Podcasts about MySQL

In this episode, Jake and Michael reflect on parenting, discuss Apple's new Liquid Glass UI, finding smarter ways to use video on the web, plus share thoughts on AI overload, Laracon prep, and why Wistia might be your next favourite video tool.In this episode:- Apple's Liquid Glass UI- Kit.com and Wistia for video- Reflections on AI, tech bubbles, and accessibility- Laracon US and vox pop interviews- The emotional ride of watching your kids grow up

Welcome to episode 306 of The Cloud Pod – where the forecast is always cloudy!  This week, we have a bunch of announcements concerning the newest offering from Anthropic – Claude Sonnet 4 and Opus 4, plus container security, Azure MySQL Maintenance, Vertex AI, and Mistral AI. Plus, we've got a Cloud Journey installment AND an aftershow – so get comfy and get ready for a trip to the clouds! Titles we almost went with this week: ECS Failures Now Have 4x the Excuses Nailing Down Your Container Security, One Patch at a Time HashiCorp’s New Recipe: Terraform, AI, and a Pinch of MCP Teaching an Old DNS New IPv6 Tricks Dash-ing through the Klusters, in an AWS Console Google’s Generative AI Playground Gets a Glow-Up Vertex AI Studio: Now with 200% More Darkness! Like our souls Claude Opus 4 Strikes a Chord on Google Cloud Sovereign-teed to Please: Google Cloud’s Royal Treatment Google’s Cloud Kingdom Expands its Borders Shall I Compare Thee to a Summer’s AI? Anthropic Drops Sonne(t) 4 Knowledge on Vertex Mistral AI Chats Up a Storm on Google Cloud Google Cloud’s Vertex AI Gets a Dose of Mistral Magic .NET Aspire on Azure: The App Service Strikes Back Default Outbound Access Retires, Decides Florida Isn’t for Everyone  AI Is Going Great – or How ML Makes Money  01:52 Introducing Claude 4 Claude has launched the latest models in Claude Opus 4 and Claude Sonnet 4, setting new standards for coding, advancing reasoning and AI agents. Maybe they'll actually follow instructions when told to shut down? (Looking at you, ChatGPT.) Claude Opus 4 is “the world's best coding model” with sustained performance on complex, long-running tasks and agent workflows.  Opus 4 has 350 billion parameters, making it one of the largest publicly available language models.  It demonstrates strong performance on academic benchmarks, including research.  Sonnet 4 is a smaller 10 billion parameter model optimized for dialogue, making it well-suited for conversational AI applications.  Alongside the models, they are also announcing: Extended thinking with tool use (beta): Both models can use tools – like web search – during extended thinking, allowing Claude to alternate between reasoning and tool use to improve its responses. New Model Capabilities: Both models can use tools in parallel, follow instructions more precisely, and when given access to local files by developers — demonstrate significantly improved memory capabilities, extracting and saving key facts maintain continuity and build tacit knowledge over time Claude code is now generally available: After receiving extensive positive feedback during our research preview, they are expanding how developers can collaborate with Claude.  Claude code now supports background tasks via gith

Nesse episódio trouxemos as notícias e novidades do mundo da programação que nos chamaram atenção dos dias 17/05 a 23/05.

Nesse episódio trouxemos as notícias e novidades do mundo da programação que nos chamaram atenção dos dias 17/05 a 23/05.

✨ The Changers podcast: https://open.spotify.com/show/19XFAVLLrQP5z8KQRLTGE7Miten kasvetaan menestyväksi yritysjohtajaksi? Studiossa yritysjohtaja Mårten Mickos. Jakso on kuvattu 31.3.2025. ⌚ AIKALEIMAT (0:00) Syyte USA:ssa (2:52) Lapsuus (8:42) Fysiikka (11:03) Kaverit (13:49) Otaniemi (18:41) Normien rikkominen (22:21) Ryhmähenki (26:10) Haasteiden kohtaaminen (33:55) Optimismi ja pessimismi (40:09) Raha (49:33) Rikastuminen (57:23) Moraali (1:01:49) Opiskelijatoiminta (1:04:14) Partio (1:07:44) Identiteetti (1:14:19) Tunneäly (1:23:23) Neuvottelu (1:28:55) Tunteiden näyttäminen (1:32:48) Empatia (1:36:27) Menestyminen (1:51:25) Sinnikkyys (1:59:47) Polycon (2:11:50) Cap table (2:18:03) Osakassopimus (2:25:24) Hyvä tiimi (2:31:52) Myynti (2:36:48) Yksinkertaisuus (2:39:11) Match on Sports (2:51:50) MySQL (3:02:08) Avoin lähdekoodi (3:09:02) Hajautettu organisaatio (3:15:20) Potkut (3:17:23) "Founder mode"

In this episode, Jake and Michael discuss Jake's new stealth grill, his eldest son's takeover of the state finals (and metric's takeover of measurement), and Michael goes through the process of refining over 150 talk submissions down to the final Laracon AU schedule.

The Disturbing Reality of AI-Generated AbuseA new study reveals nearly 35,000 publicly downloadable AI models capable of generating deepfake pornography—often targeting women and celebrities. These "model variants" can be tweaked to create millions of non-consensual intimate images, fuelling a growing crisis in digital exploitation. Researchers identified 35,000 deepfake model variants in public repositories, with each model being used to generate countless AI-generated explicit images of real people. While 35,000 models may seem small compared to the vast internet, each one represents a potential weapon for harassment. Experts warn that without stricter regulation, deepfake abuse could spiral further out of control. The study was led by Will Hawkins from the Oxford Internet Institute. He joins Gareth and Ghislaine on the show.    MySQL at 30: The Unsung Hero Powering Facebook, the Web & Your Data - The Database That Quietly Runs the InternetYou've probably never heard of it—but your data lives on MySQL. Celebrating its 30th anniversary this year, this open-source database is the invisible backbone of Facebook, countless websites, and nearly every major online service. It's an open-source success story, which relies on simplicity and speed and it's easy to set up. We spoke with Peter Zaitsev, who joined MySQL in 2002 and later co-founded Percona, a leading database consultancy.  Next time you log into Facebook or book a flight, remember - there's a 30-year-old database working behind the scenes. Not bad for a tech "underdog."   The programme is presented by Gareth Mitchell and the studio expert is Ghislaine Boddington. More on this week's stories: Dramatic rise in publicly downloadable deep fake image generators 1995-2025: MySQL at 30!   Production Manager: Liz Tuohy Editor: Ania Lichtarowicz For the PodExtra version of the show please subscribe via this link: https://somewhere-on-earth-the-global-tech-podcast-the-podextra-edition.pod.fan/ Follow us on all the socials: Join our Facebook group Instagram BlueSky   If you like Somewhere on Earth, please rate and review it on Apple Podcasts or Spotify   Contact us by email: hello@somewhereonearth.co  Send us a voice note via WhatsApp: +44 7486 329 484   Find a Story + Make it News = Change the World Learn more about your ad choices. Visit megaphone.fm/adchoices

Aprende Grafana y domina el monitoreo de datos en tiempo real

In this episode, Jake and Michael discuss using interfaces as a dictionary of constants, working with and testing inputs passed down multiple layers of the application, and refactoring legacy code with PHP's ArrayAccess interface.

In a new season of the Oracle University Podcast, Lois Houston and Nikita Abraham dive into the world of Oracle GoldenGate 23ai, a cutting-edge software solution for data management. They are joined by Nick Wagner, a seasoned expert in database replication, who provides a comprehensive overview of this powerful tool.   Nick highlights GoldenGate's ability to ensure continuous operations by efficiently moving data between databases and platforms with minimal overhead. He emphasizes its role in enabling real-time analytics, enhancing data security, and reducing costs by offloading data to low-cost hardware. The discussion also covers GoldenGate's role in facilitating data sharing, improving operational efficiency, and reducing downtime during outages.   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: Editorial Services with Oracle University, and with me is Lois Houston: Director of Innovation Programs. Lois: Hi everyone! Welcome to a new season of the podcast. This time, we're focusing on the fundamentals of Oracle GoldenGate. Oracle GoldenGate helps organizations manage and synchronize their data across diverse systems and databases in real time.  And with the new Oracle GoldenGate 23ai release, we'll uncover the latest innovations and features that empower businesses to make the most of their data. Nikita: Taking us through this is Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. He's been doing database replication for about 25 years and has been focused on GoldenGate on and off for about 20 of those years.  01:18 Lois: In today's episode, we'll ask Nick to give us a general overview of the product, along with some use cases and benefits. Hi Nick! To start with, why do customers need GoldenGate? Nick: Well, it delivers continuous operations, being able to continuously move data from one database to another database or data platform in efficiently and a high-speed manner, and it does this with very low overhead. Almost all the GoldenGate environments use transaction logs to pull the data out of the system, so we're not creating any additional triggers or very little overhead on that source system. GoldenGate can also enable real-time analytics, being able to pull data from all these different databases and move them into your analytics system in real time can improve the value that those analytics systems provide. Being able to do real-time statistics and analysis of that data within those high-performance custom environments is really important. 02:13 Nikita: Does it offer any benefits in terms of cost?  Nick: GoldenGate can also lower IT costs. A lot of times people run these massive OLTP databases, and they are running reporting in those same systems. With GoldenGate, you can offload some of the data or all the data to a low-cost commodity hardware where you can then run the reports on that other system. So, this way, you can get back that performance on the OLTP system, while at the same time optimizing your reporting environment for those long running reports. You can improve efficiencies and reduce risks. Being able to reduce the amount of downtime during planned and unplanned outages can really make a big benefit to the overall operational efficiencies of your company.  02:54 Nikita: What about when it comes to data sharing and data security? Nick: You can also reduce barriers to data sharing. Being able to pull subsets of data, or just specific pieces of data out of a production database and move it to the team or to the group that needs that information in real time is very important. And it also protects the security of your data by only moving in the information that they need and not the entire database. It also provides extensibility and flexibility, being able to support multiple different replication topologies and architectures. 03:24 Lois: Can you tell us about some of the use cases of GoldenGate? Where does GoldenGate truly shine?  Nick: Some of the more traditional use cases of GoldenGate include use within the multicloud fabric. Within a multicloud fabric, this essentially means that GoldenGate can replicate data between on-premise environments, within cloud environments, or hybrid, cloud to on-premise, on-premise to cloud, or even within multiple clouds. So, you can move data from AWS to Azure to OCI. You can also move between the systems themselves, so you don't have to use the same database in all the different clouds. For example, if you wanted to move data from AWS Postgres into Oracle running in OCI, you can do that using Oracle GoldenGate. We also support maximum availability architectures. And so, there's a lot of different use cases here, but primarily geared around reducing your recovery point objective and recovery time objective. 04:20 Lois: Ah, reducing RPO and RTO. That must have a significant advantage for the customer, right? Nick: So, reducing your RPO and RTO allows you to take advantage of some of the benefits of GoldenGate, being able to do active-active replication, being able to set up GoldenGate for high availability, real-time failover, and it can augment your active Data Guard and Data Guard configuration. So, a lot of times GoldenGate is used within Oracle's maximum availability architecture platinum tier level of replication, which means that at that point you've got lots of different capabilities within the Oracle Database itself. But to help eke out that last little bit of high availability, you want to set up an active-active environment with GoldenGate to really get true zero RPO and RTO. GoldenGate can also be used for data offloading and data hubs. Being able to pull data from one or more source systems and move it into a data hub, or into a data warehouse for your operational reporting. This could also be your analytics environment too. 05:22 Nikita: Does GoldenGate support online migrations? Nick: In fact, a lot of companies actually get started in GoldenGate by doing a migration from one platform to another. Now, these don't even have to be something as complex as going from one database like a DB2 on-premise into an Oracle on OCI, it could even be simple migrations. A lot of times doing something like a major application or a major database version upgrade is going to take downtime on that production system. You can use GoldenGate to eliminate that downtime. So this could be going from Oracle 19c to Oracle 23ai, or going from application version 1.0 to application version 2.0, because GoldenGate can do the transformation between the different application schemas. You can use GoldenGate to migrate your database from on premise into the cloud with no downtime as well. We also support real-time analytic feeds, being able to go from multiple databases, not only those on premise, but being able to pull information from different SaaS applications inside of OCI and move it to your different analytic systems. And then, of course, we also have the ability to stream events and analytics within GoldenGate itself.  06:34 Lois: Let's move on to the various topologies supported by GoldenGate. I know GoldenGate supports many different platforms and can be used with just about any database. Nick: This first layer of topologies is what we usually consider relational database topologies. And so this would be moving data from Oracle to Oracle, Postgres to Oracle, Sybase to SQL Server, a lot of different types of databases. So the first architecture would be unidirectional. This is replicating from one source to one target. You can do this for reporting. If I wanted to offload some reports into another server, I can go ahead and do that using GoldenGate. I can replicate the entire database or just a subset of tables. I can also set up GoldenGate for bidirectional, and this is what I want to set up GoldenGate for something like high availability. So in the event that one of the servers crashes, I can almost immediately reconnect my users to the other system. And that almost immediately depends on the amount of latency that GoldenGate has at that time. So a typical latency is anywhere from 3 to 6 seconds. So after that primary system fails, I can reconnect my users to the other system in 3 to 6 seconds. And I can do that because as GoldenGate's applying data into that target database, that target system is already open for read and write activity. GoldenGate is just another user connecting in issuing DML operations, and so it makes that failover time very low. 07:59 Nikita: Ok…If you can get it down to 3 to 6 seconds, can you bring it down to zero? Like zero failover time?   Nick: That's the next topology, which is active-active. And in this scenario, all servers are read/write all at the same time and all available for user activity. And you can do multiple topologies with this as well. You can do a mesh architecture, which is where every server talks to every other server. This works really well for 2, 3, 4, maybe even 5 environments, but when you get beyond that, having every server communicate with every other server can get a little complex. And so at that point we start looking at doing what we call a hub and spoke architecture, where we have lots of different spokes. At the end of each spoke is a read/write database, and then those communicate with a hub. So any change that happens on one spoke gets sent into the hub, and then from the hub it gets sent out to all the other spokes. And through that architecture, it allows you to really scale up your environments. We have customers that are doing up to 150 spokes within that hub architecture. Within active-active replication as well, we can do conflict detection and resolution, which means that if two users modify the same row on two different systems, GoldenGate can actually determine that there was an issue with that and determine what user wins or which row change wins, which is extremely important when doing active-active replication. And this means that if one of those systems fails, there is no downtime when you switch your users to another active system because it's already available for activity and ready to go. 09:35 Lois: Wow, that's fantastic. Ok, tell us more about the topologies. Nick: GoldenGate can do other things like broadcast, sending data from one system to multiple systems, or many to one as far as consolidation. We can also do cascading replication, so when data moves from one environment that GoldenGate is replicating into another environment that GoldenGate is replicating. By default, we ignore all of our own transactions. But there's actually a toggle switch that you can flip that says, hey, GoldenGate, even though you wrote that data into that database, still push it on to the next system. And then of course, we can also do distribution of data, and this is more like moving data from a relational database into something like a Kafka topic or a JMS queue or into some messaging service. 10:24 Raise your game with the Oracle Cloud Applications skills challenge. Get free training on Oracle Fusion Cloud Applications, Oracle Modern Best Practice, and Oracle Cloud Success Navigator. Pass the free Oracle Fusion Cloud Foundations Associate exam to earn a Foundations Associate certification. Plus, there's a chance to win awards and prizes throughout the challenge! What are you waiting for? Join the challenge today by visiting visit oracle.com/education. 10:58 Nikita: Welcome back! Nick, does GoldenGate also have nonrelational capabilities?  Nick: We have a number of nonrelational replication events in topologies as well. This includes things like data lake ingestion and streaming ingestion, being able to move data and data objects from these different relational database platforms into data lakes and into these streaming systems where you can run analytics on them and run reports. We can also do cloud ingestion, being able to move data from these databases into different cloud environments. And this is not only just moving it into relational databases with those clouds, but also their data lakes and data fabrics. 11:38 Lois: You mentioned a messaging service earlier. Can you tell us more about that? Nick: Messaging replication is also possible. So we can actually capture from things like messaging systems like Kafka Connect and JMS, replicate that into a relational data, or simply stream it into another environment. We also support NoSQL replication, being able to capture from MongoDB and replicate it onto another MongoDB for high availability or disaster recovery, or simply into any other system. 12:06 Nikita: I see. And is there any integration with a customer's SaaS applications? Nick: GoldenGate also supports a number of different OCI SaaS applications. And so a lot of these different applications like Oracle Financials Fusion, Oracle Transportation Management, they all have GoldenGate built under the covers and can be enabled with a flag that you can actually have that data sent out to your other GoldenGate environment. So you can actually subscribe to changes that are happening in these other systems with very little overhead. And then of course, we have event processing and analytics, and this is the final topology or flexibility within GoldenGate itself. And this is being able to push data through data pipelines, doing data transformations. GoldenGate is not an ETL tool, but it can do row-level transformation and row-level filtering.  12:55 Lois: Are there integrations offered by Oracle GoldenGate in automation and artificial intelligence? Nick: We can do time series analysis and geofencing using the GoldenGate Stream Analytics product. It allows you to actually do real time analysis and time series analysis on data as it flows through the GoldenGate trails. And then that same product, the GoldenGate Stream Analytics, can then take the data and move it to predictive analytics, where you can run MML on it, or ONNX or other Spark-type technologies and do real-time analysis and AI on that information as it's flowing through.  13:29 Nikita: So, GoldenGate is extremely flexible. And given Oracle's focus on integrating AI into its product portfolio, what about GoldenGate? Does it offer any AI-related features, especially since the product name has “23ai” in it? Nick: With the advent of Oracle GoldenGate 23ai, it's one of the two products at this point that has the AI moniker at Oracle. Oracle Database 23ai also has it, and that means that we actually do stuff with AI. So the Oracle GoldenGate product can actually capture vectors from databases like MySQL HeatWave, Postgres using pgvector, which includes things like AlloyDB, Amazon RDS Postgres, Aurora Postgres. We can also replicate data into Elasticsearch and OpenSearch, or if the data is using vectors within OCI or the Oracle Database itself. So GoldenGate can be used for a number of things here. The first one is being able to migrate vectors into the Oracle Database. So if you're using something like Postgres, MySQL, and you want to migrate the vector information into the Oracle Database, you can. Now one thing to keep in mind here is a vector is oftentimes like a GPS coordinate. So if I need to know the GPS coordinates of Austin, Texas, I can put in a latitude and longitude and it will give me the GPS coordinates of a building within that city. But if I also need to know the altitude of that same building, well, that's going to be a different algorithm. And GoldenGate and replicating vectors is the same way. When you create a vector, it's essentially just creating a bunch of numbers under the screen, kind of like those same GPS coordinates. The dimension and the algorithm that you use to generate that vector can be different across different databases, but the actual meaning of that data will change. And so GoldenGate can replicate the vector data as long as the algorithm and the dimensions are the same. If the algorithm and the dimensions are not the same between the source and the target, then you'll actually want GoldenGate to replicate the base data that created that vector. And then once GoldenGate replicates the base data, it'll actually call the vector embedding technology to re-embed that data and produce that numerical formatting for you.  15:42 Lois: So, there are some nuances there… Nick: GoldenGate can also replicate and consolidate vector changes or even do the embedding API calls itself. This is really nice because it means that we can take changes from multiple systems and consolidate them into a single one. We can also do the reverse of that too. A lot of customers are still trying to find out which algorithms work best for them. How many dimensions? What's the optimal use? Well, you can now run those in different servers without impacting your actual AI system. Once you've identified which algorithm and dimension is going to be best for your data, you can then have GoldenGate replicate that into your production system and we'll start using that instead. So it's a nice way to switch algorithms without taking extensive downtime. 16:29 Nikita: What about in multicloud environments?  Nick: GoldenGate can also do multicloud and N-way active-active Oracle replication between vectors. So if there's vectors in Oracle databases, in multiple clouds, or multiple on-premise databases, GoldenGate can synchronize them all up. And of course we can also stream changes from vector information, including text as well into different search engines. And that's where the integration with Elasticsearch and OpenSearch comes in. And then we can use things like NVIDIA and Cohere to actually do the AI on that data.  17:01 Lois: Using GoldenGate with AI in the database unlocks so many possibilities. Thanks for that detailed introduction to Oracle GoldenGate 23ai and its capabilities, Nick.  Nikita: We've run out of time for today, but Nick will be back next week to talk about how GoldenGate has evolved over time and its latest features. And if you liked what you heard today, head over to mylearn.oracle.com and take a look at the Oracle GoldenGate 23ai Fundamentals course to learn more. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 17:33 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. 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.

In this episode, Jake and Michael discuss the ramp up of Laracon AU planning, touch base on Jake's unorthodox usage of Laravel Horizon, and Michael finally coming around to using AI.

In this episode, Dave Rubinstein interviews Heikki Linnakangas, co-founder of Neon, a company that provides Postgres solutions. They discuss: The factors that have contributed to adoption of PostgreSQLWhy PostgreSQL has leapfrogged over MySQL in popularityWhat to expect in PostgreSQL 18

Kevin Weil is the chief product officer at OpenAI, where he oversees the development of ChatGPT, enterprise products, and the OpenAI API. Prior to OpenAI, Kevin was head of product at Twitter, Instagram, and Planet, and was instrumental in the development of the Libra (later Novi) cryptocurrency project at Facebook.In this episode, you'll learn:1. How OpenAI structures its product teams and maintains agility while developing cutting-edge AI2. The power of model ensembles—using multiple specialized models together like a company of humans with different skills3. Why writing effective evals (AI evaluation tests) is becoming a critical skill for product managers4. The surprisingly enduring value of chat as an interface for AI, despite predictions of its obsolescence5. How “vibe coding” is changing how companies operate6. What OpenAI looks for when hiring product managers (hint: high agency and comfort with ambiguity)7. “Model maximalism” and why today's AI is the worst you'll ever use again8. Practical prompting techniques that improve AI interactions, including example-based prompting—Brought to you by:• Eppo—Run reliable, impactful experiments• Persona—A global leader in digital identity verification• OneSchema—Import CSV data 10x faster—Where to find Kevin Weil:• X: https://x.com/kevinweil• LinkedIn: https://www.linkedin.com/in/kevinweil/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Kevin's background(04:06) OpenAI's new image model(06:52) The role of chief product officer at OpenAI(10:18) His recruitment story and joining OpenAI(17:20) The importance of evals in AI(24:59) Shipping quickly and consistently(28:34) Product reviews and iterative deployment(39:35) Chat as an interface for AI(43:59) Collaboration between researchers and product teams(46:41) Hiring product managers at OpenAI(48:45) Embracing ambiguity in product management(51:41) The role of AI in product teams(53:21) Vibe coding and AI prototyping(55:55) The future of product teams and fine-tuned models(01:04:36) AI in education(01:06:42) Optimism and concerns about AI's future(01:16:37) Reflections on the Libra project(01:20:37) Lightning round and final thoughts—Referenced:• OpenAI: https://openai.com/• The AI-Generated Studio Ghibli Trend, Explained: https://www.forbes.com/sites/danidiplacido/2025/03/27/the-ai-generated-studio-ghibli-trend-explained/• Introducing 4o Image Generation: https://openai.com/index/introducing-4o-image-generation/• Waymo: https://waymo.com/• X: https://x.com• Facebook: https://www.facebook.com/• Instagram: https://www.instagram.com/• Planet: https://www.planet.com/• Sam Altman on X: https://x.com/sama• A conversation with OpenAI's CPO Kevin Weil, Anthropic's CPO Mike Krieger, and Sarah Guo: https://www.youtube.com/watch?v=IxkvVZua28k• OpenAI evals: https://github.com/openai/evals• Deep Research: https://openai.com/index/introducing-deep-research/• Ev Williams on X: https://x.com/ev• OpenAI API: https://platform.openai.com/docs/overview• Dwight Eisenhower quote: https://www.brainyquote.com/quotes/dwight_d_eisenhower_164720• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder & CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• StackBlitz: https://stackblitz.com/• Claude 3.5 Sonnet: https://www.anthropic.com/news/claude-3-5-sonnet• Anthropic: https://www.anthropic.com/• Four-minute mile: https://en.wikipedia.org/wiki/Four-minute_mile• Chad: https://chatgpt.com/g/g-3F100ZiIe-chad-open-a-i• Dario Amodei on LinkedIn: https://www.linkedin.com/in/dario-amodei-3934934/• Figma: https://www.figma.com/• Julia Villagra on LinkedIn: https://www.linkedin.com/in/juliavillagra/• Andrej Karpathy on X: https://x.com/karpathy• Silicon Valley CEO says ‘vibe coding' lets 10 engineers do the work of 100—here's how to use it: https://fortune.com/2025/03/26/silicon-valley-ceo-says-vibe-coding-lets-10-engineers-do-the-work-of-100-heres-how-to-use-it/• Cursor: https://www.cursor.com/• Windsurf: https://codeium.com/windsurf• GitHub Copilot: https://github.com/features/copilot• Patrick Srail on X: https://x.com/patricksrail• Khan Academy: https://www.khanacademy.org/• CK-12 Education: https://www.ck12.org/• Sora: https://openai.com/sora/• Sam Altman's post on X about creative writing: https://x.com/sama/status/1899535387435086115• Diem (formerly known as Libra): https://en.wikipedia.org/wiki/Diem_(digital_currency)• Novi: https://about.fb.com/news/2020/05/welcome-to-novi/• David Marcus on LinkedIn: https://www.linkedin.com/in/dmarcus/• Peter Zeihan on X: https://x.com/PeterZeihan• The Wheel of Time on Prime Video: https://www.amazon.com/Wheel-Time-Season-1/dp/B09F59CZ7R• Top Gun: Maverick on Prime Video: https://www.amazon.com/Top-Gun-Maverick-Joseph-Kosinski/dp/B0DM2LYL8G• Thinking like a gardener not a builder, organizing teams like slime mold, the adjacent possible, and other unconventional product advice | Alex Komoroske (Stripe, Google): https://www.lennysnewsletter.com/p/unconventional-product-advice-alex-komoroske• MySQL: https://www.mysql.com/—Recommended books:• Co-Intelligence: Living and Working with AI: https://www.amazon.com/Co-Intelligence-Living-Working-Ethan-Mollick/dp/059371671X• The Accidental Superpower: Ten Years On: https://www.amazon.com/Accidental-Superpower-Ten-Years/dp/1538767341• Cable Cowboy: https://www.amazon.com/Cable-Cowboy-Malone-Modern-Business/dp/047170637X—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

In this episode, Jake and Michael discuss Michael's new recording gear, building integrations with external APIs using Saloon, and configuring Laravel Horizon.

RJJ Software's Software Development Service This episode of The Modern .NET Show is supported, in part, by RJJ Software's Podcasting Services, whether your company is looking to elevate its UK operations or reshape its US strategy, we can provide tailored solutions that exceed expectations. Show Notes "So I've been focused on the code to cloud journey, I like to call it, for the template. And two years ago, my goal was to provide a solution that could take you from code to cloud in 45 minutes or less. So I wanted it to be "file new project" to deploy a solution on Azure—because that's where my main focus is—within 45 minutes."— Jason Taylor Welcome friends to The Modern .NET Show; the premier .NET podcast, focusing entirely on the knowledge, tools, and frameworks that all .NET developers should have in their toolbox. We are the go-to podcast for .NET developers worldwide, and I am your host: Jamie "GaProgMan" Taylor. In this episode, Jason Taylor (no relation) joined us to talk about his journey from Classic ASP to .NET and Azure. He also discusses clean architecture's maintainability, and his open-source Clean Architecture Solution template for ASP .NET Core, along with strategies for learning new frameworks and dealing with complexity. "Right now the template supports PostgreSQL, SQLite, and SQL Server. If you want to support MySQL, it's relatively easy to do because there's already a Bicep module or a Terraform module that you can go in and use it. So I went from 45 minutes to now I can get things up and running in like, I don't know, two minutes of effort and 15 minutes of waiting around while I make my coffee"— Jason Taylor Along the way, we talk about some of the complexities involved with creating a template which supports multiple different frontend technologies and .NET Aspire (which was news to me when we recorded), all the while maintaining the goal of being the simplest approach for enterprise development with Clean Architecture. Anyway, without further ado, let's sit back, open up a terminal, type in `dotnet new podcast` and we'll dive into the core of Modern .NET. Supporting the Show If you find this episode useful in any way, please consider supporting the show by either leaving a review (check our review page for ways to do that), sharing the episode with a friend or colleague, buying the host a coffee, or considering becoming a Patron of the show. Full Show Notes The full show notes, including links to some of the things we discussed and a full transcription of this episode, can be found at: https://dotnetcore.show/season-7/from-code-to-cloud-in-15-minutes-jason-taylors-expert-insights-and-the-clean-architecture-template/ Jason's Links: Jason's Clean Architecture repo on GitHub Jason's Northwind Traders with Clean Architecture repo on Github Connect with Jason Jason's RapidBlazor repo on GitHub Other Links: C# DevKit for Visual Studio Code Code, Coffee, and Clever Debugging: Leslie Richardson's Microsoft Journey and the C# Dev Kit in Visual Studio Code with Leslie Richardson dotnet scaffold devcontainers .NET Aspire Azure Developer CLI GitHub CLI Obsidian Supporting the show: Leave a rating or review Buy the show a coffee Become a patron Getting in Touch: Via the contact page Joining the Discord Remember to rate and review the show on Apple Podcasts, Podchaser, or wherever you find your podcasts, this will help the show's audience grow. Or you can just share the show with a friend. And don't forget to reach out via our Contact page. We're very interested in your opinion of the show, so please get in touch. You can support the show by making a monthly donation on the show's Patreon page at: https://www.patreon.com/TheDotNetCorePodcast. Music created by Mono Memory Music, licensed to RJJ Software for use in The Modern .NET Show

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

Luca Casanato, member of the Deno core team, delves into the intricacies of debugging applications using Deno and OpenTelemetry. Discover how Deno's native integration with OpenTelemetry enhances application performance monitoring, simplifies instrumentation compared to Node.js, and unlocks new insights for developers! Links https://lcas.dev https://x.com/lcasdev https://github.com/lucacasonato https://mastodon.social/@lcasdev https://www.linkedin.com/in/luca-casonato-15946b156 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: Luca Casonato.

In this episode of The Eric Ries Show, I sit down with Marten Mickos, a serial tech CEO who has been at the forefront of some of the most transformative moments in open-source technology. From leading MySQL through its groundbreaking journey to guiding HackerOne as a pioneering bug bounty platform, Marten's career is a masterclass in building innovative, trust-driven organizations.Our wide-ranging conversation explores Marten's remarkable journey through tech leadership, touching on his experiences building game-changing companies and, more recently, his work coaching emerging CEOs. We dive deep into the world of open source, company culture, and the nuanced art of leadership.In our conversation today, we talk about the following topics: • How MySQL revolutionized open-source databases and became Facebook's database• The strategic decision to make MySQL open source and leverage Linux distributions• The art of building a beloved open-source project while creating a profitable business model• How a lawsuit solidified MySQL's position in the open-source database market• The role of transparency and direct feedback in building organizational trust• Why Marten was drawn to HackerOne's disruptive approach to cybersecurity• Marten's transition to coaching new CEOs • Marten's unique "contrast framework" for making complex decisions• And much more!—Brought to you by:• Wilson Sonsini – Wilson Sonsini is the innovation economy's law firm. ⁠⁠Learn more⁠⁠.• Gusto – Gusto is an easy payroll and benefits software built for small businesses. ⁠⁠⁠⁠Get 3 months free⁠⁠⁠⁠.—Where to find Marten Mickos: • LinkedIn: https://www.linkedin.com/in/martenmickos/• Bluesky: https://bsky.app/profile/martenmickos.bsky.social—Where to find Eric:• Newsletter:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ericries.carrd.co/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • Podcast:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://ericriesshow.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • YouTube:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@theericriesshow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ —In This Episode We Cover:(00:00) Intro(03:15) The first time Eric used MySQL(07:10) The origins of MySQL and how Marten got involved (13:22) Why MySQL pivoted to open source to leverage the power of Linux distros(17:03) Open source vs. closed (18:56) Building profitable open-source companies (24:52) The fearless company culture at MySQL and the Progress lawsuit(29:30) The value of not cutting any corners (33:35) How a dolphin became part of the MySQL logo (35:55) What it was like to build a company of true believers(38:47) Marten's management approach emphasizes kindness and direct feedback (42:12) Marten's hiring philosophy(45:14) Why MySQL sold to Sun Microsystems and tried to avoid Oracle (50:24) How Oracle has made MySQL even better(52:22) Why Marten decided to lead at HackerOne(55:41) An overview of HackerOne(59:31) How HackerOne got started and landed the Department of Defense contract(1:03:19) The trust-building power of transparency(1:08:30) Marten's successor and the state of HackerOne now(1:09:23) Marten's work coaching CEOs(1:14:20) Common issues CEOs struggle with (1:16:45) Marten's contrast framework (1:26:12) The book of Finnish poetry that inspired Marten's love of polarities—You can find the transcript and references at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.ericriesshow.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠—Production and marketing by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Eric may be an investor in the companies discussed.

AWS Morning Brief for the week of March 17th, with Corey Quinn. Links:Amazon Bedrock now supports multi-agent collaborationAmazon RDS for MySQL announces Extended Support minor 5.7.44-RDS.20250213Amazon Route 53 Traffic Flow introduces a new visual editor to improve DNS policy editingApplication Load Balancer announces integration with Amazon VPC IPAMAnnouncing the end of support for Node.js 14.x and 16.x in AWS CDKWatch the recordings from AWS Developer Day 2025How GoDaddy built a category generation system at scale with batch inference for Amazon BedrockFormula 1® unlocks the most competitive season yet with AWSSecure cloud innovation starts at re:Inforce 2025

Today, we have Sam Lambert back on the show! Sam is the CEO of PlanetScale, and if you follow him on X, you know he's one of the sharpest voices in the database space—cutting through the hype with deep experience and a no-nonsense approach. In this episode, we dive into PlanetScale's new Metal offering, which has been battle-tested with PlanetScale's high-scale cloud business partners and is now GA. Sam also shares why staying profitable is crucial—not just for the business but for the stability and reliability it guarantees for customers. While many cloud infrastructure companies chase the next hype cycle, Sam prefers to keep it boring—delivering rock-solid performance with no surprises Finally, we close with Sam's thoughts on other happenings in the database space -- Aurora DSQL, Aurora Limitless, MySQL benchmarks, and multi-region strong consistency. Tune in for a deep dive into databases, cloud infrastructure, and what it takes to build a sustainable, high-performance tech company. Timestamps 01:34 Start 06:42 PlanetScale Metal 11:15 The problem with separation of storage and compute 15:02 EBS Tax 17:32 How does Vitess handle durability 22:58 Metal recommended for all PlanetScale users? 27:20 The hidden expense of IOPS for cloud databases 37:41 Timeline of creating PlanetScale Metal 41:32 Focus on profitability 47:52 Removal of hobby plan 57:45 Deprecation of PlanetScale Boost 01:00:24 DSQL 01:01:51 Aurora Limitless 01:04:15 AWS as a partner 01:07:00 The spectacle of AWS re:Invent 01:12:22 Benchmarks and benchmarketing 01:15:51 AWS Databases + multi-region strong consistency

In this episode, Jake and Michael discuss circles of influence and information, eloquently handling return of single values from the database, and monitoring tools for your applications.

In this episode, Jeremy Maldonado shares his experiences and insights on server management, highlighting the importance of learning from mistakes, the power of automation, and finding balance between Linux and Windows environments. He discusses the challenges and rewards of managing servers, the pivotal role of Ansible in streamlining operations, and the confidence required to maintain a reliable infrastructure. Jeremy encourages listeners to view setbacks as opportunities for growth while reminding us to be kind to ourselves throughout our professional journeys.

In this special episode of the Oracle University Podcast, hosts Lois Houston and Nikita Abraham dive into Oracle Fusion Cloud Applications and the new courses and certifications on offer. They are joined by Oracle Fusion Apps experts Patrick McBride and Bill Lawson who introduce the concept of Oracle Modern Best Practice (OMBP), explaining how it helps organizations maximize results by mapping Fusion Application features to daily business processes. They also discuss how the new courses educate learners on OMBP and its role in improving Fusion Cloud Apps implementations.   OMBP: https://www.oracle.com/applications/modern-best-practice/ 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 everyone! For the last two months, we've been focusing on all things MySQL. But today, we wanted to share some really exciting news about new courses and certifications on Oracle Fusion Cloud Applications that feature Oracle Modern Best Practice, or OMBP, and Oracle Cloud Success Navigator.  00:57 Nikita: And to tell us more about this, we have two very special guests joining us today. Patrick McBride is a Senior Director from the Fusion Application Development organization. He leads the Oracle Modern Best Practice Program office for Oracle. And Bill Lawson is a Senior Director for Cloud Applications Product Management here at Oracle University. We'll first ask Patrick about Oracle Modern Best Practice and then move on to Bill for details about the new training and certification we're offering. Patrick, Bill, thanks for being here today.   Patrick: Hey, Niki and Lois, thanks for the invitation. Happy to be here.  Bill: Hi Niki, Lois. 01:32 Lois: Patrick, let's start with some basic information about what OMBP are. Can you tell us a little about why they were created? Patrick: Sure, love to. So, modern best practices are more than just a business process. They're really about translating features and technology into actionable capabilities in our product. So, we've created these by curating industry leading best practices we've collected from our customers over the years. And ensure that the most modern technologies that we've built into the Fusion Application stack are represented inside of those business processes. Our goal is really to help you as customers improve your business operations by easily finding and applying those technologies to what you do every day.  02:18 Nikita: So, by understanding these modern best practice and the technology that enables it, you're really unlocking the full potential of Fusion Apps.  Patrick: Absolutely. So, the goal is that modern best practice make it really easy for customers, implementers, partners, to see the opportunity and take action. 02:38 Lois: That's great. OK, so, let's talk about implementations, Patrick. How do Oracle Modern Best Practice support customers throughout the lifecycle of an Oracle Fusion Cloud implementation? Patrick: What we found during many implementers' journey with taking our solution and trying to apply it with customers is that customers come in with a long list of capabilities that they're asking us to replicate. What they've always done in the past. And what modern best practice is trying to do is help customers to reimage the art of the possible…what's possible with Fusion by taking advantage of innovative features like AI, like IoT, like, you know, all of the other solutions that we built in to help you automate your processes to help you get the most out of the solution using the latest and greatest technology. So, if you're an implementer, there's a number of ways a modern best practice can help during an implementation. First is that reimagine exercise where you can help the customer see what's possible. And how we can do it in a better way. I think more importantly though, as you go through your implementation, many customers aren't able to get everything done by the time they have to go live. They have a list of things they've deferred and modern best practices really establishes itself as a road map for success, so you can go back to it at the completion and see what's left for the opportunity to take advantage of and you can use it to track kind of the continuous innovation that Oracle delivers with every release and see what's changed with that business process and how can I get the most out of it. 04:08 Nikita: Thanks, Patrick. That's a great primer on OMBP that I'm sure everyone will find very helpful. Patrick: Thanks, Niki. We want our customers to understand the value of modern best practices so they can really maximize their investment in Oracle technology today and in the future as we continue to innovate. 04:24 Lois: Right. And the way we're doing that is through new training and certifications that are closely aligned with OMBP. Bill, what can you tell us about this? Bill: Yes, sure. So, the new Oracle Fusion Applications Foundations training program is designed to help partners and customers understand Oracle Modern Best Practice and how they improve the entire implementation journey with Fusion Cloud Applications. As a learner, you will understand how to adhere to these practices and how they promise a greater level of success and customer satisfaction. So, whether you're designing, or implementing, or going live, you'll be able to get it right on day one. So, like Patrick was saying, these OMBPs are reimagined, industry-standard business processes built into Fusion Applications. So, you'll also discover how technologies like AI, Mobile, and Analytics help you automate tasks and make smarter decisions. You'll see how data flows between processes and get tips for successful go-lives. So, the training we're offering includes product demonstrations, key metrics, and design considerations to give you a solid understanding of modern best practice. It also introduces you to Oracle Cloud Success Navigator and how it can be leveraged and relied upon as a trusted source to guide you through every step of your cloud journey, so from planning, designing, and implementation, to user acceptance testing and post-go-live innovations with each quarterly new release of Fusion Applications and those new features. And then, the training also prepares you for Oracle Cloud Applications Foundations certifications.                   05:55 Nikita: Which applications does the training focus on, Bill? Bill: Sure, so the training focuses on four key pillars of Fusion Apps and the associated OMBP with them. For Human Capital Management, we cover Human Resources and Talent Management. For Enterprise Resource Planning, it's all about Financials, Project Management, and Risk Management. In Supply Chain Management, you'll look at Supply Chain, Manufacturing, Inventory, Procurement, and more. And for Customer Experience, we'll focus on Marketing, Sales, and Service. 06:24 Lois: That's great, Bill. Now, who is the training and certification for?  Bill: That's a great question. So, it's really for anyone who wants to get the most out of Oracle Fusion Cloud Applications. It doesn't matter if you're an experienced professional or someone new to Fusion Apps, this is a great place to start. It's even recommended for professionals with experience in implementing other applications, like on-premise products. The goal is to give you a solid foundation in Oracle Modern Best Practice and show you how to use them to improve your implementation approach. We want to make it easy for anyone, whether you're an implementer, a global process owner, or an IT team employee, to identify every way Fusion Applications can improve your organization. So, if you're new to Fusion Apps, you'll get a comprehensive overview of Oracle Fusion Applications and how to use OMBP to improve business operations. If you're already certified in Oracle Cloud Applications and have years of experience, you'll still benefit from learning how OMBP fits into your work.  If you're an experienced Fusion consultant who is new to Oracle Modern Best Practice processes, this is a good place to begin and learn how to apply them and the latest technology enablers during implementations.  And, lastly, if you're an on-premise or you have non-Fusion consultant skills looking to upskill to Fusion, this is a great way to begin acquiring the knowledge and skills needed to transition to Fusion and migrate your existing expertise. 07:53 Raise your game with the Oracle Cloud Applications skills challenge. Get free training on Oracle Fusion Cloud Applications, Oracle Modern Best Practice, and Oracle Cloud Success Navigator. Pass the free Oracle Fusion Cloud Foundations Associate exam to earn a Foundations Associate certification. Plus, there's a chance to win awards and prizes throughout the challenge! What are you waiting for? Join the challenge today by visiting oracle.com/education. 08:27 Nikita: Welcome back! Bill, how long is it going to take me to complete this training program? Bill: So, we wanted to make this program detailed enough so our learners find it valuable, obviously. But at the same time, we didn't want to make it too long. So, each course is approximately 5 hours or more, and provides folks with all the requisite knowledge they need to get started with Oracle Modern Best Practice and Fusion Applications.  08:51 Lois: Bill, is there anything that I need to know before I take this course? Are there any prerequisites? Bill: No, Lois, there are no prerequisites. Like I was saying, whether you're fresh out of college or a seasoned professional, this is a great place to start your journey into Fusion Apps and Oracle Modern Best Practice. 09:06 Nikita: That's great, you know, that there are no barriers to starting. Now, Bill, what can you tell us about the certification that goes along with this new program?  Bill: The best part, Niki, is that it's free. In fact, the training is also free. We have four courses and corresponding Foundation Associate–level certifications for Human Capital Management, Enterprise Resource Planning, Supply Chain Management, and Customer Experience. So, completing the training prepares you for an hour-long exam with 25 questions. It's a pretty straightforward way to validate your expertise in Oracle Modern Best Practice and Fusion Apps implementation considerations. 09:40 Nikita: Ok. Say I take this course and certification. What can I do next? Where should my learning journey take me? Bill: So, you're building knowledge and expertise with Fusion Applications, correct? So, once you take this training and certification, I recommend that you identify a product area you want to specialize in. So, if you take the Foundations training for HCM, you can dive deeper into specialized paths focused on implementing Human Resources, Workforce Management, Talent Management, or Payroll applications, for example.  The same goes for other product areas. If you finish the certification for Foundations in ERP, you may choose to specialize in Finance or Project Management and get your professional certifications there as your next step. So, once you have this foundational knowledge, moving on to advanced learning in these areas becomes much easier. We offer various learning paths with associated professional-level certifications to deepen your knowledge and expertise in Oracle Fusion Cloud Applications. So, you can learn more about these courses by visiting oracle.com/education/training/ to find out more of what Oracle University has to offer. 10:43 Lois: Right. I love that we have a clear path from foundational-level training to more advanced levels. So, as your skills grow, we've got the resources to help you move forward.  Nikita: That's right, Lois. Thanks for walking us through all this, Patrick and Bill. We really appreciate you taking the time to join us on the podcast. Bill: Yeah, it's always a pleasure to join you on the podcast. Thank you very much. Patrick: Oh, thanks for having me, Lois. Happy to be here. Lois: Well, that's all the time we have for today. If you have questions or suggestions about anything we discussed today, you can write to us at ou-podcast_ww@oracle.com. That's ou-podcast_ww@oracle.com. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 11:29 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.

In this episode, Lois Houston and Nikita Abraham chat with MySQL expert Perside Foster on the importance of keeping MySQL performing at its best. They discuss the essential tools for monitoring MySQL, tackling slow queries, and boosting overall performance.   They also explore HeatWave, the powerful real-time analytics engine that brings machine learning and cross-cloud flexibility into MySQL.   MySQL 8.4 Essentials: https://mylearn.oracle.com/ou/course/mysql-84-essentials/141332/226362 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: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last two episodes, we spoke about MySQL backups, exploring their critical role in data recovery, error correction, data migration, and more. Lois: Today, we're switching gears to talk about monitoring MySQL instances. We'll also explore the features and benefits of HeatWave with Perside Foster, a MySQL Principal Solution Engineer at Oracle. 01:02 Nikita: Hi, Perside! We're thrilled to have you here for one last time this season. So, let's start by discussing the importance of monitoring systems in general, especially when it comes to MySQL. Perside: Database administrators face a lot of challenges, and these sometimes appear in the form of questions that a DBA must answer. One of the most basic question is, why is the database slow? To address this, the next step is to determine which queries are taking the longest. Queries that take a long time might be because they are not correctly indexed. Then we get to some environmental queries or questions. How can we find out if our replicas are out of date? If lag is too much of a problem? Can I restore my last backup? Is the database storage likely to fill up any time soon? Can and should we consider adding more servers and scaling out the system? And when it comes to users and making sure they're behaving correctly, has the database structure changed? And if so, who did it and what did they do? And more generally, what security issues have arisen? How can I see what has happened and how can I fix it? Performance is always at the top of the list of things a DBA worries about. The underlying hardware will always be a factor but is one of the things a DBA has the least flexibility with changing over the short time. The database structure, choice of data types and the overall size of retained data in the active data set can be a problem. 03:01 Nikita: What are some common performance issues that database administrators encounter? Perside: The sort of SQL queries that the application runs can be an issue. 90% of performance problems come from the SQL index and schema group.  03:18 Lois: Perside, can you give us a checklist of the things we should monitor? Perside: Make sure your system is working. Monitor performance continually. Make sure replication is working. Check your backup. Keep an eye on disk space and how it grows over time. Check when long running queries block your application and identify those queries. Protect your database structure from unauthorized changes. Make sure the operating system itself is working fine and check that nothing unusual happened at that level. Keep aware of security vulnerabilities in your software and operating system and ensure that they are kept updated. Verify that your database memory usage is under control. 04:14 Lois: That's a great list, Perside. Thanks for that. Now, what tools can we use to effectively monitor MySQL?     Perside: The slow query log is a simple way to monitor long running queries. Two variables control the log queries. Long_query_time. If a query takes longer than this many seconds, it gets logged. And then there's min_exam_row_limit. If a query looks at more than this many rows, it gets logged. The slow query log doesn't ordinarily record administrative statements or queries that don't use indexes. Two variables control this, log_slow_admin_statements and log_queries_not_using_indexes. Once you have found a query that takes a long time to run, you can focus on optimizing the application, either by limiting this type of query or by optimizing it in some way. 05:23 Nikita: Perside, what tools can help us optimize slow queries and manage data more efficiently? Perside: To help you with processing the slow query log file, you can use the MySQL dump slow command to summarize slow queries. Another important monitoring feature of MySQL is the performance schema. It's a system database that provides statistics of how MySQL executes at a low level. Unlike user databases, performance schema does not persist data to disk. It uses its own storage engine that is flushed every time we start MySQL. And it has almost no interaction with the storage media, making it very fast. This performance information belongs only to the specific instance, so it's not replicated to other systems. Also, performance schema does not grow infinitely large. Instead, each row is recorded in a fixed size ring buffer. This means that when it's full, it starts again at the beginning. The SYS schema is another system database that's strongly related to performance schema. 06:49 Nikita: And how can the SYS schema enhance our monitoring efforts in MySQL? Perside: It contains helper objects like views and stored procedures. They help simplify common monitoring tasks and can help monitor server health and diagnose performance issues. Some of the views provide insights into I/O hotspots, blocking and locking issues, statements that use a lot of resources in various statistics on your busiest tables and indexes. 07:26 Lois: Ok… can you tell us about some of the features within the broader Oracle ecosystem that enhance our ability to monitor MySQL? Perside: As an Oracle customer, you also have access to Oracle Enterprise Manager. This tool supports a huge range of Oracle products. And for MySQL, it's used to monitor performance, system availability, your replication topology, InnoDB performance characteristics and locking, bad queries caught by the MySQL Enterprise firewall, and events that are raised by the MySQL Enterprise audit. 08:08 Nikita: What would you say are some of the standout features of Oracle Enterprise Manager? Perside: When you use MySQL in OCI, you have access to some really powerful features. HeatWave MySQL enables continuous monitoring of query statistics and performance. The health monitor is part of the MySQL server and gathers raw data about the performance of queries. You can see summaries of this information in the Performance Hub in the OCI Console. For example, you can see average statement latency or top 100 statements executed. MySQL metrics lets you drill in with your own custom monitoring queries. This works well with existing OCI features that you might already know. The observability and management framework lets you filter by resource type and across several dimensions. And you can configure OCI alarms to be notified when some condition is reached. 09:20 Lois: Perside, could you tell us more about MySQL metrics? Perside: MySQL metrics uses the raw performance data gathered by the health monitor to measure the important characteristic of your servers. This includes CPU and storage usage and information relevant to your database connection and queries executed. With MySQL metrics, you can create your own custom monitoring queries that you can use to feed graphics. This gives you an up to the minute representation of all the performance characteristics that you're interested in. You can also create alarms that trigger on some performance condition. And you can be notified through the OCI alarms framework so that you can be aware instantly when you need to deal with some issue.  10:22 Are you keen to stay ahead in today's fast-paced world? We've got your back! Each quarter, Oracle rolls out game-changing updates to its Fusion Cloud Applications. And to make sure you're always in the know, we offer New Features courses that give you an insider's look at all of the latest advancements. Don't miss out! Head over to mylearn.oracle.com to get started. 10:47 Nikita: Welcome back! Now, let's dive into the key features of HeatWave, the cloud service that integrates with MySQL. Can you tell us what HeatWave is all about? Perside: HeatWave is the cloud service for MySQL. MySQL is the world's leading database for web applications. And with HeatWave, you can run your online transaction processing or OLTP apps in the cloud. This gives you all the benefits of cloud deployments while keeping your MySQL-based web application running just like they would on your own premises. As well as OLTP applications, you need to run reports with Business Intelligence and Analytics Dashboards or Online Analytical Processing, or OLAP reports. The HeatWave cluster provides accelerated analytics queries without requiring extraction or transformation to a separate reporting system. This is achieved with an in-memory analytics accelerator, which is part of the HeatWave service. In addition, HeatWave enables you to create Machine Learning models to embed artificial intelligence right there in the database. The ML accelerator performs classification, regression, time-series forecasting, anomaly detection, and other functions provided by the various models that you can embed in your architecture. HeatWave can also work directly with storage outside the database. With HeatWave Lakehouse, you can run queries directly on data stored in object storage in a variety of formats without needing to import that data into your MySQL database. 12:50 Lois: With all of these exciting features in HeatWave, Perside, what core MySQL benefits can users continue to enjoy? Perside: The reason why you chose MySQL in the first place, it's still a relational database and with full transactional support, low latency, and high throughput for your online transaction processing app. It has encryption, compression, and high availability clustering. It also has the same large database support with up to 256 terabytes support. It has advanced security features, including authentication, data masking, and database firewall. But because it's part of the cloud service, it comes with automated patching, upgrades, and backup. And it is fully supported by the MySQL team. 13:50 Nikita: Ok… let's get back to what the HeatWave service entails. Perside: The HeatWave service is a fully managed MySQL. Through the web-based console, you can deploy your instances and manage backups, enable high availability, resize your instances, create read replicas, and perform many common administration tasks without writing a single line of SQL. It brings with it the power of OCI and MySQL Enterprise Edition. As a managed service, many routine DBA tests are automated. This includes keeping the instances up to date with the latest version and patches. You can run analytics queries right there in the database without needing to extract and transform your databases, or load them in another dedicated analytics system. 14:52 Nikita: Can you share some common use cases for HeatWave? Perside: You have your typical OLTP workloads, just like you'd run on prem, but with the benefit of being managed in the cloud. Analytic queries are accelerated by HeatWave. So your reporting applications and dashboards are way faster. You can run both OLTP and analytics workloads from the same database, keeping your reports up to date without needing a separate reporting infrastructure. 15:25 Lois: I've heard a lot about HeatWave AutoML. Can you explain what that is? Perside: HeatWave AutoML enables in-database artificial intelligence and Machine Learning. Externally sourced data stores, such as sensor data exported to CSV, can be read directly from object store. And HeatWave generative AI enables chatbots and LLM content creation. 15:57 Lois: Perside, tell us about some of the key features and benefits of HeatWave. Perside: Autopilot is a suite of AI-powered tools to improve the performance and applicability of your HeatWave queries. Autopilot includes two features that help cut costs when you provision your service. There's auto provisioning and auto shape prediction. They analyze your existing use case and tell you exactly which shape you must provision for your nodes and how many nodes you need. Auto parallel loading is used when you import data into HeatWave. It splits the import automatically into an optimum number of parallel streams to speed up your import. And then there's auto data placement. It distributes your data across the HeatWave cluster node to improve your query retrieval performance. Auto encoding chooses the correct data storage type for your string data, cutting down storage and retrieval time. Auto error recovery automatically recovers a fail node and reloads data if that node becomes unresponsive. Auto scheduling prioritizes incoming queries intelligently. An auto change propagation brings data optimally from your DB system to the acceleration cluster. And then there's auto query time estimation and auto query plan improvement. They learn from your workload. They use those statistics to perform on node adaptive optimization. This optimization allows each query portion to be executed on every local node based on that node's actual data distribution at runtime. Finally, there's auto thread pooling. It adjusts the enterprise thread pool configuration to maximize concurrent throughput. It is workload-aware, and minimizes resource contention, which can be caused by too many waiting transactions. 18:24 Lois: How does HeatWave simplify analytics within MySQL and with external data sources? Perside: HeatWave in Oracle Cloud Infrastructure provides all the features you need for analytics, all in one system. Your classic OLTP application run on the MySQL database that you know and love, provision in a DB system. On-line analytical processing is done right there in the database without needing to extract and load it to another analytic system. With HeatWave Lakehouse, you can even run your analytics queries against external data stores without loading them to your DB system. And you can run your machine learning models and LLMs in the same HeatWave service using HeatWave AutoML and generative AI. HeatWave is not just available in Oracle Cloud Infrastructure. If you're tied to another cloud vendor, such as AWS or Azure, you can use HeatWave from your applications in those cloud too, and at a great price. 19:43 Nikita: That's awesome! Thank you, Perside, for joining us throughout this season on MySQL. These conversations have been so insightful. If you're interested in learning more about the topics we discussed today, head over to mylearn.oracle.com and search for the MySQL 8.4: Essentials course.  Lois: This wraps up our season on the essentials of MySQL. But before we go, we just want to remind you to write to us if you have any feedback, questions, or ideas for future episodes. Drop us an email at ou-podcast_ww@oracle.com. That's ou-podcast_ww@oracle.com. Nikita: Until next time, 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. 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.

Jake and Michael discuss those features you ship that nobody uses but everybody has feedback for, testing a system where the valid state can change based on user input, and compliance auditing and adherence.

Lois Houston and Nikita Abraham continue their conversation with MySQL expert Perside Foster, with a closer look at MySQL Enterprise Backup. They cover essential features like incremental backups for quick recovery, encryption for data security, and monitoring with MySQL Enterprise Monitor—all to help you manage backups smoothly and securely.   MySQL 8.4 Essentials: https://mylearn.oracle.com/ou/course/mysql-84-essentials/141332/226362 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: Editorial Services  with Oracle University, and with me is Lois Houston, Director of Innovation Programs.  Lois: Hi there! Last week was the first of a two-part episode covering the different types of backups and why they're important. Today, we'll look at how we can use MySQL Enterprise Backup for efficient and consistent backups. 00:52 Nikita: And of course, we've got Perside Foster with us again to walk us through all the details. Perside, could you give us an overview of MySQL Enterprise Backup?  Perside: MySQL Enterprise Backup is a form of physical backup at its core, so it's much faster for large data sets than logical backups, such as the most commonly used MySQL Dump. Because it backs up the data files, it's non-locking and enables either complete system backup or partial backup, focusing only on specific databases. 01:29 Lois: And what are the benefits of using MySQL Enterprise Backup? Perside: You can back up to local storage or direct-to-common-cloud storage types. You can perform incremental backups, which can speed up your backup process greatly. Incremental backups enable point-in-time recovery. It's useful when you need to restore to a point in time before some application or human error occurred. Backups can be compressed to save archival storage requirements and encrypted for regulatory compliance and offline data security. 02:09 Nikita: So we know MySQL Enterprise Backup is an impressive tool, but could you talk more about some of the main features it supports for creating and managing backups? Specifically, which tools are integrated within MySQL Enterprise to support different backup scenarios? Perside: MySQL Enterprise Backup supports SBT, implemented by many common Tape storage systems. MySQL Enterprise Backup supports optimistic backup. This process deals with busy tables separately from the rest of the database. It can record changes that happen in the database during the backup for consistency. In a large data set, this can make a huge difference in performance. MySQL Enterprise Backup runs on all supported platforms. It's available when you have a MySQL Enterprise Edition license. And it comes with Enterprise Edition, but it also is available as a separate package. You can get the most recent version from eDelivery, where you can also get a trial version. If you need a previous release, you can get that from My Oracle Support. It's also available in all versions of MySQL, whether you run a Long-Term support version or an Innovation Release. For LTS releases, MySQL Enterprise Backup supports MySQL instances of the same LTS release. For Innovation releases, it supports the previous LTS release and any subsequent Innovation version within the same LTS family. 04:03 Nikita: How does MySQL Enterprise Monitor manage and track backup processes? Perside: MySQL Enterprise Monitor has a dashboard for monitoring MySQL Enterprise Backup. The dashboard monitors the health of backup process and usage throughout the entire Enterprise fleet, not just a single server. It supports drilling down into specific sub-operations within a backup job. You can see information about full backups, partial backups, and incremental backups. You can configure alerts that will notify you in the event of delays, failures, or backups that have not been performed in some configuration time period. 04:53 Lois: Ok…let's get into the mechanics. I understand that MySQL Enterprise Backup uses binary logs as part of its backup process. Can you explain how these logs fit into the bigger picture of maintaining database integrity? Perside: MySQL Enterprise Backup is a utility designed specifically for backing up MySQL systems in the most efficient and flexible way. At its simplest, it performs a physical backup of the data files, so it is fast. However, it also records the changes that were made during the time it took to do the backup. So, the result is that you get a consistent backup of the data at the time the backup completed. This backup is not tied to the host system and can be moved to other hosts. It can be used for archiving and is fully supported as part of the MySQL Enterprise Edition. It is, however, tied to the specific version of MySQL from which the backup was taken. So, you cannot use it for upgrades where the destination server is an upgrade from the source. For example, if you take a backup from MySQL 5.7, you can't directly restore it to MySQL 8.0. As a part of MySQL Enterprise Edition, it's not part of the freely available Community Edition. 06:29 Lois: Perside, how do MySQL's binary logs track changes over time? And why is this so critical for backups? Perside: The binary logs record changes to the database. These changes are recorded in a sequential set of files numbered incrementally. MySQL logs changes either in statement-based form, where each log entry records the statement that gives rise to the change, or in row-based form where the actual change row data is recorded. If you select mixed format, then MySQL records statements for most operations and records row for changes where the statement might result in a different row value each time it's run, for example, where there's a generated value like autoincrement. The current log file grows as changes are recorded. When it reaches its maximum configured size, that log file is closed, and the next sequential file is created for new logs. You can make this happen automatically by using the FLUSH BINARY LOGS command. This does not delete any existing log files. 07:59 Nikita: But what happens if you want to delete the log files? Perside: If you want to delete all log files, you can do so manually with the PURGE BINARY LOGS command, either specifying a file or a date time.  08:14 Lois: When it comes to tracking transactions, MySQL provides a couple of methods, right? Can you explain the differences between Global Transaction Identifiers and the traditional log file sequence? Perside: Log files positioning is one of two formats, either legacy, where you specify transactions with a log file in a sequence number, or by using global transaction identifiers, or GTIDs, where each transaction is identified with a universally unique identifier or UUID. When you apply a transaction to the source server, that is when the GTID is attached to the transaction. This makes it particularly useful in replication topologies so that each transaction is uniquely identified by both its server ID and the transaction sequence number. When such a transaction is replicated to other hosts, the transaction retains its original GTID so that you can track when that transaction has propagated to the replicas and has been applied. The global transaction identifier is unique across the entire network. 09:49 Have you mastered the basics of AI? Are you ready to take your skills to the next level? Unlock the potential of advanced AI with our OCI Generative AI Professional course and certification that covers topics like LLMs, the OCI Generative AI Service, and building Q&A chatbots for real-world applications. Head over to mylearn.oracle.comand find out more. 10:19 Nikita: Welcome back! Let's move on to replication. How does MySQL's legacy log format handle transactions, and what does that mean for replication timing across different servers? Perside: Legacy format binary logs are non-transactional. This means that a transaction made up of multiple modifications is logged as a sequence of changes. It's possible that different hosts in a replication network apply those changes at different times. Each server that uses legacy binary logging maintain the current applied log position as coordinates based on a combination of binary log files in the position within that log file. 11:11 Nikita: Troubleshooting with legacy logs can be quite complex, right? So, how does the lack of unique transaction IDs make it more difficult to address replication issues? Perside: Because each server has its own log with its own transactions, these modification could have entirely different coordinates, making it challenging to find the specific modification point if you need to do any deep dive troubleshooting, for example, if one replica fell partway through applying a transaction and you need to partially roll it back manually. On the other hand, when you enable GTIDs, the transaction applied on the source host has that globally unique identifier attached to the whole transaction as a sequence of unique IDs. When the second or subsequent servers apply those transactions, they have exactly the same identifier, making it both transaction-safe for MySQL and also easier to troubleshoot if you need to. 12:26 Lois: How can you use binary logs to perform a point-in-time recovery in MySQL? Perside: First, you restore the last full backup. Once you've restarted the restart server, find the current log position of that backup. Either it's GTID or log sequence number. The SHOW BINARY LOG STATUS command shows this information. Then you can use the MySQL binlog utility to replay events from the binary log files, specifying the start and stop position containing the range of log operations that you wish to apply. You can pipe the output of the MySQL bin log to the MySQL client if you want to execute the changes immediately, or you can redirect the output to a script file if you want to examine and perhaps edit the changes. 13:29 Nikita: And how do you save binary logs? Perside: You can save binary logs to use in disaster recovery, for point-in-time restores, or for incremental backups. One way to do this is to flush the logs so that the log file closes and ready for copying. And then copy it to a different server to protect against hardware media failures. You can also use the MySQL binlog utility to create a copy of a set of binary log files in the same format, but to a different file or set of files. This can be useful if you want to run MySQL binlog continuously, copying from the source server binary log to a new location, perhaps in network storage. If you do this, remember that MySQL binlog does not run as a service or daemon, so you'll need to monitor it to make sure it's running continually.  14:39 Lois: Can you take us through how the MySQL Enterprise Backup process works? What does it do when performing a backup? Perside: First, it performs a physical file copy of necessary data and log files. This can be done while the server is fully operational, and it has minimal impact on performance. Once this initial copy is taken, it applies a low impact backup lock on the instance. If you have any tables that are not using InnoDB, the backup cannot guarantee transaction-safe consistency for those tables. It applies a weed lock to those tables so that it can guarantee consistency. Then it briefly locks all logging activity to take a consistent view of the current coordinates of various logs. It releases the weed lock on non-transactional tables. Using the log coordinates that were taken earlier in the process, it gathers all logs for transactions that have occurred since then. Bear in mind that the backup process takes place while the system is active. So, for a consistent backup, it must record not only the data files, but all changes that occurred during the backup. Then it releases the backup lock. The last piece of information recorded is any metadata for the backup itself, including its timing and contents in the final redo log. That completes the backup operation. 16:30 Nikita: And where are the files stored? Perside: The files contained in the backup are saved to the backup location, which can be on the local system or in network storage. The files contained in the backup location include files from the MySQL data directory. Some raw files include InnoDB tablespace, plus any InnoDB file per table tablespace files, and InnoDB log files. Other files might include data files belonging to other storage engines, perhaps MyISAM files. The various log files in instance configuration files are also retained. 17:20 Lois: What steps do you follow to restore a MySQL Enterprise Backup, and how do you guarantee consistency, especially when dealing with incremental backups? Perside: To restore from a backup using MySQL Enterprise Backup, you must first remove any previous files from the data directory. The restore process will fail if you attempt to restore over an existing system or backup. Then you restore the database with appropriate options. If you only restore a single backup, you can use copy, back, and apply log to ensure that the restored system has a consistency state. If you perform a full backup in subsequent incremental backups, you might need to restore multiple times using copy-back, and then use copy-back-and-apply-log only for the final consistent restore operation. The restart server might be on the same host or might be a different host with different configuration. This means that you might have to change some configuration on the restored server, including the operating system ownership of the restored data directory and various MySQL configuration files. If you want to retain the MySQL configuration files from the source server to reproduce on a new server, you should copy those files separately. MySQL Enterprise Backup focuses on the data rather than the server configuration. It does, however, produce configuration files appropriate for the backup. These are similar to the MySQL configuration files, but only contain options relevant for the backup process itself. There's also variables that have been changed to non-default values and all global variable values. These files must be renamed and possibly edited before they are suitable to become configuration files in the newly restored server. For example, the mysqld-auto.cnf file contains a JSON-formatted set of persisted variables. The backup process stores this as the newly named backup mysqld-auto.cnf. If you want to use it in the restored server, you must rename it and place it in the appropriate location so that the restored server can read it. This also applies in part to the auto.cnf file, which contain identifying information for the server. If you are replacing the original server or restoring on the same host, then you can keep the original values. However, this information must be unique within a network. So, if you are restoring this backup to create a replica in a replication topology, you must not include that file and instead start MySQL without it so that it creates its own unique identifying information. 21:14 Nikita: Let's discuss securing and optimizing backups. How does MySQL Enterprise Backup handle encryption and compression, and what are the critical considerations for each? Perside: You can encrypt backups so that they are secure while moving them around or archiving them. The encrypt option performs the encryption. And you can specify the encryption key either on the command line as a string or a key file that has been generated with some cryptographic algorithm. Encryption only applies to image files, not to backup directories. You can also compress backup with different levels of compression, with higher levels requiring more CPU, but resulting in greater savings in storage. Compression only works with InnoDB data files. If your organization has media management software for performing backups, perhaps to a tape array, then you can use the SBT interface supported in MySQL Enterprise Backup. 22:34 Lois: Before we wrap up, could you share how MySQL Enterprise Backup facilitates the management of backups across a multi-server environment? Perside: As an enterprise solution, it's easy to run MySQL Enterprise Backup in a multi-server environment. We've already mentioned backing up to cloud storage, but you can, of course, back up to a directory or image on network storage that can be mounted locally, perhaps with NFS or some other file system. The "with time" option enables multiple backups within the same backup directory, where each in its own subdirectory named with the timestamp. This is especially useful when you want to run the same backup script repeatedly.  23:32 Lois: Thank you for that detailed overview, Perside. This wraps up our discussion of the various backup types, their pros and cons, and how to select the right option for your needs. In our next session, we'll explore the different MySQL monitoring strategies and look at the features as well as benefits of Heatwave. 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 MySQL 8.4 Essentials course. Until then, this is Nikita Abraham… Lois: And Lois Houston signing off! 24:06 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.

AWS Morning Brief for the week of February 17, with Corey Quinn. Links:Amazon DynamoDB now supports auto-approval of quota adjustmentsAmazon Elastic Block Store (EBS) now adds full snapshot size information in Console and APIAmazon RDS for MySQL announces Extended Support minor 5.7.44-RDS.20250103Amazon Redshift Serverless announces reduction in IP Address Requirements to 3 per SubnetAWS Deadline Cloud now supports Adobe After Effects in Service-Managed FleetsAWS Network Load Balancer now supports removing availability zonesAWS CloudTrail network activity events for VPC endpoints now generally availableHarness Amazon Bedrock Agents to Manage SAP InstancesTimestamp writes for write hedging in Amazon DynamoDBUpdating AWS SDK defaults – AWS STS service endpoint and Retry StrategyLearning AWS best practices from Amazon Q in the ConsoleAutomating Cost Optimization Governance with AWS ConfigAmazon Q Developer in chat applications rename - Summary of changes - AWS Chatbot

Join Lois Houston and Nikita Abraham as they kick off a two-part episode on MySQL backups with MySQL expert Perside Foster. In this conversation, they explore the critical role of backups in data recovery, error correction, data migration, and more.   Perside breaks down the differences between logical and physical backups, discussing their pros and cons, and shares valuable insights on how to create a reliable backup strategy to safeguard your data.   MySQL 8.4 Essentials: https://mylearn.oracle.com/ou/course/mysql-84-essentials/141332/226362 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: 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 everyone! This is Episode 6 in our series on MySQL, and today we're focusing on how to back up our MySQL instances. This is another two-parter and we've got Perside Foster back with us.    00:49 Lois: Perside is a MySQL Principal Solution Engineer at Oracle and she's here to share her insights on backup strategies and tools. In this episode, we'll be unpacking the types of backups available and discussing their pros and cons. Nikita: But first let's start right at the beginning. Perside, why is it essential for us to back up our databases? 01:10 Perside: The whole point of a database is to store and retrieve your business data, your intellectual property. When you back up your data, you are able to do disaster recovery so that your business can continue after some catastrophic event. You can recover from error and revert to a previous known good version of the data. You can migrate effectively from one system to another, or you can create replicas for load balancing or parallel system. You can retain data for archival purposes. Also, you can move large chunks of data to other systems, for example, to create a historical reporting application. And then you can create test environments for applications that are in development and that need real world test data. 02:10 Lois: Yes, and creating a robust backup strategy takes planning, doesn't it? Perside: As with any complex business critical process, there are challenges with coming up with a backup strategy that you can trust. This requires some careful planning. Any backup process needs to read the data. And in a production system, this will involve adding input/output operations to what might be an already busy system. The resources required might include memory or disk I/O operation and of course, you'll want to avoid downtime, so you might need to schedule the backup for a time when the system is not at peak usage. You'll also need to consider whether the backup is on network storage or some local storage so that you don't exceed limitations for those resources. It isn't enough just to schedule the backup. You'll also need to ensure that they succeed, which you can do with monitoring and consistency check. No backup is effective unless you can use it to restore your data, so you should also test your restore process regularly. If you have business requirements or regulatory commitments that control your data storage policies, you need to ensure your backup also align with those policies. Remember, every backup is a copy of your data at that moment in time. So it is subject to all of your data retention policies, just like your active data. 04:02 Nikita: Let's talk backup types. Perside, can you break them down for us? Perside: The first category is logical backup. A logical backup creates a script of SQL statements that will re-create the data structure and roles of the live database. Descript can be moved to another server as required. And because it's a script, it needs to be created by and executed on a running server. Because of this, the backup process takes up resources from the source server and is usually slower than a physical media backup.  04:45 Nikita: Ok… what's the next type? Perside: The next category is physical backup. This is a backup of the actual data file in the server. Bear in mind that the file copy process takes time, and if the database server is active during that time, then the later parts of the copy data will be inconsistent with those parts copied earlier. Ideally, the file must be stable during the backup so that the database state at the start of the copy process is consistent with the state at the end. If there is inconsistency in the data file, then MySQL detects that when the server starts up and it performs a crash recovery. From MySQL's perspective, there is no difference between a database backup copied from a running server and restarting a server after a crash. In each case, the data files were not saved in a consistent state and crash recovery can take a lot of time on large databases. 06:02 Lois: I see… how can MySQL Enterprise Backup help with this? Perside: MySQL Enterprise Backup has features that enable a consistent backup from a running server. If you create file system copies, either by copying the data files or by performing a file system snapshot, then you must either shut the server down before the copy and undergo crash recovery on the server that starts with those copied files. 06:35 Lois: And aside from logical and physical backups, are there other techniques to back up data? Perside: The binary log enables point-in-time recovery. You can enable replication in a couple of ways. If you start replication and then stop it at a particular time, the replica effectively contains a live backup of the data at the time that you stopped replication. You can also enable a defined replication lag so that the replica is always a known period of time behind the production database. You can also use transportable tablespaces, which are tables or sets of tables in a specific file that you can copy to another server. 07:34 AI is being used in nearly every industry…healthcare, manufacturing, retail, customer service, transportation, agriculture, you name it! And it's only going to get more prevalent and transformational in the future. It's no wonder that AI skills are the most sought-after by employers. If you're ready to dive in to AI, check out the OCI AI Foundations training and certification e that's available for free! It's the perfect starting point to build your AI knowledge. So, get going! Head over to mylearn.oracle.com to find out more. 08:14 Nikita: Welcome back! I want to return to the topic of crafting an effective backup strategy. Perside, any advice here? Perside: We can use the different backup types to come up with an effective backup strategy based on how we intend to restore the data. A full backup is a complete copy of the database at some point in time. This can take a lot of time to complete and to restore. An incremental backup contains only the changes since the last backup, as recorded in the binary log files. To restore an incremental backup, you must have restored the previous full backup and any incremental backups taken since then. For example, you might have four incremental backups taken after the last full backup. Each incremental backup contains only the changes since the previous backup. If you want to restore to the point at which you took the fourth incremental backup, then you must restore the full backup and each incremental backup in turn. A differential backup contains all changes since the last full backup. It contains only those portions of the database that are different from the full backup. Over time, the differential backup takes longer because it contains more changes. However, it is easier to restore because if you want to restore to the point at which you took a particular differential backup, you must restore the last full backup and only the differential backup that you require. You can ignore the intermediate differential backups. 10:13 Lois: Can you drill into the different types of backups and explain how each technique is used in various situations? Perside: One of the physical backup techniques is taking a snapshot of the storage medium. The advantages of a snapshot include its quickness. A snapshot is quick to create and restore. It is well-suited to situations where you need to quickly revert to a previous version of the database. For example, in a development environment. A storage snapshot is often a feature of the underlying file system. Linux supports logical volume management or LVM, and many storage area networks or network-attached storage platforms have native snapshot features. You can also use a storage snapshot to supplement a more scheduled logical backup structure. This way, the snapshot enables quick reversion to a previous type, and the logical backup can be used for other purposes, such as archiving or disaster recovery. 11:28 Nikita: Are there any downsides to using snapshots? Perside: First one includes issues with consistency. Because taking a snapshot is quick and does not cause a database performance hit, you might take the snapshot while the system is running. When you restore such a snapshot, MySQL must perform a crash recovery. If you want a consistent snapshot, you must shut down MySQL in advance. Another problem is that the snapshot is a copy of the file system and not of the database. So if you want to transfer it to another system, you must create a database backup from the storage. This adds step in time. A snapshot records the state of the disk at a specific point in time. Initially, the snapshot is practically empty. When a data page changes, the original version of that page is written to the snapshot. Over time, the snapshot storage grows as more data pages are modified. So multiple snapshots result in multiple writes whenever a snapshot data page is changed. To avoid performance deterioration, you should remove or release snapshots when they are no longer in use. Also, because snapshots are tied to the storage medium, they're not suited to moving backups between systems. 13:03 Lois: How about logical backups? How do we create those? Perside: The mysqldump utility has long been a standard way to create logical backups. It creates a script made up of the SQL statement that creates the data and structure in a database or server.  13:21 Nikita: Perside, what are the advantages and disadvantages of mysql dump? Perside: It is an excellent solution for preserving the database structure or for backing up small databases. Logical backups naturally require that the server is running. And they use system resources to produce the SQL statements, so they are less likely for very large databases. The output is a human-readable text file with SQL statements that you can edit as a text file. It can be managed by a source code management system. This allows you to maintain a known good version of the database structure, one that matches your application source code version, which can also include sample data. The mysqldump disadvantages are it needs to run against an active server. So if your production server is busy, you must take action to ensure a consistent backup. This requires locking tables or using the single transaction option, which can result in application delays as the backup completes in a consistent way. Mysqldump does not track changes since the last backup, so it has no way of recording only those rows that have changed. This means it's not suited to perform differential or incremental backups. The scripts must be executed against a running server, so it is slower to restore than using a data dump or physical backup. Additionally, if the database structure has indexes of foreign keys, these conditions must be checked and updated as the data is imported. You can disable these checks during the import but must handle any risks that come from doing so. Because the backup is nothing more than an SQL script, it is easy to restore. You can simply use the MySQL client or any other client tool that can process scripts.  15:46 Nikita: Is there an alternative tool for logical backups? Perside: MySQL Shell is another utility that supports logical backup and restore. Unlike mysqldump, it dumps data in a form that can be processed in parallel, which makes it much faster to use for larger data sets. This enables it to export to or import from remote storage where it can stream data without requiring the whole file before starting the input. It can process multiple chunks of imported data in parallel, and you can monitor progress as it completes. You can also pause import and resume later. For example, in the event of network outage. You can dump and restart table structure, including indexes and primary keys. The utilities in MySQL Shell are exposed through functions. The dumpInstance and dumpSchema utilities back up the whole server or specified schemas respectively. And loadDump is how you restore from such a dump. 17:07 Lois: Thanks for that rundown, Perside! This concludes our first part on MySQL backups. Next week, we'll take a look at advanced backup methods and the unique features of MySQL Enterprise Backup. Nikita: And if you want to learn more about everything we discussed today, head over to mylearn.oracle.com and explore the MySQL 8.4 Essentials course. Until then, this is Nikita Abraham… Lois: And Lois Houston signing off! 17:37 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.

Databases are hard. Making the right choices early and keeping things running smoothly even when budget pressures and customer requests start piling on — that's the hard part that many solopreneurs and indie founders struggle with.I certainly do.So here's a journey through my learnings and experiences from running SaaS offerings with sizeable —talking about terabytes of data— databases.The blog post: https://thebootstrappedfounder.com/indie-hacking-databases-at-scale/ The podcast episode: https://tbf.fm/episodes/374-indie-hacking-databases-at-scaleCheck out Podscan to get alerts when you're mentioned on podcasts: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

In this episode, Jake and Michael discuss the blockbuster trade of Luka Dončić to the the Los Angeles Lakers in exchange for Anthony Davis, the just-announced Laracon US, and pitch our talks for the very same conference.

Picking up from Part 1, hosts Lois Houston and Nikita Abraham continue their deep dive into MySQL security with MySQL Solution Engineer Ravish Patel. In this episode, they focus on user authentication techniques and tools such as MySQL Enterprise Audit and MySQL Enterprise Firewall.   MySQL 8.4 Essentials: https://mylearn.oracle.com/ou/course/mysql-84-essentials/141332/226362 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 everyone! Last week, we began exploring MySQL security, covering regulatory compliance and common security threats.  Nikita: This week, we're continuing the conversation by digging deeper into MySQL's user authentication methods and taking a closer look at some powerful security tools in the MySQL Enterprise suite. 00:57 Lois: And we're joined once again by Ravish Patel, a MySQL Solution Engineer here at Oracle. Welcome, Ravish! How does user authentication work in MySQL? Ravish: MySQL authenticates users by storing account details in a system database. These accounts are authenticated with three elements, username and hostname commonly separated with an @ sign along with a password.  The account identifier has the username and host. The host identifier specifies where the user connects from. It specifies either a DNS hostname or an IP address. You can use a wild card as part of the hostname or IP address if you want to allow this username to connect from a range of hosts. If the host value is just the percent sign wildcard, then that username can connect from any host. Similarly, if you create the user account with an empty host, then the user can connect from any host.  01:55 Lois: Ravish, can MySQL Enterprise Edition integrate with an organization's existing accounts?  Ravish: MySQL Enterprise authentication integrates with existing authentication mechanisms in your infrastructure. This enables centralized account management, policies, and authentication based on group membership and assigned corporate roles, and MySQL supports a wide range of authentication plugins. If your organization uses Linux, you might already be familiar with PAM, also known as Pluggable Authentication Module. This is a standard interface in Linux and can be used to authenticate to MySQL. Kerberos is another widely used standard for granting authorization using a centralized service. The FIDO Alliance, short for Fast Identify Online, promotes an interface for passwordless authentication. This includes methods for authenticating with biometrics RUSB security tokens. And MySQL even supports logging into centralized authentication services that use LDAP, including having a dedicated plugin to connect to Windows domains. 03:05 Nikita: So, once users are authenticated, how does MySQL handle user authorization? Ravish: The MySQL privilege system uses the GRANT keyword. This grants some privilege X on some object Y to some user Z, and optionally gives you permission to grant the same privilege to others. These can be global administrative privileges that enable users to perform tasks at the server level, or they can be database-specific privileges that allow users to modify the structure or data within a database. 03:39 Lois: What about database privileges? Ravish: Database privileges can be fine-grained from the largest to the smallest. At the database level, you can permit users to create, alter, and delete whole databases. The same privileges apply at the table, view, index, and stored procedure levels. And in addition, you can control who can execute stored procedures and whether they do so with their own identity or with the privileges of the procedure's owner. For tables, you can control who can select, insert, update, and delete rows in those tables. You can even specify the column level, who can select, insert, and update data in those columns. Now, any privileged system carries with it the risk that you might forget an important password and lock yourself out. In MySQL, if you forget the password to the root account and don't have any other admin-level accounts, you will not be able to administer the MySQL server. 04:39 Nikita: Is there a way around this? Ravish: There is a way around this as long as you have physical access to the server that runs the MySQL process. If you launch the MySQL process with the --skip grant tables option, then MySQL will not load the privilege tables from the system database when it starts. This is clearly a dangerous thing to do, so MySQL also implicitly disables network access when you use that option to prevent users from connecting over the network. When you use this option, any client connection to MySQL succeeds and has root privileges. This means you should control who has shell access to the server during this time and you should restart the server or enable privileged system with the command flush privileges as soon as you have changed the root password. The privileges we have already discussed are built into MySQL and are always available. MySQL also makes use of dynamic privileges, which are privileges that are enabled at runtime and which can be granted once they are enabled.  In addition, plugins and components can define privileges that relate to features of those plugins. For example, the enterprise firewall plugin defines the firewall admin privilege and the audit admin privilege is defined by the enterprise audit plugin.  06:04 Are you working towards an Oracle Certification this year? Join us at one of our certification prep live events in the Oracle University Learning Community. Get insider tips from seasoned experts and learn from others who have already taken their certifications. Go to community.oracle.com/ou to jump-start your journey towards certification today! 06:28 Nikita: Welcome back! Ravish, I want to move on to MySQL Enterprise security tools. Could you start with MySQL Enterprise Audit? Ravish: MySQL Enterprise Audit is an extension available in Enterprise Edition that makes it easier to comply with regulations that require observability and control over who does what in your database servers. It provides visibility of connections, authentication, and individual operations. This is a necessary part of compliance with various regulations, including GDPR, NIS2, HIPAA, and so on. You can control who has access to the audited events so that the audits themselves are protected. As well as configuring what you audit, you can also configure rotation policies so that unmonitored audit logs don't fill up your storage space. The configuration can be performed while the server is running with minimal effect on production applications. You don't need to restart the server to enable or disable auditing or to change the filtering options. You can output the audit logs in either XML or JSON format, depending on how you want to perform further searching and processing. If you need it, you can compress the logs to save space and you can encrypt the logs to provide address protection of audited identities and data modifications. The extension is available either as a component or if you prefer, as the legacy plugin. 07:53 Lois: But how does it all work? Ravish: Well, first, as a DBA, you'll enable the audit plugin and attach it to your running server. You can then configure filters to audit your connections and queries and record who does what, when they do it, and so on. Then once the system is up and running, it audits whenever a user authenticates, accesses data, or even when they perform schema changes. The logs are recorded in whatever format that you have configured. You can then monitor the audited events at will with MySQL tools such as Workbench or with any software that can view and manipulate XML or JSON files. You can even configure Enterprise Audit to export the logs to an external Audit Vault, enabling collection, and archiving of audit information from all over your enterprise. In general, you won't audit every action on every server. You can configure filters to control what specific information ends up in the logs. 08:50 Nikita: Why is this sort of filtering necessary, Ravish? Ravish: As a DBA, this enables you to create a custom designed audit process to monitor things that you're really interested in. Rules can be general or very fine grained, which enables you to reduce the overall log size, reduces the performance impact on the database server and underlying storage, makes it easier to process the log file once you've gathered data, and filters are configured with the easily used JSON file format. 09:18 Nikita: So what information is audited? Ravish: You can see who did what, when they did it, what commands they use, and whether they succeeded. You can also see where they connected from, which can be useful when identifying man in the middle attacks or stolen credentials. The log also records any available client information, including software versions and information about the operating system and much more. 09:42 Lois: Can you tell us about MySQL Enterprise Firewall, which I understand is a specific tool to learn and protect the SQL statements that MySQL executes? Ravish: MySQL Enterprise Firewall can be enabled on MySQL Enterprise Edition with a plugin. It uses an allow list to set policies for acceptable queries. You can apply this allow list to either specific accounts or groups. Queries are protected in real time. Every query that executes is verified per server and checked to make sure that it conforms to query structures that are defined in the allow list. This makes it very useful to block SQL injection attacks. Only transactions that match well-formed queries in the allow list are permitted. So any attempt to inject other types of SQL statements are blocked. Not only does it block such statements, but it also sends an alert to the MySQL error log in real time. This gives you visibility on any security gaps in your applications. The Enterprise Firewall has a learning mode during which you can train the firewall to identify the correct sort of query. This makes it easy to create the allow list based on a known good workload that you can create during development before your application goes live. 10:59 Lois: Does MySQL Enterprise Firewall operate seamlessly and transparently with applications? Ravish: Your application simply submits queries as normal and the firewall monitors incoming queries with no application changes required. When you use the Enterprise Firewall, you don't need to change your application. It can submit statements as normal to the MySQL server. This adds an extra layer of protection in your applications without requiring any additional application code so that you can protect against malicious SQL injection attacks. This not only applies to your application, but also to any client that configured user runs. 11:37 Nikita: How does this firewall system work?  Ravish: When the application submits a SQL statement, the firewall verifies that the statement is in a form that matches the policy defined in the allow list before it passes to the server for execution.  It blocks any statement that is in a form that's outside of policy.  In many cases, a badly formed query can only be executed if there is some bug in the application's data validation. You can use the firewall's detection and alerting features to let when it blocks such a query, which will help you quickly detect such bugs, even when the firewall continues to block the malicious queries. 12:14 Lois: Can you take us through some of the encryption and masking features available in MySQL Enterprise Edition?  Ravish: Transparent data encryption is a great way to protect against physical security disclosure. If someone gains access to the database files on the file system through a vulnerability of the operating system, or even if you've had a laptop stolen, your data will still be protected. This is called Data at Rest Encryption. It protects not only the data rows in tablespaces, but also other locations that store some version of the data, such as undo logs, redo logs, binary logs and relay logs. It is a strong encryption using the AES 256 algorithm. Once we enable transparent data encryption, it is, of course, transparent to the client software, applications, and users. Applications continue to submit SQL statements, and the encryption and decryptions happen in flight. The application code does not need to change. All data types, table structure, and database names remain the same. It's even transparent to the DBAs. The same data types, table structure, and so on is still how the DBA interacts with the system while creating indexes, views, and procedures. In fact, DBAs don't even need to be in possession of any encryption keys to perform their admin tasks. It is entirely transparent. 13:32 Nikita: What kind of management is required for encryption? Ravish: There is, of course, some key management required at the outside. You must keep the keys safe and put policies in place so that you store and rotate keys effectively, and ensure that you can recover those keys in the event of some disaster. This key management integrates with common standards, including KMIP and KMS. 13:53 Lois: Before we close, I want to ask you about the role of data masking in MySQL. Ravish: Data masking is when we replace some part of the private information with a placeholder. You can mask portions of a string based on the string position using the letter X or some other character. You can also create a table that contains a dictionary of suitable replacement words and use that dictionary to mask values in your data. There are specific functions that work with known formats of data, for example, social security numbers as used in the United States, national insurance numbers from the United Kingdom, and Canadian social insurance numbers. You can also mask various account numbers, such as primary account numbers like credit cards or IBAN numbers as used in the European Bank system. There are also functions to generate random values, which can be useful in test databases. This might be a random number within some range, or an email address, or a compliant credit card number, or social security number. You can also create random information using the dictionary table that contains suitable example values. 14:58 Nikita: Thank you, Ravish, for taking us through MySQL security. We really cannot overstate the importance of this, especially in today's data-driven world.  Lois: That's right, Niki. Cyber threats are increasingly sophisticated these days. You really have to be on your toes when it comes to security. If you're interested in learning more about this, the MySQL 8.4 Essentials course on mylearn.oracle.com is a great next step.  Nikita: We'd also love to hear your thoughts on our podcast so please feel free to share your comments, suggestions, or questions by emailing us at ou-podcast_ww@oracle.com. That's ou-podcast_ww@oracle.com. In our next episode, we'll journey into the world of MySQL backups. Until then, this is Nikita Abraham… Nikita: And Lois Houston, signing off! 15:51 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.

News includes the exciting release of Oban Web as open source with newly added MySQL support, nine new ElixirConf 2024 videos have been published, a new full-stack web framework called Hologram that transpiles Elixir to JavaScript was announced, PhoenixTest gained Playwright driver support for enhanced testing capabilities, Protoss reached feature-complete status as it moves to version 1.0, and several Elixir conferences were announced including Code BEAM Lite Stockholm and GigCityElixir, and more! Show Notes online - http://podcast.thinkingelixir.com/238 (http://podcast.thinkingelixir.com/238) Elixir Community News https://oban.pro/articles/oss-web-and-new-oban (https://oban.pro/articles/oss-web-and-new-oban?utm_source=thinkingelixir&utm_medium=shownotes) – Oban Web has been officially released as OpenSource, including MySQL support in Oban v2.19 and Oban Web v2.11. https://www.youtube.com/playlist?list=PLqj39LCvnOWbW2Zli4LurDGc6lL5ij-9Y (https://www.youtube.com/playlist?list=PLqj39LCvnOWbW2Zli4LurDGc6lL5ij-9Y?utm_source=thinkingelixir&utm_medium=shownotes) – Nine new ElixirConf 2024 videos have been published and added to the official YouTube playlist. https://hologram.page/ (https://hologram.page/?utm_source=thinkingelixir&utm_medium=shownotes) – Introduction of Hologram, a new full stack isomorphic Elixir web framework that transpiles Elixir to JavaScript for client-side code. https://github.com/bartblast/hologram (https://github.com/bartblast/hologram?utm_source=thinkingelixir&utm_medium=shownotes) – The GitHub repository for Hologram, currently at version 0.2.0. https://hexdocs.pm/phoenixtestplaywright/PhoenixTest.Playwright.html (https://hexdocs.pm/phoenix_test_playwright/PhoenixTest.Playwright.html?utm_source=thinkingelixir&utm_medium=shownotes) – PhoenixTest now has a Playwright driver, enabling three layers of Phoenix testing with a common assertion layer. https://github.com/ityonemo/protoss (https://github.com/ityonemo/protoss?utm_source=thinkingelixir&utm_medium=shownotes) – Protoss, a library for powerful Elixir protocols, is now feature-complete and moving to version 1.0. Looking for maintainer. https://ashweekly.substack.com/p/ash-weekly-issue-1 (https://ashweekly.substack.com/p/ash-weekly-issue-1?utm_source=thinkingelixir&utm_medium=shownotes) – Launch of Ash Weekly newsletter to keep up with Ash Framework updates and news. https://ash-project.github.io/ash_phoenix/nested-forms.html (https://ash-project.github.io/ash_phoenix/nested-forms.html?utm_source=thinkingelixir&utm_medium=shownotes) – AshPhoenix update featuring improved handling for nested forms. https://sessionize.com/code-beam-lite-stockholm-2025 (https://sessionize.com/code-beam-lite-stockholm-2025?utm_source=thinkingelixir&utm_medium=shownotes) – Call for speakers open until February 20th for Code BEAM Lite Stockholm, happening June 2nd 2025. NervesConf EU and Goatmire Elixir announced for September 10-12 in Varberg, Sweden. https://www.gigcityelixir.com/ (https://www.gigcityelixir.com/?utm_source=thinkingelixir&utm_medium=shownotes) – GigCityElixir conference announced in Chattanooga, TN, May 9-10, preceded by NervesConf on May 8th. 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 - Bluesky (https://bsky.app/profile/thinkingelixir.com) - Message the show - X (https://x.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 on X - @brainlid (https://x.com/brainlid) - Mark Ericksen on Bluesky - @brainlid.bsky.social (https://bsky.app/profile/brainlid.bsky.social) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel on Bluesky - @david.bernheisel.com (https://bsky.app/profile/david.bernheisel.com) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

Security takes center stage in this episode as Lois Houston and Nikita Abraham are joined by MySQL Solution Engineer Ravish Patel. Together, they explore MySQL's security features, addressing key topics like regulatory compliance.   Ravish also shares insights on protecting data through encryption, activity monitoring, and access control to guard against threats like SQL injection and malware.   MySQL 8.4 Essentials: https://mylearn.oracle.com/ou/course/mysql-84-essentials/141332/226362 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: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead of Editorial Services. Nikita: Hey everyone! In our last episode, we took a look at MySQL database design. Today is the first of a two-part episode on MySQL security.  Lois: In Part 1, we'll discuss how MySQL supports regulatory compliance and how to spot and handle common security risks.  00:55 Nikita: Joining us today is Ravish Patel, a MySQL Solution Engineer at Oracle. Hi Ravish! Let's start by talking about how MySQL supports regulatory compliance. 01:06 Ravish: Some of the most important international regulations that we have surrounding data and organizations include the GDPR, HIPAA, Sarbanes-Oxley, the UK Data Protection Act, and the NIS2. Although each regulatory framework differs in the details, in general, you must be able to comply with certain key requirements and all of which are enabled by MySQL. First, you must be able to monitor user activity on the system, which includes keeping track of when new users are created, when the schema changes, and when backups are taken and used. You must protect data, for example, by ensuring that databases that are stored on disk are encrypted at REST and ensuring that only authorized users have privileges to access and modify the data. You must have the appropriate retention policies in place for your data, ensuring that backups are held securely and used only for the purpose intended. You must be able to audit access to the data so that you can trace which users gained access to records or when they were modified. All of these facilities are available in MySQL, either as part of the core community edition features or made available through enterprise features. 02:21 Lois: What kind of risks might we encounter, Ravish, and how can we address them? Ravish: As your system grows in complexity, you're likely going to have more risks associated with it. Some of those risks are associated with the human factors that come with any computer system. These might be errors that are introduced when people perform work on the system, either administrative work on the environment or database or work that developers and testers perform when working on a changing system. You might even have malicious users trying to exploit the system or good faith users or support staff who make changes without proper consideration or protection from knock-on effects. At the foundation are the necessary components of the system, each of which might be vulnerable to human error or malicious actors. Every piece of the system exposes possible risks, whether that's the application presented to users, the underlying database, the operating system or network that it works on, or processes such as backups that place copies of your data in other locations. More complex environments add more risks. High availability architectures multiply the number of active systems. Consolidating multiple application databases on a single server exposes every database to multiple vectors for bugs and human error. Older, less well supported applications might give more challenges for maintenance. Engaging external contractors might reduce your control over authorized users. And working in the cloud can increase your network footprint and your reliance on external vendors.  03:53 Nikita: What are risks that specifically impact the database? Ravish: The database server configuration might not be optimal. And this can be changed by users with proper access. To mitigate this risk, you might enable version control of the configuration files and ensure that only certain users are authorized. Application and administrator accounts might have more data privileges than required, which adds risk of human error or malicious behavior. To mitigate this, you should ensure that users are only granted necessary permissions. In particular, structural modifications and administrative tasks might be more widely accessible than desired. Not every developer needs full administrative rights on a database. And certainly, an application should not have such privileges. You should limit administrative privileges only to those users who need that authorization. 04:45 Nikita: Okay, quick question, Ravish. How do authentication and password security fit into this picture? Ravish: Authentication is often a weak point. And password security is one of the most common issues in large applications. Ensure that you have strong password policies in place. And consider using authentication mechanisms that don't solely rely on passwords, such as pass-through authentication or multifactor authentication. 05:11 Lois: So, it sounds like auditing operations are a critical part of this process, right? Ravish: When something bad happens, you can only repair it or learn from it if you know exactly what has happened and how. You should ensure that you audit key operations so you can recover from error or malicious actions. If a developer laptop is lost or stolen or someone gains access to an underlying operating system, then your data might become vulnerable. You can mitigate this by encrypting your data in place.  This also applies to backups and, where possible, securing the connection between your application and the database to encrypt data in flight. 05:54 Did you know that Oracle University offers free courses on Oracle Cloud Infrastructure? You'll find training on everything from multicloud, database, networking, and security to artificial intelligence and machine learning, all free for our subscribers. So, what are you waiting for? Pick a topic, head over to mylearn.oracle.com and get started. 06:18 Nikita: Welcome back! Before the break, we touched on the importance of auditing. Now, Ravish, what role does encryption play in securing these operations? Ravish: Encryption is only useful if the keys are secure. Make sure to keep your encryption assets secure, perhaps by using a key vault. Every backup that you take contains a copy of your data. If these backups are not kept securely, then you are at risk, just as if your database wasn't secure. So keep your backups encrypted. 06:47 Lois: From what we've covered so far, it's clear that monitoring is essential for database security. Is that right? Ravish: Without monitoring, you can't track what happens on an ongoing basis. For example, you will not be aware of a denial-of-service attack until the application slows down or becomes unavailable. If you implement monitoring, you can identify a compromised user account or unusual query traffic as it happens. A poorly coded application might enable queries that do more than they should. A database firewall can be configured to permit only queries that conform to a specific pattern. 07:24 Nikita: There are so many potential types of attacks out there, right? Could you tell us about some specific ones, like SQL injection and buffer overflow attacks? Ravish: A SQL injection attack is a particular form of attack that modifies a SQL command to inject a different command to the one that was intended by the developer. You can configure an allow list in a database firewall to block such queries and perform a comprehensive input validation inside the application so that such queries cannot be inserted. A buffer overflow attack attempts to input more data than can fit in the appropriate memory location. These are usually possible when there is an unpatched bug in the application or even in the database or operating system software. Validation and the database firewall can catch this sort of attack before it even hits the database. And frequent patching of the platforms can mitigate risks that come from unpatched bugs. Malicious acts from inside the organization might also be possible. So good access control and authorization can prevent this. And monitoring and auditing can detect it if it occurs. 08:33 Lois: What about brute force attacks? How do they work? Ravish: A brute force attack is when someone tries passwords repeatedly until they find the correct one. MySQL can lock out an account if there have been too many incorrect attempts. Someone who has access to the physical network on which the application and database communicate can monitor or eavesdrop that network. However, if you encrypt the communications in flight, perhaps by using TLS or SSL connections, then that communication cannot be monitored. 09:04 Nikita: How do the more common threats like malware, Trojan horses, and ransomware impact database security? Ravish: Malware, ransomware, and Trojan horses can be a problem if they get to the server platforms or if client systems are compromised and have too much permissions. If the account that is compromised has only limited access and if the database is encrypted in place, then you can minimize the risks associated even if such an event occurs. There are also several risks directly associated with people who want to do the harm. So it's vital to protect personal information from any kind of disclosure, particularly sensitive information, such as credit card numbers. Encryption and access control can protect against this. 09:49 Lois: And then there are denial-of-service and spoofing attacks as well, right? How can we prevent those? Ravish: A denial-of-service attack prevents users from accessing the system. You can prevent any single user from performing too many queries by setting resource users limits. And you can limit the total number of connections as well. Sometimes, a user might gain access to a privileged level that is not appropriate. Password protection, multifactor authentication, and proper access control will protect against this. And auditing will help you discover if it has occurred. A spoofing attack is when an attacker intercepts and uses information to authenticate a user. This can be mitigated with strong access control and password policies. An attacker might attempt to modify or delete data or even auditing information. Again, this can be mitigated with tighter access controls and caught with monitoring and auditing. If the attack is successful, you can recover from it easily if you have a strong backup strategy in place. 10:50 Nikita: Ravish, are there any overarching best practices for keeping a database secure? Ravish: The MySQL installation itself should be kept up-to-date. This is the easiest if you install from a package manager on Windows or Linux. Your authentication systems should be kept strong with password policies or additional authentication systems that supplement or replace passwords entirely. Authorization should be kept tightly controlled by minimizing the number of active accounts and ensuring that those accounts have only the minimal privileges. You should control and monitor changes on the system. You can limit such changes with the database firewall and with tight access controls and observe changes with monitoring, auditing, and logging. Data encryption is also necessary to protect data from disclosure. MySQL supports encryption in place with Transparent Data Encryption, also known as TDE, and a variety of encryption functions and features. And you can encrypt data in flight with SSL or TLS. And of course, it's not just about the database itself but how it's used in the wider enterprise. You should ensure that replicas are secure and that your disaster recovery procedures do not open up to additional risks. And keep your backups encrypted. 12:06 Lois: Is there anything else we should keep in mind as part of these best practices? Ravish: The database environment is also worth paying attention to. The operating system and network should be as secure as you can keep them.  You should keep your platform software patched so that you are protected from known exploits caused by bugs. If your operating system has hardening guidelines, you should always follow those. And the Center of Internet Security maintains a set of benchmarks with configuration recommendations for many products designed to protect against threats. 12:38 Nikita: And that's a wrap on Part 1! Thank you, Ravish, for guiding us through MySQL's role in ensuring compliance and telling us about the various types of attacks. If you want to dive deeper into these topics, head over to mylearn.oracle.com to explore the MySQL 8.4 Essentials course. Lois: In our next episode, we'll continue to explore how user authentication works in MySQL and look at a few interesting MySQL Enterprise security tools that are available. Until then, this is Lois Houston…  Nikita: And Nikita Abraham, signing off! 13:12 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.

This week, the team behind JavaScript runtime Bun drops some major updates into Bun 1.2. Bun introduces a built-in S3 storage API, a built-in Postgres client (with MySQL coming soon), 90% compatibility with Node.js, and it's faster than ever before.Tailwind CSS v4 is out as well, and it boasts a new higher performance engine for 5x faster full builds, support for cutting edge CSS features like cascade layers, custom properties, and container queries, a simplified initial install and config to get going, and a first-party Vite plugin.And React Scan, built by Aiden Bai who also built Million.js, is also out this week with v0.1. Install React Scan into any React app and it will auto detect performance issues due to excessive re-rendering, and highlight the components causing the issues.News:Paige - Tailwind CSS v4.0Jack - React Scan 0.1 (aka Million.js)TJ - Bun 1.2Bonus News:StargateOpenAI's new Operator AI agent can do things on the web for youFire Starters:Link to text fragments with #:~:text=What Makes Us Happy this Week:Paige - Puzzles (like the ones from Buffalo Games)Jack - Klipsch G-17 air wireless speakerTJ - Not So Super, AppleThanks as always to our sponsor, the Blue Collar Coder channel on YouTube. You can join us in our Discord channel, explore our website and reach us via email, or Tweet us on X @front_end_fire and BlueSky.Front-end Fire websiteBlue Collar Coder on YouTubeBlue Collar Coder on DiscordReach out via emailTweet at us on X @front_end_fireFollow us on Bluesky @front-end-fire.com

Ryan is an Advocate at Redgate focusing on PostgreSQL. Ryan has been working as a PostgreSQL advocate, developer, DBA, and product manager for over 20 years, primarily working with time-series data on PostgreSQL and the Microsoft Data Platform.   Ryan is a long-time DBA, starting with MySQL and Postgres in the late '90s. He spent more than 15 years working with SQL Server before returning to PostgreSQL full-time in 2018. He's at the top of his game when learning something new about the data platform or teaching others about the technology he loves.   Topics of Discussion: [4:10] What made Ryan a database guy? [6:11] CodeMash. [6:58] Discovering the potential of SQL Server. [12:02] The state of the database in 2025 and the things generalist developers should know. [15:27] The challenge of interfacing between database types. [19:57] Is Microsoft Fabric the future? [22:44] Postgres for .NET developers. [24:46] Nuances of migrating from SQL Server to Postgres. [26:01] Postgres resources for data professionals. [35:29] Postgres and its innovative edge. [38:30] What is a vector database? [39:45] The power of Postgres indexing.   Mentioned in this Episode: Clear Measure Way Architect Forum Software Engineer Forum Programming with Palermo — New Video Podcast! Email us at programming@palermo.net. Clear Measure, Inc. (Sponsor) .NET DevOps for Azure: A Developer's Guide to DevOps Architecture the Right Way, by Jeffrey Palermo — Available on Amazon! Jeffrey Palermo's Twitter — Follow to stay informed about future events! Ep 245 with Ryan Booz Figuring Out Fabric SQLGene Training Introduction to PostgreSQL for the data professional. Kindle Edition Postgres Playground pgEdge “Name Collision of the Year”   Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

In this episode Michael, Gladys, Mark and Sarah talk to guest Diana Vicezar from the Microsoft Entra team about security Generative AI applications. Note, this is a short, simple intro episode to introduce three follow-on episodes. We also cover security news about TLS 1.3 and Azure Event Grid, big updates to Microsoft Defender for Cloud, Azure Database for MySQL, SQL Managed Instance and Confidential Ledger.

Jake and Michael recap their Christmas and New Year break, talk through lingering packages, Laravel 11 upgrades, and breaking changes in PHPUnit.

Nordic Innovation: A Strong Foundation for Global SuccessTom Henriksson, a seasoned venture capitalist and General Partner at OpenOcean, began his career in the 1990s when Finland's startup ecosystem was just emerging. Reflecting on that time, he said, “You could count the truly interesting companies with global potential on one hand.” Today, the Nordics are recognized for their robust STEM education and data-driven innovations, producing companies that excel in analytics and automation despite a small population base.OpenOcean's Mission: Building Data-Driven EnterprisesTom shared how OpenOcean, founded in 2010, was inspired by the success of MySQL, one of Europe's earliest unicorns. “We wanted to help entrepreneurs from small places in Europe make similar journeys,” he explained. With its fourth fund aiming to raise €135 million, OpenOcean continues to invest in early-stage, data-intensive business software across Europe, focusing on practical AI applications and vertical-specific solutions.AI in the Enterprise: Challenges and OpportunitiesAI has revolutionized how businesses operate, but enterprises still struggle to adopt these technologies effectively. OpenOcean invests in solutions that simplify adoption, such as tools enabling engineers to handle tasks traditionally reserved for data scientists. Tom highlighted a success story: Cambridge, a Finnish company leveraging AI to optimize product launches. “It not only predicts success but also suggests improvements, reducing waste and enhancing profitability,” he noted.The Legendary Poker Tournament: Networking with a TwistEach year during Slush, OpenOcean hosts a poker tournament that has become an iconic part of the event. Tom credited its origins to Riku Seppälä, a former OpenOcean principal, and shared a legendary anecdote: “The first prize was 3.1415 Bitcoins, which one winner used to pay his rent, while another held onto them—an ultimate poker win!” Beyond the fun, the event fosters meaningful connections in the startup ecosystem.By blending a focus on data-intensive startups with a deep commitment to the Nordic ecosystem, Tom Henriksson and OpenOcean continue to shape the future of venture capital. Whether helping enterprises navigate AI or hosting iconic events like their poker tournament, their mission remains clear: empowering entrepreneurs to build scalable, impactful businesses. Find Tom on:LinkedIn: Tom HenrikssonTwitter/X: @openoceanvc Find Ben on:LinkedIn: Ben CostantiniTwitter/X: @bencostantini --Be sure to follow Sesamers on Instagram, LinkedIn, and X for more cool stories from the people we catch during the best Tech events!

On this Screaming in the Cloud Replay, Corey is joined by Nipun Agarwal, Senior Vice President of MySQL HeatWave Development at Oracle, to discuss the release of MySQL HeatWave and how it will benefit users among the sea of database offerings on AWS. Nipun reveals why Oracle decided to develop HeatWave, how HeatWave is providing meaningful cost savings to users, and how HeatWave has been optimized for the cloud. Nipun explains how they've lowered the barriers to entry for new users of HeatWave, and Oracle's focus on implementing customer feedback when developing new offerings.Show Highlights(0:00) Intro(0:55) The Duckbill Group sponsor read(1:28) The significance of HeatWave coming to AWS(2:20) What is MySQL HeatWave?(5:13) What jumped out to Corey during his conversations with Nipun on Oracle(8:40) What's “under the hood” of MySQL HeatWave(14:12) How Oracle built out its pricing for MySQL HeatWave(16:41) Why MySQL HeatWave doesn't show up on AWS bills(21:27) The Duckbill Group sponsor read(22:09) Oracle's historical customer base and the company's credit system(24:30) The point behind MySQL HeatWave(27:51) How MySQL HeatWave runs(33:53) Where you can find more from Nipun and OracleAbout Nipun AgarwalNipun Agarwal is a Senior Vice President, MySQL HeatWave and Advanced Development, Oracle. His interests include distributed data processing, machine learning, cloud technologies and security. Nipun was part of the Oracle Database team where he introduced a number of new features. He has been awarded over 170 patents., Nipun Agarwal is Senior Vice President of MySQL Database & HeatWave Development. He leads a global engineering organization responsible for Oracle's MySQL innovations that enable organizations to use a single database for both transactional and analytical workloads. His interests include data processing, distributed systems, machine learning, cloud computing and security. Prior to his current position, Nipun was with Oracle Labs and the Oracle Database team, where he introduced a number of new features. He has been awarded over 175 patents.LinksOracle: https://oracle.comMySQL HeatWave info: https://www.oracle.com/mysql/ MySQL Service on AWS and OCI login (Oracle account required): https://cloud.mysql.comOriginal Episodehttps://www.lastweekinaws.com/podcast/screaming-in-the-cloud/heatwave-and-the-latest-evolution-of-mysql-with-nipun-agarwal/SponsorThe Duckbill Group: duckbillgroup.com

Join Jake, Michael, David, TJ, and Chris for a merry chat about tech, holiday antics, and the entertaining Wheel of Fortune game!## SocialXhttps://x.com/northsouthaudiohttps://x.com/JacobBennett https://x.com/michaeldyryndahttps://x.com/davidhemphillhttps://x.com/heytjmillerhttps://x.com/cmgmyrBlueskyhttps://bsky.app/profile/northmeetssouth.audiohttps://bsky.app/profile/dyrynda.auhttps://bsky.app/profile/jakebennett.bsky.socialhttps://bsky.app/profile/tjmiller.bsky.socialhttps://bsky.app/profile/chrisgmyr.dev (00:00) - Introductions (00:30) - Holiday Humor (02:28) - Brain Fog Chat (05:25) - Favorite Christmas Movies (08:26) - Significant 2024 Events (15:32) - Wheel of Fortune Game (59:14) - Wrapping Up

In this episode, I'm joined by Jesmar Canol, COO of ProxySQL, to explore the  journey behind the creation of this open source solution that has become a game-changer for database management.  From his early days in IT to addressing the challenges that database administrators (DBAs) face daily, Jesmar shares the story of how ProxySQL evolved from a side project into a vital tool for empowering database teams around the world. We discuss the complexities of managing MySQL and PostgreSQL infrastructures, ProxySQL's unique approach to query routing, load balancing, and its ability to maintain high availability even in the most demanding environments. Jesmar explains why ProxySQL's open-source model is critical in fostering trust and transparency, and how it helps organizations adapt to the growing demands for cloud-native and on-premise database solutions. Jesmar also offers insights into the challenges of running a distributed team, the evolution of database management in an era of increasing automation, and the emerging trends shaping the future of this space. Whether you're a seasoned DBA, a tech leader, or simply curious about the transformative power of open source solutions, this episode is packed with valuable takeaways.

Thank you to the folks at Sustain (https://sustainoss.org/) for providing the hosting account for CHAOSSCast! CHAOSScast- Episode 99 Hosts: Ray Paik Daniël van Eeden Guests: Dawn Foster We're taking a break this week so we're very pleased to share instead an episode from our friends over at Data in the Hallway, hosted by Ray Paik and co-hosted by Daniël van Eeden. Their podcast dives deep into the fascinating world of database technology, open source innovation, and the incredible stories shaping global tech communities. In this episode, Ray and Daniël sit down with Dawn Foster, Director of Data Science at the CHAOSS Project, to explore the re-licensing of major open source projects—a significant trend over the past decade. Together, they unpack the ripple effects this has on community health, share advice for developers navigating Contributor License Agreements, and examine the financial and business implications of re-licensing. They also reflect on whether this trend is likely to continue in the years ahead. Throughout the episode, they reference some excellent resources, including case-study data from CHAOSS, guides on open source organizational dynamics, and financial analyses that add depth to the discussion. Links to these resources are in the show notes if you want to explore further. The case-study data: https://github.com/chaoss/wg-data-science/tree/main/dataset/license-changes/fork-case-study More details about the organizational dynamics in OSS: https://chaoss.community/practitioner-guide-organizational-participation/ Discussion on the topic by James Governor, Amanda Brock, and Dawn Foster: https://www.youtube.com/watch?v=wliDVF3FpI0 Business implications/financial analysis: https://redmonk.com/rstephens/2024/08/26/software-licensing-changes-and-their-impact-on-financial-outcomes/ Magic Roundabout: https://en.wikipedia.org/wiki/MagicRoundabout(Hemel_Hempstead) If you have any feedback or suggestions for episodes, you can email us at podcast@pingcap.com. ————————— About PingCAP and TiDB ————————— PingCAP is the creator of #TiDB, an advanced, #OpenSource, #DistributedSQL database with #MySQL compatibility. TiDB powers modern applications with a streamlined tech stack, elastic scaling, real-time analytics, and continuous access to data—all in a single database. ————————— Developer resources —————————

What if your organization could unlock the full potential of AI without ever compromising on privacy or sharing sensitive data? In this episode of Tech Talks Daily, I am joined by Alexander Alten, Co-Founder and CEO of Scalytics, to explore how he is building the next-generation infrastructure layer for AI agents. Alexander brings a wealth of expertise, having led data and product teams at industry giants like Cloudera, Allianz, and Healthgrades. With a background in startups such as X-Warp and Infinite Devices, he has a proven track record of developing customer-centric, data-driven solutions that not only disrupt conventional norms but also fuel measurable growth. During our conversation at the IT Press Tour in Malta,  Alexander introduces Scalytics Connect, a modern AI data platform designed to accelerate insights while preserving privacy. He unpacks the challenges of breaking down data silos and explains why centralizing data may not always be the optimal solution. We also demystify federated learning, shedding light on its potential to empower businesses, particularly in regulated industries, to collaborate on AI models without exposing their data. The discussion extends to the value of open-source technologies and why they often emerge as long-term winners, citing examples like MySQL, Postgres, and WordPress. Alexander shares how Scalytics leverages open-source principles to provide scalable and transparent machine learning solutions for businesses looking to outperform in an increasingly data-driven world. As AI continues to redefine the way we work and innovate, Alexander's insights provide a roadmap for navigating the complexities of decentralized machine learning, privacy-first AI, and scalable technology. Could his approach to AI and data collaboration be the key to unlocking your organization's potential? Tune in to find out, and don't forget to share your thoughts on the future of AI-powered innovation.

Join Michael and Jake as they explore noisy coffee machines, dive into documentation challenges with Prezet, and discuss the nuances of sandbox environments. (00:00) - Introduction (01:14) - Noisy Coffee Machines (03:06) - Basketball Recap (03:48) - Documentation with Prezet (07:31) - Carrier Documentation Permissions (10:07) - Code Rendering Issues (12:28) - Developer World Update (20:21) - Sandbox Environment Strategy (27:48) - Next Steps (28:16) - Conclusion

Join Michael and Jake as they dive into Laracon AU highlights, reveal new coffee adventures, and discuss dynamic directories and tenant file storage.

Alessio will be at AWS re:Invent next week and hosting a casual coffee meetup on Wednesday, RSVP here! And subscribe to our calendar for our Singapore, NeurIPS, and all upcoming meetups!We are still taking questions for our next big recap episode! Submit questions and messages on Speakpipe here for a chance to appear on the show!If you've been following the AI agents space, you have heard of Lindy AI; while founder Flo Crivello is hesitant to call it "blowing up," when folks like Andrew Wilkinson start obsessing over your product, you're definitely onto something.In our latest episode, Flo walked us through Lindy's evolution from late 2022 to now, revealing some design choices about agent platform design that go against conventional wisdom in the space.The Great Reset: From Text Fields to RailsRemember late 2022? Everyone was "LLM-pilled," believing that if you just gave a language model enough context and tools, it could do anything. Lindy 1.0 followed this pattern:* Big prompt field ✅* Bunch of tools ✅* Prayer to the LLM gods ✅Fast forward to today, and Lindy 2.0 looks radically different. As Flo put it (~17:00 in the episode): "The more you can put your agent on rails, one, the more reliable it's going to be, obviously, but two, it's also going to be easier to use for the user."Instead of a giant, intimidating text field, users now build workflows visually:* Trigger (e.g., "Zendesk ticket received")* Required actions (e.g., "Check knowledge base")* Response generationThis isn't just a UI change - it's a fundamental rethinking of how to make AI agents reliable. As Swyx noted during our discussion: "Put Shoggoth in a box and make it a very small, minimal viable box. Everything else should be traditional if-this-then-that software."The Surprising Truth About Model LimitationsHere's something that might shock folks building in the space: with Claude 3.5 Sonnet, the model is no longer the bottleneck. Flo's exact words (~31:00): "It is actually shocking the extent to which the model is no longer the limit. It was the limit a year ago. It was too expensive. The context window was too small."Some context: Lindy started when context windows were 4K tokens. Today, their system prompt alone is larger than that. But what's really interesting is what this means for platform builders:* Raw capabilities aren't the constraint anymore* Integration quality matters more than model performance* User experience and workflow design are the new bottlenecksThe Search Engine Parallel: Why Horizontal Platforms Might WinOne of the spiciest takes from our conversation was Flo's thesis on horizontal vs. vertical agent platforms. He draws a fascinating parallel to search engines (~56:00):"I find it surprising the extent to which a horizontal search engine has won... You go through Google to search Reddit. You go through Google to search Wikipedia... search in each vertical has more in common with search than it does with each vertical."His argument: agent platforms might follow the same pattern because:* Agents across verticals share more commonalities than differences* There's value in having agents that can work together under one roof* The R&D cost of getting agents right is better amortized across use casesThis might explain why we're seeing early vertical AI companies starting to expand horizontally. The core agent capabilities - reliability, context management, tool integration - are universal needs.What This Means for BuildersIf you're building in the AI agents space, here are the key takeaways:* Constrain First: Rather than maximizing capabilities, focus on reliable execution within narrow bounds* Integration Quality Matters: With model capabilities plateauing, your competitive advantage lies in how well you integrate with existing tools* Memory Management is Key: Flo revealed they actively prune agent memories - even with larger context windows, not all memories are useful* Design for Discovery: Lindy's visual workflow builder shows how important interface design is for adoptionThe Meta LayerThere's a broader lesson here about AI product development. Just as Lindy evolved from "give the LLM everything" to "constrain intelligently," we might see similar evolution across the AI tooling space. The winners might not be those with the most powerful models, but those who best understand how to package AI capabilities in ways that solve real problems reliably.Full Video PodcastFlo's talk at AI Engineer SummitChapters* 00:00:00 Introductions * 00:04:05 AI engineering and deterministic software * 00:08:36 Lindys demo* 00:13:21 Memory management in AI agents * 00:18:48 Hierarchy and collaboration between Lindys * 00:21:19 Vertical vs. horizontal AI tools * 00:24:03 Community and user engagement strategies * 00:26:16 Rickrolling incident with Lindy * 00:28:12 Evals and quality control in AI systems * 00:31:52 Model capabilities and their impact on Lindy * 00:39:27 Competition and market positioning * 00:42:40 Relationship between Factorio and business strategy * 00:44:05 Remote work vs. in-person collaboration * 00:49:03 Europe vs US Tech* 00:58:59 Testing the Overton window and free speech * 01:04:20 Balancing AI safety concerns with business innovation Show Notes* Lindy.ai* Rick Rolling* Flo on X* TeamFlow* Andrew Wilkinson* Dust* Poolside.ai* SB1047* Gathertown* Sid Sijbrandij* Matt Mullenweg* Factorio* Seeing Like a StateTranscriptAlessio [00:00:00]: Hey everyone, welcome 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 Smol.ai.Swyx [00:00:12]: Hey, and today we're joined in the studio by Florent Crivello. Welcome.Flo [00:00:15]: Hey, yeah, thanks for having me.Swyx [00:00:17]: Also known as Altimore. I always wanted to ask, what is Altimore?Flo [00:00:21]: It was the name of my character when I was playing Dungeons & Dragons. Always. I was like 11 years old.Swyx [00:00:26]: What was your classes?Flo [00:00:27]: I was an elf. I was a magician elf.Swyx [00:00:30]: Well, you're still spinning magic. Right now, you're a solo founder and CEO of Lindy.ai. What is Lindy?Flo [00:00:36]: Yeah, we are a no-code platform letting you build your own AI agents easily. So you can think of we are to LangChain as Airtable is to MySQL. Like you can just pin up AI agents super easily by clicking around and no code required. You don't have to be an engineer and you can automate business workflows that you simply could not automate before in a few minutes.Swyx [00:00:55]: You've been in our orbit a few times. I think you spoke at our Latent Space anniversary. You spoke at my summit, the first summit, which was a really good keynote. And most recently, like we actually already scheduled this podcast before this happened. But Andrew Wilkinson was like, I'm obsessed by Lindy. He's just created a whole bunch of agents. So basically, why are you blowing up?Flo [00:01:16]: Well, thank you. I think we are having a little bit of a moment. I think it's a bit premature to say we're blowing up. But why are things going well? We revamped the product majorly. We called it Lindy 2.0. I would say we started working on that six months ago. We've actually not really announced it yet. It's just, I guess, I guess that's what we're doing now. And so we've basically been cooking for the last six months, like really rebuilding the product from scratch. I think I'll list you, actually, the last time you tried the product, it was still Lindy 1.0. Oh, yeah. If you log in now, the platform looks very different. There's like a ton more features. And I think one realization that we made, and I think a lot of folks in the agent space made the same realization, is that there is such a thing as too much of a good thing. I think many people, when they started working on agents, they were very LLM peeled and chat GPT peeled, right? They got ahead of themselves in a way, and us included, and they thought that agents were actually, and LLMs were actually more advanced than they actually were. And so the first version of Lindy was like just a giant prompt and a bunch of tools. And then the realization we had was like, hey, actually, the more you can put your agent on Rails, one, the more reliable it's going to be, obviously, but two, it's also going to be easier to use for the user, because you can really, as a user, you get, instead of just getting this big, giant, intimidating text field, and you type words in there, and you have no idea if you're typing the right word or not, here you can really click and select step by step, and tell your agent what to do, and really give as narrow or as wide a guardrail as you want for your agent. We started working on that. We called it Lindy on Rails about six months ago, and we started putting it into the hands of users over the last, I would say, two months or so, and I think things really started going pretty well at that point. The agent is way more reliable, way easier to set up, and we're already seeing a ton of new use cases pop up.Swyx [00:03:00]: Yeah, just a quick follow-up on that. You launched the first Lindy in November last year, and you were already talking about having a DSL, right? I remember having this discussion with you, and you were like, it's just much more reliable. Is this still the DSL under the hood? Is this a UI-level change, or is it a bigger rewrite?Flo [00:03:17]: No, it is a much bigger rewrite. I'll give you a concrete example. Suppose you want to have an agent that observes your Zendesk tickets, and it's like, hey, every time you receive a Zendesk ticket, I want you to check my knowledge base, so it's like a RAG module and whatnot, and then answer the ticket. The way it used to work with Lindy before was, you would type the prompt asking it to do that. You check my knowledge base, and so on and so forth. The problem with doing that is that it can always go wrong. You're praying the LLM gods that they will actually invoke your knowledge base, but I don't want to ask it. I want it to always, 100% of the time, consult the knowledge base after it receives a Zendesk ticket. And so with Lindy, you can actually have the trigger, which is Zendesk ticket received, have the knowledge base consult, which is always there, and then have the agent. So you can really set up your agent any way you want like that.Swyx [00:04:05]: This is something I think about for AI engineering as well, which is the big labs want you to hand over everything in the prompts, and only code of English, and then the smaller brains, the GPU pours, always want to write more code to make things more deterministic and reliable and controllable. One way I put it is put Shoggoth in a box and make it a very small, the minimal viable box. Everything else should be traditional, if this, then that software.Flo [00:04:29]: I love that characterization, put the Shoggoth in the box. Yeah, we talk about using as much AI as necessary and as little as possible.Alessio [00:04:37]: And what was the choosing between kind of like this drag and drop, low code, whatever, super code-driven, maybe like the Lang chains, auto-GPT of the world, and maybe the flip side of it, which you don't really do, it's like just text to agent, it's like build the workflow for me. Like what have you learned actually putting this in front of users and figuring out how much do they actually want to add it versus like how much, you know, kind of like Ruby on Rails instead of Lindy on Rails, it's kind of like, you know, defaults over configuration.Flo [00:05:06]: I actually used to dislike when people said, oh, text is not a great interface. I was like, ah, this is such a mid-take, I think text is awesome. And I've actually come around, I actually sort of agree now that text is really not great. I think for people like you and me, because we sort of have a mental model, okay, when I type a prompt into this text box, this is what it's going to do, it's going to map it to this kind of data structure under the hood and so forth. I guess it's a little bit blackmailing towards humans. You jump on these calls with humans and you're like, here's a text box, this is going to set up an agent for you, do it. And then they type words like, I want you to help me put order in my inbox. Oh, actually, this is a good one. This is actually a good one. What's a bad one? I would say 60 or 70% of the prompts that people type don't mean anything. Me as a human, as AGI, I don't understand what they mean. I don't know what they mean. It is actually, I think whenever you can have a GUI, it is better than to have just a pure text interface.Alessio [00:05:58]: And then how do you decide how much to expose? So even with the tools, you have Slack, you have Google Calendar, you have Gmail. Should people by default just turn over access to everything and then you help them figure out what to use? I think that's the question. When I tried to set up Slack, it was like, hey, give me access to all channels and everything, which for the average person probably makes sense because you don't want to re-prompt them every time you add new channels. But at the same time, for maybe the more sophisticated enterprise use cases, people are like, hey, I want to really limit what you have access to. How do you kind of thread that balance?Flo [00:06:35]: The general philosophy is we ask for the least amount of permissions needed at any given moment. I don't think Slack, I could be mistaken, but I don't think Slack lets you request permissions for just one channel. But for example, for Google, obviously there are hundreds of scopes that you could require for Google. There's a lot of scopes. And sometimes it's actually painful to set up your Lindy because you're going to have to ask Google and add scopes five or six times. We've had sessions like this. But that's what we do because, for example, the Lindy email drafter, she's going to ask you for your authorization once for, I need to be able to read your email so I can draft a reply, and then another time for I need to be able to write a draft for them. We just try to do it very incrementally like that.Alessio [00:07:15]: Do you think OAuth is just overall going to change? I think maybe before it was like, hey, we need to set up OAuth that humans only want to kind of do once. So we try to jam-pack things all at once versus what if you could on-demand get different permissions every time from different parts? Do you ever think about designing things knowing that maybe AI will use it instead of humans will use it? Yeah, for sure.Flo [00:07:37]: One pattern we've started to see is people provisioning accounts for their AI agents. And so, in particular, Google Workspace accounts. So, for example, Lindy can be used as a scheduling assistant. So you can just CC her to your emails when you're trying to find time with someone. And just like a human assistant, she's going to go back and forth and offer other abilities and so forth. Very often, people don't want the other party to know that it's an AI. So it's actually funny. They introduce delays. They ask the agent to wait before replying, so it's not too obvious that it's an AI. And they provision an account on Google Suite, which costs them like $10 a month or something like that. So we're seeing that pattern more and more. I think that does the job for now. I'm not optimistic on us actually patching OAuth. Because I agree with you, ultimately, we would want to patch OAuth because the new account thing is kind of a clutch. It's really a hack. You would want to patch OAuth to have more granular access control and really be able to put your sugar in the box. I'm not optimistic on us doing that before AGI, I think. That's a very close timeline.Swyx [00:08:36]: I'm mindful of talking about a thing without showing it. And we already have the setup to show it. Why don't we jump into a screen share? For listeners, you can jump on the YouTube and like and subscribe. But also, let's have a look at how you show off Lindy. Yeah, absolutely.Flo [00:08:51]: I'll give an example of a very simple Lindy and then I'll graduate to a much more complicated one. A super simple Lindy that I have is, I unfortunately bought some investment properties in the south of France. It was a really, really bad idea. And I put them on a Holydew, which is like the French Airbnb, if you will. And so I received these emails from time to time telling me like, oh, hey, you made 200 bucks. Someone booked your place. When I receive these emails, I want to log this reservation in a spreadsheet. Doing this without an AI agent or without AI in general is a pain in the butt because you must write an HTML parser for this email. And so it's just hard. You may not be able to do it and it's going to break the moment the email changes. By contrast, the way it works with Lindy, it's really simple. It's two steps. It's like, okay, I receive an email. If it is a reservation confirmation, I have this filter here. Then I append a row to this spreadsheet. And so this is where you can see the AI part where the way this action is configured here, you see these purple fields on the right. Each of these fields is a prompt. And so I can say, okay, you extract from the email the day the reservation begins on. You extract the amount of the reservation. You extract the number of travelers of the reservation. And now you can see when I look at the task history of this Lindy, it's really simple. It's like, okay, you do this and boom, appending this row to this spreadsheet. And this is the information extracted. So effectively, this node here, this append row node is a mini agent. It can see everything that just happened. It has context over the task and it's appending the row. And then it's going to send a reply to the thread. That's a very simple example of an agent.Swyx [00:10:34]: A quick follow-up question on this one while we're still on this page. Is that one call? Is that a structured output call? Yeah. Okay, nice. Yeah.Flo [00:10:41]: And you can see here for every node, you can configure which model you want to power the node. Here I use cloud. For this, I use GPT-4 Turbo. Much more complex example, my meeting recorder. It looks very complex because I've added to it over time, but at a high level, it's really simple. It's like when a meeting begins, you record the meeting. And after the meeting, you send me a summary and you send me coaching notes. So I receive, like my Lindy is constantly coaching me. And so you can see here in the prompt of the coaching notes, I've told it, hey, you know, was I unnecessarily confrontational at any point? I'm French, so I have to watch out for that. Or not confrontational enough. Should I have double-clicked on any issue, right? So I can really give it exactly the kind of coaching that I'm expecting. And then the interesting thing here is, like, you can see the agent here, after it sent me these coaching notes, moves on. And it does a bunch of other stuff. So it goes on Slack. It disseminates the notes on Slack. It does a bunch of other stuff. But it's actually able to backtrack and resume the automation at the coaching notes email if I responded to that email. So I'll give a super concrete example. This is an actual coaching feedback that I received from Lindy. She was like, hey, this was a sales call I had with a customer. And she was like, I found your explanation of Lindy too technical. And I was able to follow up and just ask a follow-up question in the thread here. And I was like, why did you find too technical about my explanation? And Lindy restored the context. And so she basically picked up the automation back up here in the tree. And she has all of the context of everything that happened, including the meeting in which I was. So she was like, oh, you used the words deterministic and context window and agent state. And that concept exists at every level for every channel and every action that Lindy takes. So another example here is, I mentioned she also disseminates the notes on Slack. So this was a meeting where I was not, right? So this was a teammate. He's an indie meeting recorder, posts the meeting notes in this customer discovery channel on Slack. So you can see, okay, this is the onboarding call we had. This was the use case. Look at the questions. How do I make Lindy slower? How do I add delays to make Lindy slower? And I was able, in the Slack thread, to ask follow-up questions like, oh, what did we answer to these questions? And it's really handy because I know I can have this sort of interactive Q&A with these meetings. It means that very often now, I don't go to meetings anymore. I just send my Lindy. And instead of going to like a 60-minute meeting, I have like a five-minute chat with my Lindy afterwards. And she just replied. She was like, well, this is what we replied to this customer. And I can just be like, okay, good job, Jack. Like, no notes about your answers. So that's the kind of use cases people have with Lindy. It's a lot of like, there's a lot of sales automations, customer support automations, and a lot of this, which is basically personal assistance automations, like meeting scheduling and so forth.Alessio [00:13:21]: Yeah, and I think the question that people might have is memory. So as you get coaching, how does it track whether or not you're improving? You know, if these are like mistakes you made in the past, like, how do you think about that?Flo [00:13:31]: Yeah, we have a memory module. So I'll show you my meeting scheduler, Lindy, which has a lot of memories because by now I've used her for so long. And so every time I talk to her, she saves a memory. If I tell her, you screwed up, please don't do this. So you can see here, oh, it's got a double memory here. This is the meeting link I have, or this is the address of the office. If I tell someone to meet me at home, this is the address of my place. This is the code. I guess we'll have to edit that out. This is not the code of my place. No dogs. Yeah, so Lindy can just manage her own memory and decide when she's remembering things between executions. Okay.Swyx [00:14:11]: I mean, I'm just going to take the opportunity to ask you, since you are the creator of this thing, how come there's so few memories, right? Like, if you've been using this for two years, there should be thousands of thousands of things. That is a good question.Flo [00:14:22]: Agents still get confused if they have too many memories, to my point earlier about that. So I just am out of a call with a member of the Lama team at Meta, and we were chatting about Lindy, and we were going into the system prompt that we sent to Lindy, and all of that stuff. And he was amazed, and he was like, it's a miracle that it's working, guys. He was like, this kind of system prompt, this does not exist, either pre-training or post-training. These models were never trained to do this kind of stuff. It's a miracle that they can be agents at all. And so what I do, I actually prune the memories. You know, it's actually something I've gotten into the habit of doing from back when we had GPT 3.5, being Lindy agents. I suspect it's probably not as necessary in the Cloud 3.5 Sunette days, but I prune the memories. Yeah, okay.Swyx [00:15:05]: The reason is because I have another assistant that also is recording and trying to come up with facts about me. It comes up with a lot of trivial, useless facts that I... So I spend most of my time pruning. Actually, it's not super useful. I'd much rather have high-quality facts that it accepts. Or maybe I was even thinking, were you ever tempted to add a wake word to only memorize this when I say memorize this? And otherwise, don't even bother.Flo [00:15:30]: I have a Lindy that does this. So this is my inbox processor, Lindy. It's kind of beefy because there's a lot of different emails. But somewhere in here,Swyx [00:15:38]: there is a rule where I'm like,Flo [00:15:39]: aha, I can email my inbox processor, Lindy. It's really handy. So she has her own email address. And so when I process my email inbox, I sometimes forward an email to her. And it's a newsletter, or it's like a cold outreach from a recruiter that I don't care about, or anything like that. And I can give her a rule. And I can be like, hey, this email I want you to archive, moving forward. Or I want you to alert me on Slack when I have this kind of email. It's really important. And so you can see here, the prompt is, if I give you a rule about a kind of email, like archive emails from X, save it as a new memory. And I give it to the memory saving skill. And yeah.Swyx [00:16:13]: One thing that just occurred to me, so I'm a big fan of virtual mailboxes. I recommend that everybody have a virtual mailbox. You could set up a physical mail receive thing for Lindy. And so then Lindy can process your physical mail.Flo [00:16:26]: That's actually a good idea. I actually already have something like that. I use like health class mail. Yeah. So yeah, most likely, I can process my physical mail. Yeah.Swyx [00:16:35]: And then the other product's idea I have, looking at this thing, is people want to brag about the complexity of their Lindys. So this would be like a 65 point Lindy, right?Flo [00:16:43]: What's a 65 point?Swyx [00:16:44]: Complexity counting. Like how many nodes, how many things, how many conditions, right? Yeah.Flo [00:16:49]: This is not the most complex one. I have another one. This designer recruiter here is kind of beefy as well. Right, right, right. So I'm just saying,Swyx [00:16:56]: let people brag. Let people be super users. Oh, right.Flo [00:16:59]: Give them a score. Give them a score.Swyx [00:17:01]: Then they'll just be like, okay, how high can you make this score?Flo [00:17:04]: Yeah, that's a good point. And I think that's, again, the beauty of this on-rails phenomenon. It's like, think of the equivalent, the prompt equivalent of this Lindy here, for example, that we're looking at. It'd be monstrous. And the odds that it gets it right are so low. But here, because we're really holding the agent's hand step by step by step, it's actually super reliable. Yeah.Swyx [00:17:22]: And is it all structured output-based? Yeah. As far as possible? Basically. Like, there's no non-structured output?Flo [00:17:27]: There is. So, for example, here, this AI agent step, right, or this send message step, sometimes it gets to... That's just plain text.Swyx [00:17:35]: That's right.Flo [00:17:36]: Yeah. So I'll give you an example. Maybe it's TMI. I'm having blood pressure issues these days. And so this Lindy here, I give it my blood pressure readings, and it updates a log that I have of my blood pressure that it sends to my doctor.Swyx [00:17:49]: Oh, so every Lindy comes with a to-do list?Flo [00:17:52]: Yeah. Every Lindy has its own task history. Huh. Yeah. And so you can see here, this is my main Lindy, my personal assistant, and I've told it, where is this? There is a point where I'm like, if I am giving you a health-related fact, right here, I'm giving you health information, so then you update this log that I have in this Google Doc, and then you send me a message. And you can see, I've actually not configured this send message node. I haven't told it what to send me a message for. Right? And you can see, it's actually lecturing me. It's like, I'm giving it my blood pressure ratings. It's like, hey, it's a bit high. Here are some lifestyle changes you may want to consider.Alessio [00:18:27]: I think maybe this is the most confusing or new thing for people. So even I use Lindy and I didn't even know you could have multiple workflows in one Lindy. I think the mental model is kind of like the Zapier workflows. It starts and it ends. It doesn't choose between. How do you think about what's a Lindy versus what's a sub-function of a Lindy? Like, what's the hierarchy?Flo [00:18:48]: Yeah. Frankly, I think the line is a little arbitrary. It's kind of like when you code, like when do you start to create a new class versus when do you overload your current class. I think of it in terms of like jobs to be done and I think of it in terms of who is the Lindy serving. This Lindy is serving me personally. It's really my day-to-day Lindy. I give it a bunch of stuff, like very easy tasks. And so this is just the Lindy I go to. Sometimes when a task is really more specialized, so for example, I have this like summarizer Lindy or this designer recruiter Lindy. These tasks are really beefy. I wouldn't want to add this to my main Lindy, so I just created a separate Lindy for it. Or when it's a Lindy that serves another constituency, like our customer support Lindy, I don't want to add that to my personal assistant Lindy. These are two very different Lindys.Alessio [00:19:31]: And you can call a Lindy from within another Lindy. That's right. You can kind of chain them together.Flo [00:19:36]: Lindys can work together, absolutely.Swyx [00:19:38]: A couple more things for the video portion. I noticed you have a podcast follower. We have to ask about that. What is that?Flo [00:19:46]: So this one wakes me up every... So wakes herself up every week. And she sends me... So she woke up yesterday, actually. And she searches for Lenny's podcast. And she looks for like the latest episode on YouTube. And once she finds it, she transcribes the video and then she sends me the summary by email. I don't listen to podcasts as much anymore. I just like read these summaries. Yeah.Alessio [00:20:09]: We should make a latent space Lindy. Marketplace.Swyx [00:20:12]: Yeah. And then you have a whole bunch of connectors. I saw the list briefly. Any interesting one? Complicated one that you're proud of? Anything that you want to just share? Connector stories.Flo [00:20:23]: So many of our workflows are about meeting scheduling. So we had to build some very open unity tools around meeting scheduling. So for example, one that is surprisingly hard is this find available times action. You would not believe... This is like a thousand lines of code or something. It's just a very beefy action. And you can pass it a bunch of parameters about how long is the meeting? When does it start? When does it end? What are the meetings? The weekdays in which I meet? How many time slots do you return? What's the buffer between my meetings? It's just a very, very, very complex action. I really like our GitHub action. So we have a Lindy PR reviewer. And it's really handy because anytime any bug happens... So the Lindy reads our guidelines on Google Docs. By now, the guidelines are like 40 pages long or something. And so every time any new kind of bug happens, we just go to the guideline and we add the lines. Like, hey, this has happened before. Please watch out for this category of bugs. And it's saving us so much time every day.Alessio [00:21:19]: There's companies doing PR reviews. Where does a Lindy start? When does a company start? Or maybe how do you think about the complexity of these tasks when it's going to be worth having kind of like a vertical standalone company versus just like, hey, a Lindy is going to do a good job 99% of the time?Flo [00:21:34]: That's a good question. We think about this one all the time. I can't say that we've really come up with a very crisp articulation of when do you want to use a vertical tool versus when do you want to use a horizontal tool. I think of it as very similar to the internet. I find it surprising the extent to which a horizontal search engine has won. But I think that Google, right? But I think the even more surprising fact is that the horizontal search engine has won in almost every vertical, right? You go through Google to search Reddit. You go through Google to search Wikipedia. I think maybe the biggest exception is e-commerce. Like you go to Amazon to search e-commerce, but otherwise you go through Google. And I think that the reason for that is because search in each vertical has more in common with search than it does with each vertical. And search is so expensive to get right. Like Google is a big company that it makes a lot of sense to aggregate all of these different use cases and to spread your R&D budget across all of these different use cases. I have a thesis, which is, it's a really cool thesis for Lindy, is that the same thing is true for agents. I think that by and large, in a lot of verticals, agents in each vertical have more in common with agents than they do with each vertical. I also think there are benefits in having a single agent platform because that way your agents can work together. They're all like under one roof. That way you only learn one platform and so you can create agents for everything that you want. And you don't have to like pay for like a bunch of different platforms and so forth. So I think ultimately, it is actually going to shake out in a way that is similar to search in that search is everywhere on the internet. Every website has a search box, right? So there's going to be a lot of vertical agents for everything. I think AI is going to completely penetrate every category of software. But then I also think there are going to be a few very, very, very big horizontal agents that serve a lot of functions for people.Swyx [00:23:14]: That is actually one of the questions that we had about the agent stuff. So I guess we can transition away from the screen and I'll just ask the follow-up, which is, that is a hot topic. You're basically saying that the current VC obsession of the day, which is vertical AI enabled SaaS, is mostly not going to work out. And then there are going to be some super giant horizontal SaaS.Flo [00:23:34]: Oh, no, I'm not saying it's either or. Like SaaS today, vertical SaaS is huge and there's also a lot of horizontal platforms. If you look at like Airtable or Notion, basically the entire no-code space is very horizontal. I mean, Loom and Zoom and Slack, there's a lot of very horizontal tools out there. Okay.Swyx [00:23:49]: I was just trying to get a reaction out of you for hot takes. Trying to get a hot take.Flo [00:23:54]: No, I also think it is natural for the vertical solutions to emerge first because it's just easier to build. It's just much, much, much harder to build something horizontal. Cool.Swyx [00:24:03]: Some more Lindy-specific questions. So we covered most of the top use cases and you have an academy. That was nice to see. I also see some other people doing it for you for free. So like Ben Spites is doing it and then there's some other guy who's also doing like lessons. Yeah. Which is kind of nice, right? Yeah, absolutely. You don't have to do any of that.Flo [00:24:20]: Oh, we've been seeing it more and more on like LinkedIn and Twitter, like people posting their Lindys and so forth.Swyx [00:24:24]: I think that's the flywheel that you built the platform where creators see value in allying themselves to you. And so then, you know, your incentive is to make them successful so that they can make other people successful and then it just drives more and more engagement. Like it's earned media. Like you don't have to do anything.Flo [00:24:39]: Yeah, yeah. I mean, community is everything.Swyx [00:24:41]: Are you doing anything special there? Any big wins?Flo [00:24:44]: We have a Slack community that's pretty active. I can't say we've invested much more than that so far.Swyx [00:24:49]: I would say from having, so I have some involvement in the no-code community. I would say that Webflow going very hard after no-code as a category got them a lot more allies than just the people using Webflow. So it helps you to grow the community beyond just Lindy. And I don't know what this is called. Maybe it's just no-code again. Maybe you want to call it something different. But there's definitely an appetite for this and you are one of a broad category, right? Like just before you, we had Dust and, you know, they're also kind of going after a similar market. Zapier obviously is not going to try to also compete with you. Yeah. There's no question there. It's just like a reaction about community. Like I think a lot about community. Lanespace is growing the community of AI engineers. And I think you have a slightly different audience of, I don't know what.Flo [00:25:33]: Yeah. I think the no-code tinkerers is the community. Yeah. It is going to be the same sort of community as what Webflow, Zapier, Airtable, Notion to some extent.Swyx [00:25:43]: Yeah. The framing can be different if you were, so I think tinkerers has this connotation of not serious or like small. And if you framed it to like no-code EA, we're exclusively only for CEOs with a certain budget, then you just have, you tap into a different budget.Flo [00:25:58]: That's true. The problem with EA is like, the CEO has no willingness to actually tinker and play with the platform.Swyx [00:26:05]: Maybe Andrew's doing that. Like a lot of your biggest advocates are CEOs, right?Flo [00:26:09]: A solopreneur, you know, small business owners, I think Andrew is an exception. Yeah. Yeah, yeah, he is.Swyx [00:26:14]: He's an exception in many ways. Yep.Alessio [00:26:16]: Just before we wrap on the use cases, is Rick rolling your customers? Like a officially supported use case or maybe tell that story?Flo [00:26:24]: It's one of the main jobs to be done, really. Yeah, we woke up recently, so we have a Lindy obviously doing our customer support and we do check after the Lindy. And so we caught this email exchange where someone was asking Lindy for video tutorials. And at the time, actually, we did not have video tutorials. We do now on the Lindy Academy. And Lindy responded to the email. It's like, oh, absolutely, here's a link. And we were like, what? Like, what kind of link did you send? And so we clicked on the link and it was a recall. We actually reacted fast enough that the customer had not yet opened the email. And so we reacted immediately. Like, oh, hey, actually, sorry, this is the right link. And so the customer never reacted to the first link. And so, yeah, I tweeted about that. It went surprisingly viral. And I checked afterwards in the logs. We did like a database query and we found, I think, like three or four other instances of it having happened before.Swyx [00:27:12]: That's surprisingly low.Flo [00:27:13]: It is low. And we fixed it across the board by just adding a line to the system prompt that's like, hey, don't recall people, please don't recall.Swyx [00:27:21]: Yeah, yeah, yeah. I mean, so, you know, you can explain it retroactively, right? Like, that YouTube slug has been pasted in so many different corpuses that obviously it learned to hallucinate that.Alessio [00:27:31]: And it pretended to be so many things. That's the thing.Swyx [00:27:34]: I wouldn't be surprised if that takes one token. Like, there's this one slug in the tokenizer and it's just one token.Flo [00:27:41]: That's the idea of a YouTube video.Swyx [00:27:43]: Because it's used so much, right? And you have to basically get it exactly correct. It's probably not. That's a long speech.Flo [00:27:52]: It would have been so good.Alessio [00:27:55]: So this is just a jump maybe into evals from here. How could you possibly come up for an eval that says, make sure my AI does not recall my customer? I feel like when people are writing evals, that's not something that they come up with. So how do you think about evals when it's such like an open-ended problem space?Flo [00:28:12]: Yeah, it is tough. We built quite a bit of infrastructure for us to create evals in one click from any conversation history. So we can point to a conversation and we can be like, in one click we can turn it into effectively a unit test. It's like, this is a good conversation. This is how you're supposed to handle things like this. Or if it's a negative example, then we modify a little bit the conversation after generating the eval. So it's very easy for us to spin up this kind of eval.Alessio [00:28:36]: Do you use an off-the-shelf tool which is like Brain Trust on the podcast? Or did you just build your own?Flo [00:28:41]: We unfortunately built our own. We're most likely going to switch to Brain Trust. Well, when we built it, there was nothing. Like there was no eval tool, frankly. I mean, we started this project at the end of 2022. It was like, it was very, very, very early. I wouldn't recommend it to build your own eval tool. There's better solutions out there and our eval tool breaks all the time and it's a nightmare to maintain. And that's not something we want to be spending our time on.Swyx [00:29:04]: I was going to ask that basically because I think my first conversations with you about Lindy was that you had a strong opinion that everyone should build their own tools. And you were very proud of your evals. You're kind of showing off to me like how many evals you were running, right?Flo [00:29:16]: Yeah, I think that was before all of these tools came around. I think the ecosystem has matured a fair bit.Swyx [00:29:21]: What is one thing that Brain Trust has nailed that you always struggled to do?Flo [00:29:25]: We're not using them yet, so I couldn't tell. But from what I've gathered from the conversations I've had, like they're doing what we do with our eval tool, but better.Swyx [00:29:33]: And like they do it, but also like 60 other companies do it, right? So I don't know how to shop apart from brand. Word of mouth.Flo [00:29:41]: Same here.Swyx [00:29:42]: Yeah, like evals or Lindys, there's two kinds of evals, right? Like in some way, you don't have to eval your system as much because you've constrained the language model so much. And you can rely on open AI to guarantee that the structured outputs are going to be good, right? We had Michelle sit where you sit and she explained exactly how they do constraint grammar sampling and all that good stuff. So actually, I think it's more important for your customers to eval their Lindys than you evaling your Lindy platform because you just built the platform. You don't actually need to eval that much.Flo [00:30:14]: Yeah. In an ideal world, our customers don't need to care about this. And I think the bar is not like, look, it needs to be at 100%. I think the bar is it needs to be better than a human. And for most use cases we serve today, it is better than a human, especially if you put it on Rails.Swyx [00:30:30]: Is there a limiting factor of Lindy at the business? Like, is it adding new connectors? Is it adding new node types? Like how do you prioritize what is the most impactful to your company?Flo [00:30:41]: Yeah. The raw capabilities for sure are a big limit. It is actually shocking the extent to which the model is no longer the limit. It was the limit a year ago. It was too expensive. The context window was too small. It's kind of insane that we started building this when the context windows were like 4,000 tokens. Like today, our system prompt is more than 4,000 tokens. So yeah, the model is actually very much not a limit anymore. It almost gives me pause because I'm like, I want the model to be a limit. And so no, the integrations are ones, the core capabilities are ones. So for example, we are investing in a system that's basically, I call it like the, it's a J hack. Give me these names, like the poor man's RLHF. So you can turn on a toggle on any step of your Lindy workflow to be like, ask me for confirmation before you actually execute this step. So it's like, hey, I receive an email, you send a reply, ask me for confirmation before actually sending it. And so today you see the email that's about to get sent and you can either approve, deny, or change it and then approve. And we are making it so that when you make a change, we are then saving this change that you're making or embedding it in the vector database. And then we are retrieving these examples for future tasks and injecting them into the context window. So that's the kind of capability that makes a huge difference for users. That's the bottleneck today. It's really like good old engineering and product work.Swyx [00:31:52]: I assume you're hiring. We'll do a call for hiring at the end.Alessio [00:31:54]: Any other comments on the model side? When did you start feeling like the model was not a bottleneck anymore? Was it 4.0? Was it 3.5? 3.5.Flo [00:32:04]: 3.5 Sonnet, definitely. I think 4.0 is overhyped, frankly. We don't use 4.0. I don't think it's good for agentic behavior. Yeah, 3.5 Sonnet is when I started feeling that. And then with prompt caching with 3.5 Sonnet, like that fills the cost, cut the cost again. Just cut it in half. Yeah.Swyx [00:32:21]: Your prompts are... Some of the problems with agentic uses is that your prompts are kind of dynamic, right? Like from caching to work, you need the front prefix portion to be stable.Flo [00:32:32]: Yes, but we have this append-only ledger paradigm. So every node keeps appending to that ledger and every filled node inherits all the context built up by all the previous nodes. And so we can just decide, like, hey, every X thousand nodes, we trigger prompt caching again.Swyx [00:32:47]: Oh, so you do it like programmatically, not all the time.Flo [00:32:50]: No, sorry. Anthropic manages that for us. But basically, it's like, because we keep appending to the prompt, the prompt caching works pretty well.Alessio [00:32:57]: We have this small podcaster tool that I built for the podcast and I rewrote all of our prompts because I noticed, you know, I was inputting stuff early on. I wonder how much more money OpenAN and Anthropic are making just because people don't rewrite their prompts to be like static at the top and like dynamic at the bottom.Flo [00:33:13]: I think that's the remarkable thing about what we're having right now. It's insane that these companies are routinely cutting their costs by two, four, five. Like, they basically just apply constraints. They want people to take advantage of these innovations. Very good.Swyx [00:33:25]: Do you have any other competitive commentary? Commentary? Dust, WordWare, Gumloop, Zapier? If not, we can move on.Flo [00:33:31]: No comment.Alessio [00:33:32]: I think the market is,Flo [00:33:33]: look, I mean, AGI is coming. All right, that's what I'm talking about.Swyx [00:33:38]: I think you're helping. Like, you're paving the road to AGI.Flo [00:33:41]: I'm playing my small role. I'm adding my small brick to this giant, giant, giant castle. Yeah, look, when it's here, we are going to, this entire category of software is going to create, it's going to sound like an exaggeration, but it is a fact it is going to create trillions of dollars of value in a few years, right? It's going to, for the first time, we're actually having software directly replace human labor. I see it every day in sales calls. It's like, Lindy is today replacing, like, we talk to even small teams. It's like, oh, like, stop, this is a 12-people team here. I guess we'll set up this Lindy for one or two days, and then we'll have to decide what to do with this 12-people team. And so, yeah. To me, there's this immense uncapped market opportunity. It's just such a huge ocean, and there's like three sharks in the ocean. I'm focused on the ocean more than on the sharks.Swyx [00:34:25]: So we're moving on to hot topics, like, kind of broadening out from Lindy, but obviously informed by Lindy. What are the high-order bits of good agent design?Flo [00:34:31]: The model, the model, the model, the model. I think people fail to truly, and me included, they fail to truly internalize the bitter lesson. So for the listeners out there who don't know about it, it's basically like, you just scale the model. Like, GPUs go brr, it's all that matters. I think it also holds for the cognitive architecture. I used to be very cognitive architecture-filled, and I was like, ah, and I was like a critic, and I was like a generator, and all this, and then it's just like, GPUs go brr, like, just like let the model do its job. I think we're seeing it a little bit right now with O1. I'm seeing some tweets that say that the new 3.5 SONNET is as good as O1, but with none of all the crazy...Swyx [00:35:09]: It beats O1 on some measures. On some reasoning tasks. On AIME, it's still a lot lower. Like, it's like 14 on AIME versus O1, it's like 83.Flo [00:35:17]: Got it. Right. But even O1 is still the model. Yeah.Swyx [00:35:22]: Like, there's no cognitive architecture on top of it.Flo [00:35:23]: You can just wait for O1 to get better.Alessio [00:35:25]: And so, as a founder, how do you think about that, right? Because now, knowing this, wouldn't you just wait to start Lindy? You know, you start Lindy, it's like 4K context, the models are not that good. It's like, but you're still kind of like going along and building and just like waiting for the models to get better. How do you today decide, again, what to build next, knowing that, hey, the models are going to get better, so maybe we just shouldn't focus on improving our prompt design and all that stuff and just build the connectors instead or whatever? Yeah.Flo [00:35:51]: I mean, that's exactly what we do. Like, all day, we always ask ourselves, oh, when we have a feature idea or a feature request, we ask ourselves, like, is this the kind of thing that just gets better while we sleep because models get better? I'm reminded, again, when we started this in 2022, we spent a lot of time because we had to around context pruning because 4,000 tokens is really nothing. You really can't do anything with 4,000 tokens. All that work was throwaway work. Like, now it's like it was for nothing, right? Now we just assume that infinite context windows are going to be here in a year or something, a year and a half, and infinitely cheap as well, and dynamic compute is going to be here. Like, we just assume all of these things are going to happen, and so we really focus, our job to be done in the industry is to provide the input and output to the model. I really compare it all the time to the PC and the CPU, right? Apple is busy all day. They're not like a CPU wrapper. They have a lot to build, but they don't, well, now actually they do build the CPU as well, but leaving that aside, they're busy building a laptop. It's just a lot of work to build these things. It's interesting because, like,Swyx [00:36:45]: for example, another person that we're close to, Mihaly from Repl.it, he often says that the biggest jump for him was having a multi-agent approach, like the critique thing that you just said that you don't need, and I wonder when, in what situations you do need that and what situations you don't. Obviously, the simple answer is for coding, it helps, and you're not coding, except for, are you still generating code? In Indy? Yeah.Flo [00:37:09]: No, we do. Oh, right. No, no, no, the cognitive architecture changed. We don't, yeah.Swyx [00:37:13]: Yeah, okay. For you, you're one shot, and you chain tools together, and that's it. And if the user really wantsFlo [00:37:18]: to have this kind of critique thing, you can also edit the prompt, you're welcome to. I have some of my Lindys, I've told them, like, hey, be careful, think step by step about what you're about to do, but that gives you a little bump for some use cases, but, yeah.Alessio [00:37:30]: What about unexpected model releases? So, Anthropic released computer use today. Yeah. I don't know if many people were expecting computer use to come out today. Do these things make you rethink how to design, like, your roadmap and things like that, or are you just like, hey, look, whatever, that's just, like, a small thing in their, like, AGI pursuit, that, like, maybe they're not even going to support, and, like, it's still better for us to build our own integrations into systems and things like that. Because maybe people will say, hey, look, why am I building all these API integrationsFlo [00:38:02]: when I can just do computer use and never go to the product? Yeah. No, I mean, we did take into account computer use. We were talking about this a year ago or something, like, we've been talking about it as part of our roadmap. It's been clear to us that it was coming, My philosophy about it is anything that can be done with an API must be done by an API or should be done by an API for a very long time. I think it is dangerous to be overly cavalier about improvements of model capabilities. I'm reminded of iOS versus Android. Android was built on the JVM. There was a garbage collector, and I can only assume that the conversation that went down in the engineering meeting room was, oh, who cares about the garbage collector? Anyway, Moore's law is here, and so that's all going to go to zero eventually. Sure, but in the meantime, you are operating on a 400 MHz CPU. It was like the first CPU on the iPhone 1, and it's really slow, and the garbage collector is introducing a tremendous overhead on top of that, especially a memory overhead. For the longest time, and it's really only been recently that Android caught up to iOS in terms of how smooth the interactions were, but for the longest time, Android phones were significantly slowerSwyx [00:39:07]: and laggierFlo [00:39:08]: and just not feeling as good as iOS devices. Look, when you're talking about modules and magnitude of differences in terms of performance and reliability, which is what we are talking about when we're talking about API use versus computer use, then you can't ignore that, right? And so I think we're going to be in an API use world for a while.Swyx [00:39:27]: O1 doesn't have API use today. It will have it at some point, and it's on the roadmap. There is a future in which OpenAI goes much harder after your business, your market, than it is today. Like, ChatGPT, it's its own business. All they need to do is add tools to the ChatGPT, and now they're suddenly competing with you. And by the way, they have a GPT store where a bunch of people have already configured their tools to fit with them. Is that a concern?Flo [00:39:56]: I think even the GPT store, in a way, like the way they architect it, for example, their plug-in systems are actually grateful because we can also use the plug-ins. It's very open. Now, again, I think it's going to be such a huge market. I think there's going to be a lot of different jobs to be done. I know they have a huge enterprise offering and stuff, but today, ChatGPT is a consumer app. And so, the sort of flow detail I showed you, this sort of workflow, this sort of use cases that we're going after, which is like, we're doing a lot of lead generation and lead outreach and all of that stuff. That's not something like meeting recording, like Lindy Today right now joins your Zoom meetings and takes notes, all of that stuff.Swyx [00:40:34]: I don't see that so farFlo [00:40:35]: on the OpenAI roadmap.Swyx [00:40:36]: Yeah, but they do have an enterprise team that we talk to You're hiring GMs?Flo [00:40:42]: We did.Swyx [00:40:43]: It's a fascinating way to build a business, right? Like, what should you, as CEO, be in charge of? And what should you basically hireFlo [00:40:52]: a mini CEO to do? Yeah, that's a good question. I think that's also something we're figuring out. The GM thing was inspired from my days at Uber, where we hired one GM per city or per major geo area. We had like all GMs, regional GMs and so forth. And yeah, Lindy is so horizontal that we thought it made sense to hire GMs to own each vertical and the go-to market of the vertical and the customization of the Lindy templates for these verticals and so forth. What should I own as a CEO? I mean, the canonical reply here is always going to be, you know, you own the fundraising, you own the culture, you own the... What's the rest of the canonical reply? The culture, the fundraising.Swyx [00:41:29]: I don't know,Flo [00:41:30]: products. Even that, eventually, you do have to hand out. Yes, the vision, the culture, and the foundation. Well, you've done your job as a CEO. In practice, obviously, yeah, I mean, all day, I do a lot of product work still and I want to keep doing product work for as long as possible.Swyx [00:41:48]: Obviously, like you're recording and managing the team. Yeah.Flo [00:41:52]: That one feels like the most automatable part of the job, the recruiting stuff.Swyx [00:41:56]: Well, yeah. You saw myFlo [00:41:59]: design your recruiter here. Relationship between Factorio and building Lindy. We actually very often talk about how the business of the future is like a game of Factorio. Yeah. So, in the instance, it's like Slack and you've got like 5,000 Lindys in the sidebar and your job is to somehow manage your 5,000 Lindys. And it's going to be very similar to company building because you're going to look for like the highest leverage way to understand what's going on in your AI company and understand what levels do you have to make impact in that company. So, I think it's going to be very similar to like a human company except it's going to go infinitely faster. Today, in a human company, you could have a meeting with your team and you're like, oh, I'm going to build a facility and, you know, now it's like, okay,Swyx [00:42:40]: boom, I'm going to spin up 50 designers. Yeah. Like, actually, it's more important that you can clone an existing designer that you know works because the hiring process, you cannot clone someone because every new person you bring in is going to have their own tweaksFlo [00:42:54]: and you don't want that. Yeah.Swyx [00:42:56]: That's true. You want an army of mindless dronesFlo [00:42:59]: that all work the same way.Swyx [00:43:00]: The reason I bring this, bring Factorio up as well is one, Factorio Space just came out. Apparently, a whole bunch of people stopped working. I tried out Factorio. I never really got that much into it. But the other thing was, you had a tweet recently about how the sort of intentional top-down design was not as effective as just build. Yeah. Just ship.Flo [00:43:21]: I think people read a little bit too much into that tweet. It went weirdly viral. I was like, I did not intend it as a giant statement online.Swyx [00:43:28]: I mean, you notice you have a pattern with this, right? Like, you've done this for eight years now.Flo [00:43:33]: You should know. I legit was just hearing an interesting story about the Factorio game I had. And everybody was like, oh my God, so deep. I guess this explains everything about life and companies. There is something to be said, certainly, about focusing on the constraint. And I think it is Patrick Collison who said, people underestimate the extent to which moonshots are just one pragmatic step taken after the other. And I think as long as you have some inductive bias about, like, some loose idea about where you want to go, I think it makes sense to follow a sort of greedy search along that path. I think planning and organizing is important. And having older is important.Swyx [00:44:05]: I'm wrestling with that. There's two ways I encountered it recently. One with Lindy. When I tried out one of your automation templates and one of them was quite big and I just didn't understand it, right? So, like, it was not as useful to me as a small one that I can just plug in and see all of. And then the other one was me using Cursor. I was very excited about O1 and I just up frontFlo [00:44:27]: stuffed everythingSwyx [00:44:28]: I wanted to do into my prompt and expected O1 to do everything. And it got itself into a huge jumbled mess and it was stuck. It was really... There was no amount... I wasted, like, two hours on just, like, trying to get out of that hole. So I threw away the code base, started small, switched to Clouds on it and build up something working and just add it over time and it just worked. And to me, that was the factorial sentiment, right? Maybe I'm one of those fanboys that's just, like, obsessing over the depth of something that you just randomly tweeted out. But I think it's true for company building, for Lindy building, for coding.Flo [00:45:02]: I don't know. I think it's fair and I think, like, you and I talked about there's the Tuft & Metal principle and there's this other... Yes, I love that. There's the... I forgot the name of this other blog post but it's basically about this book Seeing Like a State that talks about the need for legibility and people who optimize the system for its legibility and anytime you make a system... So legible is basically more understandable. Anytime you make a system more understandable from the top down, it performs less well from the bottom up. And it's fine but you should at least make this trade-off with your eyes wide open. You should know, I am sacrificing performance for understandability, for legibility. And in this case, for you, it makes sense. It's like you are actually optimizing for legibility. You do want to understand your code base but in some other cases it may not make sense. Sometimes it's better to leave the system alone and let it be its glorious, chaotic, organic self and just trust that it's going to perform well even though you don't understand it completely.Swyx [00:45:55]: It does remind me of a common managerial issue or dilemma which you experienced in the small scale of Lindy where, you know, do you want to organize your company by functional sections or by products or, you know, whatever the opposite of functional is. And you tried it one way and it was more legible to you as CEO but actually it stopped working at the small level. Yeah.Flo [00:46:17]: I mean, one very small example, again, at a small scale is we used to have everything on Notion. And for me, as founder, it was awesome because everything was there. The roadmap was there. The tasks were there. The postmortems were there. And so, the postmortem was linkedSwyx [00:46:31]: to its task.Flo [00:46:32]: It was optimized for you. Exactly. And so, I had this, like, one pane of glass and everything was on Notion. And then the team, one day,Swyx [00:46:39]: came to me with pitchforksFlo [00:46:40]: and they really wanted to implement Linear. And I had to bite my fist so hard. I was like, fine, do it. Implement Linear. Because I was like, at the end of the day, the team needs to be able to self-organize and pick their own tools.Alessio [00:46:51]: Yeah. But it did make the company slightly less legible for me. Another big change you had was going away from remote work, every other month. The discussion comes up again. What was that discussion like? How did your feelings change? Was there kind of like a threshold of employees and team size where you felt like, okay, maybe that worked. Now it doesn't work anymore. And how are you thinking about the futureFlo [00:47:12]: as you scale the team? Yeah. So, for context, I used to have a business called TeamFlow. The business was about building a virtual office for remote teams. And so, being remote was not merely something we did. It was, I was banging the remote drum super hard and helping companies to go remote. And so, frankly, in a way, it's a bit embarrassing for me to do a 180 like that. But I guess, when the facts changed, I changed my mind. What happened? Well, I think at first, like everyone else, we went remote by necessity. It was like COVID and you've got to go remote. And on paper, the gains of remote are enormous. In particular, from a founder's standpoint, being able to hire from anywhere is huge. Saving on rent is huge. Saving on commute is huge for everyone and so forth. But then, look, we're all here. It's like, it is really making it much harder to work together. And I spent three years of my youth trying to build a solution for this. And my conclusion is, at least we couldn't figure it out and no one else could. Zoom didn't figure it out. We had like a bunch of competitors. Like, Gathertown was one of the bigger ones. We had dozens and dozens of competitors. No one figured it out. I don't know that software can actually solve this problem. The reality of it is, everyone just wants to get off the darn Zoom call. And it's not a good feeling to be in your home office if you're even going to have a home office all day. It's harder to build culture. It's harder to get in sync. I think software is peculiar because it's like an iceberg. It's like the vast majority of it is submerged underwater. And so, the quality of the software that you ship is a function of the alignment of your mental models about what is below that waterline. Can you actually get in sync about what it is exactly fundamentally that we're building? What is the soul of our product? And it is so much harder to get in sync about that when you're remote. And then you waste time in a thousand ways because people are offline and you can't get a hold of them or you can't share your screen. It's just like you feel like you're walking in molasses all day. And eventually, I was like, okay, this is it. We're not going to do this anymore.Swyx [00:49:03]: Yeah. I think that is the current builder San Francisco consensus here. Yeah. But I still have a big... One of my big heroes as a CEO is Sid Subban from GitLab.Flo [00:49:14]: Mm-hmm.Swyx [00:49:15]: Matt MullenwegFlo [00:49:16]: used to be a hero.Swyx [00:49:17]: But these people run thousand-person remote businesses. The main idea is that at some company

It's annoying when they skip a week but Adam and Dax explain why, Dax's Default, how did Adam's phone break, politics happened and we have some theories, Liz's Theories, and Dax considers getting hair while Adam ponders getting a wig.Want to carry on the conversation? Join us in Discord. Or send us an email at sliceoffalittlepieceofbacon@tomorrow.fm.The ultimate MySQL database platform — PlanetScalethe decisionGuillermo Rauch on TrumpZoo Tycoon Game OverviewTopics:(00:00) - Adam's bandwidth issues (00:37) - It's annoying when they skip a week (03:58) - Adam shares from tough life stuff (18:00) - Dax's embarrasing thing (26:04) - Dax's Default: PlanetScale (27:14) - The decision video (33:05) - How did Adam's phone break? (35:27) - The politics happened (55:45) - Dry or humidity? (58:10) - Who should we have on the podcast? (01:02:35) - Liz's theories (01:07:21) - Dax's hair thoughts and Adam's beard thoughts

In this episode, Jake and Michael discuss large file uploads without double-handling them, generating API documentation for your app, and the mad rush of the last two weeks before this year's Laracon AUShow linksRodecaster DuoMichael's tattooStoplightScrambleVitepressLaracon AU

In this episode, Jake and Michael discuss (clever?) ways to generate dynamic images for OpenGraph purposes, how a missing slot variable sent Michael mad, Jake's adventures with API-based file uploads, and some more details about Laracon AU.

Jake and Michael discuss all the latest Laravel releases, tutorials, and happenings in the community.This episode is sponsored by Sentry - code breaks, fix it faster. Don't just observe, take action today!Show linksLaravel 11.24 Released PHP 8.4 Release Candidate 1 is here Laravel MongoDB releases version 5.0 Laravel Roundup - October The September 2024 Laravel Worldwide Meetup is Today MySql Explain with Tobias Petry PHP and LLMs with Alfred Nutile Laravel Config Checker Package LangCountry launches v4 Fetch PHP is a Lightweight HTTP Library Inspired by JavaScript's fetch() Validate Console Command Input With the Command Validator Package A Deep Dive into Sessions in Laravel Split Log Levels Between Stdout and Stderr With Laravel