevski is trying to make it easy by building a universal query engine, with pluggable datasources, all in Rust.This week we dive into Trustfall to figure out how it works. How do you model nearly anything as a datasource? How do you make it easy to extend? And what does it take to optimize a query that's going to be spread out over multiple systems and potentially multiple servers? Questions, questions, questions - all about the act of asking our systems questions.

Send us a textWeb Crawler DesignsCan a simple idea like building a web crawler teach you the intricacies of system design? Join me, Ben Kitchell, as we uncover this fascinating intersection. Returning from a brief pause, I'm eager to guide you through the essential building blocks of a web crawler, from queuing seed URLs to parsing new links autonomously. These basic functionalities are your gateway to creating a minimum viable product or acing that system design interview. You'll gain insights into potential extensions like scheduled crawling and page prioritization, ensuring a strong foundation for tackling real-world challenges.Managing a billion URLs a month is no small feat, and scaling such a system requires meticulous planning. We'll break down the daunting numbers into digestible pieces, exploring how to efficiently store six petabytes of data annually. By examining different database models, you'll learn how to handle URLs, track visit timestamps, and keep data searchable. The focus is on creating a robust system that not only scales but does so in a way that meets evolving demands without compromising on performance.Navigating the complexities of designing a web crawler means making critical decisions about data storage and system architecture. We'll weigh the benefits of using cloud storage solutions like AWS S3 and Azure Blob Storage against maintaining dedicated servers. Discover the role of REST APIs in seamless user and service interactions, and explore search functionalities using Cassandra, Amazon Athena, or Google's BigQuery. Flexibility and foresight are key as we build systems that adapt to future needs. Thank you for your continued support—let's keep learning and growing on this exciting system design journey together.Support the showDedicated to the memory of Crystal Rose.Email me at LearnSystemDesignPod@gmail.comJoin the free Discord Consider supporting us on PatreonSpecial thanks to Aimless Orbiter for the wonderful music.Please consider giving us a rating on ITunes or wherever you listen to new episodes.

Joël and Stephanie go back to fundamentals as they pick apart some recent conversations they've been having around the office. Together they discuss the advantages of GraphQL over a REST API, how they utilise JSONB over a regular column or table, and the use-cases for and against a frontend framework like React. But what's the theme that ties all these conversations together? — The article mentioned in this episode was Why I'm over GraphQL (https://bessey.dev/blog/2024/05/24/why-im-over-graphql/) Your hosts for this episode have been thoughtbot's own Stephanie Minn and Joël Quenneville (https://www.linkedin.com/in/joel-quenneville-96b18b58/). If you would like to support the show, head over to our GitHub page (https://github.com/sponsors/thoughtbot), or check out our website (https://bikeshed.thoughtbot.com). Got a question or comment about the show? Why not write to our hosts: hosts@bikeshed.fm This has been a thoughtbot (https://thoughtbot.com/) podcast. Stay up to date by following us on social media - LinkedIn (https://www.linkedin.com/company/150727/) - Mastodon (https://thoughtbot.social/@thoughtbot) - Instagram (https://www.instagram.com/thoughtbot/) © 2024 thoughtbot, inc.

What do you need to start customizing the next generation of Oracle Fusion Apps? How do you create new pages for business processes? What level of expertise do you require for this?   Join Lois Houston and Nikita Abraham as they get answers to all these questions and more from Senior Principal OCI Instructor Joe Greenwald.   Survey: https://customersurveys.oracle.com/ords/surveys/t/oracle-university-gtm/survey?k=focus-group-2-link-share-5   Develop Fusion Applications Using Visual Builder Studio: https://mylearn.oracle.com/ou/course/develop-fusion-applications-using-visual-builder-studio/138392/   Build Visual Applications Using Oracle Visual Builder Studio: https://mylearn.oracle.com/ou/course/build-visual-applications-using-oracle-visual-builder-studio/137749/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started. 00:26 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead of Editorial Services. Nikita: Hi there! You're listening to our Best of 2024 series, where over the next few weeks, we'll be revisiting four of our most popular episodes of the year. Lois: Today's episode is #2 of 4, and we're throwing it back to another episode with our friend and Senior Principal OCI Instructor Joe Greenwald. This episode is all about extending Oracle Cloud Applications that are being built using Visual Builder for the front-end. 01:04 Nikita: Right, Lois. We began by asking Joe to explain what's happening with the redesign and re-architecture of Oracle Cloud Applications using Visual Builder Studio, or VBS.  Joe: That's right, Niki. Oracle is redesigning and rebuilding its entire suite of Fusion Cloud Applications, over 330 different products, utilizing over 60,000 engineers — that is “60,” not “16”—at Oracle to develop the next generation of Oracle Fusion Applications. What's most exciting is that the same tools the engineers are using to accomplish this are available to our partners and our customers to use to extend the functionality and capabilities of Fusion Applications to meet their custom needs and processes.  01:45 Lois: That's pretty awesome! We want to use this time today to ask you about extensions, the types of extensions you can create, and how to use Visual Builder Studio to create those extensions. Nikita: Yeah, can we start with you telling us what an extension is? I've gotten the sense that Oracle uses the term extension as both a noun and a verb and that's a bit confusing to me. Joe: Yeah, good catch, Niki. Yes, Oracle does use the term extension in two ways: both as a noun and a verb. As a noun, an extension is a container for the code changes that you make to your applications. Basically, it's a Git repository that Oracle creates and manages for you. So, the extension container holds the code changes you make to your page layouts: the fields, their positioning, showing and hiding fields, that sort of thing, as well as page functionality. These code changes you make are stored in the extension and it is this extension with your code changes that is merged with the main Git branch eventually and then deployed using continuous integration/continuous deployment jobs defined in Visual Builder Studio, which manages the project and its assets. Your extension is a Git branch that is an asset of the project. Once your extension code is merged with the main branch and deployed, then the next time someone brings up the application, they'll see the changes you've made in the app. 02:59 Lois: And as a verb? Joe: As a verb, extension means to extend the functionality and the look and feel of the application, though I prefer the term customization or configuration to describe this aspect, as the documentation does, and to avoid confusion, though I'll admit I'm not always consistent about the terms I use. 03:16 Lois: What types of customizations, or extensions, and I'm using the verb now, are available for Fusion Apps in Visual Builder Studio? Joe: There are three different ways Fusion Apps can be customized effectively, configured, or extended. The first way is what we call a basic extension, where you're rearranging hiding, or showing, or moving around fields and sections on the page that have been set up to be extendable by the Fusion Application development teams. Things like hiding fields, showing fields, hiding sections, showing sections…  03:45 Nikita: So fairly basic actions… Joe: Yeah exactly and they can be done in Visual Builder Studio Designer by people with minimal VB training, Visual Builder training. And, most recently, if you have access to it, you can do it in the new Express mode, where the page shows you just those things you can work with and just the tools you need to work with the page. This is new and makes it much easier for folks who are not highly technical to make basic changes to the page layout. 04:09 Lois: People like me! That sounds easy enough. Joe: And the next type of extension is more of an intermediate change and requires some training with Visual Builder Studio because you're creating rules that govern the display of layouts based on certain conditions on the page. These are highly flexible, powerful, and useful for creating customized page layouts based on a variety of factors from page size and orientation to the role of the person using it to values in the actual fields on the page itself. These rules can be combined to create complex rule-based conditions that display exactly what the user should see, given the conditions of the page and their role. I would also include making changes to action chains, which execute sequences of behaviors and navigation, and the actual structure of the application, but this is more advanced.  Lastly, is creating mashup applications, which are stand-alone Visual Builder visual applications, which use data from Fusion apps, and customer data sources, like their own database tables, and potentially third-party APIs to create brand new pages and applications with new functionality, new processes, new procedures, new displays, all of which look just like Fusion Applications and use the same data as Fusion applications. 05:18 Lois: Joe, how do I get started if I want to extend a page?  Joe: The easiest way to do it is to open a page in Fusion Applications and then select Edit Page in Visual Builder Studio from the Profile menu. You're then prompted for a project to hold the Git repository for the extension container. And since there's probably already one that exists, after you select the project, an extension Git container is assigned to you. Unless this is the very first time the application has been extended in which case it creates an extension for you. When creating customizations or configurations, we recommend that each application be done in its own separate project. So, for example, if you're working on Customer Experience Sales, you might do it in Project A and if you're working on extensions with HCM, you might do it in Project B. And if you decide to create your own pages and flows in your own app, you might do that in Project C.  06:04 Nikita: But why do you need to do this? Joe: That's just to keep things nice and separate and organized. The tool, Visual Builder Studio, doesn't really care, but it makes for cleaner development and can help with the management of the development teams. 06:14 Nikita: Ok, Joe, I have a question. How do I know if the page I'm on in Fusion Apps can be edited in Visual Builder? I know there are a lot of legacy pages still out there and they can co-exist with the new VB-based pages. Joe: If the URL of the page you're on has the word /Redwood in it instead of /faces, then you know this is a page that was created using Visual Builder Studio and you'll be able to extend it and make changes to it using the Edit in Visual Builder Studio option. So, if you select Edit in Visual Builder Studio, then the page you are on opens inside Visual Builder Studio Designer and you can make changes to any part of the page that has been explicitly enabled for extension by the development team. 06:53 Lois: That's an important part, right? The application is not extendable by default.  Joe: That's right, Lois. It is all locked down and you can't make any changes to it by default. The development team must specifically enable certain parts of the page: sections, fields, layouts, variables, types, action chains, etc. as extendable for you to be able to make changes to it. This ensures the changes the development team makes to the application in the future won't break your extensions. And conversely, the development team can choose to not extend portions that they do not want you to touch or mess with. Then if they do change that bit of the app in the future, it won't break the application and you won't get a big surprise. So, using the Edit page in Visual Builder Studio, you can make both basic changes, like moving, showing, and hiding fields and sections, as well as the more intermediate types of configurations, like using dynamic components to create rule-based layouts that change dynamically based on several conditions such as page size, roles of the user, and field values on the page itself. 07:51 Nikita: What happens if two developers make changes and essentially overwrite each other's customizations — say one hides a field and another later exposes it? Joe: Well, whoever commits their changes and deploys last wins. The other developer's changes get overwritten. So, this is something the team would want to consider carefully. It is possible to roll back to an earlier version if one must. And this can be done in Visual Builder Studio — the part that manages project assets like Git repositories. And there are Oracle blog posts about how to do that if you're interested in learning more. 08:20 Lois: Joe, earlier you mentioned creating new pages and flows, but so far you've only talked about modifying existing extendable pages. How do I create new pages and flows? Joe: In a Visual Builder extension, a set of pages and flows is called an App UI. When I use the terms pages and flows, what I'm talking about is a set of pages that are logically related—whatever logical means to the designer and developer—in a group called a flow that you can navigate between. But you can also navigate between flows and even between applications. So, without getting too technical, each application has a default flow, which has a default page where that flow starts when the app first comes up. So, you can think of an App UI as a collection of flows and their pages, and a URL that accesses the default flow and its default page. That's the page you would see first when accessing that URL. Of course, this can be configured and changed by the developer, as needed. Now, when Oracle creates the original application (for example, digital sales, helpdesk, or something like that), we create an App UI, which contains the pages and flows for that application and is the “entry point” into the app, accessing that App UI's default flow and its default page and then things flow on from there. Partners and customers can create their own application extensions that are dependent on an Oracle application and even create their own App UI – their own sets of pages and flows to accommodate their own processing and workflow needs. This gives them the ability to add their own processes and rules, and still leverage and navigate to the core application that Oracle built. For example, say Oracle delivered digital sales as an Oracle Cloud Application built using Visual Builder to a customer and the customer needs to add a few pages to do some validation or other type of business processing before entering the digital sales application. What the customer does, in this case, is create a new extension of the Oracle Digital Sales app and an App UI of their own, which would be the set of pages and flows that contain the processing they want to start with before then navigating into the digital sales app to use Oracle's application. 10:22 Nikita: Wait, did I hear that correctly? We're creating an extension of an extension or creating an extension on an existing extension? Joe: I know, right? I realize this can sound confusing the first time you hear it or the second time or even the third time. It took me a while to get my head around what they're talking about. Let's start with a Fusion application. In a Fusion application, everything is an extension of something. This is just how the code base and the architecture are organized and how they manage the Git repositories and the code base itself. So, Oracle created a base application called the Unified App. The Unified Application contains the basic page structure and common functionality needed for all applications. For example, it contains the header at the top that has the profile and the footer at the bottom of the page that has that little Ask Oracle icon. Within that page, between the header and the footer, are the pages that are created by the developers, whether they be Oracle engineers or partners or customers. They display the contents of the page with the data and the layouts and all of that. In a sense, you can think of the Unified App as an index page, the starting page of the web application. Though that's not completely true technically, it's good enough for illustrative purposes. So, Oracle starts with the Unified App and then a development team extends that Unified App to build their product. This is how digital sales did it. This is how customer experience did it. This is how helpdesk did it. They start with the Unified App and they extend that and create an App UI that contains the flows and pages for their specific application, and then add functionality for all the pages and flows, as needed for the design. Partners and customers can then create a new extension that extends the Oracle Application and add their own App UI and their own URL if they want their pages accessed first, before navigating to the Oracle application. For example, if the digital sales application has functionality you'd like to leverage, like it has data services or fragments or page layouts that you want to reuse or other things, you extend the digital sales application, and this extension holds your code changes. You could then create a new App UI, and once deployed, users can use that URL for the new App UI to access your new pages. And your page can then navigate to the Oracle app when it needs to. Though I will say to date, we're really not seeing much demand for this particular use case, but it is possible. 12:33 Lois: Is that the only option available to customers and partners—to extend an existing Oracle application? Joe: No, Lois. We're seeing customers and partners create brand new Fusion applications of their own, based on the Unified App Oracle created. In a sense, doing the same thing that our development teams here are doing.  Remember, I said an Oracle development team starts with the Unified App, which has common functionality and look and feel for all applications, and then extends that to add business rules processing, flows, App UI, whatever they need for their specific Oracle application. We're seeing our partners and customers wanting to build their own applications. Maybe a customer or partner wants to create a Time & Expense application and leverage the Fusion application data and the APIs available, but define their own flows, their own pages, their own processing. This is very easy to do. They'd start by extending the Unified App just like the Oracle development teams do, and then build their own App UI and within that, their own flows, pages, and custom processing. The nice thing about it is that the application looks and works and feels just like a Fusion application and it appears alongside other Fusion applications, because it is a Fusion application. 13:43 Did you know that the Oracle University Learning Community regularly holds live events hosted by Oracle expert instructors. Find out how to prepare for your certification exams. Learn about the latest technology advances and features. Ask questions in real time and learn from an Oracle subject matter expert. From Ask Me Anything about certification to Ask the Instructor coaching sessions, you'll be able to achieve your learning goals for 2024 in no time. Join a live event today and witness firsthand the transformative power of the Oracle University Learning Community. Visit mylearn.oracle.com to get started.  14:24 Nikita: Welcome back! So Joe, it sounds like there are two different paths or life cycles to create extensions for future applications in Visual Builder Studio. Is that correct? Joe: Yes, exactly. So one path to extending the functionality of Fusion apps is to edit the page in Visual Builder Studio, which opens the page in Visual Builder Designer, and you then make changes to the existing pages, depending on what the development team has made extendable.  14:49 Nikita: But you can't create new pages and flows in this scenario, right? Joe: This is strictly about modifying an existing page. The other path is creating a new application extension, which is a new application from scratch or extending an existing Oracle application or even an existing partner or customer application. Again, we're not seeing this typically being done too much. Most partners and customers create new applications or make customizations to existing pages. But the architecture does support it. So, your partner might create a new application based on the production app released by Oracle, and you could extend their application. Or a development team at your site could extend Oracle's application and you could then extend that team's application. This is mechanically possible, although I question the use case behind that. Usually, we see our apps being extended – becoming a dependency when there's code that can be leveraged or reused for a new app and its new App UI. 15:40 Lois: Joe, what did you mean when you say one extension is a dependency of another? Can you talk a bit about dependencies, what that means, how it looks to the developer? Joe: When you extend an application, it becomes a dependency to your application, and you get access to all the resources within that dependency that are marked as extendable by the developer who created that extension. Most useful are things like service connections to REST APIs from Fusion apps data sources, reusable code fragments, and layouts that you can leverage in those cases where you want to create a new App UI. When an extension is listed as a dependency, you'll see this graphically in Visual Builder Studio Designer. When you see an extension listed as a dependency, it means you can reference any of that extension's resources that have been marked extendable by the developer. Recall all resources are closed off or hidden by default, but development teams can mark resources as open to being extended and reused, and then you can see and use those resources. So, you can easily add and remove extensions as dependencies in Visual Builder Designer as needed. Now, this can be a nice way to modularize and reuse your resources and assets. To summarize: I can modify an existing page – this is most common, extend an existing application and create a new App UI, which is not common, or I can extend the unified app to create a new app and a new App UI and add other extensions as dependences, as needed, to leverage their services, fragments, and layouts when building my own pages – this is pretty common as well. 17:04 Nikita: There's one thing I'd like to come back to, Joe. You mentioned something called a mashup application earlier. Can you tell us a little more about that? Joe: To recap: I mentioned a couple of different ways that you can extend Fusion applications. One is changing layouts or creating rule-based layouts. You can also extend existing apps and create your own App UI on top of them or create your own Fusion app from scratch. But these are Fusion apps and they have restrictions.  These can only run within the Fusion applications ecosystem, which means they can only be accessed by people who are registered in the Fusion application ecosystem, and there are some other restrictions (for example, in terms of the APIs you can access). And you also have no access to customer data tables. Mashup applications use the stand-alone Visual Builder Cloud Service, which enables you to create custom visual applications. These are visual applications that run outside the Fusion apps ecosystem. Users only need to be identified to the Identity Cloud Service, IDCS, and then they can get access to these mashup apps, depending on the roles and privileges given to them, of course. These mashup applications can access Fusion apps API data, as well as customer database tables, Excel spreadsheet data, CSV files, and third-party APIs. And all this data can appear on the same page, in the same app, using the same Redwood components, so they look and work just like Fusion applications. 18:22 Lois: I know in the past there's been some friction to making changes in Fusion applications. Partner and customer developers use different tools than the ones Oracle engineers use and there have been some deployment issues. To wrap up things, can you tell us why customers should use Visual Builder Studio to customize Fusion apps? Joe: Glad to, Lois. The big benefit to customers is that they are using the exact same tools, Visual Builder Designer for page design work and Visual Builder Studio for project and code management, to build the customizations and extensions that Oracle is using to create the applications and extensions that are delivered to them. I can't emphasize enough how big a deal this is and how wonderful it is for the customer. We're constantly making the Visual Builder Designer interface easier and easier to work with. We're currently releasing a new version of Visual Builder Designer—the Express mode version. This version of Designer is lightweight and has only the necessary features required to allow you to make changes to pages and layouts, and create and manage dynamic rule-based layouts. If you need more (for example, you need to create service connections, fragments, and do a lot more of that type of advanced work), then use the advanced version of the Designer. Both are available to you, assuming that your user has the appropriate permission and the Fusion app you are using has implemented Express Designer. 19:37 Lois: OK Joe, what courses does Oracle University offer for me if I wanted to learn more about developing extensions for Fusion apps and creating mashup apps using Visual Builder Studio? Joe: Oracle University has several courses. We have the Develop Visual Applications Using Visual Builder Studio, which focuses on creating the stand-alone custom bespoke mashup visual applications. We also have our Design and Develop Redwood Applications course, which goes into detail about working with the Redwood page templates and components. All these courses are free and available today. And all you need to do is log in to mylearn.oracle.com to get started. 20:10 Nikita: We hope you enjoyed that conversation. Just a quick reminder before we close about the short survey we've put together to get your thoughts on the podcast. It'll take just a few minutes and will help us make the podcast even better. Just click the link in the show notes to participate. Join us next week for another throwback episode. Until then, this is Nikita Abraham... Lois: And Lois Houston, signing off! 20:33 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. 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The next generation of front-end user interfaces for Oracle Fusion Applications is being built using Visual Builder Studio and Oracle JavaScript Extension Toolkit. However, many of the terms associated with these tools can be confusing.   In this episode, Lois Houston and Nikita Abraham are joined by Senior Principal OCI Instructor Joe Greenwald. Together, they take you through the different terminologies, how they relate to each other, and how they can be used to deliver the new Oracle Fusion Applications as well as stand-alone, bespoke visual web applications.   Survey: https://customersurveys.oracle.com/ords/surveys/t/oracle-university-gtm/survey?k=focus-group-2-link-share-5   Develop Fusion Applications Using Visual Builder Studio: https://mylearn.oracle.com/ou/course/develop-fusion-applications-using-visual-builder-studio/138392/   Build Visual Applications Using Oracle Visual Builder Studio: https://mylearn.oracle.com/ou/course/build-visual-applications-using-oracle-visual-builder-studio/137749/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started. 00:26 Nikita: Hello and welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! If you've been following along with us, you'll know that we've had some really interesting seasons this year. We covered Autonomous Database, Artificial Intelligence, Visual Builder Studio and Redwood, OCI Container Engine for Kubernetes, and Oracle Database 23ai New Features. Nikita: And we've had some pretty awesome special guests. Do go back and check out those episodes if any of those topics interest you. 01:04 Lois: As we close out the year, we thought this would be a good time to revisit some of our best episodes. Over the next few weeks, you'll be able to listen to four of our most popular episodes of the year.  Nikita: Right, this is the best of the best–according to you–our listeners.   Lois: Today's episode is #1 of 4 and is a throwback to a discussion with Senior Principal OCI Instructor Joe Greenwald on Visual Builder Studio. Nikita: We asked Joe about Visual Builder Studio and Oracle JavaScript Extension Toolkit, also known as JET. Together, they form the basis of the technology for the next generation of front-end user interfaces for Oracle Fusion Applications, as well as many other Oracle applications, including most Oracle Cloud Infrastructure (OCI) interfaces. 01:48 Lois: We looked at the different terminologies and technologies, how they relate to each other, and how they deliver the new Oracle Fusion Applications and stand-alone, bespoke visual web applications.  So, let's dive right in. Nikita: Joe, I'm somewhat thrown by the terminology around Visual Builder, Visual Studio, and JET. Can you help streamline that for us? Lois: Yeah, things that are named the same sometimes refer to different things, and sometimes things with a different name refer to the same thing. 02:18 Joe: Yeah, I know where you're coming from. So, let's start with Visual Builder Studio. It's abbreviated as VBS and can go by a number of different names. Some of the most well-known ones are Visual Builder Studio, VBS, Visual Builder, Visual Builder Stand-Alone, and Visual Builder Cloud Service. Clearly, this can be very confusing. For the purposes of these episodes as well as the training courses I create, I use certain definitions.  02:42 Lois: Can you take us through those? Joe: Absolutely, Lois. Visual Builder Studio refers to a product that comes free with an OCI account and allows you to manage your project-related assets. This includes the project itself, which is a container for all of its assets. You can assign teams to your projects, as well as secure the project and declare roles for the different team members. You manage GIT repositories with full graphical and command-line GIT support, define package, build, and deploy jobs, and create and run continuous integration/continuous deployment graphical and code-managed pipelines for your applications. These can be visual applications, created using the Visual Builder Integrated Development Environment, the IDE, or non-visual apps, such as Java microservices, docker builds, NPM apps, and things like that. And you can define environments, which determine where your build jobs can be deployed. You can also define issues, which allow you to identify, track, and manage things like bugs, defects, and enhancements. And these can be tracked in code review merge requests and build jobs, and be mapped to agile sprints and scrum boards. There's also support for wikis for team collaboration, code snippets, and the management of the repository and the project itself. So, VBS supports code reviews before code is merged into GIT branches for package, build, and deploy jobs using merge requests. 04:00 Nikita: OK, what exactly do you mean by that? Joe: Great. So, for example, you could have developers working in one GIT branch and when they're done, they would push their private code changes into that remote branch. Then, they'd submit a merge request and their changes would be reviewed. Once the changes are approved, their code branch is merged into the main branch and then automatically runs a CI/CD package (continuous integration/continuous deployment) package, build, and deploy job on the code. Also, the CI/CD package, build, and deploy jobs can run against any branches, not just the main branch. So Visual Builder Studio is intended for managing the project and all of its assets. 04:37 Lois: So Joe, what are the different tools used in developing web applications? Joe: Well, Visual Builder, Visual Builder Studio Designer, Visual Builder Designer, Visual Builder Design-Time, Visual Builder Cloud Service, Visual Builder Stand-Alone all kind of get lumped together. You can kinda see why. What I'm referring to here are the tools that we use to build a visual web application composed of HTML5, CSS3, JavaScript, and JSON (JavaScript Object Notation) for metadata. I call this Visual Builder Designer. This is an Integrated Development Environment, it's the “IDE” which runs in your browser. You use a combination of drag and drop, setting properties, and writing and modifying custom and generated code to develop your web applications. You work within a workspace, which is your own private copy of a remote Git branch. When you're ready to start development work, you open an existing workspace or create a new one based on a clone of the remote branch you want to work on. Typically, a new branch would be created for the development work or you would join an existing branch. 05:38 Nikita: What's a workspace, Joe? Is it like my personal laptop and drive? Joe: A workspace is your own private code area that stores any changes you make on the Oracle servers, so your code changes are never lost—even when working in a browser-based, network-based tool. A good analogy is, say I was working at home on my own machine. And I would make a copy of a remote GIT branch and then copy that code down to my local machine, make my code changes, do my testing, etc. and then commit my work—create a logical save point periodically—and then when I'm ready, I'd push that code up into the remote branch so it can be reviewed and merged with the main branch. My local machine is my workspace. However, since this code is hosted up by Oracle on our servers, and the code and the IDE are all running in your browser, the workspace is a simulation of a local work area on your own computer. So, the workspace is a hosted allocation of resources for you that's private. Other people can't see what's going on in your workspace. Your workspace has a clone of the remote branch that you're working with and the changes you make are isolated to your cloned code in your workspace. 06:41 Lois: Ok… the code is actually hosted on the server, so each time you make a change in the browser, the change is written back to the server? Is it possible that you might lose your edits if there's a networking interruption? Joe: I want to emphasize that while I started out not personally being a fan of web-based integrated development environments, I have been using these tools for over three years and in all that time, while I have lost a connection at times—networks are still subject to interruptions—I've never lost any changes that I've made. Ever. 07:11 Nikita: Is there a way to save where you are in your work so that you could go back to it later if you need to? Joe: Yes, Niki, you're asking about commits and savepoints, like in a Git repository or a Git branch. When you reach a logical stopping or development point in your work, you would create a commit or a savepoint. And when you're ready, you would push that committed code in your workspace up to the remote branch where it can be reviewed and then eventually merged, usually with the main Git branch, and then continuous integration/continuous package and deployment build jobs are run. Now, I'm only giving you a high-level overview, but we cover all this and much more in detail with hands-on practices in our Visual Builder developer courses. Right now, I'm just trying to give you a sense of how these different tools are used. 07:52 Lois: Yeah, that makes sense, Joe. It's a lot to cover in a short amount of time. Now, we've discussed the Visual Builder Designer IDE and workspace. But can you tell us more about Visual Builder Cloud Service and stand-alone environments? What are they used for? What features do they provide? Are they the same or different things? Joe: Visual Builder Cloud Service or Visual Builder Stand-Alone, as it's sometimes called, is a service that Oracle hosts on its servers. It provides hosting for the deployed web application source code as well as database tables for business objects that we build and maintain to store your customer data. This data can come from XLS or CSV files, or even your own Oracle database customer table data. A custom REST proxy makes calls to external third-party REST services on your behalf and supports several popular authentication mechanisms. There is also integration with the Identity Cloud Service (IDCS) to manage users and their access to your web apps. Visual Builder Cloud Service is a for-fee product. You pay licensing fees for how much you use because it's a hosted service. Visual Builder Studio, the project asset management aspect I discussed earlier, is free with a standard OCI license. Now, keep in mind these are separate from something like Visual Builder Design Time and the service that's running in Fusion application environments. What I'm talking about now is creating stand-alone, bespoke, custom visual applications. These are applications that are built using industry-standard HTML5, CSS3, JavaScript, and JSON for metadata and are hosted on the Oracle servers.  09:30 Are you looking for practical use cases to help you plan and apply configurations that solve real-world challenges?  With the new Applied Learning courses for Cloud Applications, you'll be able to practically apply the concepts learned in our implementation courses and work through case studies featuring key decisions and configurations encountered during a typical Oracle Cloud Applications implementation. Applied learning scenarios are currently available for General Ledger, Payables, Receivables, Accounting Hub, Global Human Resources, Talent Management, Inventory, and Procurement, with many more to come!  Visit mylearn.oracle.com to get started. 10:12 Nikita: Welcome back! Joe, you said Visual Builder Cloud Service or Stand-Alone is a for-fee service. Is there a way I can learn about using Visual Builder Designer to build bespoke visual applications without a fee? Joe: Yes. Actually, we've added an option where you can run the Visual Builder Designer and learn how to create web apps without using the app hosting or the business object database that stores your customer data or the REST proxy for authentication or the Identity Cloud Service. So you don't get those features, but you can still learn the fundamentals of developing with Visual Builder Designer. You can call third-party APIs, you can download the source, and run it locally, for example, in a Tomcat server. This is a great and free way to learn how to develop with the Visual Builder Designer. 10:55 Lois: Joe, I want to know more about the kinds of apps you can build in VB Designer and the capabilities that VB Cloud Service provides. Joe: Visual Builder Designer allows you to build custom, bespoke web applications made of interactive webpages; flows of pages for navigation; events that respond when things happen in the app, for example, GUI events like a button is clicked or values are entered into a text field; variables to store the state of the application and the ability to make REST calls, all from your browser. These applications have full access to the Oracle Fusion Applications APIs, given that you have the right security permissions and credentials of course. They can access your customer business data as business objects in our internally hosted database tables or your own customer database tables. They can access third-party APIs, and all these different data sources can appear in the same visual application, on the same page, at the same time. They use the Identity Cloud Service to identify which users can log in and authenticate against the application. And they all use the new Redwood graphical user interface components and page templates, so they have the same look and feel of all Oracle applications. 12:02 Nikita: But what if you're building or extending Oracle Fusion Applications? Don't things change a little bit? Joe: Good point, Niki. Yes. While you still work within Visual Builder Studio, that doesn't change, VBS maintains your project and all your project-related assets, that is still the same. However, in this case, there is no separate hosted Visual Builder Cloud Service or Stand-Alone instance. In this case, Visual Builder is hosted inside of Fusion apps itself as part of the installation. I won't go into the details of how the architecture works, but the Visual Builder instance that you're running your code against is part of Fusion applications and is included in the architecture as well as the billing. All your code changes are maintained and stored within a single container called an extension. And this extension is a Git repository that is created for you, or you can create it yourself, depending on how you choose to work within Visual Builder Studio. You create an extension to hold the source code changes that provide a customization or configuration. This means making a change to an existing page or a set of pages or even adding new pages and flows to your Oracle Fusion Applications. You use Visual Builder Studio and Visual Builder Designer in a similar way as to how you would use them for bespoke stand-alone visual applications. 13:12 Lois: I'm trying to envision how this workflow is used. How is it different from bespoke VB app development? Or is it different at all? Joe: So, recall that the Visual Builder Designer is effectively the Integrated Development Environment, the IDE, where you make your code changes by working with both the raw HTML5, CSS3, and JavaScript code, if need be, or the Page Designer for drag and drop, and setting properties and then Live mode to test your work. You use a version of VB Designer to view and modify your customizations, and the code is stored in a Git repository called an extension. So, in that sense, the work of developing pages and flows and such is the same. You still start by creating or, more typically, joining a project and then either create a new extension from scratch or base it on an existing application, or go directly to the page that you want to edit and, on that page, select from your profile menu to edit in Visual Builder Studio. Now, this is a different lifecycle path from bespoke visual applications. With them, you're not extending an app or modifying individual pages in the same way. You get a choice of which project you want to add your extension to when you're working with Fusion apps and potentially which repository to store your customizations, unless one already exists and then it's assigned automatically to hold your code changes. So you make your changes and edits to the portions of the application that have been opened for extensibility by the development team. This is another difference. Once you make your code changes, the workflow is pretty much the same as for a bespoke visual application: do your development work, commit your changes, push your changes to the remote branch. And then typically, your code is reviewed and if the code passes and is approved, it's merged with the main branch. Then, the package and deploy jobs run to deploy the main code to the production environment or whatever environment you're targeting. And once the package and deploy jobs complete, the code base is updated and users who log in see the changes that you've made. 15:03 Nikita: You mentioned creating apps that combine data from Fusion cloud, applications, customer data, and third-party APIs into one page. Why is it necessary? Why can't you just do all that in one Fusion Applications extension? Joe: When you create extensions, you are working within the Oracle Fusion Applications ecosystem, that's what they actually call it, which includes a defined a set of users who have been predefined and are, therefore, known to Fusion Applications. So, if you're a user and you're not part of that Fusion Apps ecosystem, you can't access the pages. Period. That's how Fusion Apps works to maintain its security and integrity. Secondly, you're working pretty much solely with the Fusion Applications APIs data sources coming directly from Fusion Applications, which are also available to you when you're creating bespoke visual apps. When you're working with Fusion Applications in Visual Builder, you don't have access to these business objects that give you access to your own customer database data through Visual Builder-generated REST APIs. Business objects are available only to bespoke visual applications in the hosted VB Cloud Service instance. So, your data sources are restricted to the Oracle Fusion Applications APIs and some third-party APIs that work within a narrow set of authentication mechanisms currently, although there are plans to expand this in the future. A mashup app that allows you now to access all these data sources while creating apps that leverage the Redwood Component System, so they look and work like Fusion Apps. They're a highly popular option for our partners and customers. 16:28 Lois: So, to review, we have two different approaches. You can create a visual application using the for-fee, hosted Visual Builder Cloud Service/Stand-Alone or the one that comes with Oracle Integration Cloud, or you can use the extension architecture for Fusion applications, where you use the designer and create your extensions, and the code is delivered and deployed to Fusion applications code. You haven't talked about JET yet though, Joe. What is that? Joe: So, JET is an abbreviation. It stands for Oracle JavaScript Extension Toolkit and JET is the underlying technology that makes Visual Builder, visual applications, and Visual Builder Extensions for Fusion Applications possible. Oracle JavaScript Extension Toolkit provides a module-based, open-source toolkit that leverages modern JavaScript, TypeScript, CSS3, and HTML5 to deliver web applications. It's targeted at JavaScript developers working on client-side applications. It is not for backend development.  It's a collection of popular, powerful JavaScript libraries and a set of Oracle-contributed JavaScript libraries that make it very simple, easy, and efficient to build front-end applications that can consume and interact with Oracle products and services, especially Oracle Cloud services, but of course it can work with any type of third-party API. 17:44 Nikita: How are JET applications architected, Joe, and how does that relate to Visual Builder pages and flows? Joe: The architecture of JET applications is what's called a single page architecture. We've all seen these. These are where you have a single webpage—think of your index page that provides the header and footer for your webpage—and then the middle portion or the middle content of the page, represented by modules, allow you to navigate from one page or module to another. It also provides the data mapping so that the data elements in the variables and the state of the application, as well as the graphical user interface elements that provide the fields and functionality for the interface for the application, these are all maintained on the client side. If you're working in pure JET, then you work with these modules at the raw JavaScript code level. And there are a lot of JavaScript developers who want to work like this and create their custom applications from the code up, so to speak. However, it also provides the basis for Visual Builder visual applications and Fusion Apps visual extensions in Visual Builder. 18:41 Lois: How does JET support VB Apps? You didn't talk much about having to write a bunch of JavaScript and HTML5, so I got the impression that this is all done for you by VB Designer? Joe: Visual Builder applications are composed of HTML5, CSS3, and JavaScript code that is usually generated by the developer when she drags and drops components on to the page designer canvas or sets properties or creates action chains to respond to events. But there's also a lot of JavaScript object notation (JSON) metadata created at the time that describes the pages, the flows, the navigation, the REST services, the variables, their data types, and other assets needed for the app to function. This JSON metadata is translated at runtime using a large JavaScript extension toolkit library called the Visual Builder Runtime that runs in the browser and real time translates the metadata and other assets in the Visual Builder source code into JET code and assets, which are actually executed at runtime. And it's very quick, very fast, very efficient, and provides a layer of abstraction between the raw JET code and the Visual Builder architecture of pages, flows, action chains for executing code and events to handle things that occur in the user interface, including saving the state in variables that are mapped to GUI components. For example, if you have an Input text component, you need to have a variable to store the value that was entered into that Input text component between page refreshes. The data can move from the Input text component to the variable, and from the variable to that Input text component if it's changed programmatically, for example. So, JET manages binding these data values to variables and the UI components on the page. So, a change to a variable value or a change to the contents of the component causes the others to change automatically. Now, this is only a small part of what JET and the frameworks and libraries it uses do for the applications. JET also provides more complex GUI components like lists and tables, and selection lists, and check boxes, and all the sorts of things you would expect in a modern GUI application. 20:37 Nikita: You mentioned a layer of abstraction between Visual Builder Studio Designer and JET. What's the benefit of working in Visual Builder Designer versus JET itself? Joe: The benefit of Visual Builder is that you work at a higher level of abstraction than having to get down into the more detailed levels of deep JavaScript code, working with modules, data mappings, HTML code, single page architecture navigation, and the related functionalities. You can work at a higher level, a graphical level, where you can drag and drop things onto a design canvas and set properties. The VB architecture insulates you from the more technical bits of JET. Now, this frees the developer to concentrate more on application and page design, implementing logic and business rules, and creating a pleasing workflow and look and feel for the user. This keeps them from having to get caught up in the details of getting this working at the code level. Now if needed, you can write custom JavaScript, HTML5, and CSS3 code, though much less than in a JET app, and all that is part of the VB application source, which becomes part of the code used by JET to execute the application itself. And yet it all works seamlessly together. 21:38 Lois: Joe, I know we have courses in JavaScript, HTML, and CSS. But does a developer getting ready to work in Visual Builder Designer have to go take those courses first or can they start working in VB Designer right away? Joe: Yeah, that question does often comes up: Do I need to learn JET to work with Visual Builder? No, you don't. That's all taken care for you in the products themselves. I don't really think it helps that much to learn JET if you are going to be a VB developer. In some ways, it could even be a bit distracting since some of things you learn to do in JET, you would have to unlearn or not do so much because of what VB does it for you. The things you would have to do manually in code in JET are done for you. This is why we call VB a low code development tool. I mean, you certainly can if you want to, but I would spend more time learning about the different GUI components, page templates, the Visual Builder architecture — events, action chains, and the data provider variables and types. Now, I know JET myself. I started with that before learning Visual Builder, but I use very little of my JET knowledge as a VB developer. Visual Builder Designer provides a nice, abstracted, clean layer of modern visual development on top of JET, while leveraging the power and flexibility of JET and keeping the lower-level details out of my way. 22:49 Nikita: Joe, where can I go to get started with Visual Builder? Joe: Well, for more information, I recommend you take a look at our Develop Fusion Applications course if you're working with Fusion Applications and Visual Builder Studio. The other course is Develop Visual Applications with Visual Builder Studio and that's if you're creating stand-alone bespoke applications. Both these courses are free. We also have a comprehensive course that covers JavaScript, HTML5, and CSS3, and while it's not required that you take that to be successful, it can be helpful down the road. I would also say that some basic knowledge of HTML5, CSS3, and JavaScript will certainly support you and serve you well when working with Visual Builder. You learn more as you go along and you find that you need to create more sophisticated applications. I would also mention that a lot of the look and feel of the applications in Visual Builder visual applications and Fusion apps extensions and customizations come through JET components, JET styles, and JET variables, and CSS variables, so that's something that you would want to pursue at some point. There's a JET cookbook out there. You can search for Oracle JET and look for the JET cookbook and that's a good introduction to all of that. 23:50 Nikita: We hope you enjoyed that conversation. To learn about some of the courses Joe mentioned, visit mylearn.oracle.com to get started. Lois: Before we wrap up, we've got a favor to ask. We've created a short survey to capture your thoughts on the podcast. It'll only take a few minutes of your time. Just click the link in the show notes and share your feedback. We want to make sure we're delivering the best experience possible so don't hesitate to let us know what's on your mind! Thanks for your support. Join us next week for another throwback episode. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 24:30 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Fredrik snackar Postgresql med Svante Richter. Är Postgresql gravt underanvänd när man tänker på allt databasen klarar av? Är vi onödigt rädda för att låta databasen sköta om saker, och vad beror det i så fall på? Vad händer när man försöker ersätta hela sitt backend med Postgresql? Och vad vill man möjligen inte ha Postgresql till? Inspelat under Øredev 2024, där Svante höll en presentation på samma ämne. Avsnittet sponsras av 46elks, som bygger lättanvända API:er för telefoni och SMS. Hur enkla? Här är ett Curl-exempel: curl https://api.46elks.com/a1/sms -u API_USERNAME:API_PASSWORD -d to=+46766861004 -d message="Hej kodsnacklyssnare! Testa att skicka ditt första SMS med Curl." -d from=Kodsnack Skicka notiser per SMS, ring upp folk, ordna telefonväxlar, och mycket mer. API-dokumentationen hittar du på 46elks.se/docs. Registrera dig via 46elks.se/kodsnack och få 200 krediter att experimentera med! Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @thieta, @krig, och @bjoreman på Mastodon, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Øredev Advent of code Kåre Kåre byggde sitt eget språk för Advent of code Kodsnacks Advent of code-repo Svante nås med fördel i vår Slack-kanal Andra avsnitt med Svante Postgresql Axis Svantes presentation är inte ute än - länk kommer när den publicerats Svantes presentationsbilder Postgrest - skapar REST-API automatiskt CRUD - Create, read, update and delete ORM Mongodb Nosql Timescale Postgis Extensions för Postgresql 46elks - veckans sponsor 46elks.se/kodsnack - registrera dig och få 200 krediter att experimentera med! Webhooks Lua JSON-schema Rabbitmq Dead letter queue Foreign keys Next.js HTMX Stöd oss på Ko-fi Nginx Webdav Haskell Lars Wikman Unison - tvåvägsversion av Rsync Ocaml Elm Noredink Neon Supabase Hetzner Elasticsearch Openstreetmap Dynamodb Neo4j Nearest neighbor Lagringsmotorer för Postgresql Devoteam Skruv - Svantes mikroramverk Krausest-prestandamätningarna Svante jämför Skruv mot Titlar Tryck undan ångesten Använda men underanvända Lösa problem som man själv skapat Vilken fråga blir det faktiskt? Databasfunktioner i Lua Är detta smart? Gå på tok för långt Vilka bitar var det som skavde? En hel klass av problem Databasen är någon annans domän Vem låter man inkräkta mest? Undvika att behöva ha ett backend Den kortaste vägen mellan två noder Jag är lite radikal

2024-10-22 Weekly News — Episode 221Watch the video version on YouTube at https://youtube.com/live/j-e_y4OwuCw?feature=shareHosts: Gavin Pickin - Senior Developer at Ortus SolutionsThanks to our Sponsor - Ortus SolutionsThe makers of ColdBox, CommandBox, ForgeBox, TestBox and all your favorite box-es out there including BoxLang.A few ways to say thanks back to Ortus Solutions:Buy Tickets to Into the Box 2025 in Washington DC https://t.co/cFLDUJZEyMApril 30, 2025 - May 2, 2025 - Washington, DCLike and subscribe to our videos on YouTube. Help ORTUS reach for the Stars - Star and Fork our ReposStar all of your Github Box Dependencies from CommandBox with https://www.forgebox.io/view/commandbox-github Subscribe to our Podcast on your Podcast Apps and leave us a reviewSign up for a free or paid account on CFCasts, which is releasing new content regularlyBOXLife store: https://www.ortussolutions.com/about-us/shopBuy Ortus's Books102 ColdBox HMVC Quick Tips and Tricks on GumRoad (http://gum.co/coldbox-tips)Now on Amazon! In hardcover too!!!https://www.amazon.com/dp/B0CJHB712MLearn Modern ColdFusion (CFML) in 100+ Minutes - Free online https://modern-cfml.ortusbooks.com/ or buy an EBook or Paper copy https://www.ortussolutions.com/learn/books/coldfusion-in-100-minutes Patreon Support ()We have 59 patreons: https://www.patreon.com/ortussolutions. News and AnnouncementsLucee 6.1.1 (6.1.1.100-RC) Release CandidateThere is a new Lucee 6.1.1.100-RC release candidate available for testing. Give it a try and share your feedback with us.What's New?This release focuses mainly on bug fixes, along with a few useful enhancements.https://dev.lucee.org/t/lucee-6-1-1-6-1-1-100-rc-release-candidate/14353 ColdFusion 2023 and 2021 October 15th, 2024 updatesWe are pleased to announce that we have released general updates to ColdFusion (2023 release) Update 11 and ColdFusion (2021 release) Update 17. The updates include bug fixes and enhancements in Administrator, Language, CFSetup, Database, and other areas. They also contain library upgrades, such as netty, ehcache, etc. The updates also include enhancements to whitespace management and client variable support in CFPM.Known issues in the updateThe PDF Services page in ColdFusion Administrator does not load even with the HTMLToPDF package installedhttps://coldfusion.adobe.com/2024/10/released-coldfusion-2023-and-2021-october-15th-2024-updates/CF Summit India AnnouncedWe are excited to announce that the Adobe ColdFusion India Summit 2024 is happening on December 7, 2024, and this year, we're bringing the event to two vibrant cities: Bengaluru and Noida. Whether you're a seasoned developer or just beginning your journey in web development, this free summit offers a unique opportunity to learn, connect, and grow with the best minds in the industry.https://coldfusion.adobe.com/2024/10/get-ready-for-adobe-coldfusion-india-summit-2024/ Announcing Java updates of Oct 2024 for 8, 11, 17, 21, and 23: thoughts and resourcesIt's that time again: there are new JVM updates released today (Oct 15, 2024) for the current long-term support (LTS) releases of Oracle Java, 8, 11, 17, and 21, as well as the new short-term release 23. (The previous short-term release, Java 22, is no longer updated.)TLDR: The new updates are 1.8.0_431 (aka 8u431), 11.0.25, 17.0.13, 21.0.5, and 23.0.1 respectively. Crazy that there are now 5 current Java releases, I realize. More below, including more on each of them including what changed as well as bug fixes and the security fixes each version contains (including their CVE scores regarding urgency of concerns), which are offered in Oracle resources I list below.https://www.carehart.org/blog/2024/10/15/java_updates_oct_2024 PayPal's NVP/SOAP API for Website Payments Pro accounts suddenly stopped working sometime early OctoberPayPal's NVP/SOAP API for Website Payments Pro accounts suddenly stopped working sometime around October 4th (possibly Sep 30). Some developers that reported having the issue were using legacy classic ASP and others were using ColdFusion. I believe we've been using the PayPal DoDirectPayment API since it was introduced back in 2002.At some point, PayPal added the following undated disclaimer to their documentation. (According to Microsoft Copilot, "PayPal's NVP (Name-Value Pair) API was marked as "legacy" around October 12th, 2021".)CFPayment (retired) supports WPP & Payflow, but not the new REST API method. Searching online for "ColdFusion (or cfml) paypal rest api" didn't return anything beneficial, so it became apparent that there was a need for a solution... any solution.James Moberg has an updated Paypal Rest API Cfc available here: https://dev.to/gamesover/coldfusion-paypal-rest-api-cfc-339p Secure Your ColdFusion Perpetual License Before Adobe's Subscription-Only SwitchFollowing Adobe's announcement at the Adobe ColdFusion Summit in Las Vegas, ColdFusion will transition to a subscription-only licensing model. This major shift in licensing strategy means developers and organizations have a limited window to secure their final perpetual ColdFusion license.While we don't know the date for the Adobe switch, FusionReactor customers have an exclusive opportunity to secure their last perpetual license and save significantly in the process. This final offer has been extended to December 31, 2024, giving organizations more months to make this crucial decision.https://fusion-reactor.com/blog/secure-your-coldfusion-perpetual-license-before-adobes-subscription-only-switch/ Microsoft Copilot is a little Snarky about ColdFusion

In this episode, Senior Principal APEX and Apps Dev Instructor Brent Dayley joins hosts Lois Houston and Nikita Abraham to discuss Oracle AI Vector Search. Brent provides an in-depth overview, shedding light on the brand-new vector data type, vector embeddings, and the vector workflow.   Oracle Database 23ai: Oracle AI Vector Search Fundamentals: https://mylearn.oracle.com/ou/course/oracle-database-23ai-oracle-ai-vector-search-fundamentals/140188/   Oracle Database 23ai: SQL Workshop: https://mylearn.oracle.com/ou/course/oracle-database-23ai-sql-workshop/137830/   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   Twitter: https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Radhika Banka, and the OU Studio Team for helping us create this episode.   ---------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started!   00:26 Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs here at Oracle University. Joining me as always is our Team Lead of our Editorial Services, Nikita Abraham. Nikita: Hi everyone! Thanks for tuning in over the last few months as we've been discussing all the Oracle Database 23ai new features. We're coming to the end of the season, and to close things off, in this episode and the next one, we're going to be talking about the fundamentals of Oracle AI Vector Search. In today's episode, we'll try to get an overview of what vector search is, why Oracle Vector Search stands out, and dive into the new vector data type. We'll also get insights into vector embedding models and the vector workflow. 01:11 Lois: To take us through all of this, we're joined by Brent Dayley, who is a Senior Principal APEX and Apps Development Instructor with Oracle University. Hi Brent! Thanks for joining us today. Can you tell us about the new vector data type? Brent: So this data type was introduced in Oracle Database 23ai. And it allows you to store vector embeddings alongside other business data. Now, the vector data type allows a foundation to store vector embeddings. 01:42 Lois: And what are vector embeddings, Brent? Brent: Vector embeddings are mathematical representations of data points. They assign mathematical representations based on meaning and context of your unstructured data. You have to generate vector embeddings from your unstructured data either outside or within the Oracle Database. In order to get vector embeddings, you can either use ONNX embedding machine learning models or access third-party REST APIs. Embeddings can be used to represent almost any type of data, including text, audio, or visual, such as pictures. And they are used in proximity searches. 02:28 Nikita: Hmmm, proximity search. And similarity search, right? Can you break down what similarity search is and how it functions? Brent: So vector data tends to be unevenly distributed and clustered into groups that are semantically related. Doing a similarity search based on a given query vector is equivalent to retrieving the k nearest vectors to your query vector in your vector space. What this means is that basically you need to find an ordered list of vectors by ranking them, where the first row is the closest or most similar vector to the query vector. The second row in the list would be the second closest vector to the query vector, and so on, depending on your data set. What we need to do is to find the relative order of distances. And that's really what matters rather than the actual distance. Now, similarity searches tend to get data from one or more clusters, depending on the value of the query vector and the fetch size. Approximate searches using vector indexes can limit the searches to specific clusters. Exact searches visit vectors across all clusters. 03:44 Lois: Ok. I want to move on to vector embedding models. What are they and why are they valuable? Brent: Vector embedding models allow you to assign meaning to what a word, or a sentence, or the pixels in an image, or perhaps audio. It allows you to quantify features or dimensions. Most modern vector embeddings use a transformer model. Bear in mind that convolutional neural networks can also be used. Depending on the type of your data, you can use different pretrained open source models to create vector embeddings. As an example, for textual data, sentence transformers can transform words, sentences, or paragraphs into vector embeddings. 04:33 Nikita: And what about visual data? Brent: For visual data, you can use residual network also known as ResNet to generate vector embeddings. You can also use visual spectrogram representation for audio data. And that allows us to use the audio data to fall back into the visual data case. Now, these can also be based on your own data set. Each model also determines the number of dimensions for your vectors. 05:02 Lois: Can you give us some examples of this, Brent? Brent: Cohere's embedding model, embed English version 3.0, has 1,024 dimensions. Open AI's embedding model, text-embedding-3-large, has 3,072 dimensions. 05:24 Want to get the inside scoop on Oracle University? Head over to the Oracle University Learning Community. Attend exclusive events. Read up on the latest news. Get first-hand access to new products. Read the OU Learning Blog. Participate in Challenges. And stay up-to-date with upcoming certification opportunities. Visit mylearn.oracle.com to get started.  05:50 Nikita: Welcome back! Let's now get into the practical side of things. Brent, how do you import embedding models? Brent: Although you can generate vector embeddings outside the Oracle Database using pre-trained open source embeddings or your own embedding models, you also have the option of doing those within the Oracle Database. In order to use those within the Oracle Database, you need to use models that are compatible with the Open Neural Network Exchange Standard, or ONNX, also known as Onyx. Oracle Database implements an Onyx runtime directly within the database, and this is going to allow you to generate vector embeddings directly inside the Oracle Database using SQL. 06:35 Lois: Brent, why should people choose to use Oracle AI Vector Search? Brent: Now one of the biggest benefits of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data, all in one single system. This is very powerful, and also a lot more effective because you don't need to add a specialized vector database. And this eliminates the pain of data fragmentation between multiple systems. It also supports Retrieval Augmented Generation, also known as RAG. Now this is a breakthrough generative AI technique that combines large language models and private business data. And this allows you to deliver responses to natural language questions. RAG provides higher accuracy and avoids having to expose private data by including it in the large language model training data. 07:43 Nikita: In the last part of our conversation today, I want to ask you about the Oracle AI Vector Search workflow, starting with generating vector embeddings. Brent: Generate vector embeddings from your data, either outside the database or within the database. Now, embeddings are a mathematical representation of what your data meaning is. So what does this long sentence mean, for instance? What are the main keywords out of it? You can also generate embeddings not only on your typical string type of data, but you can also generate embeddings on other types of data, such as pictures or perhaps maybe audio wavelengths. 08:28 Lois: Could you give us some examples? Brent: Maybe we want to convert text strings to embeddings or convert files into text. And then from text, maybe we can chunk that up into smaller chunks and then generate embeddings on those chunks. Maybe we want to convert files to embeddings, or maybe we want to use embeddings for end-to-end search. Now you have to generate vector embeddings from your unstructured data, either outside or within the Oracle Database. You can either use the ONNX embedding machine learning models or you can access third-party REST APIs. You can import pre-trained models in ONNX format for vector generation within the database. You can download pre-trained embedding machine learning models, convert them into the ONNX format if they are not already in that format. Then you can import those models into the Oracle Database and generate vector embeddings from your data within the database. Oracle also allows you to convert pre-trained models to the ONNX format using Oracle machine learning for Python. This enables the use of text transformers from different companies. 09:51 Nikita: Ok, so that was about generating vector embeddings. What about the next step in the workflow—storing vector embeddings? Brent: So you can create one or more columns of the vector data type in your standard relational data tables. You can also store those in secondary tables that are related to the primary tables using primary key foreign key relationships. You can store vector embeddings on structured data and relational business data in the Oracle Database. You do store the resulting vector embeddings and associated unstructured data with your relational business data inside the Oracle Database. 10:30 Nikita: And the third step is creating vector indexes? Brent: Now you may want to create vector indexes in the event that you have huge vector spaces. This is an optional step, but this is beneficial for running similarity searches over those huge vector spaces. So once you have generated the vector embeddings and stored those vector embeddings and possibly created the vector indexes, you can then query your data with similarity searches. This allows for Native SQL operations and allows you to combine similarity searches with relational searches in order to retrieve relevant data. 11:15 Lois: Ok. I think I've got it. So, Step 1, generate the vector embeddings from your unstructured data. Step 2, store the vector embeddings. Step 3, create vector indices. And Step 4, combine similarity and keyword search. Brent: Now there is another optional step. You could generate a prompt and send it to a large language model for a full RAG inference. You can use the similarity search results to generate a prompt and send it to your generative large language model in order to complete your RAG pipeline. 11:59 Lois: Thank you for sharing such valuable insights about Oracle AI Vector Search, Brent. We can't wait to have you back next week to talk about vector indices and memory. Nikita: And if you want to know more about Oracle AI Vector Search, visit mylearn.oracle.com and check out the Oracle Database 23ai: Oracle AI Vector Search Fundamentals course. Lois: Yes, and if you're serious about advancing in your development journey, we recommend taking the Oracle Database 23ai SQL workshop. It's designed for those who might be familiar with SQL from other database platforms or even those completely new to SQL. Nikita: Yeah, we'll add the link to the workshop in the show notes so you can find it easily. Until next week, this is Nikita Abraham… Lois: And Lois Houston signing off! 12:45 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

GraphQL is an open-source query language for APIs and a runtime for executing those queries. It was developed by Facebook to address the problem of over-fetching or under-fetching data, which is a common issue with traditional REST APIs. Matt Bessey is a Principal Engineer and Software Architect. Earlier this year Matt wrote a blog post The post The End of GraphQL with Matt Bessey appeared first on Software Engineering Daily.

GraphQL is an open-source query language for APIs and a runtime for executing those queries. It was developed by Facebook to address the problem of over-fetching or under-fetching data, which is a common issue with traditional REST APIs. Matt Bessey is a Principal Engineer and Software Architect. Earlier this year Matt wrote a blog post The post The End of GraphQL with Matt Bessey appeared first on Software Engineering Daily.

On this episode, PJ talks about his time using Figma's REST API and how with so much data collected, we just need it to do a little more for us.

Alba Rivas, a passionate Salesforce Developer Advocate, shares her insights on the power of Salesforce's Models API. Discover how this API enables developers to seamlessly integrate large language models via REST API or Apex, eliminating the need for custom code or prompt templates. Alba highlights the critical role of the Einstein Trust Layer in ensuring data security, compliance, and efficient integration with external models. Gain knowledge on AI implementation, including maintaining audit trails, monitoring data for toxicity, and using human feedback for refining AI systems.  We also explore the flexibility and capabilities of the Models API, offering a centralized interface for developers, along with resources like Postman collections, codeLive sessions, and an upcoming Trailhead module to support your journey in mastering generative AI with Salesforce. Show Highlights: Discussion on the Einstein Trust Layer's role in ensuring security, data masking, and compliance for generative AI applications. Comparison between using prompt templates and the Models API. Importance of maintaining an audit trail. Focus on the value of human feedback in refining AI systems and the benefits of a centralized interface for developers. Insights into practical AI implementation and the flexibility offered by the Models API. Links: Postman Collection - https://www.postman.com/salesforce-developers/workspace~34382471-0c97-40e5-a206-f947271665c4/collection/onih7sc/models-apis-beta Einstein Models API Documentation - https://developer.salesforce.com/docs/einstein/genai/guide/models-api.html codeLive: Exploring the New Models API (Beta) - https://www.youtube.com/watch?v=hf95_ghGeNI

In this episode, we sit down with expert Akshata Sawant to discuss MuleSoft Intelligent Document Processing (IDP). Akshata delves into how IDP uses AI to extract and organize data from a variety of unstructured documents, such as purchase orders, invoices, and driving licenses, using AWS Textract for exact information extraction. The discussion highlights IDP's seamless integration with multiple systems via REST API endpoints and its application in automating the processing of daily invoices received via email.  Learn about IDP's ability to handle different document structures without needing multiple templates and the crucial role of human oversight when the AI's confidence ratio falls below 80%, ensuring accuracy and reliability. This episode offers a comprehensive look at how IDP can revolutionize document workflows, balancing automation with human intervention, and provides valuable resources for further learning. Show Highlights: Using AI to extract and organize data from unstructured documents Integration of AWS Textract for exact information extraction and seamless integration via REST API endpoints Accessing and using IDP through the Anypoint Platform with ease Managing different document structures without multiple templates and the role of human oversight for accuracy Links: Getting Started with Intelligent Document Processing: Part 1 - https://www.youtube.com/watch?v=Yi160vy7gg4 Getting Started with Intelligent Document Processing: Part 2 - https://www.youtube.com/watch?v=iGvAypuzAvw How to Get Started With Intelligent Document Processing - https://blogs.mulesoft.com/dev-guides/how-to-get-started-with-intelligent-document-processing/  

Summary In this episode of the Blue Security Podcast, Andy and Adam discuss Microsoft's Defender for Storage, a cloud-native security solution for Azure Blob Storage, Azure Files, and Azure Data Lake Storage. They highlight the three major impacts on data workloads: malicious file uploads, sensitive data exfiltration, and data corruption. The solution offers activity monitoring, malware scanning, and sensitive data threat detection. They also mention the pricing model, the integration with Microsoft Purview, and the ease of deployment using Azure Policy and Logic Apps. Takeaways -Defender for Storage is a cloud-native security solution for Azure Blob Storage, Azure Files, and Azure Data Lake Storage. -The solution protects against malicious file uploads, sensitive data exfiltration, and data corruption. -It offers activity monitoring, malware scanning, and sensitive data threat detection. -Integration with Microsoft Purview allows for seamless inheritance of sensitivity settings. -Deployment can be done through the Azure portal, Azure Policy, or infrastructure as code using the REST API. -Logic Apps can be used to automate responses and streamline security operations. -A pre-purchase plan is available for Defender for Cloud workloads, offering programmatic discounts and predictable billing. ---------------------------------------------------- YouTube Video Link:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://youtu.be/_DNCcy4V5Uo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ---------------------------------------------------- Documentation: ⁠https://learn.microsoft.com/en-us/azure/defender-for-cloud/defender-for-storage-introduction ---------------------------------------------------- Contact Us: Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bluesecuritypod.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/bluesecuritypod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/company/bluesecpod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/c/BlueSecurityPodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ----------------------------------------------------------- Andy Jaw Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/ajawzero⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/andyjaw/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Email: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠andy@bluesecuritypod.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ---------------------------------------------------- Adam Brewer Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/ajbrewer⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/adamjbrewer/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Email: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠adam@bluesecuritypod.com

Full notes for this episode at: https://solfate.com/podcast/61Follow @SolfatePod on Twitter for updates: https://twitter.com/SolfatePodPS: We are happy to announce the launch of our Solfate Drip channel where you can collect Solfate Podcast episodes as digital collectibles! Subscribe to our Drip channel today: drip.haus/solfate (we have some fun experiments planned)Notes from the showThe conversation revolves around the introduction of Actions and Blinks, a technology platform and protocol developed by Dialect and Solana Foundation. Actions and Blinks allow users to share Solana anywhere on the internet by unfurling URLs and providing interactive experiences. The idea originated from the concept of smart messages and transaction requests in Solana Pay. The launch of Actions and Blinks was a collaborative effort between Dialect, Solana Foundation, and various wallets.The discoverability and accessibility of Blinks are limited to specific browsers with wallet extensions installed. However, there are plans to make Blinks more widely available and user-friendly. The conversation explores the challenges and potential of Blinks and Solana Actions in revolutionizing the internet. The guests discuss the uphill battle of creating an open ecosystem in the current environment of mega companies and intentional blockades.Chris and Nick highlight the power of Blinks in bringing value transfer and interaction to users without leaving the app. The technical nature of Blinks and Solana Actions is explained, emphasizing the backend REST API of Actions and the frontend UI of Blinks. The guests also mention the future possibilities of mobile integration and the importance of developer feedback and collaboration.TakeawaysActions and Blinks allow users to share Solana anywhere on the internet by unfurling URLs and providing interactive experiences.The idea of Actions and Blinks originated from the concept of smart messages and transaction requests in Solana Pay.The launch of Actions and Blinks was a collaborative effort between Dialect, Solana Foundation, and various wallets.Currently, the discoverability and accessibility of Blinks are limited to specific browsers with wallet extensions installed, but there are plans to make Blinks more widely available and user-friendly. Creating an open ecosystem in the current internet environment is an uphill battle against mega companies and intentional blockades.Blinks bring value transfer and interaction to users without leaving the app, enhancing the user experience and stickiness.Blinks and Solana Actions have a backend REST API for transaction construction and delivery, and a frontend UI for rendering and user interaction.The technical nature of Blinks and Solana Actions allows for easy development and customization, making it accessible for developers.Mobile integration and native mobile implementations of Blinks are the next steps in the evolution of the technology.Find Chris and Dialect onlineFollow Chris on twitter - @aliquotchrisFollow Dialect on twitter - @SayDialectDialect's website - dialect.toFollow us aroundNicktwitter: @nickfrostygithub: github.com/nickfrostywebsite: https://nick.afJamestwitter: @jamesrp13github: github.com/jamesrp13Solfate Podcasttwitter: @SolfatePodmore podcast episodes: solfate.com/podcastcollect episodes on Drip: drip.haus/solfate

News includes Chris McCord's speedrun video on adding a self-hosted llama2-7b to an existing application, Tyler Young's release of parameterized_test v0.2.0, major updates in Oban Pro's new launch week, potential for CRDTs being added to Mnesia DB, Zach Daniel's blog post on Igniter for code generation, and a preview of ElixirConf 2024 with exciting speakers and topics, and more! Show Notes online - http://podcast.thinkingelixir.com/213 (http://podcast.thinkingelixir.com/213) Elixir Community News - https://x.com/chris_mccord/status/1815409966611648705 (https://x.com/chris_mccord/status/1815409966611648705?utm_source=thinkingelixir&utm_medium=shownotes) – Chris McCord does a YouTube video speedrun of adding a self-hosted llama2-7b to an existing application. He's running it against Ollama and making REST API calls to it, showing how to run the Ollama server on a private Fly.io IPv6 network using auto-stop and auto-start features. - https://x.com/TylerAYoung/status/1815391743484870980 (https://x.com/TylerAYoung/status/1815391743484870980?utm_source=thinkingelixir&utm_medium=shownotes) – Tyler Young shared a new release of his library parameterizedtest, version v0.2.0, which includes support for longer test names, comments in tables, and Obsidian markdown table format. - https://github.com/s3cur3/parameterized_test (https://github.com/s3cur3/parameterized_test?utm_source=thinkingelixir&utm_medium=shownotes) – GitHub repository for Tyler Young's parameterizedtest library that makes it easier to create tests using multiple combinations in markdown tables. - https://x.com/Exadra37/status/1815694986345611683 (https://x.com/Exadra37/status/1815694986345611683?utm_source=thinkingelixir&utm_medium=shownotes) – The Mnesia database may receive native support for automated conflict resolution via CRDTs, sponsored by ErlangSolutions and developed by Vincent Lau. - https://www.youtube.com/watch?v=tHdPRyMjmW8 (https://www.youtube.com/watch?v=tHdPRyMjmW8?utm_source=thinkingelixir&utm_medium=shownotes) – Vincent Lau spoke at Code BEAM Europe 2023 about his work on adding CRDTs to Mnesia for automated conflict resolution. - https://www.erlang.org/doc/apps/mnesia/mnesia.html (https://www.erlang.org/doc/apps/mnesia/mnesia.html?utm_source=thinkingelixir&utm_medium=shownotes) – Documentation on Mnesia, a distributed key-value DBMS built into Erlang. - https://x.com/sorentwo/status/1791166342034255938 (https://x.com/sorentwo/status/1791166342034255938?utm_source=thinkingelixir&utm_medium=shownotes) – Oban Pro's launch week introduces unified migrations, worker aliases, better support for distributed databases, faster unique job checks, and the @job decorator for small jobs. - https://x.com/sorentwo/status/1807155900609904973 (https://x.com/sorentwo/status/1807155900609904973?utm_source=thinkingelixir&utm_medium=shownotes) – Improvements in Oban Pro include better batch workflows with mermaid visualizations. - https://getoban.pro/articles/pro-1-5-launch-week-day-1 (https://getoban.pro/articles/pro-1-5-launch-week-day-1?utm_source=thinkingelixir&utm_medium=shownotes) – Article on Oban Pro's launch week, detailing new features and improvements. - https://getoban.pro/articles/pro-1-5-launch-week-day-2 (https://getoban.pro/articles/pro-1-5-launch-week-day-2?utm_source=thinkingelixir&utm_medium=shownotes) – Second day of Oban Pro's launch week article series. - https://getoban.pro/articles/pro-1-5-launch-week-day-3 (https://getoban.pro/articles/pro-1-5-launch-week-day-3?utm_source=thinkingelixir&utm_medium=shownotes) – Third day of Oban Pro's launch week article series. - https://alembic.com.au/blog/igniter-rethinking-code-generation-with-project-patching (https://alembic.com.au/blog/igniter-rethinking-code-generation-with-project-patching?utm_source=thinkingelixir&utm_medium=shownotes) – Blog post by Zach Daniel about Igniter, a tool for rethinking code generation with project patching, useful for installing libraries into existing Phoenix applications. - https://2024.elixirconf.com/ (https://2024.elixirconf.com/?utm_source=thinkingelixir&utm_medium=shownotes) – ElixirConf 2024 Preview with details on scheduled speakers and topics. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Find us online - Message the show - @ThinkingElixir (https://twitter.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen - @brainlid (https://twitter.com/brainlid) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel - @bernheisel (https://twitter.com/bernheisel) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

Could integrating generative AI revolutionize your business processes? Join us as we sit down with Charles Watkins, the newest Salesforce Developer Advocate, to unveil the transformative capabilities of Salesforce's Prompt Builder. Charles shares a compelling example from his own support background, illustrating how this innovative tool can streamline the summarization and categorization of support cases. Learn how Prompt Builder simplifies switching between large language models like OpenAI and Microsoft Azure, making advanced AI accessible without complex integrations. We also decode Salesforce's cutting-edge Models API, currently in beta, offering a granular, controlled approach to generative AI. Charles discusses how the API can be harnessed to build chat-based conversational AI apps, embeddings, and more using REST API and Apex interfaces. Discover the security advantages of the Einstein Trust Layer, which ensures data privacy, and hear about the impressive response times of models like GPT-4. This episode is a treasure trove for developers eager to experiment with Salesforce's open-gen AI ecosystem. Don't miss this opportunity to get inspired and elevate your AI game. Show Highlights: The Prompt Builder tool for automating business processes like summarizing and categorizing support cases. Simplified integration with multiple large language models such as OpenAI and Microsoft Azure. The new Models API for creating chat-based AI apps, embeddings, and text generation solutions. Benefits of the Einstein Trust Layer for enhanced security and data privacy. The rapid response times of models like GPT-4 and the lack of a streaming API. Links: Applying Fundamentals of Prompt Engineering with Prompt Builder - https://developer.salesforce.com/blogs/2024/07/applying-fundamentals-of-prompt-engineering-with-prompt-builder Using Prompt Builder, Flows, and Apex to Summarize and Classify Cases Faster - https://developer.salesforce.com/blogs/2024/06/using-prompt-builder-flows-and-apex-to-summarize-and-classify-cases-faster Prompt Builder for Developers Playlist - https://www.youtube.com/playlist?list=PLgIMQe2PKPSIarRb6ud6odVxB1gv-hbO8 Einstein 1 Studio Developer Center - https://developer.salesforce.com/developer-centers/einstein-1-studio

Dive into the world of GraphQL APIs on AWS this week! We'll explore the recently launched feature in AppSync: asynchronous Lambda functions for GraphQL resolvers. But first, we'll break down the advantages of GraphQL over REST APIs and the limitations of synchronous calls in GraphQL. Then, we'll uncover the power of async Lambdas: stream data directly to your client for a more responsive experience and unlock innovative use cases, like generative AI-powered chatbots built with Lambdas. Curious how this can transform your applications? Tune in to learn more! With Derek Bingham, Developer Advocate, AWS https://www.linkedin.com/in/derekwbingham/ - Derek's blog about AppSync async Lambda resolvers https://community.aws/content/2hlqAp86YvckSS2DrVvZ1qdArqF/async-lambda-and-appsync?lang=en - AWS AppSync https://docs.aws.amazon.com/appsync/latest/devguide/what-is-appsync.html - AWS Lambda https://docs.aws.amazon.com/lambda/latest/dg/welcome.html - Streaming a response from a Lambda function https://docs.aws.amazon.com/lambda/latest/dg/configuration-response-streaming.html - AWS AppSync sample code https://github.com/aws-samples/aws-appsync-resolver-samples - Michael (App Sync Developer Advocate) YouTube channel https://www.youtube.com/@focusotter/videos

Curious about how OCI Container Engine for Kubernetes (OKE) can transform the way your development team builds, deploys, and manages cloud-native applications? Listen to hosts Lois Houston and Nikita Abraham explore OKE's key features and benefits with senior OCI instructor Mahendra Mehra.   Mahendra breaks down complex concepts into digestible bits, making it easy for you to understand the magic behind OKE.   OCI Container Engine for Kubernetes Specialist: https://mylearn.oracle.com/ou/course/oci-container-engine-for-kubernetes-specialist/134971/210836   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X (formerly Twitter): https://twitter.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Nikita: Hello and welcome to the Oracle University Podcast. I'm Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs. Lois: Hi there! If you've been listening to us these last few weeks, you'll know we've been discussing containerization, the Oracle Cloud Infrastructure Registry, and the basics of Kubernetes. Today, we'll dive into the world of OCI Container Engine for Kubernetes, also referred to as OKE.  Nikita: We're joined by Mahendra Mehra, a senior OCI instructor with Oracle University, who will take us through the key features and benefits of OKE and also talk about working with managed nodes. Hi Mahendra! Thanks for joining us today. 01:09 Lois: So, Mahendra, what is OKE exactly? Mahendra: Oracle Cloud Infrastructure Container Engine for Kubernetes is a fully managed, scalable, and highly available service that empowers you to effortlessly deploy your containerized applications to the cloud. But that's just the beginning. OKE can transform the way you and your development team build, deploy, and manage cloud native applications. 01:36 Nikita: What would you say are some of its most defining features?    Mahendra: One of the defining features of OKE is the flexibility it offers. You can specify whether you want to run your applications on virtual nodes or opt for managed nodes. Regardless of your choice, Container Engine for Kubernetes will efficiently provision them within your existing OCI tenancy on Oracle Cloud Infrastructure. Creating OKE cluster is a breeze, and you have a couple of fantastic tools at your disposal-- the console and the rest API. These make it super easy to get started. OKE relies on Kubernetes, which is an open-source system that simplifies the deployment, scaling, and management of containerized applications across clusters of hosts. Kubernetes is an incredible system that groups containers into logical units known as pods. And these pods make managing and discovering your applications very simple. Not to mention, Container Engine for Kubernetes uses Kubernetes versions that are certified as conformant by the Cloud Native Computing Foundation, also abbreviated as CNCF. And here's the icing on the cake. Container Engine for Kubernetes is ISO-compliant. The other two ISO-IEC standards—27001, 27017, and 27018. That's your guarantee of a secure and reliable platform.   03:08 Lois: That's great. But how do you access all this power? Mahendra: You can define and create your Kubernetes cluster using the intuitive console and the robust rest API. Once your clusters are up and running, you can manage them using the Kubernetes command line, also known as kubectl, the user-friendly Kubernetes dashboard, and the powerful Kubernetes API. 03:32 Nikita: I love the idea of an intuitive console and being able to manage everything from a centralized place. Lois: Yeah, that's fantastic! Mahendra, can you talk us through the magic that happens behind the scenes? What's Oracle's role in all this? Mahendra: All the master nodes or control plane nodes are managed by Oracle. This includes components like etcd, the API server, and the controller manager among others. To ensure reliability, we make sure multiple copies of these master components are distributed across different availability domains. And we don't stop there. We also manage the Kubernetes dashboard and even handle the self-healing mechanism of both the cluster and the worker nodes. All of these are meticulously created and managed within your Oracle tenancy. 04:19 Lois: And what happens at the user's end? What is their responsibility? Mahendra: At your end, you have the power to manage your worker nodes. Using different compute shapes, you can create and control them in your own user tenancy. So, as you can see, it's a perfect blend of Oracle's expertise and your control. 04:38 Nikita: So, in your opinion, why should users consider OKE their go-to solution for all things Kubernetes? Mahendra: Imagine a world where building and maintaining Kubernetes environments, be it master nodes or worker nodes, is no longer complex, costly, or even time-consuming. OKE is here to make your life easier by seamlessly integrating Kubernetes with various container life cycle management products, which includes container registries, CI/CD frameworks, networking solutions, storage options, and top-notch security features. And speaking of security, OKE gives you the tools you need to manage and control team access to production clusters, ensuring granular access to Kubernetes cluster in a straightforward process. It empowers developers to deploy containers quickly, provides devops teams with visibility and control for seamless Kubernetes management, and brings together Kubernetes container orchestration with Oracle's advanced cloud infrastructure. This results in robust control, top tier security, IAM, and consistent performance. 05:50 Nikita: OK…a lot of benefits! Mahendra, I know there have been ongoing enhancements to the OKE service. So, when creating a new cluster with Container Engine for Kubernetes, what is the cluster type we should specify?  Mahendra: The first type is the basic clusters. Basic clusters support all the core functionality provided by Kubernetes and Container Engine for Kubernetes. Basic clusters come with a service-level objective, but not a financially backed service level agreement. This means that Oracle guarantees a certain level of availability for the basic cluster, but there is no monetary compensation if that level is not met. On the other hand, we have the enhanced clusters. Enhanced clusters support all available features, including features not supported by basic clusters.  06:38 Lois: OK. So, can you tell us more about the features supported by enhanced clusters? Mahendra: As we move towards a more digitized world, the demand for infrastructure continues to rise. However, with virtual nodes, managing the infrastructure of your cluster becomes much simpler. The burden of manually scaling, upgrading, or troubleshooting worker nodes is removed, giving you more time to focus on your applications rather than the underlying infrastructure. Virtual nodes provide a great solution for managing large clusters with a high number of nodes that require frequent updates or scaling. With this feature, you can easily simplify the management of your cluster and focus on what really matters, that is your applications. Managing cluster add-ons can be a daunting task. But with enhanced clusters, you can now deploy and configure them in a more granular way. This means that you can manage both essential add-ons like CoreDNS and kube-proxy as well as a growing portfolio of optional add-ons like the Kubernetes Dashboard.  With enhanced clusters, you have complete control over the add-ons you install or disable, the ability to select specific add-on versions, and the option to opt-in or opt-out of automatic updates by Oracle. You can also manage add-on specific customizations to tailor your cluster to meet the needs of your application. 08:05 Lois: Do users need to worry about deploying add-ons themselves? Mahendra: Oracle manages the lifecycle of add-ons so that you don't have to worry about deploying them yourself. This level of control over add-ons gives you the flexibility to customize your cluster to meet the unique needs of your applications, making managing your cluster a breeze. 08:25 Lois: What about scaling? Mahendra: Scaling your clusters to meet the demands of your workload can be a challenging task. However, with enhanced clusters, you can now provision more worker nodes in a single cluster, allowing you to deploy larger workloads on the same cluster which can lead to better resource utilization and lower operational overhead. Having fewer larger environments to secure, monitor, upgrade, and manage is generally more efficient and can help you save on cost. Remember, there are limits to the number of worker nodes supported on an enhanced cluster, so you should review the Container Engine for Kubernetes limits documentation and consider the additional considerations when defining enhanced clusters with large number of managed nodes.  09:09  Nikita: Ensuring the security of my cluster would be of utmost importance to me, right? How would I do that with enhanced clusters? Mahendra: With enhanced clusters, you can now strengthen cluster security through the use of workload identity. Workload identity enables you to define OCI IAM policies that authorize specific pods to make OCI API calls and access OCI resources. By scoping the policies to Kubernetes service account associated with application pods, you can now allow the applications running inside those pods to directly access the API based on the permissions provided by the policies. 09:48 Nikita: Mahendra, what type of uptime and server availability benefits do enhanced clusters provide? Mahendra: You can now rely on a financially backed service level agreement tied to Kubernetes API server uptime and availability. This means that you can expect a certain level of uptime and availability for your Kubernetes API server, and if it degrades below the stated SLA, you'll receive compensation. This provides an extra level of assurance and helps ensure that your cluster is highly available and performant. 10:20 Lois: Mahendra, do you have any tips for us to remember when creating basic and enhanced clusters? Mahendra: When using the console to create a cluster, a new cluster is created as an enhanced cluster by default unless you explicitly choose to create a basic cluster. If you don't select any enhanced features during cluster creation, you have the option to create the new cluster as a basic cluster. When using CLI or API to create a cluster, you can specify whether to create a basic cluster or an enhanced cluster. If you don't explicitly specify the type of cluster to create, a new cluster is created as a basic cluster by default. Creating a new cluster as an enhanced cluster enables you to easily add enhanced features later even if you didn't select any enhanced features initially. If you do choose to create a new cluster as a basic cluster, you can still choose to upgrade the basic cluster to an enhanced cluster later on. However, you cannot downgrade an enhanced cluster to a basic cluster. These points are really important while you consider selection of a basic cluster or an enhanced cluster for your usage. 11:34 Do you want to stay ahead of the curve in the ever-evolving AI landscape? Look no further than our brand-new OCI Generative AI Professional course and certification. For a limited time only, we're offering both the course and certification for free! So, don't miss out on this exclusive opportunity to get certified on Generative AI at no cost. Act fast because this offer is valid only until July 31, 2024. Visit https://education.oracle.com/genai to get started. That's https://education.oracle.com/genai. 12:13 Nikita: Welcome back! I want to move on to serverless Kubernetes with virtual nodes. But I think before we do that, we first need to have a basic understanding of what managed nodes are.   Mahendra: Managed nodes run on compute instances within your tenancy, and are at least partly managed by you. In the context of Kubernetes, a node is a compute host that can be either a virtual machine or a bare metal host. As you are responsible for managing managed nodes, you have the flexibility to configure them to meet your specific requirements. You are responsible for upgrading Kubernetes on managed nodes and for managing cluster capacity. Nodes are responsible for running a collection of pods or containers, and they are comprised of two system components: the kubelet, which is the host brain, and the container runtime such as CRI-O, or containerd.  13:07 Nikita: Ok… so what are virtual nodes, then? Mahendra: Virtual nodes are fully managed and highly available nodes that look and act like real nodes to Kubernetes. They are built using the open source CNCF Virtual Kubelet Project, which provides the translation layer between OCI and Kubernetes. 13:25 Lois: So, what makes Oracle's managed virtual Kubernetes product different? Mahendra: OCI is the first major cloud provider to offer a fully managed virtual Kubelet product that provides a serverless Kubernetes experience through virtual nodes. Virtual nodes are configured by customers and are located within a single availability and fault domain within OCI. Virtual nodes have two main components: port management and container instance management. Virtual nodes delegates all the responsibility of managing the lifecycle of pods to virtual Kubernetes while on a managed node, the kubelet is responsible for managing all the lifecycle state. The key distinction of virtual nodes is that they support up to a 1,000 pods per virtual node with the expectation of supporting more in the future. 14:15 Nikita: What are the other benefits of virtual nodes? Mahendra: Virtual nodes offer a fully managed experience where customers don't have to worry about managing the underlying infrastructure of their containerized applications. Virtual nodes simplifies scaling patterns for customers. Customers can scale their containerized application up or down quickly without worrying about the underlying infrastructure, and they can focus solely on their applications. With virtual nodes, customers only pay for the resources that their containerized application use. This allows customers to optimize their costs and ensures that they are not paying for any unused resources. Virtual nodes can support over 10 times the number of pods that a normal node can. This means that customer can run more containerized applications on virtual nodes, which reduces operational burden and makes it easier to scale applications. Customers can leverage container instances in serverless offering from OCI to take advantage of many OCI functionalities natively. These functionalities include strong isolation and ultimate elasticity with respect to compute capacity. 15:26 Lois: When creating a cluster using Container Engine for Kubernetes, we have the flexibility to customize the worker nodes within the cluster, right? Could you tell us more about this customization? Mahendra: This customization includes specifying two key elements. Firstly, you can select the operating system image to be used for worker nodes. This image serves as a template for the worker node's virtual hard drive, and determines the operating system and other software installed. Secondly, you can choose the shape for your worker nodes. The shape defines the number of CPUs and the amount of memory allocated to each instance, ensuring it meets your specific requirements. This customization empowers you to tailor your OKE cluster to your exact needs. It is important to note that you can define and create OKE clusters using both the console and the REST API. This level of control is specially valuable for your development team when building, deploying, and managing cloud native applications.  You have the option to specify whether applications should run on virtual nodes or managed nodes. And Container Engine for Kubernetes efficiently provisions them on Oracle Cloud Infrastructure within your existing OCI tenancy. This flexibility ensures that you can adapt your OKE cluster to suit the specific requirements of your projects and workloads. 16:56 Lois: Thank you so much, Mahendra, for giving us your time today. For more on the topics we discussed, visit mylearn.oracle.com and look for the OCI Container Engine for Kubernetes Specialist course. Join us next week as we dive deeper into working with OKE virtual nodes. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 17:18 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

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Editor's note: One of the top reasons we have hundreds of companies and thousands of AI Engineers joining the World's Fair next week is, apart from discussing technology and being present for the big launches planned, to hire and be hired! Listeners loved our previous Elicit episode and were so glad to welcome 2 more members of Elicit back for a guest post (and bonus podcast) on how they think through hiring. Don't miss their AI engineer job description, and template which you can use to create your own hiring plan! How to Hire AI EngineersJames Brady, Head of Engineering @ Elicit (ex Spring, Square, Trigger.io, IBM)Adam Wiggins, Internal Journalist @ Elicit (Cofounder Ink & Switch and Heroku)If you're leading a team that uses AI in your product in some way, you probably need to hire AI engineers. As defined in this article, that's someone with conventional engineering skills in addition to knowledge of language models and prompt engineering, without being a full-fledged Machine Learning expert.But how do you hire someone with this skillset? At Elicit we've been applying machine learning to reasoning tools since 2018, and our technical team is a mix of ML experts and what we can now call AI engineers. This article will cover our process from job description through interviewing. (You can also flip the perspectives here and use it just as easily for how to get hired as an AI engineer!)My own journeyBefore getting into the brass tacks, I want to share my journey to becoming an AI engineer.Up until a few years ago, I was happily working my job as an engineering manager of a big team at a late-stage startup. Like many, I was tracking the rapid increase in AI capabilities stemming from the deep learning revolution, but it was the release of GPT-3 in 2020 which was the watershed moment. At the time, we were all blown away by how the model could string together coherent sentences on demand. (Oh how far we've come since then!)I'd been a professional software engineer for nearly 15 years—enough to have experienced one or two technology cycles—but I could see this was something categorically new. I found this simultaneously exciting and somewhat disconcerting. I knew I wanted to dive into this world, but it seemed like the only path was going back to school for a master's degree in Machine Learning. I started talking with my boss about options for taking a sabbatical or doing a part-time distance learning degree.In 2021, I instead decided to launch a startup focused on productizing new research ideas on ML interpretability. It was through that process that I reached out to Andreas—a leading ML researcher and founder of Elicit—to see if he would be an advisor. Over the next few months, I learned more about Elicit: that they were trying to apply these fascinating technologies to the real-world problems of science, and with a business model that aligned it with safety goals. I realized that I was way more excited about Elicit than I was about my own startup ideas, and wrote about my motivations at the time.Three years later, it's clear this was a seismic shift in my career on the scale of when I chose to leave my comfy engineering job at IBM to go through the Y Combinator program back in 2008. Working with this new breed of technology has been more intellectually stimulating, challenging, and rewarding than I could have imagined.Deep ML expertise not requiredIt's important to note that AI engineers are not ML experts, nor is that their best contribution to a tech team.In our article Living documents as an AI UX pattern, we wrote:It's easy to think that AI advancements are all about training and applying new models, and certainly this is a huge part of our work in the ML team at Elicit. But those of us working in the UX part of the team believe that we have a big contribution to make in how AI is applied to end-user problems.We think of LLMs as a new medium to work with, one that we've barely begun to grasp the contours of. New computing mediums like GUIs in the 1980s, web/cloud in the 90s and 2000s, and multitouch smartphones in the 2000s/2010s opened a whole new era of engineering and design practices. So too will LLMs open new frontiers for our work in the coming decade.To compare to the early era of mobile development: great iOS developers didn't require a detailed understanding of the physics of capacitive touchscreens. But they did need to know the capabilities and limitations of a multi-touch screen, the constrained CPU and storage available, the context in which the user is using it (very different from a webpage or desktop computer), etc.In the same way, an AI engineer needs to work with LLMs as a medium that is fundamentally different from other compute mediums. That means an interest in the ML side of things, whether through their own self-study, tinkering with prompts and model fine-tuning, or following along in #llm-paper-club. But this understanding is so that they can work with the medium effectively versus, say, spending their days training new models.Language models as a chaotic mediumSo if we're not expecting deep ML expertise from AI engineers, what are we expecting? This brings us to what makes LLMs different.We'll assume already that our ideal candidate is already inspired by, and full of ideas about, all the new capabilities AI can bring to software products. But the flip side is all the things that make this new medium difficult to work with. LLM calls are annoying due to high latency (measured in tens of seconds sometimes, rather than milliseconds), extreme variance on latency, high error rates even under normal operation. Not to mention getting extremely different answers to the same prompt provided to the same model on two subsequent calls!The net effect is that an AI engineer, even working at the application development level, needs to have a skillset comparable to distributed systems engineering. Handling errors, retries, asynchronous calls, streaming responses, parallelizing and recombining model calls, the halting problem, and fallbacks are just some of the day-in-the-life of an AI engineer. Chaos engineering gets new life in the era of AI.Skills and qualities in candidatesLet's put together what we don't need (deep ML expertise) with what we do (work with capabilities and limitations of the medium). Thus we start to see what Elicit looks for in AI engineers:* Conventional software engineering skills. Especially back-end engineering on complex, data-intensive applications.* Professional, real-world experience with applications at scale.* Deep, hands-on experience across a few back-end web frameworks.* Light devops and an understanding of infrastructure best practices.* Queues, message buses, event-driven and serverless architectures, … there's no single “correct” approach, but having a deep toolbox to draw from is very important.* A genuine curiosity and enthusiasm for the capabilities of language models.* One or more serious projects (side projects are fine) of using them in interesting ways on a unique domain.* …ideally with some level of factored cognition, e.g. breaking the problem down into chunks, making thoughtful decisions about which things to push to the language model and which stay within the realm of conventional heuristics and compute capabilities.* Personal studying with resources like Elicit's ML reading list. Part of the role is collaborating with the ML engineers and researchers on our team. To do so, the candidate needs to “speak their language” somewhat, just as a mobile engineer needs some familiarity with backends in order to collaborate effectively on API creation with backend engineers.* An understanding of the challenges that come along with working with large models (high latency, variance, etc.) leading to a defensive, fault-first mindset.* Careful and principled handling of error cases, asynchronous code (and ability to reason about and debug it), streaming data, caching, logging and analytics for understanding behavior in production.* This is a similar mindset that one can develop working on conventional apps which are complex, data-intensive, or large-scale apps. The difference is that an AI engineer will need this mindset even when working on relatively small scales!On net, a great AI engineer will combine two seemingly contrasting perspectives: knowledge of, and a sense of wonder for, the capabilities of modern ML models; but also the understanding that this is a difficult and imperfect foundation, and the willingness to build resilient and performant systems on top of it.Here's the resulting AI engineer job description for Elicit. And here's a template that you can borrow from for writing your own JD.Hiring processOnce you know what you're looking for in an AI engineer, the process is not too different from other technical roles. Here's how we do it, broken down into two stages: sourcing and interviewing.SourcingWe're primarily looking for people with (1) a familiarity with and interest in ML, and (2) proven experience building complex systems using web technologies. The former is important for culture fit and as an indication that the candidate will be able to do some light prompt engineering as part of their role. The latter is important because language model APIs are built on top of web standards and—as noted above—aren't always the easiest tools to work with.Only a handful of people have built complex ML-first apps, but fortunately the two qualities listed above are relatively independent. Perhaps they've proven (2) through their professional experience and have some side projects which demonstrate (1).Talking of side projects, evidence of creative and original prototypes is a huge plus as we're evaluating candidates. We've barely scratched the surface of what's possible to build with LLMs—even the current generation of models—so candidates who have been willing to dive into crazy “I wonder if it's possible to…” ideas have a huge advantage.InterviewingThe hard skills we spend most of our time evaluating during our interview process are in the “building complex systems using web technologies” side of things. We will be checking that the candidate is familiar with asynchronous programming, defensive coding, distributed systems concepts and tools, and display an ability to think about scaling and performance. They needn't have 10+ years of experience doing this stuff: even junior candidates can display an aptitude and thirst for learning which gives us confidence they'll be successful tackling the difficult technical challenges we'll put in front of them.One anti-pattern—something which makes my heart sink when I hear it from candidates—is that they have no familiarity with ML, but claim that they're excited to learn about it. The amount of free and easily-accessible resources available is incredible, so a motivated candidate should have already dived into self-study.Putting all that together, here's the interview process that we follow for AI engineer candidates:* 30-minute introductory conversation. Non-technical, explaining the interview process, answering questions, understanding the candidate's career path and goals.* 60-minute technical interview. This is a coding exercise, where we play product manager and the candidate is making changes to a little web app. Here are some examples of topics we might hit upon through that exercise:* Update API endpoints to include extra metadata. Think about appropriate data types. Stub out frontend code to accept the new data.* Convert a synchronous REST API to an asynchronous streaming endpoint.* Cancellation of asynchronous work when a user closes their tab.* Choose an appropriate data structure to represent the pending, active, and completed ML work which is required to service a user request.* 60–90 minute non-technical interview. Walk through the candidate's professional experience, identifying high and low points, getting a grasp of what kinds of challenges and environments they thrive in.* On-site interviews. Half a day in our office in Oakland, meeting as much of the team as possible: more technical and non-technical conversations.The frontier is wide openAlthough Elicit is perhaps further along than other companies on AI engineering, we also acknowledge that this is a brand-new field whose shape and qualities are only just now starting to form. We're looking forward to hearing how other companies do this and being part of the conversation as the role evolves.We're excited for the AI Engineer World's Fair as another next step for this emerging subfield. And of course, check out the Elicit careers page if you're interested in joining our team.Podcast versionTimestamps* [00:00:24] Intros* [00:05:25] Defining the Hiring Process* [00:08:42] Defensive AI Engineering as a chaotic medium* [00:10:26] Tech Choices for Defensive AI Engineering* [00:14:04] How do you Interview for Defensive AI Engineering* [00:19:25] Does Model Shadowing Work?* [00:22:29] Is it too early to standardize Tech stacks?* [00:32:02] Capabilities: Offensive AI Engineering* [00:37:24] AI Engineering Required Knowledge* [00:40:13] ML First Mindset* [00:45:13] AI Engineers and Creativity* [00:47:51] Inside of Me There Are Two Wolves* [00:49:58] Sourcing AI Engineers* [00:58:45] Parting ThoughtsTranscript[00:00:00] swyx: Okay, so welcome to the Latent Space Podcast. This is another remote episode that we're recording. This is the first one that we're doing around a guest post. And I'm very honored to have two of the authors of the post with me, James and Adam from Elicit. Welcome, James. Welcome, Adam.[00:00:22] James Brady: Thank you. Great to be here.[00:00:23] Hey there.[00:00:24] Intros[00:00:24] swyx: Okay, so I think I will do this kind of in order. I think James, you're, you're sort of the primary author. So James, you are head of engineering at Elicit. You also, We're VP Eng at Teespring and Spring as well. And you also , you have a long history in sort of engineering. How did you, , find your way into something like Elicit where, , it's, you, you are basically traditional sort of VP Eng, VP technology type person moving into a more of an AI role.[00:00:53] James Brady: Yeah, that's right. It definitely was something of a Sideways move if not a left turn. So the story there was I'd been doing, as you said, VP technology, CTO type stuff for around about 15 years or so, and Notice that there was this crazy explosion of capability and interesting stuff happening within AI and ML and language models, that kind of thing.[00:01:16] I guess this was in 2019 or so, and decided that I needed to get involved. , this is a kind of generational shift. And Spent maybe a year or so trying to get up to speed on the state of the art, reading papers, reading books, practicing things, that kind of stuff. Was going to found a startup actually in in the space of interpretability and transparency, and through that met Andreas, who has obviously been on the, on the podcast before asked him to be an advisor for my startup, and he countered with, maybe you'd like to come and run the engineering team at Elicit, which it turns out was a much better idea.[00:01:48] And yeah, I kind of quickly changed in that direction. So I think some of the stuff that we're going to be talking about today is how actually a lot of the work when you're building applications with AI and ML looks and smells and feels much more like conventional software engineering with a few key differences rather than really deep ML stuff.[00:02:07] And I think that's one of the reasons why I was able to transfer skills over from one place to the other.[00:02:12] swyx: Yeah, I[00:02:12] James Brady: definitely[00:02:12] swyx: agree with that. I, I do often say that I think AI engineering is about 90 percent software engineering with like the, the 10 percent of like really strong really differentiated AI engineering.[00:02:22] And that might, that obviously that number might change over time. I want to also welcome Adam onto my podcast because you welcomed me onto your podcast two years ago.[00:02:31] Adam Wiggins: Yeah, that was a wonderful episode.[00:02:32] swyx: That was, that was a fun episode. You famously founded Heroku. You just wrapped up a few years working on Muse.[00:02:38] And now you've described yourself as a journalist, internal journalist working on Elicit.[00:02:43] Adam Wiggins: Yeah, well I'm kind of a little bit in a wandering phase here and trying to take this time in between ventures to see what's out there in the world and some of my wandering took me to the Elicit team. And found that they were some of the folks who were doing the most interesting, really deep work in terms of taking the capabilities of language models and applying them to what I feel like are really important problems.[00:03:08] So in this case, science and literature search and, and, and that sort of thing. It fits into my general interest in tools and productivity software. I, I think of it as a tool for thought in many ways, but a tool for science, obviously, if we can accelerate that discovery of new medicines and things like that, that's, that's just so powerful.[00:03:24] But to me, it's a. It's kind of also an opportunity to learn at the feet of some real masters in this space, people who have been working on it since it was, before it was cool, if you want to put it that way. So for me, the last couple of months have been this crash course, and why I sometimes describe myself as an internal journalist is I'm helping to write some, some posts, including Supporting James in this article here we're doing for latent space where I'm just bringing my writing skill and that sort of thing to bear on their very deep domain expertise around language models and applying them to the real world and kind of surface that in a way that's I don't know, accessible, legible, that, that sort of thing.[00:04:03] And so, and the great benefit to me is I get to learn this stuff in a way that I don't think I would, or I haven't, just kind of tinkering with my own side projects.[00:04:12] swyx: I forgot to mention that you also run Ink and Switch, which is one of the leading research labs, in my mind, of the tools for thought productivity space, , whatever people mentioned there, or maybe future of programming even, a little bit of that.[00:04:24] As well. I think you guys definitely started the local first wave. I think there was just the first conference that you guys held. I don't know if you were personally involved.[00:04:31] Adam Wiggins: Yeah, I was one of the co organizers along with a few other folks for, yeah, called Local First Conf here in Berlin.[00:04:36] Huge success from my, my point of view. Local first, obviously, a whole other topic we can talk about on another day. I think there actually is a lot more what would you call it , handshake emoji between kind of language models and the local first data model. And that was part of the topic of the conference here, but yeah, topic for another day.[00:04:55] swyx: Not necessarily. I mean , I, I selected as one of my keynotes, Justine Tunney, working at LlamaFall in Mozilla, because I think there's a lot of people interested in that stuff. But we can, we can focus on the headline topic. And just to not bury the lead, which is we're talking about hire, how to hire AI engineers, this is something that I've been looking for a credible source on for months.[00:05:14] People keep asking me for my opinions. I don't feel qualified to give an opinion and it's not like I have. So that's kind of defined hiring process that I'm super happy with, even though I've worked with a number of AI engineers.[00:05:25] Defining the Hiring Process[00:05:25] swyx: I'll just leave it open to you, James. How was your process of defining your hiring, hiring roles?[00:05:31] James Brady: Yeah. So I think the first thing to say is that we've effectively been hiring for this kind of a role since before you, before you coined the term and tried to kind of build this understanding of what it was.[00:05:42] So, which is not a bad thing. Like it's, it was a, it was a good thing. A concept, a concept that was coming to the fore and effectively needed a name, which is which is what you did. So the reason I mentioned that is I think it was something that we kind of backed into, if you will. We didn't sit down and come up with a brand new role from, from scratch of this is a completely novel set of responsibilities and skills that this person would need.[00:06:06] However, it is a A kind of particular blend of different skills and attitudes and and curiosities interests, which I think makes sense to kind of bundle together. So in the, in the post, the three things that we say are most important for a highly effective AI engineer are first of all, conventional software engineering skills, which is Kind of a given, but definitely worth mentioning.[00:06:30] The second thing is a curiosity and enthusiasm for machine learning and maybe in particular language models. That's certainly true in our case. And then the third thing is to do with basically a fault first mindset, being able to build systems that can handle things going wrong in, in, in some sense.[00:06:49] And yeah, the I think the kind of middle point, the curiosity about ML and language models is probably fairly self evident. They're going to be working with, and prompting, and dealing with the responses from these models, so that's clearly relevant. The last point, though, maybe takes the most explaining.[00:07:07] To do with this fault first mindset and the ability to, to build resilient systems. The reason that is, is so important is because compared to normal APIs, where normal, think of something like a Stripe API or a search API or something like this. The latency when you're working with language models is, is wild, like you can get 10x variation.[00:07:32] I mean, I was looking at the stats before, actually, before, before the podcast. We do often, normally, in fact, see a 10x variation in the P90 latency over the course of, Half an hour, an hour when we're prompting these models, which is way higher than if you're working with a, more kind of conventional conventionally backed API.[00:07:49] And the responses that you get, the actual content and the responses are naturally unpredictable as well. They come back with different formats. Maybe you're expecting JSON. It's not quite JSON. You have to handle this stuff. And also the, the semantics of the messages are unpredictable too, which is, which is a good thing.[00:08:08] Like this is one of the things that you're looking for from these language models, but it all adds up to needing to. Build a resilient, reliable, solid feeling system on top of this fundamentally, well, certainly currently fundamentally shaky foundation. The models do not behave in the way that you would like them to.[00:08:28] And yeah, the ability to structure the code around them such that it does give the user this warm, reassuring, Snappy, solid feeling is is really what we're driving for there.[00:08:42] Defensive AI Engineering as a chaotic medium[00:08:42] Adam Wiggins: What really struck me as we, we dug in on the content for this article was that third point there. The, the language models is this kind of chaotic medium, this, this dragon, this wild horse you're, you're, you're riding and trying to guide in the direction that is going to be useful and reliable to users, because I think.[00:08:58] So much of software engineering is about making things not only high performance and snappy, but really just making it stable, reliable, predictable, which is literally the opposite of what you get from from the language models. And yet, yeah, the output is so useful, and indeed, some of their Creativity, if you want to call it that, which is, is precisely their value.[00:09:19] And so you need to work with this medium. And I guess the nuanced or the thing that came out of Elissa's experience that I thought was so interesting is quite a lot of working with that is things that come from distributed systems engineering. But you have really the AI engineers as we're defining them or, or labeling them on the illicit team is people who are really application developers.[00:09:39] You're building things for end users. You're thinking about, okay, I need to populate this interface with some response to user input. That's useful to the tasks they're trying to do, but you have this. This is the thing, this medium that you're working with that in some ways you need to apply some of this chaos engineering, distributed systems engineering, which typically those people with those engineering skills are not kind of the application level developers with the product mindset or whatever, they're more deep in the guts of a, of a system.[00:10:07] And so it's, those, those skills and, and knowledge do exist throughout the engineering discipline, but sort of putting them together into one person that is That feels like sort of a unique thing and working with the folks on the Elicit team who have that skills I'm quite struck by that unique that unique blend.[00:10:23] I haven't really seen that before in my 30 year career in technology.[00:10:26] Tech Choices for Defensive AI Engineering[00:10:26] swyx: Yeah, that's a Fascinating I like the reference to chaos engineering. I have some appreciation, I think when you had me on your podcast, I was still working at Temporal and that was like a nice Framework, if you live within Temporal's boundaries, you can pretend that all those faults don't exist, and you can, you can code in a sort of very fault tolerant way.[00:10:47] What is, what is you guys solutions around this, actually? Like, I think you're, you're emphasizing having the mindset, but maybe naming some technologies would help? Not saying that you have to adopt these technologies, but they're just, they're just quick vectors into what you're talking about when you're, when you're talking about distributed systems.[00:11:03] Like, that's such a big, chunky word, , like are we talking, are Kubernetes or, and I suspect we're not, , like we're, we're talking something else now.[00:11:10] James Brady: Yeah, that's right. It's more at the application level rather than at the infrastructure level, at least, at least the way that it works for us.[00:11:17] So there's nothing kind of radically novel here. It is more a careful application of existing concepts. So the kinds of tools that we reach for to handle these kind of slightly chaotic objects that Adam was just talking about, are retries and fallbacks and timeouts and careful error handling. And, yeah, the standard stuff, really.[00:11:39] There's also a great degree of dependence. We rely heavily on parallelization because, , these language models are not innately very snappy, and , there's just a lot of I. O. going back and forth. So All these things I'm talking about when I was in my earlier stages of a career, these are kind of the things that are the difficult parts that most senior software engineers will be better at.[00:12:01] It is careful error handling, and concurrency, and fallbacks, and distributed systems, and, , eventual consistency, and all this kind of stuff and As Adam was saying, the kind of person that is deep in the guts of some kind of distributed systems, a really high, high scale backend kind of a problem would probably naturally have these kinds of skills.[00:12:21] But you'll find them on, on day one, if you're building a, , an ML powered app, even if it's not got massive scale. I think one one thing that I would mention that we do do yeah, maybe, maybe two related things, actually. The first is we're big fans of strong typing. We share the types all the way from the Backend Python code all the way to the to the front end in TypeScript and find that is I mean We'd probably do this anyway But it really helps one reason around the shapes of the data which can going to be going back and forth and that's really important When you can't rely upon You you're going to have to coerce the data that you get back from the ML if you want if you want for it to be structured basically speaking and The second thing which is related is we use checked exceptions inside our Python code base, which means that we can use the type system to make sure we are handling, properly handling, all of the, the various things that could be going wrong, all the different exceptions that could be getting raised.[00:13:16] So, checked exceptions are not, not really particularly popular. Actually there's not many people that are big fans of them. For our particular use case, to really make sure that we've not just forgotten to handle, , This particular type of error we have found them useful to to, to force us to think about all the different edge cases that can come up.[00:13:32] swyx: Fascinating. How just a quick note of technology. How do you share types from Python to TypeScript? Do you, do you use GraphQL? Do you use something[00:13:39] James Brady: else? We don't, we don't use GraphQL. Yeah. So we've got the We've got the types defined in Python, that's the source of truth. And we go from the OpenAPI spec, and there's a, there's a tool that you work and use to generate types dynamically, like TypeScript types from those OpenAPI definitions.[00:13:57] swyx: Okay, excellent. Okay, cool. Sorry, sorry for diving into that rabbit hole a little bit. I always like to spell out technologies for people to dig their teeth into.[00:14:04] How do you Interview for Defensive AI Engineering[00:14:04] swyx: One thing I'll, one thing I'll mention quickly is that a lot of the stuff that you mentioned is typically not part of the normal interview loop.[00:14:10] It's actually really hard to interview for because this is the stuff that you polish out in, as you go into production, the coding interviews are typically about the happy path. How do we do that? How do we, how do we design, how do you look for a defensive fault first mindset?[00:14:24] Because you can defensive code all day long and not add functionality. to your to your application.[00:14:29] James Brady: Yeah, it's a great question and I think that's exactly true. Normally the interview is about the happy path and then there's maybe a box checking exercise at the end of the candidate says of course in reality I would handle the edge cases or something like this and that unfortunately isn't isn't quite good enough when when the happy path is is very very narrow and yeah there's lots of weirdness on either side so basically speaking, it's just a case of, of foregrounding those kind of concerns through the interview process.[00:14:58] It's, there's, there's no magic to it. We, we talk about this in the, in the po in the post that we're gonna be putting up on, on Laton space. The, there's two main technical exercises that we do through our interview process for this role. The first is more coding focus, and the second is more system designy.[00:15:16] Yeah. White whiteboarding a potential solution. And in, without giving too much away in the coding exercise. You do need to think about edge cases. You do need to think about errors. The exercise consists of adding features and fixing bugs inside the code base. And in both of those two cases, it does demand, because of the way that we set the application up and the interview up, it does demand that you think about something other than the happy path.[00:15:41] But your thinking is the right prompt of how do we get the candidate thinking outside of the, the kind of normal Sweet spot, smooth smooth, smoothly paved path. In terms of the system design interview, that's a little easier to prompt this kind of fault first mindset because it's very easy in that situation just to say, let's imagine that, , this node dies, how does the app still work?[00:16:03] Let's imagine that this network is, is going super slow. Let's imagine that, I don't know, like you, you run out of, you run out of capacity in, in, in this database that you've sketched out here, how do you handle that, that, that sort of stuff. So. It's, in both cases, they're not firmly anchored to and built specifically around language models and ways language models can go wrong, but we do exercise the same muscles of thinking defensively and yeah, foregrounding the edge cases, basically.[00:16:32] Adam Wiggins: James, earlier there you mentioned retries. And this is something that I think I've seen some interesting debates internally about things regarding, first of all, retries are, can be costly, right? In general, this medium, in addition to having this incredibly high variance and response rate, and, , being non deterministic, is actually quite expensive.[00:16:50] And so, in many cases, doing a retry when you get a fail does make sense, but actually that has an impact on cost. And so there is Some sense to which, at least I've seen the AI engineers on our team, worry about that. They worry about, okay, how do we give the best user experience, but balance that against what the infrastructure is going to, , is going to cost our company, which I think is again, an interesting mix of, yeah, again, it's a little bit the distributed system mindset, but it's also a product perspective and you're thinking about the end user experience, but also the.[00:17:22] The bottom line for the business, you're bringing together a lot of a lot of qualities there. And there's also the fallback case, which is kind of, kind of a related or adjacent one. I think there was also a discussion on that internally where, I think it maybe was search, there was something recently where there was one of the frontline search providers was having some, yeah, slowness and outages, and essentially then we had a fallback, but essentially that gave people for a while, especially new users that come in that don't the difference, they're getting a They're getting worse results for their search.[00:17:52] And so then you have this debate about, okay, there's sort of what is correct to do from an engineering perspective, but then there's also what actually is the best result for the user. Is giving them a kind of a worse answer to their search result better, or is it better to kind of give them an error and be like, yeah, sorry, it's not working right at the moment, try again.[00:18:12] Later, both are obviously non optimal, but but this is the kind of thing I think that that you run into or, or the kind of thing we need to grapple with a lot more than you would other kinds of, of mediums.[00:18:24] James Brady: Yeah, that's a really good example. I think it brings to the fore the two different things that you could be optimizing for of uptime and response at all costs on one end of the spectrum and then effectively fragility, but kind of, if you get a response, it's the best response we can come up with at the other end of the spectrum.[00:18:43] And where you want to land there kind of depends on, well, it certainly depends on the app, obviously depends on the user. I think it depends on the, feature within the app as well. So in the search case that you, that you mentioned there, in retrospect, we probably didn't want to have the fallback. And we've actually just recently on Monday, changed that to Show an error message rather than giving people a kind of degraded experience in other situations We could use for example a large language model from a large language model from provider B rather than provider A and Get something which is within the A few percentage points performance, and that's just a really different situation.[00:19:21] So yeah, like any interesting question, the answer is, it depends.[00:19:25] Does Model Shadowing Work?[00:19:25] swyx: I do hear a lot of people suggesting I, let's call this model shadowing as a defensive technique, which is, if OpenAI happens to be down, which, , happens more often than people think then you fall back to anthropic or something.[00:19:38] How realistic is that, right? Like you, don't you have to develop completely different prompts for different models and won't the, won't the performance of your application suffer from whatever reason, right? Like it may be caused differently or it's not maintained in the same way. I, I think that people raise this idea of fallbacks to models, but I don't think it's, I don't, I don't see it practiced very much.[00:20:02] James Brady: Yeah, it is, you, you definitely need to have a different prompt if you want to stay within a few percentage points degradation Like I, like I said before, and that certainly comes at a cost, like fallbacks and backups and things like this It's really easy for them to go stale and kind of flake out on you because they're off the beaten track And In our particular case inside of Elicit, we do have fallbacks for a number of kind of crucial functions where it's going to be very obvious if something has gone wrong, but we don't have fallbacks in all cases.[00:20:40] It really depends on a task to task basis throughout the app. So I can't give you a kind of a, a single kind of simple rule of thumb for, in this case, do this. And in the other, do that. But yeah, we've it's a little bit easier now that the APIs between the anthropic models and opening are more similar than they used to be.[00:20:59] So we don't have two totally separate code paths with different protocols, like wire protocols to, to speak, which makes things easier, but you're right. You do need to have different prompts if you want to, have similar performance across the providers.[00:21:12] Adam Wiggins: I'll also note, just observing again as a relative newcomer here, I was surprised, impressed, not sure what the word is for it, at the blend of different backends that the team is using.[00:21:24] And so there's many The product presents as kind of one single interface, but there's actually several dozen kind of main paths. There's like, for example, the search versus a data extraction of a certain type, versus chat with papers, versus And each one of these, , the team has worked very hard to pick the right Model for the job and craft the prompt there, but also is constantly testing new ones.[00:21:48] So a new one comes out from either, from the big providers or in some cases, Our own models that are , running on, on essentially our own infrastructure. And sometimes that's more about cost or performance, but the point is kind of switching very fluidly between them and, and very quickly because this field is moving so fast and there's new ones to choose from all the time is like part of the day to day, I would say.[00:22:11] So it isn't more of a like, there's a main one, it's been kind of the same for a year, there's a fallback, but it's got cobwebs on it. It's more like which model and which prompt is changing weekly. And so I think it's quite, quite reasonable to to, to, to have a fallback that you can expect might work.[00:22:29] Is it too early to standardize Tech stacks?[00:22:29] swyx: I'm curious because you guys have had experience working at both, , Elicit, which is a smaller operation and, and larger companies. A lot of companies are looking at this with a certain amount of trepidation as, as, , it's very chaotic. When you have, when you have , one engineering team that, that, knows everyone else's names and like, , they, they, they, they meet constantly in Slack and knows what's going on.[00:22:50] It's easier to, to sync on technology choices. When you have a hundred teams, all shipping AI products and all making their own independent tech choices. It can be, it can be very hard to control. One solution I'm hearing from like the sales forces of the worlds and Walmarts of the world is that they are creating their own AI gateway, right?[00:23:05] Internal AI gateway. This is the one model hub that controls all the things and has our standards. Is that a feasible thing? Is that something that you would want? Is that something you have and you're working towards? What are your thoughts on this stuff? Like, Centralization of control or like an AI platform internally.[00:23:22] James Brady: Certainly for larger organizations and organizations that are doing things which maybe are running into HIPAA compliance or other, um, legislative tools like that. It could make a lot of sense. Yeah. I think for the TLDR for something like Elicit is we are small enough, as you indicated, and need to have full control over all the levers available and switch between different models and different prompts and whatnot, as Adam was just saying, that that kind of thing wouldn't work for us.[00:23:52] But yeah, I've spoken with and, um, advised a couple of companies that are trying to sell into that kind of a space or at a larger stage, and it does seem to make a lot of sense for them. So, for example, if you're trying to sell If you're looking to sell to a large enterprise and they cannot have any data leaving the EU, then you need to be really careful about someone just accidentally putting in, , the sort of US East 1 GPT 4 endpoints or something like this.[00:24:22] I'd be interested in understanding better what the specific problem is that they're looking to solve with that, whether it is to do with data security or centralization of billing, or if they have a kind of Suite of prompts or something like this that people can choose from so they don't need to reinvent the wheel again and again I wouldn't be able to say without understanding the problems and their proposed solutions , which kind of situations that be better or worse fit for but yeah for illicit where really the The secret sauce, if there is a secret sauce, is which models we're using, how we're using them, how we're combining them, how we're thinking about the user problem, how we're thinking about all these pieces coming together.[00:25:02] You really need to have all of the affordances available to you to be able to experiment with things and iterate rapidly. And generally speaking, whenever you put these kind of layers of abstraction and control and generalization in there, that, that gets in the way. So, so for us, it would not work.[00:25:19] Adam Wiggins: Do you feel like there's always a tendency to want to reach for standardization and abstractions pretty early in a new technology cycle?[00:25:26] There's something comforting there, or you feel like you can see them, or whatever. I feel like there's some of that discussion around lang chain right now. But yeah, this is not only so early, but also moving so fast. , I think it's . I think it's tough to, to ask for that. That's, that's not the, that's not the space we're in, but the, yeah, the larger an organization, the more that's your, your default is to, to, to want to reach for that.[00:25:48] It, it, it's a sort of comfort.[00:25:51] swyx: Yeah, I find it interesting that you would say that , being a founder of Heroku where , you were one of the first platforms as a service that more or less standardized what, , that sort of early developer experience should have looked like.[00:26:04] And I think basically people are feeling the differences between calling various model lab APIs and having an actual AI platform where. , all, all their development needs are thought of for them. , it's, it's very much, and, and I, I defined this in my AI engineer post as well.[00:26:19] Like the model labs just see their job ending at serving models and that's about it. But actually the responsibility of the AI engineer has to fill in a lot of the gaps beyond that. So.[00:26:31] Adam Wiggins: Yeah, that's true. I think, , a huge part of the exercise with Heroku, which It was largely inspired by Rails, which itself was one of the first frameworks to standardize the SQL database.[00:26:42] And people had been building apps like that for many, many years. I had built many apps. I had made my own templates based on that. I think others had done it. And Rails came along at the right moment. We had been doing it long enough that you see the patterns and then you can say look let's let's extract those into a framework that's going to make it not only easier to build for the experts but for people who are relatively new the best practices are encoded into you.[00:27:07] That framework, , Model View Controller, to take one example. But then, yeah, once you see that, and once you experience the power of a framework, and again, it's so comforting, and you can develop faster, and it's easier to onboard new people to it because you have these standards. And this consistency, then folks want that for something new that's evolving.[00:27:29] Now here I'm thinking maybe if you fast forward a little to, for example, when React came on the on the scene, , a decade ago or whatever. And then, okay, we need to do state management. What's that? And then there's, , there's a new library every six months. Okay, this is the one, this is the gold standard.[00:27:42] And then, , six months later, that's deprecated. Because of course, it's evolving, you need to figure it out, like the tacit knowledge and the experience of putting it in practice and seeing what those real What those real needs are are, are critical, and so it's, it is really about finding the right time to say yes, we can generalize, we can make standards and abstractions, whether it's for a company, whether it's for, , a library, an open source library, for a whole class of apps and it, it's very much a, much more of a A judgment call slash just a sense of taste or , experience to be able to say, Yeah, we're at the right point.[00:28:16] We can standardize this. But it's at least my, my very, again, and I'm so new to that, this world compared to you both, but my, my sense is, yeah, still the wild west. That's what makes it so exciting and feels kind of too early for too much. too much in the way of standardized abstractions. Not that it's not interesting to try, but , you can't necessarily get there in the same way Rails did until you've got that decade of experience of whatever building different classes of apps in that, with that technology.[00:28:45] James Brady: Yeah, it's, it's interesting to think about what is going to stay more static and what is expected to change over the coming five years, let's say. Which seems like when I think about it through an ML lens, it's an incredibly long time. And if you just said five years, it doesn't seem, doesn't seem that long.[00:29:01] I think that, that kind of talks to part of the problem here is that things that are moving are moving incredibly quickly. I would expect, this is my, my hot take rather than some kind of official carefully thought out position, but my hot take would be something like the You can, you'll be able to get to good quality apps without doing really careful prompt engineering.[00:29:21] I don't think that prompt engineering is going to be a kind of durable differential skill that people will, will hold. I do think that, The way that you set up the ML problem to kind of ask the right questions, if you see what I mean, rather than the specific phrasing of exactly how you're doing chain of thought or few shot or something in the prompt I think the way that you set it up is, is probably going to be remain to be trickier for longer.[00:29:47] And I think some of the operational challenges that we've been talking about of wild variations in, in, in latency, And handling the, I mean, one way to think about these models is the first lesson that you learn when, when you're an engineer, software engineer, is that you need to sanitize user input, right?[00:30:05] It was, I think it was the top OWASP security threat for a while. Like you, you have to sanitize and validate user input. And we got used to that. And it kind of feels like this is the, The shell around the app and then everything else inside you're kind of in control of and you can grasp and you can debug, etc.[00:30:22] And what we've effectively done is, through some kind of weird rearguard action, we've now got these slightly chaotic things. I think of them more as complex adaptive systems, which , related but a bit different. Definitely have some of the same dynamics. We've, we've injected these into the foundations of the, of the app and you kind of now need to think with this defined defensive mindset downwards as well as upwards if you, if you see what I mean.[00:30:46] So I think it would gonna, it's, I think it will take a while for us to truly wrap our heads around that. And also these kinds of problems where you have to handle things being unreliable and slow sometimes and whatever else, even if it doesn't happen very often, there isn't some kind of industry wide accepted way of handling that at massive scale.[00:31:10] There are definitely patterns and anti patterns and tools and whatnot, but it's not like this is a solved problem. So I would expect that it's not going to go down easily as a, as a solvable problem at the ML scale either.[00:31:23] swyx: Yeah, excellent. I would describe in, in the terminology of the stuff that I've written in the past, I describe this inversion of architecture as sort of LLM at the core versus LLM or code at the core.[00:31:34] We're very used to code at the core. Actually, we can scale that very well. When we build LLM core apps, we have to realize that the, the central part of our app that's orchestrating things is actually prompt, prone to, , prompt injections and non determinism and all that, all that good stuff.[00:31:48] I, I did want to move the conversation a little bit from the sort of defensive side of things to the more offensive or, , the fun side of things, capabilities side of things, because that is the other part. of the job description that we kind of skimmed over. So I'll, I'll repeat what you said earlier.[00:32:02] Capabilities: Offensive AI Engineering[00:32:02] swyx: It's, you want people to have a genuine curiosity and enthusiasm for the capabilities of language models. We just, we're recording this the day after Anthropic just dropped Cloud 3. 5. And I was wondering, , maybe this is a good, good exercise is how do people have Curiosity and enthusiasm for capabilities language models when for example the research paper for cloud 3.[00:32:22] 5 is four pages[00:32:23] James Brady: Maybe that's not a bad thing actually in this particular case So yeah If you really want to know exactly how the sausage was made That hasn't been possible for a few years now in fact for for these new models but from our perspective as when we're building illicit What we primarily care about is what can these models do?[00:32:41] How do they perform on the tasks that we already have set up and the evaluations we have in mind? And then on a slightly more expansive note, what kinds of new capabilities do they seem to have? Can we elicit, no pun intended, from the models? For example, well, there's, there's very obvious ones like multimodality , there wasn't that and then there was that, or it could be something a bit more subtle, like it seems to be getting better at reasoning, or it seems to be getting better at metacognition, or Or it seems to be getting better at marking its own work and giving calibrated confidence estimates, things like this.[00:33:19] So yeah, there's, there's plenty to be excited about there. It's just that yeah, there's rightly or wrongly been this, this, this shift over the last few years to not give all the details. So no, but from application development perspective we, every time there's a new model release, there's a flow of activity in our Slack, and we try to figure out what's going on.[00:33:38] What it can do, what it can't do, run our evaluation frameworks, and yeah, it's always an exciting, happy day.[00:33:44] Adam Wiggins: Yeah, from my perspective, what I'm seeing from the folks on the team is, first of all, just awareness of the new stuff that's coming out, so that's, , an enthusiasm for the space and following along, and then being able to very quickly, partially that's having Slack to do this, but be able to quickly map that to, okay, What does this do for our specific case?[00:34:07] And that, the simple version of that is, let's run the evaluation framework, which Lissa has quite a comprehensive one. I'm actually working on an article on that right now, which I'm very excited about, because it's a very interesting world of things. But basically, you can just try, not just, but try the new model in the evaluations framework.[00:34:27] Run it. It has a whole slew of benchmarks, which includes not just Accuracy and confidence, but also things like performance, cost, and so on. And all of these things may trade off against each other. Maybe it's actually, it's very slightly worse, but it's way faster and way cheaper, so actually this might be a net win, for example.[00:34:46] Or, it's way more accurate. But that comes at its slower and higher cost, and so now you need to think about those trade offs. And so to me, coming back to the qualities of an AI engineer, especially when you're trying to hire for them, It's this, it's, it is very much an application developer in the sense of a product mindset of What are our users or our customers trying to do?[00:35:08] What problem do they need solved? Or what what does our product solve for them? And how does the capabilities of a particular model potentially solve that better for them than what exists today? And by the way, what exists today is becoming an increasingly gigantic cornucopia of things, right? And so, You say, okay, this new model has these capabilities, therefore, , the simple version of that is plug it into our existing evaluations and just look at that and see if it, it seems like it's better for a straight out swap out, but when you talk about, for example, you have multimodal capabilities, and then you say, okay, wait a minute, actually, maybe there's a new feature or a whole new There's a whole bunch of ways we could be using it, not just a simple model swap out, but actually a different thing we could do that we couldn't do before that would have been too slow, or too inaccurate, or something like that, that now we do have the capability to do.[00:35:58] I think of that as being a great thing. I don't even know if I want to call it a skill, maybe it's even like an attitude or a perspective, which is a desire to both be excited about the new technology, , the new models and things as they come along, but also holding in the mind, what does our product do?[00:36:16] Who is our user? And how can we connect the capabilities of this technology to how we're helping people in whatever it is our product does?[00:36:25] James Brady: Yeah, I'm just looking at one of our internal Slack channels where we talk about things like new new model releases and that kind of thing And it is notable looking through these the kind of things that people are excited about and not It's, I don't know the context, the context window is much larger, or it's, look at how many parameters it has, or something like this.[00:36:44] It's always framed in terms of maybe this could be applied to that kind of part of Elicit, or maybe this would open up this new possibility for Elicit. And, as Adam was saying, yeah, I don't think it's really a I don't think it's a novel or separate skill, it's the kind of attitude I would like to have all engineers to have at a company our stage, actually.[00:37:05] And maybe more generally, even, which is not just kind of getting nerd sniped by some kind of technology number, fancy metric or something, but how is this actually going to be applicable to the thing Which matters in the end. How is this going to help users? How is this going to help move things forward strategically?[00:37:23] That kind of, that kind of thing.[00:37:24] AI Engineering Required Knowledge[00:37:24] swyx: Yeah, applying what , I think, is, is, is the key here. Getting hands on as well. I would, I would recommend a few resources for people listening along. The first is Elicit's ML reading list, which I, I found so delightful after talking with Andreas about it.[00:37:38] It looks like that's part of your onboarding. We've actually set up an asynchronous paper club instead of my discord for people following on that reading list. I love that you separate things out into tier one and two and three, and that gives people a factored cognition way of Looking into the, the, the corpus, right?[00:37:55] Like yes, the, the corpus of things to know is growing and the water is slowly rising as far as what a bar for a competent AI engineer is. But I think, , having some structured thought as to what are the big ones that everyone must know I think is, is, is key. It's something I, I haven't really defined for people and I'm, I'm glad that this is actually has something out there that people can refer to.[00:38:15] Yeah, I wouldn't necessarily like make it required for like the job. Interview maybe, but , it'd be interesting to see like, what would be a red flag. If some AI engineer would not know, I don't know what, , I don't know where we would stoop to, to call something required knowledge, , or you're not part of the cool kids club.[00:38:33] But there increasingly is something like that, right? Like, not knowing what context is, is a black mark, in my opinion, right?[00:38:40] I think it, I think it does connect back to what we were saying before of this genuine Curiosity about and that. Well, maybe it's, maybe it's actually that combined with something else, which is really important, which is a self starting bias towards action, kind of a mindset, which again, everybody needs.[00:38:56] Exactly. Yeah. Everyone needs that. So if you put those two together, or if I'm truly curious about this and I'm going to kind of figure out how to make things happen, then you end up with people. Reading, reading lists, reading papers, doing side projects, this kind of, this kind of thing. So it isn't something that we explicitly included.[00:39:14] We don't have a, we don't have an ML focused interview for the AI engineer role at all, actually. It doesn't really seem helpful. The skills which we are checking for, as I mentioned before, this kind of fault first mindset. And conventional software engineering kind of thing. It's, it's 0. 1 and 0.[00:39:32] 3 on the list that, that we talked about. In terms of checking for ML curiosity and there are, how familiar they are with these concepts. That's more through talking interviews and culture fit types of things. We want for them to have a take on what Elisa is doing. doing, certainly as they progress through the interview process.[00:39:50] They don't need to be completely up to date on everything we've ever done on day zero. Although, , that's always nice when it happens. But for them to really engage with it, ask interesting questions, and be kind of bought into our view on how we want ML to proceed. I think that is really important, and that would reveal that they have this kind of this interest, this ML curiosity.[00:40:13] ML First Mindset[00:40:13] swyx: There's a second aspect to that. I don't know if now's the right time to talk about it, which is, I do think that an ML first approach to building software is something of a different mindset. I could, I could describe that a bit now if that, if that seems good, but yeah, I'm a team. Okay. So yeah, I think when I joined Elicit, this was the biggest adjustment that I had to make personally.[00:40:37] So as I said before, I'd been, Effectively building conventional software stuff for 15 years or so, something like this, well, for longer actually, but professionally for like 15 years. And had a lot of pattern matching built into my brain and kind of muscle memory for if you see this kind of problem, then you do that kind of a thing.[00:40:56] And I had to unlearn quite a lot of that when joining Elicit because we truly are ML first and try to use ML to the fullest. And some of the things that that means is, This relinquishing of control almost, at some point you are calling into this fairly opaque black box thing and hoping it does the right thing and dealing with the stuff that it sends back to you.[00:41:17] And that's very different if you're interacting with, again, APIs and databases, that kind of a, that kind of a thing. You can't just keep on debugging. At some point you hit this, this obscure wall. And I think the second, the second part to this is the pattern I was used to is that. The external parts of the app are where most of the messiness is, not necessarily in terms of code, but in terms of degrees of freedom, almost.[00:41:44] If the user can and will do anything at any point, and they'll put all sorts of wonky stuff inside of text inputs, and they'll click buttons you didn't expect them to click, and all this kind of thing. But then by the time you're down into your SQL queries, for example, as long as you've done your input validation, things are pretty pretty well defined.[00:42:01] And that, as we said before, is not really the case. When you're working with language models, there is this kind of intrinsic uncertainty when you get down to the, to the kernel, down to the core. Even, even beyond that, there's all that stuff is somewhat defensive and these are things to be wary of to some degree.[00:42:18] Though the flip side of that, the really kind of positive part of taking an ML first mindset when you're building applications is that you, If you, once you get comfortable taking your hands off the wheel at a certain point and relinquishing control, letting go then really kind of unexpected powerful things can happen if you lean on the, if you lean on the capabilities of the model without trying to overly constrain and slice and dice problems with to the point where you're not really wringing out the most capability from the model that you, that you might.[00:42:47] So, I was trying to think of examples of this earlier, and one that came to mind was we were working really early when just after I joined Elicit, we were working on something where we wanted to generate text and include citations embedded within it. So it'd have a claim, and then a, , square brackets, one, in superscript, something, something like this.[00:43:07] And. Every fiber in my, in my, in my being was screaming that we should have some way of kind of forcing this to happen or Structured output such that we could guarantee that this citation was always going to be present later on that the kind of the indication of a footnote would actually match up with the footnote itself and Kind of went into this symbolic.[00:43:28] I need full control kind of kind of mindset and it was notable that Andreas Who's our CEO, again, has been on the podcast, was was the opposite. He was just kind of, give it a couple of examples and it'll probably be fine. And then we can kind of figure out with a regular expression at the end. And it really did not sit well with me, to be honest.[00:43:46] I was like, but it could say anything. I could say, it could literally say anything. And I don't know about just using a regex to sort of handle this. This is a potent feature of the app. But , this is that was my first kind of, , The starkest introduction to this ML first mindset, I suppose, which Andreas has been cultivating for much longer than me, much longer than most, of yeah, there might be some surprises of stuff you get back from the model, but you can also It's about finding the sweet spot, I suppose, where you don't want to give a completely open ended prompt to the model and expect it to do exactly the right thing.[00:44:25] You can ask it too much and it gets confused and starts repeating itself or goes around in loops or just goes off in a random direction or something like this. But you can also over constrain the model. And not really make the most of the, of the capabilities. And I think that is a mindset adjustment that most people who are coming into AI engineering afresh would need to make of yeah, giving up control and expecting that there's going to be a little bit of kind of extra pain and defensive stuff on the tail end, but the benefits that you get as a, as a result are really striking.[00:44:58] The ML first mindset, I think, is something that I struggle with as well, because the errors, when they do happen, are bad. , they will hallucinate, and your systems will not catch it sometimes if you don't have large enough of a sample set.[00:45:13] AI Engineers and Creativity[00:45:13] swyx: I'll leave it open to you, Adam. What else do you think about when you think about curiosity and exploring capabilities?[00:45:22] Do people are there reliable ways to get people to push themselves? for joining us on Capabilities, because I think a lot of times we have this implicit overconfidence, maybe, of we think we know what it is, what a thing is, when actually we don't, and we need to keep a more open mind, and I think you do a particularly good job of Always having an open mind, and I want to get that out of more engineers that I talk to, but I, I, I, I struggle sometimes.[00:45:45] Adam Wiggins: I suppose being an engineer is, at its heart, this sort of contradiction of, on one hand, yeah,

Håkan Fahlstedt kommer förbi och berättar om ännu ett härligt sätt att lämna IT-fåran: Starta godisbutik i Småland! Två veckor innan påsk dessutom. En del sena nätter och ett ton godis senare är alla väldigt nöjda, och ambitionen har bara höjts.Längs vägen diskuteras också hur bortskämd man är med till exempel marginaler i IT-branschen, varför det inte känns kul med e-handel, och hur man kan göra affär av att sortera Lego.Länkarbrtk.sebartek@brtk.seHåkan FahlstedtEriksmålaEmmabodaErlangAvsnittet med Mårten RångeJan Skansholm verkade en period skriva alla böcker om programmering på svenskaGodisaffärenCRMNo code/low codeHåkans LegoförsäljningTheme parkBricklinkbigganskonfektyr.seCitatVad håller man på med?Men nu sa hon jaVarenda order saknade någontingVärlden är inte ett REST-APIBehöva lita på PostnordHundra procent godishandlare

// Abstract Diego, David, Ads, and Katharine, bring to light the risks, vulnerabilities, and evolving security landscape of machine learning as we venture into the AI-driven future. They underscore the importance of education in managing AI risks and the critical role privacy engineering plays in this narrative. They explore the legal and ethical implications of AI technologies, fostering a vital conversation on the balance between utility and privacy. // Bio Diego Oppenheimer - Moderator Diego Oppenheimer is a serial entrepreneur, product developer and investor with an extensive background in all things data. Currently, he is a Partner at Factory a venture fund specialized in AI investments as well as a co-founder at Guardrails AI. Previously he was an executive vice president at DataRobot, Founder and CEO at Algorithmia (acquired by DataRobot) and shipped some of Microsoft's most used data analysis products including Excel, PowerBI and SQL Server. Diego is active in AI/ML communities as a founding member and strategic advisor for the AI Infrastructure Alliance and MLops.Community and works with leaders to define AI industry standards and best practices. Diego holds a Bachelor's degree in Information Systems and a Masters degree in Business Intelligence and Data Analytics from Carnegie Mellon University. Ads Dawson A mainly self-taught, driven, and motivated proficient application, network infrastructure & cyber security professional holding over eleven years experience from start-up to large-size enterprises leading the incident response process and specializing in extensive LLM/AI Security, Web Application Security and DevSecOps protecting REST API endpoints, large-scale microservice architectures in hybrid cloud environments, application source code as well as EDR, threat hunting, reverse engineering, and forensics. Ads have a passion for all things blue and red teams, be that offensive & API security, automation of detection & remediation (SOAR), or deep packet inspection for example. Ads is also a networking veteran and love a good PCAP to delve into. One of my favorite things at Defcon is hunting for PWNs at the "Wall of Sheep" village and inspecting malicious payloads and binaries. Katharine Jarmul Katharine Jarmul is a privacy activist and data scientist whose work and research focuses on privacy and security in data science workflows. She recently authored Practical Data Privacy for O'Reilly and works as a Principal Data Scientist at Thoughtworks. Katharine has held numerous leadership and independent contributor roles at large companies and startups in the US and Germany -- implementing data processing and machine learning systems with privacy and security built in and developing forward-looking, privacy-first data strategy. David Haber David has started and grown several technology companies. He developed safety-critical AI in the healthcare space and for autonomous flight. David has educated thousands of people and Fortune 500 companies on the topic of AI. Outside of work, he loves to spend time with his family and enjoys training for the next Ironman. A big thank you to our Premium Sponsors,  @Databricks  and  @baseten  for their generous support! // Sign up for our Newsletter to never miss an event: https://mlops.community/join/ // Watch all the conference videos here: https://home.mlops.community/home/collections // Check out the MLOps Community podcast: https://open.spotify.com/show/7wZygk3mUUqBaRbBGB1lgh?si=242d3b9675654a69 // Read our blog: mlops.community/blog // Join an in-person local meetup near you: https://mlops.community/meetups/ // MLOps Swag/Merch: https://mlops-community.myshopify.com/ // Follow us on Twitter: https://twitter.com/mlopscommunity //Follow us on Linkedin: https://www.linkedin.com/company/mlopscommunity/

In this episode of Ruby for All, Andrew and Julie chat about their recent experiences, including a ski trip with challenges due to a storm, and discuss burnout and returning to regular podcasting. Special guest, Ryan Caldwell, a software engineer at GitHub working on Copilot, joins the conversation to discuss his work, particularly on chat-related features of Copilot.  Ryan shares insights on programming languages, leaning into his transitions between Ruby, Java, and Go, and navigating the differences between dynamically and statically typed languages.  The conversation covers the benefits and challenges of implementing type checking in Ruby with Sorbet, especially in large projects like GitHub.  Ryan advocates for learning Ruby on Rails, praises its efficiency for staring profitable projects, and provides tips for using Copilot Chat effectively.  Press download now to hear more! [00:00:23] Julie fills us in on a recent skiing trip to went on in California, the huge storm they encountered and leaving early to avoid being stranded, the broken chain on their car, and a scary moment on a slope with her kids. Andrew shares he experienced burnout but sees improvement. [00:02:47] Ryan Caldwell introduces himself and tells us what he does. [00:03:53] Andrew asks Ryan about the programming languages used for Copilot, leading to a discussion about using Go for its REST API, the manageability of the project, and Ryan's transition from Codespaces to Copilot after paternity leave. [00:04:49] Andrew wonders why Go was chosen, and Ryan explains the team's familiarity with Go and the language's simplicity. [00:06:12] Ryan reflects his first programming language and journey through JavaScript, Python, Java, and Ruby, highlighting his appreciation for Ruby. He talks about learning Ruby on the job, and his fondness for Rails. [00:08:02] Ryan discusses the challenges of picking up new languages and his approach to learning through project involvement. [00:09:24] Andrew asks about the shift from dynamic to typed languages, and Ryan shares his experiences transitioning from Ruby to Go. [00:11:53] We hear about Ryan's work on type checking with Sorbet at GitHub, and he shares that Sorbet helped find edge cases and bugs, improving the code by requiring changes to the structure to prevent these issues. [00:15:09] Ryan feels the biggest benefit of Sorbet is enforcing developers to consider boundaries and contracts between classes, which encourages thoughtful coding and design. A downside he mentions is the time and confusion involved in the migration process, particularly for team members unfamiliar with the new syntax.[00:17:11] Julie inquires if Ryan would do anything differently regarding Sorbet implementation. He reflects on the challenge of estimating the time required for implementing Sorbet, dealing with complex code, and the difficulty of refactoring legacy code without comprehensive tests. [00:18:44] Would Ryan go back to Ruby/Rails without Sorbet? He states that he would for personal projects for speed but appreciates Sorbet in team settings for defining clear code boundaries. [00:19:31] Ryan suggests that small teams should consider Sorbet if it solves a specific problem, rather than adopting it without a clear purpose. [00:21:40] Ryan discusses his pride in streamlining the authentication process across different clients in Copilot, leading to a simplified codebase for the team. A tip he shares is to provide as much context as possible when using Copilot Chat to get better responses. [00:25:35] Andrew talks about custom instructions for ChatGPT, like ensuring all output is in bullet points, and wonders if such a feature exists for Copilot.[00:28:46] Ryan advises newer developers to be intentional about what they chose to learn in software development, emphasizing the importance of investing learning time wisely. And yes, Ruby on Rails is still worth learning in 2024. [00:31:03] Find out where you can follow Ryan on the interwebs.Panelists:Andrew MasonJulie J.Guest:Ryan CaldwellSponsors:HoneybadgerGoRailsLinks:Andrew Mason X/TwitterAndrew Mason WebsiteJulie J. X/TwitterJulie J. WebsiteRyan Caldwell X/TwitterRyan Caldwell GitHubGitHub CopilotGoSorbetVisual Studio Code-GitHub Copilot Series (YouTube) (00:00) - Ski Trip and Snow Adventures (02:47) - Ryan Caldwell's Intro (03:53) - Copilot's Language Choice (06:12) - Journey Through Programming (09:24) - Dynamic to Typed Languages (11:53) - Sorbet's Impact at GitHub (17:11) - Reflecting on Sorbet (21:40) - Copilot's Authentication Success (25:35) - Customizing Copilot Chat (28:46) - Learning Paths in Software Dev (31:03) - Where to Follow Ryan

Learn about IBKR's modern REST API to trade, monitor, and manage your IBKR account.  Andrew Wise, U.S. API Support Supervisor, gives explanation of Client Portal Gateway and authentication process.

Bryan Farris is an accomplished entrepreneur, full-stack developer, and expert strategist. With a decade of prowess in shaping groundbreaking web and mobile applications, as the President and CEO of Goldfish he oversees a skilled team that offers a comprehensive array of end-to-end solutions. ‌With a (trademarked) Motivation-Driven Design Process, they are able to offer business strategy consulting, mobile and web development, project management, media and graphics, and investor pitch preparation. They are the go-to partner for clients seeking 360 Degree Consulting to successfully launch products. ‌Bryan's proficiency encompasses software programming, coupled with a talent for crafting robust database structures and developing backend servers and REST APIs. Writing code that matters with an emphasis on the business model part of it makes his project management mastery effortless, from initial planning to seamless distribution. ‌Bryan is passionate about driving social and environmental impact through his work, having previously worked in affordable housing in Pakistan as a 2010 Acumen Fund Global Fellow. Away from the digital realm, Bryan is a passionate world traveler, having explored 134 countries and territories. ‌When he's not building products, you'll find him playing ice hockey, watching the San Jose Sharks or American Football (shoutout to the San Francisco 49ers!), reading, or watching the news. What you will learn Unpack the importance of software in today's world, with insights into how it's transforming industries Get a deeper understanding of AI's role in the software industry Explore the concept of user motivation in product design and development Uncover the secret behind ChatGPT's prompt engineering Learn more about the evolution of app development Explore the concept of using ChatGPT and AI to create “bait” applications Understand the challenges involved in navigating the fast-paced software industry Plus loads more!

We're back for Episode 122! In this episode Cody and Eric catch up on the news +  Battle Of The Systems: Gradius 3 (SNES) vs. Thunderforce III (Genesis) We are doing news for the first monthly episode and then "catching up" later in the month.   Episode Guide ---------------- 8:38 - Quick Questions 36:18 - Patreon Song 41:33 - Eric's Take - State Of Gaming 2024 1:02:02 - Tea Time With Tim - Switch 2  1:18:30 - News 2:16:06 -  Battle Of The Systems: Gradius 3 (SNES) vs. Thunderforce III (Genesis) News -     (cody) https://www.timeextension.com/news/2023/12/mobile-fc-creation-kit-turns-the-nintendo-famicom-into-a-handheld  (Eric) New Shooter Looks Like Cuphead Meets Doom - https://kotaku.com/fps-retro-cartoon-shooter-mouse-new-trailer-gameplay-1851093499  (Tim) - New Ultimate 64 / Ultimante 1541 Firmware – Major update with network / internet capability, Assembley 64 online game database instant game search, download and run the game. Control C64 from webinterface via browser upload and run games also REST API.  https://ultimate64.com/Firmware  (Cody) https://www.timeextension.com/news/2023/12/evercades-indie-heroes-collection-3-arrives-early-next-year  (Eric) - World's First Flash Cart for Nintendo Switch Unveiled - https://isp.page/news/hackers-appear-to-have-created-a-working-switch-flash-cart/#gsc.tab=0  (Tim) - Thrust, the classic Gravitar style game famously released for the C64 with legendary music by Rob Hubbard a previously unlreased DOS version has now been found and has been made available by Frank Gasking over at Games That Weren't.   https://www.gamesthatwerent.com/2023/09/thrust/    (Cody) https://www.timeextension.com/news/2023/12/amiga-mini-the-a500-gets-new-firmware-and-a-free-game  (Eric) Castlevania SON on Saturn patched and updated - https://www.timeextension.com/news/2024/01/saturn-fans-can-now-play-the-ultimate-version-of-castlevania-symphony-of-the-night  (Tim) Forest Saver - Save the Trees in this new Commodore 64 game by INTOINSIDE  https://www.indieretronews.com/2023/12/forest-saver-save-trees-in-this-new.html  (Cody) https://www.timeextension.com/news/2023/12/atari-owns-almost-half-of-polymega-maker-playmaji  (Eric) - New Gamecube ODE drive – Flippydrive (solderless) - https://www.timeextension.com/news/2024/01/usd38-flippydrive-ode-lets-you-keep-your-gamecubes-optical-drive  (Cody https://www.timeextension.com/news/2023/12/a-new-shenmue-fangame-dreams-of-saturn-has-released-just-in-time-for-christmas  (Eric) Kid beats Tetris - https://www.popsci.com/technology/tetris-beat-kill-screen/  (Cody) https://www.timeextension.com/news/2023/12/check-out-these-sega-dreamcast-and-mega-drive-wireless-headphones  (Eric) - Hyperkin's Portable Genesis / Mega Drive Docks Like A Nintendo Switch - https://www.timeextension.com/news/2024/01/hyperkins-portable-genesis-mega-drive-docks-like-a-nintendo-switch  (Cody)- https://www.gamecrate.com/hidden-disc-booting-function-in-alien-resurrection-ps1-game-revealed-after-over-20-years  (Eric) - Earthbound 30th Anniversary - https://gamerant.com/earthbound-30th-anniversary-nintendo-celebration-plans-what/  (Cody) https://www.timeextension.com/reviews/sinden-light-gun-old-school-arcade-blasting-on-your-modern-day-tv  (Eric) a way to unlock an extended Bomberman SS prototype inside of an old Sega demo disc that was bundled along with some Sega Saturn consoles in Japan. - https://www.timeextension.com/news/2024/01/new-bomberman-patch-unlocks-hidden-prototype-in-saturn-demo  (Cody) - https://retrododo.com/robocop-vs-predator-is-a-new-game-boy-style-fan-game/  (Eric) - New add-ons coming for Atari Gamestation Pro - https://youtu.be/YW5QVaKkUSQ?si=vYhT59PbeNmws9Pe  (Cody) - https://www.timeextension.com/news/2024/01/paku-paku-is-pac-man-simplified-and-its-fantastic      Please give us a review on Apple Podcasts! Thanks for listening! You can always reach us at podcast@pixelgaiden.com. Send us an email if we missed anything in the show notes you need. You can now support us on Patreon.  Thank you to Henrik Ladefoged, Roy Fielding, Matthew Ackerman, Josh Malone, Daniel James, 10MARC, Eric Sandgren, Brian Arsenault, Retro Gamer Nation, Maciej Sosnowski, Paradroyd, RAM OK ROM OK, Mitsoyama, David Vincent, Ant Stiller, Mr. Toast, Jason Holland, Mark Scott, Vicky Lamburn, Mark Richardson, Scott Partelow, Paul Jacobson, Steve Rasmussen, Retro Gamer Nation, and Adam from Commodore Chronicles for making this show possible through their generous donation to the show.   Support our sponsor Retro Rewind for all of your Commodore needs! Use our page at https://retrorewind.ca/pixelgaiden and our discount code PG10 for 10%

On today's episode, Elixir Wizards Owen Bickford and Dan Ivovich compare notes on building web applications with Elixir and the Phoenix Framework versus Ruby on Rails. They discuss the history of both frameworks, key differences in architecture and approach, and deciding which programming language to use when starting a project. Both Phoenix and Rails are robust frameworks that enable developers to build high-quality web apps—Phoenix leverages functional programming in Elixir and Erlang's networking for real-time communication. Rails follows object-oriented principles and has a vast ecosystem of plug-ins. For data-heavy CRUD apps, Phoenix's immutable data pipelines provide some advantages. Developers can build great web apps with either Phoenix or Rails. Phoenix may have a slight edge for new projects based on its functional approach, built-in real-time features like LiveView, and ability to scale efficiently. But, choosing the right tech stack depends heavily on the app's specific requirements and the team's existing skills. Topics discussed in this episode: History and evolution of Phoenix Framework and Ruby on Rails Default project structure and code organization preferences in each framework Comparing object-oriented vs functional programming paradigms CRUD app development and interaction with databases Live reloading capabilities in Phoenix LiveView vs Rails Turbolinks Leveraging WebSockets for real-time UI updates Testing frameworks like RSpec, Cucumber, Wallaby, and Capybara Dependency management and size of standard libraries Scalability and distribution across nodes Readability and approachability of object-oriented code Immutability and data pipelines in functional programming Types, specs, and static analysis with Dialyzer Monkey patching in Ruby vs extensible core language in Elixir Factors to consider when choosing between frameworks Experience training new developers on Phoenix and Rails Community influences on coding styles Real-world project examples and refactoring approaches Deployment and dev ops differences Popularity and adoption curves of both frameworks Ongoing research into improving Phoenix and Rails Links Mentioned in this Episode: SmartLogic.io (https://smartlogic.io/) Dan's LinkedIn (https://www.linkedin.com/in/divovich/) Owen's LinkedIn (https://www.linkedin.com/in/owen-bickford-8b6b1523a/) Ruby https://www.ruby-lang.org/en/ Rails https://rubyonrails.org/ Sams Teach Yourself Ruby in 21 Days (https://www.overdrive.com/media/56304/sams-teach-yourself-ruby-in-21-days) Learn Ruby in 7 Days (https://www.thriftbooks.com/w/learn-ruby-in-7-days---color-print---ruby-tutorial-for-guaranteed-quick-learning-ruby-guide-with-many-practical-examples-this-ruby-programming-book--to-build-real-life-software-projects/18539364/#edition=19727339&idiq=25678249) Build Your Own Ruby on Rails Web Applications (https://www.thriftbooks.com/w/build-your-own-ruby-on-rails-web-applications_patrick-lenz/725256/item/2315989/?utm_source=google&utm_medium=cpc&utm_campaign=low_vol_backlist_standard_shopping_customer_acquisition&utm_adgroup=&utm_term=&utm_content=593118743925&gad_source=1&gclid=CjwKCAiA1MCrBhAoEiwAC2d64aQyFawuU3znN0VFgGyjR0I-0vrXlseIvht0QPOqx4DjKjdpgjCMZhoC6PcQAvD_BwE#idiq=2315989&edition=3380836) Django https://github.com/django Sidekiq https://github.com/sidekiq Kafka https://kafka.apache.org/ Phoenix Framework https://www.phoenixframework.org/ Phoenix LiveView https://hexdocs.pm/phoenixliveview/Phoenix.LiveView.html#content Flask https://flask.palletsprojects.com/en/3.0.x/ WebSockets API https://developer.mozilla.org/en-US/docs/Web/API/WebSockets_API WebSocket connection for Phoenix https://github.com/phoenixframework/websock Morph Dom https://github.com/patrick-steele-idem/morphdom Turbolinks https://github.com/turbolinks Ecto https://github.com/elixir-ecto Capybara Testing Framework https://teamcapybara.github.io/capybara/ Wallaby Testing Framework https://wallabyjs.com/ Cucumber Testing Framework https://cucumber.io/ RSpec https://rspec.info/

On this episode of the Scouting For Growth podcast, Sabine VdL talks to Terence Bennett, CEO of DreamFactory Software, brings with him a decade's worth of insights into cybersecurity, adversarial simulations, and API advancements. He's a veteran, both in serving his country and in protecting digital domains. Today, we're delving deep into Terence's illustrious career, the current cyber threat landscape, and the role of emerging technologies in shaping our digital future. KEY TAKEAWAYS If you spend enough time with computers I think you just figure out there's a whole other world in this application of computing and technology. I spent a lot of time in hacking forums online in the early 2000s, through that I got into early web development and learning the different ways to use the internet in ways it wasn't intended. 10 years later I came back around to cybersecurity as a professional. BEST MOMENTS‘RedTeaming is about simulating an adversary as they go about the process of trying to get into a network, computer, or facility.'‘As cybersecurity, as a field, has matured you'll find very specific signatures to different kinds of attackers in the way the actually go about doing that.'‘The challenges are, first and foremost, having enough people with the right skills to actually build APIs. Public facing APIs account for 14% of the total number of APIs that teams are buildings, and in some cases they account for 50-75% of a company's revenue.'‘Ransomware attacks are the 800lb gorilla in the room when it comes to threats… as we see organisations accelerating through digitisation/monetisation strategies, they're connecting more and more data and systems that used to be offline and you can't avoid that.' ABOUT THE GUESTTerence Bennett is the CEO of DreamFactory Software, a leader in API code automation based in Northern California. He has over 10 years of experience in cybersecurity, REST APIs, operations, and leadership. Previously, Bennett was the COO of Integrate.io and a member of Google's offensive security "RedTeam".  At Google, he helped craft realistic cybersecurity exercises to test the company's defences. He has deep expertise in cybersecurity, penetration testing, and adversarial simulation. Bennett served in the U.S. Navy for over 8 years as a Naval Intelligence Officer and Surface Warfare Officer.  He was deployed in support of Operation Iraqi Freedom and worked at the Naval Criminal Investigative Service (NCIS). He is a Certified Information Systems Security Professional (CISSP) and holds a Bachelor's degree in Economics from the U.S. Naval Academy and a Master's degree from the U.S. Naval War College.  Bennett is passionate about giving back through public service. He serves on the Board of Directors for the American Red Cross - Northern California Coastal Region and the Board of Advisors for Shields & Stripes, assisting veterans with PTSD. As a leader in API automation, Bennett frequently speaks on how code automation is changing software development. He aims to simplify complex integration challenges and help companies go to market faster. https://www.linkedin.com/in/terencehbennett/ https://www.dreamfactory.com/  ABOUT THE HOSTSabine is a corporate strategist turned entrepreneur. She is the CEO and Managing Partner of Alchemy Crew, a venture lab that accelerates the curation, validation, and commercialization of new tech business models. Sabine is renowned within the insurance sector for building some of the most renowned tech startup accelerators around the world working with over 30 corporate insurers and accelerating over 100 startup ventures. Sabine is the co-editor of the bestseller The INSURTECH Book, a top 50 Women in Tech, a FinTech and InsurTech Influencer, an investor & multi-award winner.  Twitter: SabineVdLLinkedIn: Sabine VanderLindenInstagram: sabinevdLofficialFacebook: SabineVdLOfficialTikTok: sabinevdlofficialEmail: podcast@sabinevdl.comWebsite: www.sabinevdl.comThis show was brought to you by Progressive Media

Laurent Doguin, Director of Developer Relations & Strategy at Couchbase, joins Corey on Screaming in the Cloud to talk about the work that Couchbase is doing in the world of databases and developer relations, as well as the role of AI in their industry and beyond. Together, Corey and Laurent discuss Laurent's many different roles throughout his career including what made him want to come back to a role at Couchbase after stepping away for 5 years. Corey and Laurent dig deep on how Couchbase has grown in recent years and how it's using artificial intelligence to offer an even better experience to the end user.About LaurentLaurent Doguin is Director of Developer Relations & Strategy at Couchbase (NASDAQ: BASE), a cloud database platform company that 30% of the Fortune 100 depend on.Links Referenced: Couchbase: https://couchbase.com XKCD #927: https://xkcd.com/927/ dbdb.io: https://dbdb.io DB-Engines: https://db-engines.com/en/ Twitter: https://twitter.com/ldoguin LinkedIn: https://www.linkedin.com/in/ldoguin/ TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Are you navigating the complex web of API management, microservices, and Kubernetes in your organization? Solo.io is here to be your guide to connectivity in the cloud-native universe!Solo.io, the powerhouse behind Istio, is revolutionizing cloud-native application networking. They brought you Gloo Gateway, the lightweight and ultra-fast gateway built for modern API management, and Gloo Mesh Core, a necessary step to secure, support, and operate your Istio environment.Why struggle with the nuts and bolts of infrastructure when you can focus on what truly matters - your application. Solo.io's got your back with networking for applications, not infrastructure. Embrace zero trust security, GitOps automation, and seamless multi-cloud networking, all with Solo.io.And here's the real game-changer: a common interface for every connection, in every direction, all with one API. It's the future of connectivity, and it's called Gloo by Solo.io.DevOps and Platform Engineers, your journey to a seamless cloud-native experience starts here. Visit solo.io/screaminginthecloud today and level up your networking game.Corey: Welcome to Screaming in the Cloud, I'm Corey Quinn. This promoted guest episode is brought to us by our friends at Couchbase. And before we start talking about Couchbase, I would rather talk about not being at Couchbase. Laurent Doguin is the Director of Developer Relations and Strategy at Couchbase. First, Laurent, thank you for joining me.Laurent: Thanks for having me. It's a pleasure to be here.Corey: So, what I find interesting is that this is your second time at Couchbase, where you were a developer advocate there for a couple of years, then you had five years of, we'll call it wilderness I suppose, and then you return to be the Director of Developer Relations. Which also ties into my personal working thesis of, the best way to get promoted at a lot of companies is to leave and then come back. But what caused you to decide, all right, I'm going to go work somewhere else? And what made you come back?Laurent: So, I've joined Couchbase in 2014. Spent about two or three years as a DA. And during those three years as a developer advocate, I've been advocating SQL database and I—at the time, it was mostly DBAs and ops I was talking to. And DBA and ops are, well, recent, modern ops are writing code, but they were not the people I wanted to talk to you when I was a developer advocate. I came from a background of developer, I've been a platform engineer for an enterprise content management company. I was writing code all day.And when I came to Couchbase, I realized I was mostly talking about Docker and Kubernetes, which is still cool, but not what I wanted to do. I wanted to talk about developers, how they use database to be better app, how they use key-value, and those weird thing like MapReduce. At the time, MapReduce was still, like, a weird thing for a lot of people, and probably still is because now everybody's doing SQL. So, that's what I wanted to talk about. I wanted to… engage with people identify with, really. And so, didn't happen. Left. Built a Platform as a Service company called Clever Cloud. They started about four or five years before I joined. We went from seven people to thirty-one LFs, fully bootstrapped, no VC. That's an interesting way to build a company in this age.Corey: Very hard to do because it takes a lot of upfront investment to build software, but you can sort of subsidize that via services, which is what we've done here in some respects. But yeah, that's a hard road to walk.Laurent: That's the model we had—and especially when your competition is AWS or Azure or GCP, so that was interesting. So entrepreneurship, it's not for everyone. I did my four years there and then I realized, maybe I'm going to do something else. I met my former colleagues of Couchbase at a software conference called Devoxx, in France, and they told me, “Well, there's a new sheriff in town. You should come back and talk to us. It's all about developers, we are repositioning, rehandling the way we do marketing at Couchbase. Why not have a conversation with our new CMO, John Kreisa?”And I said, “Well, I mean, I don't have anything to do. I actually built a brewery during that past year with some friends. That was great, but that's not going to feed me or anything. So yeah, let's have a conversation about work.” And so, I talked to John, I talked to a bunch of other people, and I realized [unintelligible 00:03:51], he actually changed, like, there was a—they were purposely going [against 00:03:55] developer, talking to developer. And that was not the case, necessarily, five, six years before that.So, that's why I came back. The product is still amazing, the people are still amazing. It was interesting to find a lot of people that still work there after, what, five years. And it's a company based in… California, headquartered in California, so you would expect people to, you know, jump around a bit. And I was pleasantly surprised to find the same folks there. So, that was also one of the reasons why I came back.Corey: It's always a strong endorsement when former employees rejoin a company. Because, I don't know about you, but I've always been aware of those companies you work for, you leave. Like, “Aw, I'm never doing that again for love or money,” just because it was such an unpleasant experience. So, it speaks well when you see companies that do have a culture of boomerangs, for lack of a better term.Laurent: That's the one we use internally, and there's a couple. More than a couple.Corey: So, one thing that seems to have been a thread through most of your career has been an emphasis on developer experience. And I don't know if we come at it from the same perspective, but to me, what drives nuts is honestly, with my work in cloud, bad developer experience manifests as the developer in question feeling like they're somehow not very good at their job. Like, they're somehow not understanding how all this stuff is supposed to work, and honestly, it leads to feeling like a giant fraud. And I find that it's pernicious because even when I intellectually know for a fact that I'm not the dumbest person ever to use this tool when I don't understand how something works, the bad developer experience manifests to me as, “You're not good enough.” At least, that's where I come at it from.Laurent: And also, I [unintelligible 00:05:34] to people that build these products because if we build the products, the user might be in the same position that we are right now. And so, we might be responsible for that experience [unintelligible 00:05:43] a developer, and that's not a great feeling. So, I completely agree with you. I've tried to… always on software-focused companies, whether it was Nuxeo, Couchbase, Clever Cloud, and then Couchbase. And I guess one of the good thing about coming back to a developer-focused era is all the product alignments.Like, a lot of people talk about product that [grows 00:06:08] and what it means. To me what it means was, what it meant—what it still means—building a product that developer wants to use, and not just want to, sometimes it's imposed to you, but actually are happy to use, and as you said, don't feel completely stupid about it in front of the product. It goes through different things. We've recently revamped our Couchbase UI, Couchbase Capella UI—Couchbase Capella is a managed cloud product—and so we've added a lot of in-product getting started guidelines, snippets of code, to help developers getting started better and not have that feeling of, “What am I doing? Why is it not working and what's going on?”Corey: That's an interesting decision to make, just because historically, working with a bunch of tools, the folks who are building the documentation working with that tool, tend to generally be experts at it, so they tend to optimize for improving things for the experience of someone has been using it for five years as opposed to the newcomer. So, I find that the longer a product is in existence, in many cases, the worse the new user experience becomes because companies tend to grow and sprawl in different ways, the product does likewise. And if you don't know the history behind it, “Oh, your company, what does it do?” And you look at the website and there's 50 different offerings that you have—like, the AWS landing page—it becomes overwhelming very quickly. So, it's neat to see that emphasis throughout the user interface on the new developer experience.On the other side of it, though, how are the folks who've been using it for a while respond to those changes? Because it's frustrating for me at least, when I log into a new account, which happens periodically within AWS land, and I have this giant series of onboarding pop-ups that I have to click to make go away every single time. How are they responding to it?Laurent: Yeah, it's interesting. One of the first things that struck me when I joined Couchbase the first time was the size of the technical documentation team. Because the whole… well, not the whole point, but part of the reason why they exist is to do that, to make sure that you understand all the differences and that it doesn't feel like the [unintelligible 00:08:18] what the documentation or the product pitch or everything. Like, they really, really, really emphasize on this from the very beginning. So, that was interesting.So, when you get that culture built into the products, well, the good thing is… when people try Couchbase, they usually stick with Couchbase. My main issue as a Director of the Developer Relations is not to make people stick with Couchbase because that works fairly well with the product that we have; it's to make them aware that we exist. That's the biggest issue I have. So, my goal as DevRel is to make sure that people get the trial, get through the trial, get all that in-app context, all that helps, get that first sample going, get that first… I'm not going to say product built because that's even a bit further down the line, but you know, get that sample going. We have a code playground, so when you're in the application, you get to actually execute different pieces of code, different languages. And so, we get those numbers and we're happy to see that people actually try that. And that's a, well, that's a good feeling.Corey: I think that there's a definite lack of awareness almost industry-wide around the fact that as the diversity of your customers increases, you have to have different approaches that meet them at various points along the journey. Because things that I've seen are okay, it's easy to ass—even just assuming a binary of, “Okay, I've done this before a thousand times; this is the thousand and first, I don't need the Hello World tutorial,” versus, “Oh, I have no idea what I'm doing. Give me the Hello World tutorial,” there are other points along that continuum, such as, “Oh, I used to do something like this, but it's been three years. Can you give me a refresher,” and so on. I think that there's a desire to try and fit every new user into a predefined persona and that just doesn't work very well as products become more sophisticated.Laurent: It's interesting, we actually have—we went through that work of defining those personas because there are many. And that was the origin of my departure. I had one person, ops slash DBA slash the person that maintain this thing, and I wanted to talk to all the other people that built the application space in Couchbase. So, we broadly segment things into back-end, full-stack, and mobile because Couchbase is also a mobile database. Well, we haven't talked too much about this, so I can explain you quickly what Couchbase is.It's basically a distributed JSON database with an integrated caching layer, so it's reasonably fast. So it does cache, and when the key-value is JSON, then you can create with SQL, you can do full-text search, you can do analytics, you can run user-defined function, you get triggers, you get all that actual SQL going on, it's transactional, you get joins, ANSI joins, you get all those… windowing function. It's modern SQL on the JSON database. So, it's a general-purpose database, and it's a general-purpose database that syncs.I think that's the important part of Couchbase. We are very good at syncing cluster of databases together. So, great for multi-cloud, hybrid cloud, on-prem, whatever suits you. And we also sync on the device, there's a thing called Couchbase Mobile, which is a local database that runs in your phone, and it will sync automatically to the server. So, a general-purpose database that syncs and that's quite modern.We try to fit as much way of growing data as possible in our database. It's kind of a several-in-one database. We call that a data platform. It took me a while to warm up to the word platform because I used to work for an enterprise content management platform and then I've been working for a Platform as a Service and then a data platform. So, it took me a bit of time to warm up to that term, but it explained fairly well, the fact that it's a several-in-one product and we empower people to do the trade-offs that they want.Not everybody needs… SQL. Some people just need key-value, some people need search, some people need to do SQL and search in the same query, which we also want people to do. So, it's about choices, it's about empowering people. And that's why the word platform—which can feel intimidating because it can seem complex, you know, [for 00:12:34] a lot of choices. And choices is maybe the enemy of a good developer experience.And, you know, we can try to talk—we can talk for hours about this. The more services you offer, the more complicated it becomes. What's the sweet spots? We did—our own trade-off was to have good documentation and good in-app help to fix that complexity problem. That's the trade-off that we did.Corey: Well, we should probably divert here just to make sure that we cover the basic groundwork for those who might not be aware: what exactly is Couchbase? I know that it's a database, which honestly, anything is a database if you hold it incorrectly enough; that's my entire shtick. But what is it exactly? Where does it start? Where does it stop?Laurent: Oh, where does it start? That's an interesting question. It's a… a merge—some people would say a fork—of Apache CouchDB, and membase. Membase was a distributed key-value store and CouchDB was this weird Erlang and C JSON REST API database that was built by Damian Katz from Lotus Notes, and that was in 2006 or seven. That was before Node.js.Let's not care about the exact date. The point is, a JSON and REST API-enabled database before Node.js was, like, a strong [laugh] power move. And so, those two merged and created the first version of Couchbase. And then we've added all those things that people want to do, so SQL, full-text search, analytics, user-defined function, mobile sync, you know, all those things. So basically, a general-purpose database.Corey: For what things is it not a great fit? This is always my favorite question to ask database folks because the zealot is going to say, “It's good for every use case under the sun. Use it for everything, start to finish”—Laurent: Yes.Corey: —and very few databases can actually check that box.Laurent: It's a very interesting question because when I pitch like, “We do all the things,” because we are a platform, people say, “Well, you must be doing lots of trade-offs. Where is the trade-off?” The trade-off is basically the way you store something is going to determine the efficiency of your [growing 00:14:45]—or the way you [grow 00:14:47] it. And that's one of the first thing you learn in computer science. You learn about data structure and you know that it's easier to get something in a hashmap when you have the key than passing your whole list of elements and checking your data, is it right one? It's the same for databases.So, our different services are different ways to store the data and to query it. So, where is it not good, it's where we don't have an index or a service that answer to the way you want to query data. We don't have a graph service right now. You can still do recursive common table expression for the SQL nerds out there, that will allow you to do somewhat of a graph way of querying your data, but that's not, like, actual—that's not a great experience for people were expecting a graph, like a Neo4j or whatever was a graph database experience.So, that's the trade-off that we made. We have a lot of things at the same place and it can be a little hard, intimidating to operate, and the developer experience can be a little, “Oh, my God, what is this thing that can do all of those features?” At the same time, that's just, like, one SDK to learn for all of the features we've just talked about. So, that's what we did. That's a trade-off that we did.It sucks to operate—well, [unintelligible 00:16:05] Couchbase Capella, which is a lot like a vendor-ish thing to say, but that's the value props of our managed cloud. It's hard to operate, we'll operate this for you. We have a Kubernetes operator. If you are one of the few people that wants to do Kubernetes at home, that's also something you can do. So yeah, I guess what we cannot do is the thing that Route 53 and [Unbound 00:16:26] and [unintelligible 00:16:27] DNS do, which is this weird DNS database thing that you like so much.Corey: One thing that's, I guess, is a sign of the times, but I have to confess that I'm relatively skeptical around, when I pull up couchbase.com—as one does; you're publicly traded; I don't feel that your company has much of a choice in this—but the first thing it greets me with is Couchbase Capella—which, yes, that is your hosted flagship product; that should be the first thing I see on the website—then it says, “Announcing Capella iQ, AI-powered coding assistance for developers.” Which oh, great, not another one of these.So, all right, give me the pitch. What is the story around, “Ooh, everything that has been a problem before, AI is going to make it way better.” Because I've already talked to you about developer experience. I know where you stand on these things. I have a suspicion you would not be here to endorse something you don't believe in. How does the AI magic work in this context?Laurent: So, that's the thing, like, who's going to be the one that get their products out before the other? And so, we're announcing it on the website. It's available on the private preview only right now. I've tried it. It works.How does it works? The way most chatbot AI code generation work is there's a big model, large language model that people use and that people fine-tune into in order to specialize it to the tasks that they want to do. The way we've built Couchbase iQ is we picked a very famous large language model, and when you ask a question to a bot, there's a context, there's a… the size of the window basically, that allows you to fit as much contextual information as possible. The way it works and the reason why it's integrated into Couchbase Capella is we make sure that we preload that context as much as possible and fine-tune that model, that [foundation 00:18:19] model, as much as possible to do whatever you want to do with Couchbase, which usually falls into several—a couple of categories, really—well maybe three—you want to write SQL, you want to generate data—actually, that's four—you want to generate data, you want to generate code, and if you paste some SQL code or some application code, you want to ask that model, what does do? It's especially true for SQL queries.And one of the questions that many people ask and are scared of with chatbot is how does it work in terms of learning? If you give a chatbot to someone that's very new to something, and they're just going to basically use a chatbot like Stack Overflow and not really think about what they're doing, well it's not [great 00:19:03] right, but because that's the example that people think most developer will do is generate code. Writing code is, like, a small part of our job. Like, a substantial part of our job is understanding what the code does.Corey: We spend a lot more time reading code than writing it, if we're, you know—Laurent: Yes.Corey: Not completely foolish.Laurent: Absolutely. And sometimes reading big SQL query can be a bit daunting, especially if you're new to that. And one of the good things that you get—Corey: Oh, even if you're not, it can still be quite daunting, let me assure you.Laurent: [laugh]. I think it's an acquired taste, let's be honest. Some people like to write assembly code and some people like to write SQL. I'm sort of in the middle right now. You pass your SQL query, and it's going to tell you more or less what it does, and that's a very nice superpower of AI. I think that's [unintelligible 00:19:48] that's the one that interests me the most right now is using AI to understand and to work better with existing pieces of code.Because a lot of people think that the cost of software is writing the software. It's maintaining the codebase you've written. That's the cost of the software. That's our job as developers should be to write legacy code because it means you've provided value long enough. And so, if in a company that works pretty well and there's a lot of legacy code and there's a lot of new people coming in and they'll have to learn all those things, and to be honest, sometimes we don't document stuff as much as we should—Corey: “The code is self-documenting,” is one of the biggest lies I hear in tech.Laurent: Yes, of course, which is why people are asking retired people to go back to COBOL again because nobody can read it and it's not documented. Actually, if someone's looking for a company to build, I guess, explaining COBOL code with AI would be a pretty good fit to do in many places.Corey: Yeah, it feels like that's one of those things that would be of benefit to the larger world. The counterpoint to that is you got that many business processes wrapped around something running COBOL—and I assure you, if you don't, you would have migrated off of COBOL long before now—it's making sure that okay well, computers, when they're in the form of AI, are very, very good at being confident-sounding when they talk about things, but they can also do that when they're completely wrong. It's basically a BS generator. And that is a scary thing when you're taking a look at something that broad. I mean, I'll use the AI coding assistance for things all the time, but those things look a lot more like, “Okay, I haven't written CloudFormation from scratch in a while. Build out the template, just because I forget the exact sequence.” And it's mostly right on things like that. But then you start getting into some of the real nuanced areas like race conditions and the rest, and often it can make things worse instead of better. That's the scary part, for me, at least.Laurent: Most coding assistants are… and actually, each time you ask its opinion to an AI, they say, “Well, you should take this with a grain of salt and we are not a hundred percent sure that this is the case.” And this is, make sure you proofread that, which again, from a learning perspective, can be a bit hard to give to new students. Like, you're giving something to someone and might—that assumes is probably as right as Wikipedia but actually, it's not. And it's part of why it works so well. Like, the anthropomorphism that you get with chatbots, like, this, it feels so human. That's why it get people so excited about it because if you think about it, it's not that new. It's just the moment it took off was the moment it looked like an assertive human being.Corey: As you take a look through, I guess, the larger ecosystem now, as well as the database space, given that is where you specialize, what do you think people are getting right and what do you think people are getting wrong?Laurent: There's a couple of ways of seeing this. Right now, when I look at from the outside, every databases is going back to SQL, I think there's a good reason for that. And it's interesting to put into perspective with AI because when you generate something, there's probably less chance to generate something wrong with SQL than generating something with code directly. And I think five generation—was it four or five generation language—there some language generation, so basically, the first innovation is assembly [into 00:23:03] in one and then you get more evolved languages, and at some point you get SQL. And SQL is a way to very shortly express a whole lot of business logic.And I think what people are doing right now is going back to SQL. And it's been impressive to me how even new developers that were all about [ORMs 00:23:25] and [no-DMs 00:23:26], and you know, avoiding writing SQL as much as possible, are actually back to it. And that's, for an old guy like me—well I mean, not that old—it feels good. I think SQL is coming back with a vengeance and that makes me very happy. I think what people don't realize is that it also involves doing data modeling, right, and stuff because database like Couchbase that are schemaless exist. You should store your data without thinking about it, you should still do data modeling. It's important. So, I think that's the interesting bits. What are people doing wrong in that space? I'm… I don't want to say bad thing about other databases, so I cannot even process that thought right now.Corey: That's okay. I'm thrilled to say negative things about any database under the sun. They all haunt me. I mean, someone wants to describe SQL to me is the chess of the programming world and I feel like that's very accurate. I have found that it is far easier in working with databases to make mistakes that don't wash off after a new deployment than it is in most other realms of technology. And when you're lucky and have a particular aura, you tend to avoid that stuff, at least that was always my approach.Laurent: I think if I had something to say, so just like the XKCD about standards: like, “there's 14 standards. I'm going to do one that's going to unify them all.” And it's the same with database. There's a lot… a [laugh] lot of databases. Have you ever been on a website called dbdb.io?Corey: Which one is it? I'm sorry.Laurent: Dbdb.io is the database of databases, and it's very [laugh] interesting website for database nerds. And so, if you're into database, dbdb.io. And you will find Couchbase and you will find a whole bunch of other databases, and you'll get to know which database is derived from which other database, you get the history, you get all those things. It's actually pretty interesting.Corey: I'm familiar with DB-Engines, which is sort of like the ranking databases by popularity, and companies will bend over backwards to wind up hitting all of the various things that they want in that space. The counterpoint with all of it is that it's… it feels historically like there haven't exactly been an awful lot of, shall we say, huge innovations in databases for the past few years. I mean, sure, we hear about vectors all the time now because of the joy that's AI, but smarter people than I are talking about how, well that's more of a feature than it is a core database. And the continual battle that we all hear about constantly is—and deal with ourselves—of should we use a general-purpose database, or a task-specific database for this thing that I'm doing remains largely unsolved.Laurent: Yeah, what's new? And when you look at it, it's like, we are going back to our roots and bringing SQL again. So, is there anything new? I guess most of the new stuff, all the interesting stuff in the 2010s—well, basically with the cloud—were all about the distribution side of things and were all about distributed consensus, Zookeeper, etcd, all that stuff. Couchbase is using an RAFT-like algorithm to keep every node happy and under the same cluster.I think that's one of the most interesting things we've had for the past… well, not for the past ten years, but between, basically, 20 or… between the start of AWS and well, let's say seven years ago. I think the end of the distribution game was brought to us by the people that have atomic clock in every data center because that's what you use to synchronize things. So, that was interesting things. And then suddenly, there wasn't that much innovation in the distributed world, maybe because Aphyr disappeared from Twitter. That might be one of the reason. He's not here to scare people enough to be better at that.Aphyr was the person behind the test called the Jepsen Test [shoot 00:27:12]. I think his blog engine was called Call Me Maybe, and he was going through every distributed system and trying to break them. And that was super interesting. And it feels like we're not talking that much about this anymore. It really feels like database have gone back to the status of infrastructure.In 2010, it was not about infrastructure. It was about developer empowerment. It was about serving JSON and developer experience and making sure that you can code faster without some constraint in a distributed world. And like, we fixed this for the most part. And the way we fixed this—and as you said, lack of innovation, maybe—has brought databases back to an infrastructure layer.Again, it wasn't the case 15 years a—well, 2023—13 years ago. And that's interesting. When you look at the new generation of databases, sometimes it's just a gateway on top of a well-known database and they call that a database, but it provides higher-level services, provides higher-level bricks, better developer experience to developer to build stuff faster. We've been trying to do this with Couchbase App Service and our sync gateway, which is basically a gateway on top of a Couchbase cluster that allow you to manage authentication, authorization, that allows you to manage synchronization with your mobile device or with websites. And yeah, I think that's the most interesting thing to me in this industry is how it's been relegated back to infrastructure, and all the cool stuff, new stuff happens on the layer above that.Corey: I really want to thank you for taking the time to speak with me. If people want to learn more, where's the best place for them to find you?Laurent: Thanks for having me and for entertaining this conversation. I can be found anywhere on the internet with these six letters: L-D-O-G-U-I-N. That's actually 7 letters. Ldoguin. That's my handle on pretty much any social network. Ldoguin. So X, [BlueSky 00:29:21], LinkedIn. I don't know where to be anymore.Corey: I hear you. We'll put links to all of it in the [show notes 00:29:27] and let people figure out where they want to go on that. Thank you so much for taking the time to speak with me today. I really do appreciate it.Laurent: Thanks for having me.Corey: Laurent Doguin, Director of Developer Relations and Strategy at Couchbase. I'm Cloud Economist Corey Quinn and this episode has been brought to us by our friends at Couchbase. If you enjoyed this episode, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice, along with an angry comment that you're not going to be able to submit properly because that platform of choice did not pay enough attention to the experience of typing in a comment.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.

Stephanie discovered a new book: The Staff Engineer's Path! Joël's got some D&D goodness. Together, they revisit a decade-old blog post initially published in 2013, which discussed the application of Sandi Metz's coding guidelines and whether these rules remain relevant and practiced among developers today. Transcript: JOËL: Hello and welcome to another episode of The Bike Shed, a weekly podcast from your friends at thoughtbot about developing great software. I'm Joël Quenneville. STEPHANIE: And I'm Stephanie Minn. And together, we're here to share a bit of what we've learned along the way. JOËL: So, Stephanie, what's new in your world? STEPHANIE: So, I picked up a new book from the library [laughs], which that in itself is not very new. That is [laughs] a common occurrence in my world. But it was kind of a fun coincidence that I was just walking around the aisles of what's new in nonfiction, and staring me right in the face was an O'Reilly book, The Staff Engineer's Path. And I think in the past, I've plugged The Manager's Path by Camille Fournier on the show. And in recent years, this one was published, and it's by Tanya Reilly. And it is kind of, like, the other half of a career path for software engineers moving up in seniority at those higher levels. And it has been a really interesting companion to The Manager's Path, which I had read even though I wasn't really sure I wanted to be manager [laughs]. And now I think I get that, like, accompaniment of like, okay, like, instead of walking that path, like, what does a staff plus engineer look like? And kind of learning a little bit more about that because I know it can be really vague or ambiguous or look very different at a lot of different companies. And that has been really helpful for me, kind of looking ahead a bit. I'm not too far into it yet. But I'm looking forward to reading more and bringing back some of those learnings to the show. JOËL: I feel like at the end of the year, Stephanie, you and I are probably going to have to sit down and talk through maybe your reading list for the year and, you know, maybe shout out some favorites. I think your reading list is probably significantly longer than mine. But you're constantly referencing cool books. I think that would probably be a fun, either end-of-year episode or a beginning-of-year episode for 2024. One thing that's really interesting, though, about the contrast of these two particular books you're talking about is how it really lines up with this, like, fork in the road that a lot of us have in our careers as we get more senior. You either move into more of a management role, which can be a pretty significant departure from what you have to do as a developer, or you kind of go into this, like, ultra-senior individual contributor path. But how that looks day to day can be very different from your sort of just traditional sitting down and banging out tickets. So, it's really cool there's two books looking at both of those paths. STEPHANIE: Yeah, absolutely. And I think the mission that they were going for with these books was to kind of illuminate a little bit more about that fork and that decision because, you know, it can be easy for people to maybe just default into one or the other based on what their organization wants for them without, like, fully knowing what that means. And the more senior you get, the more vague and, like, figure it out yourself [laughs] the work becomes. And it can be very daunting to kind of just be thrown into that and be like, well, I'm in this leadership position now. People are looking to me, and I have all this responsibility, but, like, what do I do? Yeah, so I'm kind of enjoying this book, that is...it's not a technical book, which is actually kind of what I like about it. It's actually more of a leadership book, which is really important for that kind of role. Even though, you know, they are still in that IC track, but it does come with a lot of leadership responsibility. JOËL: Yes, leadership in a very different way than management. But—and this may be counterintuitive for some people, especially earlier on in their careers—going further up that individual contributor track doesn't just mean getting more intense technically. It often means you've got to focus on things more like leadership, like being a bit more strategic, aligning technical initiatives with strategic goals. STEPHANIE: Yeah, and having a bigger impact and being a force multiplier, even in both the manager and, like, the staff plus role, like, that, you know, is the thing that ties the rising level. JOËL: Yeah, in many ways, maybe the individual contributor track is slightly misnamed because while, yes, you're not managing a sort of sub-organization within the company, it's still about being a force multiplier. STEPHANIE: Yeah, that's a really great point [laughs]. Maybe we'll be able to come up with a better [laughs] name for that. JOËL: I've mentioned several times on this podcast that I've been enjoying playing Dungeons & Dragons, D&D, with some friends and some colleagues. And something that was particularly fun that some friends and I did this summer is we hired a professional DM to run one shot for us. And that was just an absolutely lovely experience. Well, as a result of that, I am now subscribed to this guy's newsletter. And he'll do, like, various D&D events at different times. One thing that was really cool that I found out recently...as we're recording this, it's the week before election day in the U.S. And because a lot of voting happens in schools, typically, schools have the day off. And so, this guy sent out an email saying he was offering to run a, like, all day...effectively, a little mini-D&D camp for school-age kids on election day so that you can do your work. You can go vote, and you don't have to...basically, he'll watch your kids for you and, like, get them introduced to playing D&D, which I think is just a really cool thing to do. STEPHANIE: I love that. It's so heartwarming [laughs]. And it's such a great idea because, oftentimes, people are still working, and so they need childcare, like, on those kinds of days. And yeah, I think D&D is such a fun thing for kids to get into, too. You know, it requires so much, like, imagination, and I can imagine it's such a blast. JOËL: I got that email, and I was like, that is such a perfect idea. I love it so much. STEPHANIE: I wanted to plug my D&D recommendation. I'm pretty sure I have mentioned it on the show before. But there is a podcast that I listen to called Not Another D&D Podcast, which is, you know, a live play Dungeons & Dragons podcast campaign that's hosted by these comedians, formerly of CollegeHumor, and it's very fun. I always laugh. They have this, like, a kind of offshoot of the main show that they do called D&D Court, which is very fun. Because, as you were saying, like, you know, you hired a DM to run your game. And I know that...I'm sure lots of people have fun stories about their home games and, like, the drama that happens [laughs] with their friends. JOËL: Absolutely. Absolutely. STEPHANIE: And so, with D&D Court, listeners can write in with their drama or their conflicts and get an official ruling from the hosts about who was right [laughs] in the situation that they write in about. JOËL: So, you get to bring your best rules lawyering to the D&D Court. STEPHANIE: Yeah, exactly [laughs]. JOËL: That sounds kind of amazing. Recently, I had someone reach out to me asking about an older blog post that we'd written about the Sandi Metz Rules. This blog post was initially published in 2013, so ten years ago, and was talking about some guidelines that Sandi Metz at the time was talking about that she was using in some of her code. And we talked about how our experience was applying those to some of our work as well. And so, the question was, you know, ten years later, is that still something that thoughtbot developers like to follow in their code? We'll link to the article in the show notes. But I'll just read out the rules here real quick. So, there's four of them. The first one is a class can be no longer than 100 lines of code. The second is a method can be no longer than five lines of code. The third is pass no more than four parameters into a method, and hash options count, so no getting clever with those. And then, finally, controllers can only instantiate one object. You only get one instance variable. And views can only talk to that one instance variable. Had you or are you familiar with these rules? Is that something that you think about or use in your daily writing of code? STEPHANIE: Yeah. So, when you proposed this topic, I had to revisit these rules. And I can't recall if I had seen them before. They seemed familiar. And I've read, you know, a couple of Sandi Metz's books, so maybe those were places where she had mentioned them. But the one thing that really struck me when I was first reading the rules was how declarative they were in terms of, like, kind of just telling you what the results should be without really saying how. So, for example, the one where you said, you know, a method should not be more than five lines [laughs], I had the silly thought of, like, well, you could just, you know, stuff everything into a single line [laughs] and just completely disregard line limit if you wanted, and it would technically still follow the rule. JOËL: If they didn't want us to do that, they wouldn't give us semicolons in Ruby. STEPHANIE: Exactly [laughs]. So, that is kind of what struck me at first. Is that something you noticed? JOËL: I think what is interesting with them is that there's not always a ton of rationale given behind them. Our article talks a little bit about some of the why that might be helpful and how that might look like in practice. I'm not sure what Sandi's original...I don't know if it was one of her books or maybe on a...it might have been on a podcast appearance she talked about them, so she might go more in-depth there. But yeah, they are a little bit declarative. It's just like, hey, here's...it's almost basically the kind of thing that can be enforced by a linter, which is perhaps the point. STEPHANIE: Ooh, that's really interesting. It's like, on one hand, I like how simple they are, right? It's like, they're very obvious. If you're not following them, you can tell. But on the other hand, they seem to be more of a supplement to the gained knowledge and experience that you kind of get from knowing how to implement those rules. I think you and I will both agree that we don't want to stuff everything [laughs] into a single line with semicolons. But if someone who maybe is newer to development and is coming to these rules, I think they might be wondering, like, how do I do this? JOËL: Do you follow these rules in your own code? STEPHANIE: I think the ones that are easier to follow, for me, and that I think I've come to do more instinctually, are the rules about class line length and method line length just because I'm kind of looking out for opportunities to pull out a method or, you know, make sure that this class is just doing one thing. And if it's starting to seem to cover a couple of different responsibilities, I'm a little bit more on the lookout. But I do like these rules as like, you know, like, hey, once you hit, you know, 100 lines in a class, like, maybe that's your cue to start thinking about opportunities for extraction. JOËL: Do you sort of consciously follow these rules or have them maybe even encoded in a linter? Or is it more you're following other things, and somehow, it just lines up with these principles? STEPHANIE: I would say that, like, I'm not thinking about them very actively. But that could be a very interesting exercise, and I think, you know, that's what folks did in the blog post. They were like, hey, we took these rules, and we really kept them in mind as we were developing. But I think kind of what we were talking about earlier about, like, what we've learned or the strategies we've learned to implement kind of converge on these rules. And the rules actually are more of the result of other ideas or heuristics that we follow. JOËL: I mean, you dropped the keyword heuristics there. And I think that brings me back to an earlier episode we did where we talked about heuristics. And one of the things that came up on that episode was the idea that, oftentimes, we use heuristics as a way to sort of flatten a lot of experience and knowledge into sort of one, like, short rule, or short phrase, or something, one guideline, even though it's sort of trying to just summarize a mountain of wisdom. And so, oftentimes, you can look at something like these rules and be like, okay, well, what's the point? Or maybe you even just follow it to the letter without really thinking about the why behind it, and that can sometimes be problematic. And on the other hand, you might know all of the ideas that go behind them. And without necessarily knowing the rule itself, you just kind of happen to follow it because you're intimately familiar with all of these other software principles that converge on those same ideas. STEPHANIE: Yeah, agreed. I think that the more interesting ones to me are the no more than four method arguments and only one instance variable per controller. JOËL: Interesting. STEPHANIE: I'm curious if those are sparking anything for you [laughs]. JOËL: I think the no more than four method arguments, to me, is probably the least controversial. It's generally accepted that having many arguments to a method is a code smell. And there's a few different code smells that are related to that. There's forms of coupling incandescence; there are data clumps, things like that. I've often heard a sort of rule of three. And so, if you're going more than three, then you might want to revisit the structure of what you're building. Four is a bit of an arbitrary cut-off, I'll agree. Most of these are arbitrary cut-offs. But I think the idea to maybe keep your method to fewer arguments is generally a good thing to do. STEPHANIE: I liked that the rule points out that hash options account because I think that's maybe where people get a little more hand-wavy, or you have your ops hash [laughs] that can be just a catch-all for anything. You know, it's like, once you start putting stuff in there, I don't know, I feel like it's a like a law of the universe. It's like, oh, people will just stuff more things in there [laughs]. And it takes obviously more effort or, like, specific energy to, like, think through what those things might represent, or some alternative ways of handling those arguments. We definitely do have, I think, a couple of episodes on value objects. But that's something that we have talked a lot about before in terms of, you know, how can we take some kind of primitive data, hashes included, and turn them into a richer object that can then be passed on its own? JOËL: Right. And an options hash is generally...it's too much of a catch-all to really have an identity as its own sort of value object. It doesn't really represent any single thing. It's just everything else bag of data. One thing that's interesting that the article notes is that a lot of the helpers in Rails take a lot of arguments and that it is absolutely not worth trying to fight the framework to try to follow these rules. So, the article does take a very pragmatic approach, I think, to the idea of these rules. STEPHANIE: Yeah. And I think there is even a caveat to the rules where it's like, you can break them if you have a good reason, or if you're working with someone else and they give you the thumbs up [laughs], which I really like a lot because it almost kind of compels you to stop and be like, do I have a good reason of doing this? Just making sure, or I'll run it by a friend. And shifting that, I guess, that focus from kind of just coding from, like, your default mode of thinking to a more active one. JOËL: Right. There is a rule zero, which says you can break any of the other rules as long as you convince either your pair or your reviewer to give you a thumbs up on breaking the rule. So, you'd mentioned the fourth rule about a single instance variable in a controller kind of was one of the ones that stood out to you. What is particularly striking about that rule? STEPHANIE: I think this one is hard to follow, and I think the blog post mentions that as well. Because at least I've seen our web applications grow more and more complex. And it can be really challenging to just be like, what is this page doing? Like, what, you know, data does it need to know? And have that be the single thing. Because really, a lot of our web apps have a lot of things [laughs] that they're doing, and sometimes it feels like you have to capture more than one object or at least, like, a responsibility in this way. I think that's the one that I, you know, in my ideal world, I'm like, yeah, like, we have all these, like, perfectly RESTful routes. And, you know, we're only dealing with, you know, a single resource. But once you start to have some more complexity, I think that can be a little more challenging. JOËL: I think it's interesting that you mentioned RESTful routing because I think that is maybe one of the bigger things that does trigger having more instance variables in your controller actions. If you're following sort of the traditional Rails RESTful routes, every page is generally focused on a singular resource. Now, that may not necessarily line up with a table in your database, and that's fine. But you're dealing with a singular thing or perhaps, you know, in the case of an index page, a singular collection of things, which can be represented with a single instance variable. Once you start adding custom routes that may not be necessarily tied to a particular resource, now you can very easily kind of have a proliferation of all sorts of different things that interact with each other because you're no longer centered on a single thing. STEPHANIE: Yeah, that's true, which actually reminds me of something we've talked about before, too, when we were both reading Sustainable Rails. The author talks about custom routes and actually advocates for making all routes RESTful. And if you need a vanity URL or something like that, you can always alias it. That I liked, right? It's like, okay, even if, you know, your resource is not something that's like, ActiveRecord-backed, is there some abstraction or concept of a resource in there? And I actually did really like in the blog post in the example; that is one that I've used before, too. They were dealing with this idea of a dashboard, which I would, you know, say is pretty common in a lot of web applications these days. And it's funny because a dashboard can hold so much data, right? It's really, like, a composite of a lot of different things, you know, what is most, like, useful for the user to see in one place. But they were in the blog post. And this, again, this is kind of something that I've done before. They were able to capture that with the idea of, like, a dashboard as an object and that being codified using a presenter or a facade. JOËL: Right. So, instead of having a group, and a status, and a user, and all these, like, separate things that your page that you're showing is a sort of collection of all these different types of objects, you wrap them together in a dashboard object that's kind of a facade. And I guess that really does line up with the idea of RESTful routing because you're likely going to have a dashboard's controller show action that's showing the user's dashboard. So, it makes sense, you know, that show page is rendering a dashboard object. STEPHANIE: Can we talk a little bit about things not to do, or maybe things that might be a little more questionable [laughs], and if you've seen them and how you felt about them? JOËL: I think it is sometimes tricky to define your boundaries right in that sometimes you create a facade object that really is just...it doesn't really represent anything. It's just there to wrap around some other things. And sometimes that can be awkward. I think the dashboard works partly because it lines up so neatly with the sort of RESTful routing and thinking in terms of resources that you want to do at the controller layer. But drawing boundaries incorrectly or just trying to throw everything in some kind of grab bag object can...it's not a magic bullet, right? You've got to put some thought into the data modeling, even when you are pulling the facade pattern. STEPHANIE: Yeah, I think other things that I've seen before that could theoretically follow this rule maybe [laughs], you know, I'd love to hear your thoughts about it. When you start, you're like, oh, like, my controller action method does just, you know, set one instance variable. But it turns out that there's all these other instance variables that either through a hook or, like, in the parent controller or even in the view I've seen before, too [laughs]. And I'm just kind of curious if that kind of raises your eyebrow at all or if you've seen any good reasons for doing so. JOËL: I think setting instance variables in a view would absolutely cause me to raise an eyebrow. STEPHANIE: [laughs] Agreed. JOËL: Generally, don't put logic in the view. I think that we definitely have in parent controllers; we'll set other instance variables for things like maybe a current user, right? That's how we store that state. And I think that is totally fine to have around. Typically, we don't access that instance variable directly. We're referencing some kind of helper method. But yeah, I would not consider that a violation of the rule. I think another common one that might come up is when you have some kind of nested resource. And so, in your URL, you might have a nested resource where you're saying, "Oh, I'm looking at specifically this comment under this article or something like that." And then, you want to have access to both objects in the controller. So, I think that's a pretty common scenario where you might want to have both instance variables. Something that I'm thinking about...this is not a fully formed thought, so I'm curious about your opinions here. Is there an interesting distinction between variables in code that you want to use within a controller versus variables that you want to be accessible from a view? Because instance variables in a controller are kind of overloaded. They're a way of having state in a controller, but they're also a way of passing data into a view. And so, that sort of dual purpose there maybe causes them to be a little bit trickier to reason about than instance variables in a random Ruby object. What do you think? STEPHANIE: Yeah, I was actually having the same thought as you were going there, which is that it is kind of interesting that the view, you know, is that level of what you want to display to your user. But it can have access to, like, whatever you put in the controller [laughs], and that is...and, you know, in some ways, it's like, that connection needs to happen somewhere, right? And it's here. But I think that can definitely be abused sometimes, too. So, this, you know, fourth rule that we're talking about really has to do with a more traditional Rails app. But, again, with the complexity of web apps in 2023 [chuckles], you know, we also see Rails used just as an API a lot with a separate front-end framework. And your controller is rendering some JSON, which I think has that harder boundary between what is the data that the server is involved with and what we want to send to our client. And I'm curious if you have any thoughts about how this rule applies in that situation. JOËL: I think I tend to see not really any difference there. If I'm building an API, typically, I'm trying to do so in a pretty RESTful manner. Maybe I'm doing a GraphQL API, and things might be different for that. But for a traditional REST API, yeah, typically, you're fetching one resource or some sort of compound resource, in which case, you're representing that with a facade object. And yep, you can generally get away, I think, with a single instance variable with, you know, a few exceptions around maybe some extra context about maybe something like the current user, or a parent object, or something like that. I guess the view is really you're using a different mechanism for rendering JSON, and there are a bunch out there that the community uses. I think I don't really see a difference between rendering to HTML versus rendering to JSON, or XML, or whatever. How about you? STEPHANIE: That's a good point. I think I'm with you where the rule still applies. But I have also seen things get really loosey-goosey [laughs] when we decide we're rendering JSON, and now we're suddenly putting the instance variables into a hash along with other stuff. But what you said was interesting about, like, sometimes you do need that extra context, right? And, like, figuring out what the best way to package that requires a bit of, like, sustained thought, I think, because it can, you know, be really easy to be like, oh well, this is the one interface that I have to get data from the server. So, if I just sneak this in here [laughs], what's the matter? But yeah, I think, you know, that's probably why rules like this exist [laughs] to help provide some guardrails and make us think a little deeper about it. JOËL: I think sometimes, as a community, we maybe exaggerate the differences between, like, RESTful HTML view and a RESTful JSON API. I tend to think of them as more or less the same. We just have, you know, a different representation the V layer of our MVC framework. Everything else still kind of lines up. STEPHANIE: Yeah, that's a really good point. I actually hadn't thought about it that way. Because I think maybe I have been influenced by the world of GraphQL [laughs] a little bit, or it's kind of hard to have a foot in both worlds, where you maybe have to context switch a little bit about, like, the paradigms, and then you find them influencing you in different ways. Because I have seen sometimes, like, what maybe initially were meant to be traditional more, like, RESTful JSON APIs kind of start to turn into that, like, how do we get what we need from this endpoint? JOËL: I'm curious how you feel in general about the facade pattern. Is that something that you've used, something that you like? STEPHANIE: I think I would say that I don't actually reach for it, like, upfront, right? Usually, I'm still trying to maybe put some things in my models [laughs]. But I have used it before once; it kind of became clear that, like, a lot of the methods on the model had to do with more really server-side concerns. And I was, like, wanting to just pull out some presentational pieces. I think the hardest part with the facade pattern is naming. I have really struggled sometimes to think of, like, it's not quite the component that makes it up. So, what is it instead? JOËL: Right. Right. I think, for me, sometimes the naming goes the other way around in that I'll start more to kind of, like, routing our resource level and try to think about, okay, this particular view of the data that I want to have, or this particular operation that I want to do, what am I actually dealing with? What is the resource here? So, maybe I'm viewing a dashboard. Or maybe what I'm doing is creating or destroying a subscription, even though those are not necessarily tables in the database. And once I have that underlying concept, then I can start creating an object that represents that, which might be a combination of multiple ActiveRecord models that represent tables. STEPHANIE: Yeah. You're actually pointing out, like, a really great use case that we see a lot, I think, is when you start to have to reach for resources, you know, that are different ActiveRecord classes. And how do you combine them together to represent the idea that you want, you know, for your feature? JOËL: I think it's more of, like, an outside-facing perspective rather than an inside-facing perspective. So, instead of looking at, hey, these are the set of ActiveRecord classes I have because these are my database tables, how can I, like, tack on to them to make this operation work? I'll sort of start almost from, like, a zoomed-out perspective, blank slate to say, "Hey, this is the kind of operation that I'm trying to do. What sort of resource am I dealing with ideally?" And, you know, maybe the idea is, okay, I'm dealing with a dashboard. I'm trying to subscribe to something...a newsletter, so the idea is I'm creating a subscription. Then, from there, I can start looking at, okay, do I have the concept of a subscription in this application? Oh, I don't. There is no subscriptions table because that's not a thing that we track in our data mode. That's fine. But I probably need at least some kind of in-memory object to track the idea of a subscription, and then maybe from there, that grows. So, I'm kind of working from the problem towards the database rather than from the database out. STEPHANIE: Yeah, I like that a lot. The outside-in phrase that you used really triggered something for me, which is being product engineers, right? Like, having a seat at the table when the feature is in that, like, ideation phase, I think is also really important because that's where you really learn what that like, abstraction is at the user level. And also, it could be a really good place to give your input if the feature is being designed in a way that doesn't really support the, you know, kind of quality of code and, like, separation that you would like. That's the part that I'm still working on and still learning how to do. But sometimes, you know, it's, like, really critical to the job to, like, be in that room and be like, these designs; what are some places that we could extract it at that level even? And kind of, like, separate things out from there rather than having to deal with it [laughs] deep in your codebase. JOËL: I think what I'm really kind of hearing and emphasizing in what you just said is the importance of not just writing code but being involved in the product and how that really enriches you because you know the problem domain. And that allows you to then write the code that you need at the different levels of the app to best model the situation you're working with. So, we've kind of gone through all the rules and talked about them. I'm curious, though, for you, are these rules that you follow in your code? How closely do you adhere to this set of rules? Is this still something that's relevant to you in 2023 as much as it was to the authors of that blog post in 2013? STEPHANIE: I have to say they're not ones that I have thought about on a daily basis, but after this conversation, maybe they will be. And I am kind of excited to maybe, like, bring this up to other people on my team and be like, "What do you think about these rules?" Just, like, revisiting them as a group or just, like, having that conversation. Because I think that's, you know, where I am most interested in is, like, is wondering how other people incorporate them into their work and hearing different opinions from the team. And I think there's a lot of, like, generative discussion that ultimately leads to better code as a result. JOËL: I think for myself, I'm not following the rules directly. But a lot of my code ends up approximating those rules anyway because of other principles that I follow. So, in practice, while my code doesn't strictly follow those rules, it does look pretty close to that anyway. STEPHANIE: I almost think this could be a great, you know, discussion for your team, too, like, if any listeners want to...not quite a book club but kind of an article club, if you will [laughs], and see how other people on your team feel about it. Because I think that's kind of where there is, like, a really sweet spot in terms of learning and development. JOËL: On that note, shall we wrap up? STEPHANIE: Let's wrap up. Show notes for this episode can be found at bikeshed.fm. JOËL: This show has been produced and edited by Mandy Moore. STEPHANIE: If you enjoyed listening, one really easy way to support the show is to leave us a quick rating or even a review in iTunes. It really helps other folks find the show. JOËL: If you have any feedback for this or any of our other episodes, you can reach us @_bikeshed, or you can reach me @joelquen on Twitter. STEPHANIE: Or reach both of us at hosts@bikeshed.fm via email. JOËL: Thanks so much for listening to The Bike Shed, and we'll see you next week. ALL: Byeeeeeee!!!!!!!! AD: Did you know thoughtbot has a referral program? If you introduce us to someone looking for a design or development partner, we will compensate you if they decide to work with us. More info on our website at tbot.io/referral. Or you can email us at referrals@thoughtbot.com with any questions.

Today on the podcast we have Leonardo Losoviz. Leonardo is an experienced developer and the creator of the Gato GraphQL plugin for WordPress. We'll be exploring the power of Gato GraphQL, its features, and how it could change the way we use WordPress. Leonardo highlights the power of GraphQL, a technology for retrieving data from a server. It can be used in WordPress to fetch data from the server and render HTML in the editor for interactive content creation. GraphQL offers advantages over the REST API, providing flexible querying and manipulating of data, such as retrieving metadata for personalised emails or duplicating and modifying blog posts. He also explains how his plugin builds on top of this foundation to enable WordPress to make use of this powerful technology. To help users get started and unlock the full potential of Gato GraphQL, Leonardo has created a section called "recipes" within the plugin and on the website. These pre-coded GraphQL queries serve as a learning tool, explaining how GraphQL works and providing tips for more complex tasks like translating posts. The recipes gradually increase in complexity, allowing users to grasp the full capabilities of the plugin over time. So, whether you're a WordPress developer looking to enhance your website's capabilities or a content creator searching for more efficient ways to manage and distribute your content, this episode is for you. Get ready to tap into the power of Gato GraphQL and unlock a new world of possibilities within WordPress.