Podcasts about postgres

Nikolay and Michael are joined by Sugu Sougoumarane to discuss Multigres — a project he's joined Supabase to lead, building an adaptation of Vitess for Postgres! Here are some links to things they mentioned:Sugu Sougoumarane https://postgres.fm/people/sugu-sougoumaraneSupabase https://supabase.comAnnouncing Multigres https://supabase.com/blog/multigres-vitess-for-postgresVitess https://github.com/vitessio/vitessSPQR https://github.com/pg-sharding/spqrCitus https://github.com/citusdata/citusPgDog https://github.com/pgdogdev/pgdogMyths and Truths about Synchronous Replication in PostgreSQL (talk by Alexander Kukushkin) https://www.youtube.com/watch?v=PFn9qRGzTMcConsensus algorithms at scale (8 part series by Sugu) https://planetscale.com/blog/consensus-algorithms-at-scale-part-1A More Flexible Paxos (blog post by Sugu) https://www.sougou.io/a-more-flexible-paxoslibpg_query https://github.com/pganalyze/libpg_queryPL/Proxy https://github.com/plproxy/plproxyPlanetScale Postgres Benchmarking https://planetscale.com/blog/benchmarking-postgresMultiXact member exhaustion incidents (blog post by Cosmo Wolfe / Metronome) https://metronome.com/blog/root-cause-analysis-postgresql-multixact-member-exhaustion-incidents-may-2025~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith special thanks to:Jessie Draws for the elephant artwork 

Reliable software shouldn't be an accident, but for most developers it is. Jeremy Edberg, CEO of DBOS and the guy who scaled Reddit and Netflix, joins Corey Quinn to talk about his wild idea of saving your entire app into a database so it can never really break. They chat about Jeremy's "build for three" rule, a plan for scale without going crazy, why he set Reddit's servers to Arizona time to dodge daylight saving time, and how DBOS makes your app as tough as your data. Plus, Jeremy shares his brutally honest take on distributed systems cargo cult, autonomous AI testing, and why making it easy for customers to leave actually keeps them around.Public Bio: Jeremy is an angel investor and advisor for various incubators and startups, and the CEO of DBOS. He was the founding Reliability Engineer for Netflix and before that he ran ops for reddit as its first engineering hire. Jeremy also tech-edited the highly acclaimed AWS for Dummies, and he is one of the six original AWS Heroes. He is a noted speaker in serverless computing, distributed computing, availability, rapid scaling, and cloud computing, and holds a Cognitive Science degree from UC Berkeley.Show Highlights(02:08) - What DBOS actually does(04:08) - "Everything as a database" philosophy and why it works(08:26) - "95% of people will never outgrow one Postgres machine"(10:13) - Jeremy's Arizona time zone hack at Reddit (and whether it still exists)(11:22) - "Build for three" philosophy without over-engineering(17:16) - Extracting data from mainframes older than the founders(19:00) - Autonomous testing with AI trained on your app's history(20:07) - The hardest part of dev tools(22:00) - Corey's brutal pricing page audit methodology(27:15) - Why making it easy to leave keeps customers around(34:11) - Learn more about DBOSLinksDBOS website: https://dbos.devDBOS documentation: https://docs.dbos.devDBOS GitHub: https://github.com/dbos-incDBOS Discord community: https://discord.gg/fMqo9kDJeremy Edberg on Twitter: https://x.com/jedberg?lang=enAWS Heroes program: https://aws.amazon.com/developer/community/heroes/

Justin Searls describes the "full-breadth developer" and why they'll win because AI, Cloudflare comes up with a way publishers can charge crawlers for access, Hugo Bowne-Anderson explains why building AI agents fails so often, the Job Worth Calculator tells you if your job is worth the grind, and Sam Lambert announces PlanetScale for Postgres.

Justin Searls describes the "full-breadth developer" and why they'll win because AI, Cloudflare comes up with a way publishers can charge crawlers for access, Hugo Bowne-Anderson explains why building AI agents fails so often, the Job Worth Calculator tells you if your job is worth the grind, and Sam Lambert announces PlanetScale for Postgres.

In this repeat episode, Nikolas Burk, DevRel at Prisma, talks about Prisma Postgres, its unikernel architecture, and its seamless integration with cloud infrastructure. Discover how Prisma Postgres is revolutionizing database management with features like cold start elimination, real-time event handling and advanced caching strategies! Links X: https://x.com/nikolasburk LinkedIn: https://www.linkedin.com/in/nikolas-burk-1bbb7b8a Github: https://github.com/nikolasburk Resources Prisma Postgres®: Building a Modern PostgreSQL Service Using Unikernels & MicroVMs: https://www.prisma.io/blog/announcing-prisma-postgres-early-access 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, Em, at emily.kochanek@logrocket.com (mailto:emily.kochanek@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 understanding 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: Nikolas Burk.

News includes the first CVE released under EEF's new CNA program for an Erlang zip traversal vulnerability, Phoenix MacroComponents being delayed for greater potential, Supabase announcing Multigres - a Vitess-like proxy for scaling Postgres to petabyte scale, a surge of new MCP server implementations for Phoenix and Plug including Phantom, HermesMCP, ExMCP, Vancouver, and Excom, a fun blog post revealing that Erlang was the only language that didn't crash under extreme load testing against 6 other languages, LiveDebugger v0.3.0 being teased with Firefox extension support and enhanced debugging capabilities, and more! Show Notes online - http://podcast.thinkingelixir.com/258 (http://podcast.thinkingelixir.com/258) Elixir Community News https://www.honeybadger.io/ (https://www.honeybadger.io/utm_source=thinkingelixir&utm_medium=podcast) – Honeybadger.io is sponsoring today's show! Keep your apps healthy and your customers happy with Honeybadger! It's free to get started, and setup takes less than five minutes. https://cna.erlef.org/cves/cve-2025-4748.html (https://cna.erlef.org/cves/cve-2025-4748.html?utm_source=thinkingelixir&utm_medium=shownotes) – New CVE for Erlang regarding zip traversal - 4.8 severity (medium) with workaround available or update to latest patched OTP versions First CVE released under the EEF's new CNA (CVE Numbering Authority) program - a successful process milestone https://bsky.app/profile/steffend.me/post/3lrlhd5etkc2p (https://bsky.app/profile/steffend.me/post/3lrlhd5etkc2p?utm_source=thinkingelixir&utm_medium=shownotes) – Phoenix MacroComponents is being delayed in search of greater potential https://github.com/phoenixframework/phoenixliveview/pull/3846 (https://github.com/phoenixframework/phoenix_live_view/pull/3846?utm_source=thinkingelixir&utm_medium=shownotes) – Draft PR for Phoenix MacroComponents development https://x.com/supabase/status/1933627932972376097 (https://x.com/supabase/status/1933627932972376097?utm_source=thinkingelixir&utm_medium=shownotes) – Supabase announcement of Multigres project https://supabase.com/blog/multigres-vitess-for-postgres (https://supabase.com/blog/multigres-vitess-for-postgres?utm_source=thinkingelixir&utm_medium=shownotes) – Multigres - Vitess for Postgres, announcement of a new proxy for scaling Postgres databases to petabyte scale https://github.com/multigres/multigres (https://github.com/multigres/multigres?utm_source=thinkingelixir&utm_medium=shownotes) – Multigres GitHub repository Sugu, co-creator of Vitess, has joined Supabase to build Multigres https://hex.pm/packages/phantom_mcp (https://hex.pm/packages/phantom_mcp?utm_source=thinkingelixir&utm_medium=shownotes) – Phantom MCP server - comprehensive implementation supporting Streamable HTTP with Phoenix/Plug integration https://hex.pm/packages/hermes_mcp (https://hex.pm/packages/hermes_mcp?utm_source=thinkingelixir&utm_medium=shownotes) – HermesMCP - comprehensive MCP server with client, stdio and Plug adapters https://hex.pm/packages/ex_mcp (https://hex.pm/packages/ex_mcp?utm_source=thinkingelixir&utm_medium=shownotes) – ExMCP - comprehensive MCP implementation with client, server, stdio and Plug adapters, uses Horde for distribution https://hex.pm/packages/vancouver (https://hex.pm/packages/vancouver?utm_source=thinkingelixir&utm_medium=shownotes) – Vancouver MCP server - simple implementation supporting only tools https://hex.pm/packages/excom (https://hex.pm/packages/excom?utm_source=thinkingelixir&utm_medium=shownotes) – Excom MCP server - simple implementation supporting only tools https://www.youtube.com/watch?v=4dzZ44-xVds (https://www.youtube.com/watch?v=4dzZ44-xVds?utm_source=thinkingelixir&utm_medium=shownotes) – AshAI video demo showing incredible introspection capabilities for MCP frameworks https://freedium.cfd/https:/medium.com/@codeperfect/we-tested-7-languages-under-extreme-load-and-only-one-didnt-crash-it-wasn-t-what-we-expected-67f84c79dc34 (https://freedium.cfd/https:/medium.com/@codeperfect/we-tested-7-languages-under-extreme-load-and-only-one-didnt-crash-it-wasn-t-what-we-expected-67f84c79dc34?utm_source=thinkingelixir&utm_medium=shownotes) – Blog post comparing 7 languages under extreme load - Erlang was the only one that didn't crash https://github.com/software-mansion/live-debugger (https://github.com/software-mansion/live-debugger?utm_source=thinkingelixir&utm_medium=shownotes) – LiveDebugger v0.3.0 release being teased with new features https://bsky.app/profile/membrane-swmansion.bsky.social/post/3lrb4kpmmw227 (https://bsky.app/profile/membrane-swmansion.bsky.social/post/3lrb4kpmmw227?utm_source=thinkingelixir&utm_medium=shownotes) – Software Mansion preview of LiveDebugger v0.3.0 features including Firefox extension and enhanced debugging capabilities https://smartlogic.io/podcast/elixir-wizards/s14-e03-langchain-llm-integration-elixir/ (https://smartlogic.io/podcast/elixir-wizards/s14-e03-langchain-llm-integration-elixir/?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir Wizards podcast episode featuring discussion with Mark Ericksen on the Elixir LangChain project for LLM integration 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)

Nikolay and Michael are joined by Gwen Shapira to discuss multi-tenant architectures — the high level options, the pros and cons of each, and how they're trying to help with Nile. Here are some links to things they mentioned:Gwen Shapira https://postgres.fm/people/gwen-shapiraNile https://www.thenile.devSaaS Tenant Isolation Strategies (AWS whitepaper) https://docs.aws.amazon.com/whitepapers/latest/saas-tenant-isolation-strategies/saas-tenant-isolation-strategies.html Row Level Security https://www.postgresql.org/docs/current/ddl-rowsecurity.htmlCitus https://github.com/citusdata/citusPostgres.AI Bot https://postgres.ai/blog/20240127-postgres-ai-bot RLS Performance and Best Practices https://supabase.com/docs/guides/troubleshooting/rls-performance-and-best-practices-Z5JjwvCase Gwen mentioned about the planner thinking an optimisation was unsafe Re-engineering Postgres for Millions of Tenants (Gwen's recent talk at PGConf.dev) https://www.youtube.com/watch?v=EfAStGb4s88 Multi-tenant database the good, the bad, the ugly (talk by Pierre Ducroquet at PgDay Paris) https://www.youtube.com/watch?v=4uxuPfSvTGU ~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith special thanks to:Jessie Draws for the elephant artwork 

What drives someone to publish 600+ issues of a Postgres newsletter for over a decade? In Episode 28 of Talking Postgres with Claire Giordano, Peter Cooper—creator of Postgres Weekly—shares how his days of rustic programming and QBASIC fanzines on Usenet led to a newsletter empire that now reaches nearly half a million developers each week. We dig into the BBC's "big tent" editorial influence, an accidental business model that just worked, and the perils of "temporary" hacks. Plus: spam filters, a Photoshop addiction, and one very cheesy story (dairy-free).Links mentioned in this episode:Newsletter: Postgres WeeklyCooperpress: List of newslettersNewsletter: Latest issue of Postgres Weekly on Jun 19, 2025Newsletter: Postgres Weekly issue with horrible graphicNewsletter: Very first issue of Postgres Weekly on Mar 13, 2013Newsletter: Ruby Weekly, the first Cooperpress newsletterBook: Beginning Ruby Third Edition, by Peter CooperPodcast episode: How I got started as a developer (& in Postgres) with David RowleyFeed reader: FeedbinGitHub repo: feedbin/feedbinFeed reader: FeederEmail testing software: LitmusGitHub repo: MGML markup language for emailPaper: The Design of PostgresGitHub repo: PGRX for building Postgres extensions in RustPodcast news: Podnews.net for daily briefings about podcastsWikipedia page: BBC MicroWikipedia page: ZX SpectrumCal invite: LIVE recording of Ep29 of Talking Postgres to happen on Wed Jul 9, 2025

Welcome back to SED News, a podcast series from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the latest stories in software engineering, Silicon Valley, and wider tech world. In this episode, Gregor and Sean unpack what's going with Deel and Rippling, explore why Databricks and Snowflake are making big bets The post SED News: Corporate Spies, Postgres, and the Weird Life of Devs Right Now appeared first on Software Engineering Daily.

Welcome back to SED News, a podcast series from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the latest stories in software engineering, Silicon Valley, and wider tech world. In this episode, Gregor and Sean unpack what's going with Deel and Rippling, explore why Databricks and Snowflake are making big bets The post SED News: Corporate Spies, Postgres, and the Weird Life of Devs Right Now appeared first on Software Engineering Daily.

Lukas Mathis tells us to stop uploading our data to Google, Robert Vitonsky wants web devs to not guess his language using his IP, Tom from GameTorch reminds us that software talent is gold right now, Austin Parker from Honeycomb describes how LLMs are upending the observability industry, and Vitess co-creator, Sugu Sougoumarane, joins Supabase to lead their Multigres effort to bring Vitess to Postgres.

Lukas Mathis tells us to stop uploading our data to Google, Robert Vitonsky wants web devs to not guess his language using his IP, Tom from GameTorch reminds us that software talent is gold right now, Austin Parker from Honeycomb describes how LLMs are upending the observability industry, and Vitess co-creator, Sugu Sougoumarane, joins Supabase to lead their Multigres effort to bring Vitess to Postgres.

Lukas Mathis tells us to stop uploading our data to Google, Robert Vitonsky wants web devs to not guess his language using his IP, Tom from GameTorch reminds us that software talent is gold right now, Austin Parker from Honeycomb describes how LLMs are upending the observability industry, and Vitess co-creator, Sugu Sougoumarane, joins Supabase to lead their Multigres effort to bring Vitess to Postgres.

Nikolay and Michael discuss looking at queries by mean time — when it makes sense, why ordering by a percentile (like p99) might be better, and the merits of approximating percentiles in pg_stat_statements using the standard deviation column. Here are some links to things they mentioned:Approximate the p99 of a query with pg_stat_statements (blog post by Michael) https://www.pgmustard.com/blog/approximate-the-p99-of-a-query-with-pgstatstatementspg_stat_statements https://www.postgresql.org/docs/current/pgstatstatements.html Our episode about track_planning https://postgres.fm/episodes/pg-stat-statements-track-planning pg_stat_monitor https://github.com/percona/pg_stat_monitorstatement_timeout https://www.postgresql.org/docs/current/runtime-config-client.html#GUC-STATEMENT-TIMEOUT~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

In today's episode, I talk to Adam Hendel, the founding engineer of Tembo, about their project, PGMQ, and how it came to be. We discuss the design decisions behind job queues, interfacing from Rust to Postgres, and the engineering decisions that went into building the extension.

In this episode, Lois Houston and Nikita Abraham dive into key components of Oracle GoldenGate 23ai with expert insights from Nick Wagner, Senior Director of Product Management.   They break down the Distribution Service, explaining how it moves trail files between environments, replaces the classic extract pump, and ensures secure data transfer. Nick also introduces Target Initiated Paths, a method for connecting less secure environments to more secure ones, and discusses how the Receiver Service simplifies monitoring and management. The episode wraps up with a look into Initial Load, covering different methods for syncing source and target databases without downtime.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ----------------------------------------------------------------- Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead of Editorial Services with Oracle University, and with me is Lois Houston, Director of Innovation Programs.  Lois: Hey there! Last week, we spoke about the Extract process and today we're going to spend time discussing the Distribution Path, Target Initiated Path, Receiver Server, and Initial Load. These are all critical components of the GoldenGate architecture, and understanding how they work together is essential for successful data replication. 00:58 Nikita: To help us navigate these topics, we've got Nick Wagner joining us again. Nick is a Senior Director of Product Management for Oracle GoldenGate. Hi Nick! Thanks for being with us today. To kick things off, can you tell us what the distribution service is and how it works? Nick: A distribution path is used when we need to send trail files between two different GoldenGate environments. The distribution service replaces the extract pump that was used in GoldenGate classic architecture. And so the distribution service will send the trail files as they're being created to that receiver service and it will write the trail files over on the target system. The distribution service works in a kind of a streaming fashion, so it's constantly pulling the trail files that the extract is creating to see if there's any new data. As soon as it sees new data, it'll packet it up and send it across the network to the receiver service. It can use a couple of different methods to do this. The most secure and recommended method is using a WebSocket secure connection or WSS. If you're going between a microservices and a classic architecture, you can actually tell the distribution service to send it using the classic architecture method. In that case, it's the OGG option when you're configuring the distribution service. There's also some unsecured methods that would send the trail files in plain text. The receiver service is then responsible for taking that data and rewriting it into the trail file on the target site. 02:23 Lois: Nick, what are some of the key features and responsibilities of the distribution service? Nick: It's responsible for command deployment. So any time that you're going to actually make a command to the distribution service, it gets handled there directly. It can handle multiple commands concurrently. It's going to dispatch trail files to one or more receiver servers so you can actually have a single distribution path, send trail files to multiple targets. It can provide some lightweight filtering so you can decide which tables get sent to the target system. And it also is integrated in with our data streams, our pub and subscribe model that we've added in GoldenGate 23ai. 03:01 Lois: Interesting. And are there any protocols to remember when using the distribution service? Nick: We always recommend a secure WebSocket. You also have proxy support for use within cloud environments. And then if you're going to a classic architecture GoldenGate, you would use the Oracle GoldenGate protocol. So in order to communicate with the distribution service and send it commands, you can communicate directly from any web browser, client software-- installation is not required-- or you can also do it through the admin client if necessary, but you can do it directly through browsers. 03:33 Nikita: Ok, let's move on to the target initiated path. Nick, what is it and what does it do essentially? Nick: This is used when you're communicating from a less secure environment to a more secure environment. Often, this requires going through some sort of DMZ. In these situations, a connection cannot be established from the less secure environment into the more secure environment. It actually needs to be established from the more secure environment out. And so if we need to replicate data into a more secure environment, we need to actually have the target GoldenGate environment initiate that connection so that it can be established.  And that's what a target-initiated path does. 04:12 Lois: And how do you set it up? Nick: It's pretty straightforward to set up. You actually don't even need to worry about it on the source side. You actually set it up and configure it from the target. The receiver service is responsible for receiving the trail file data and writing it to the local trail file. In this situation, we have a target-initiated path created. And so that receiver service is going to write the trail files locally and the replicat is going to apply that data into that target system. 04:37 Nikita: I also want to ask you about the Receiver service. What is it really? Nick: Receiver service is pretty straightforward. It's a centrally controlled service. It allows you to view the status of your distribution path and replaces target side collectors that were available in the classic architecture of GoldenGate. You can also get statistics about the receiver service directly from the web UI.  You can get detailed information about these paths by going into the receiver service and identifying information like network details, transfer protocols, how many bytes it's received, how many bytes it's sent out. If you need to issue commands from the admin client to the receiver service, you can use the info command to get details about it. Info all will tell you everything that's running. And you can see that your receiver service is up and running. 05:28 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! 05:53 Nikita: Welcome back. In the last section of today's episode, we'll cover what Initial Load is. Nick, can you break down the basics for us? Nick: So, the initial load is really used when you need to synchronize the source and target systems. Because GoldenGate is designed for 24/7 environments, we need to be able to do that initial load without taking downtime on the source. And so all the methods that we talk about do not require any downtime for that source database. 06:18 Lois: How do you do the initial load? Nick: So there's a couple of different ways to do the initial load. And it really depends on what your topology is. If I'm doing like-to-like replication in a homogeneous environment, we'll say Oracle-to-Oracle, the best options are to use something that's integrated with GoldenGate, some sort of precise instantiation method that does not require HandleCollisions. That's something like a database backup and restoring it to a specific SDN or CSN value using a Database Snapshot. Or in some cases, we can use Oracle Data Pump integration with GoldenGate. There are some less precise instantiation options, which do require HandleCollisions. We also have dissimilar initial load methods. And this is typically when you're going between heterogeneous environments. When my source and target databases don't match and there isn't any kind of fast unload or fast load utility that I could use between those two databases. In almost all cases, this does require HandleCollisions to be used. 07:16 Nikita: Got it. So, with so many options available, are there any advantages to using GoldenGate's own initial load method?  Nick: While some databases do have very good fast load and unload utilities, there are some advantages to using GoldenGate's own initial load method. One, it supports heterogeneous replication environments. So if I'm going from Postgres to Oracle, it'll do all the data type transformation, character set transformation for me. It doesn't require any downtime, if certain conditions are met.  It actually performs transformation as the data is loaded, too, as well as filtering. And so any transformation that you would be doing in your normal transaction log replication or CDC replication can also go through the same transformation for the initial load process. GoldenGate's initial load process does read directly from the source tables. And it fetches the data in arrays. It also uses parallel processing to speed up the replication. It does also handle activity on the source tables during the initial load process, so you do not need to worry about quiescing that source database. And a lot of the initial load methods directly built into GoldenGate support distributed application analytics targets, including things like Databricks, Snowflake, BigQuery. 08:28 Lois: And what about its limitations? Or to put it differently, when should users consider using different methods? Nick: So the first thing to consider is system proximity. We want to make sure that the two systems we're working with are close together. Or if not, how are we going to send the data across? One thing to keep in mind, when we do the initial load, the source database is not quiesced. So if it takes an hour to do the initial load or 10 hours, it really doesn't matter to GoldenGate. So that's something to keep in mind. Even though we talk about performance of this, the performance really isn't as critical as one might suspect. So the important thing about data system proximity is the proximity to the extract and replicat processes that are going to be pulling the data out and pushing it across. And then how much data is generated? Are we talking about a database that's just a couple of gigabytes? Or are we talking about a database that's hundreds of terabytes? Do we want to consider outage time? Would it be faster to take a little bit of outage and use some other method to move the data across? What kind of outage or downtime windows do we have for these environments? And then another consideration is disk space. As we're pulling the data out of that source database, we need to have somewhere to store it. And if we don't have enough disk space, we need to run to temporary space or to use multiple external drives to be able to support it. So these are all different considerations. 09:50 Nikita: I think we can wind up our episode with that. Thanks, Nick, for giving us your insights.  Lois: If you'd like to learn more about the topics we covered today, head over to mylearn.oracle.com and check out the Oracle GoldenGate 23ai: Fundamentals course. Nikita: In our next episode, Nick will take us through the Replicat process. Until then, this is Nikita Abraham… Lois: And, Lois Houston signing off! 10:14 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.

Nikolay and Michael discuss logging in Postgres — mostly what to log, and why changing quite a few settings can pay off big time in the long term. Here are some links to things they mentioned:What to log https://www.postgresql.org/docs/current/runtime-config-logging.html#RUNTIME-CONFIG-LOGGING-WHATOur episode about Auditing https://postgres.fm/episodes/auditing Our episode on auto_explain https://postgres.fm/episodes/auto_explain Here are the parameters they mentioned changing:log_checkpointslog_autovacuum_min_duration log_statementlog_connections and log_disconnectionslog_lock_waitslog_temp_fileslog_min_duration_statement log_min_duration_sample and log_statement_sample_rate And finally, some very useful tools they meant to mention but forgot to!   https://pgpedia.infohttps://postgresqlco.nfhttps://why-upgrade.depesz.com/show?from=16.9&to=17.5 ~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

News includes the major OTP 28 release with priority messages functionality, ElixirConf EU 2025 videos starting to appear including Chris McCord's keynote on his new phoenix.new service and James Arthur's introduction of Phoenix Sync for real-time database synchronization, the EEF board election results and their new role as a CVE Numbering Authority for the Hex ecosystem, upcoming co-located hooks and macro components in LiveView, updates to the Elixir Lua package and MDEx with its new Markdown sigil, a new convention for AI-friendly usage_rules.md files in hex packages, and more! Show Notes online - http://podcast.thinkingelixir.com/255 (http://podcast.thinkingelixir.com/255) Elixir Community News https://www.honeybadger.io/ (https://www.honeybadger.io/?utm_source=thinkingelixir&utm_medium=podcast) – Honeybadger.io is sponsoring today's show! Keep your apps healthy and your customers happy with Honeybadger! It's free to get started, and setup takes less than five minutes. https://www.erlang.org/news/180 (https://www.erlang.org/news/180?utm_source=thinkingelixir&utm_medium=shownotes) – OTP 28 release announcement with new priority messages functionality and SBOM support https://www.erlang.org/eeps/eep-0076 (https://www.erlang.org/eeps/eep-0076?utm_source=thinkingelixir&utm_medium=shownotes) – EEP 76 specification for priority messages in OTP 28 https://www.youtube.com/playlist?list=PLvL2NEhYV4Zu421KzHuLICUqieJXI2o_Z (https://www.youtube.com/playlist?list=PLvL2NEhYV4Zu421KzHuLICUqieJXI2o_Z?utm_source=thinkingelixir&utm_medium=shownotes) – ElixirConf EU 2025 YouTube playlist with conference videos https://www.youtube.com/watch?v=ojLVHc4gLk&list=PLvL2NEhYV4Zu421KzHuLICUqieJXI2oZ&index=3 (https://www.youtube.com/watch?v=ojL_VHc4gLk&list=PLvL2NEhYV4Zu421KzHuLICUqieJXI2o_Z&index=3?utm_source=thinkingelixir&utm_medium=shownotes) – Chris McCord's keynote "Code Generators are Dead. Long Live Code Generators" https://x.com/chris_mccord/status/1923417060593356889 (https://x.com/chris_mccord/status/1923417060593356889?utm_source=thinkingelixir&utm_medium=shownotes) – Chris McCord's announcement about phoenix.new paid service https://phoenix.new/ (https://phoenix.new/?utm_source=thinkingelixir&utm_medium=shownotes) – Chris McCord's new phoenix.new paid service at Fly.io https://www.youtube.com/watch?v=4IWShnVuRCg&list=PLvL2NEhYV4Zu421KzHuLICUqieJXI2o_Z&index=2 (https://www.youtube.com/watch?v=4IWShnVuRCg&list=PLvL2NEhYV4Zu421KzHuLICUqieJXI2o_Z&index=2?utm_source=thinkingelixir&utm_medium=shownotes) – James Arthur's keynote "Introducing Phoenix Sync" from ElixirConf EU https://github.com/electric-sql/phoenix_sync/ (https://github.com/electric-sql/phoenix_sync/?utm_source=thinkingelixir&utm_medium=shownotes) – Phoenix Sync GitHub repository for real-time sync to Postgres-backed Phoenix apps https://hexdocs.pm/phoenix_sync/readme.html (https://hexdocs.pm/phoenix_sync/readme.html?utm_source=thinkingelixir&utm_medium=shownotes) – Phoenix Sync documentation on HexDocs https://github.com/josevalim/sync (https://github.com/josevalim/sync?utm_source=thinkingelixir&utm_medium=shownotes) – José Valim's sync project that inspired Phoenix Sync https://erlef.org/blog/eef/election-2025-results (https://erlef.org/blog/eef/election-2025-results?utm_source=thinkingelixir&utm_medium=shownotes) – EEF board election results for Cohort C https://x.com/TheErlef/status/1924531926008004633 (https://x.com/TheErlef/status/1924531926008004633?utm_source=thinkingelixir&utm_medium=shownotes) – EEF Twitter announcement of election results https://erlef.org/blog/eef/election-2025-candidates (https://erlef.org/blog/eef/election-2025-candidates?utm_source=thinkingelixir&utm_medium=shownotes) – Information about the EEF election candidates https://erlef.org/blog/security/eef-cna-announcement (https://erlef.org/blog/security/eef-cna-announcement?utm_source=thinkingelixir&utm_medium=shownotes) – EEF becomes CVE Numbering Authority for Hex and BEAM ecosystem https://github.com/erlef-cna (https://github.com/erlef-cna?utm_source=thinkingelixir&utm_medium=shownotes) – EEF CNA GitHub organization https://cna.erlef.org/ (https://cna.erlef.org/?utm_source=thinkingelixir&utm_medium=shownotes) – EEF CNA website https://github.com/surface-ui/surface (https://github.com/surface-ui/surface?utm_source=thinkingelixir&utm_medium=shownotes) – Surface UI project for server-side rendering components https://github.com/phoenixframework/phoenixliveview/pull/3810 (https://github.com/phoenixframework/phoenix_live_view/pull/3810?utm_source=thinkingelixir&utm_medium=shownotes) – Draft PR for co-located hooks and macro components in LiveView https://github.com/tv-labs/lua (https://github.com/tv-labs/lua?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir Lua package v0.2.x release by TvLabs https://x.com/davydog187/status/1925186045156463034 (https://x.com/davydog187/status/1925186045156463034?utm_source=thinkingelixir&utm_medium=shownotes) – Dave's tweet about ElixirConf EU Luerl talk https://www.youtube.com/watch?v=4YBBoXXH_98 (https://www.youtube.com/watch?v=4YBBoXXH_98?utm_source=thinkingelixir&utm_medium=shownotes) – "Lua on the BEAM" talk by Dave Lucia & Robert Virding https://discord.gg/6Ukp9vpj (https://discord.gg/6Ukp9vpj?utm_source=thinkingelixir&utm_medium=shownotes) – Discord link for Lua community https://x.com/germsvel/status/1922602086065148093 (https://x.com/germsvel/status/1922602086065148093?utm_source=thinkingelixir&utm_medium=shownotes) – German Velasco's video highlighting LiveDebugger tool https://bsky.app/profile/germsvel.com/post/3lp4snnkpj225 (https://bsky.app/profile/germsvel.com/post/3lp4snnkpj225?utm_source=thinkingelixir&utm_medium=shownotes) – German Velasco's BlueSky post about LiveDebugger https://podcast.thinkingelixir.com/249 (https://podcast.thinkingelixir.com/249?utm_source=thinkingelixir&utm_medium=shownotes) – Thinking Elixir episode 249 featuring LiveDebugger discussion https://hexdocs.pm/mdex/MDEx.Sigil.html (https://hexdocs.pm/mdex/MDEx.Sigil.html?utm_source=thinkingelixir&utm_medium=shownotes) – MDEx v0.7 documentation for new ~MD sigil https://hexdocs.pm/autumn (https://hexdocs.pm/autumn?utm_source=thinkingelixir&utm_medium=shownotes) – Autumn syntax highlighter package that works with MDEx https://github.com/leandrocp/mdex_mermaid (https://github.com/leandrocp/mdex_mermaid?utm_source=thinkingelixir&utm_medium=shownotes) – MDEx Mermaid plugin for adding mermaid support to Markdown https://bsky.app/profile/zachdaniel.dev/post/3lpofyykwds2i (https://bsky.app/profile/zachdaniel.dev/post/3lpofyykwds2i?utm_source=thinkingelixir&utm_medium=shownotes) – Zach Daniel's BlueSky post about usage_rules.md convention https://hexdocs.pm/usage_rules (https://hexdocs.pm/usage_rules?utm_source=thinkingelixir&utm_medium=shownotes) – Usage rules package documentation https://github.com/ash-project/usage_rules/ (https://github.com/ash-project/usage_rules/?utm_source=thinkingelixir&utm_medium=shownotes) – Usage rules GitHub repository https://blogs.windows.com/windowsdeveloper/2025/05/19/the-windows-subsystem-for-linux-is-now-open-source/ (https://blogs.windows.com/windowsdeveloper/2025/05/19/the-windows-subsystem-for-linux-is-now-open-source/?utm_source=thinkingelixir&utm_medium=shownotes) – Microsoft announcement about Windows Subsystem for Linux going open source https://www.zdnet.com/article/believe-it-or-not-microsoft-just-announced-a-linux-distribution-service-heres-why/ (https://www.zdnet.com/article/believe-it-or-not-microsoft-just-announced-a-linux-distribution-service-heres-why/?utm_source=thinkingelixir&utm_medium=shownotes) – ZDNet article explaining Microsoft's Linux strategy and Azure statistics 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) - Dave Lucia - @davydog187 (https://x.com/davydog187)

Nikolay and Michael discuss moving off managed services — when and why you might want to, and some tips on how for very large databases. Here are some links to things they mentioned:Patroni https://github.com/patroni/patronipgBackRest https://github.com/pgbackrest/pgbackrestWAL-G https://github.com/wal-g/wal-gHetzner Cloud https://www.hetzner.com/cloudPostgres Extensions Day https://pgext.daypg_wait_sampling https://github.com/postgrespro/pg_wait_samplingpg_stat_kcache https://github.com/powa-team/pg_stat_kcacheauto_explain https://www.postgresql.org/docs/current/auto-explain.htmlFivetran https://www.fivetran.compgcopydb https://github.com/dimitri/pgcopydbKafka https://kafka.apache.orgDebezium https://debezium.iomax_slot_wal_keep_size https://www.postgresql.org/docs/current/runtime-config-replication.html#GUC-MAX-SLOT-WAL-KEEP-SIZElog_statement DDL https://www.postgresql.org/docs/current/runtime-config-logging.html#GUC-LOG-STATEMENTPgBouncer pause/resume https://www.pgbouncer.org/usage.html#pause-db~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

In this episode of Remote Ruby, Chris and Andrew catch up on recent travels and food experiences, including the best Philly cheesesteaks they've ever had. The conversation shifts towards development topics, particularly testing challenges and solutions in Ruby on Rails, featuring discussions about emoji pickers, asset pipelines, and the prawn library. Chris shares updates on acquiring an old Rails app, One Month, and future plans for this project. They also explore various development hiccups and solutions, including using libraries for faster system tests and streamlining asset pipelines. The episode wraps up with insights into new tools like an official Postgres extension for VS Code and plans for future video content on their platform.LinksJudoscale- Remote Ruby listener giftOne MonthRunning Rails System Tests With Playwright Instead of Selenium by Justin SearlsAnnouncing a new IDE for PostgreSQL in VS Code from MicrosoftLou Malnati's Pizzeria Chris Oliver X/Twitter Andrew Mason X/Twitter Jason Charnes X/Twitter

Nikolay and Michael discuss heavyweight locks in Postgres — how to think about them, why you can't avoid them, and some tips for minimising issues. Here are some links to things they mentioned:Locking (docs) https://www.postgresql.org/docs/current/explicit-locking.htmlPostgres rocks, except when it blocks (blog post by Marco Slot) https://www.citusdata.com/blog/2018/02/15/when-postgresql-blocks/Lock Conflicts (tool by Hussein Nasser) https://pglocks.org/log_lock_waits (docs) https://www.postgresql.org/docs/current/runtime-config-logging.html#GUC-LOG-LOCK-WAITSHow to analyze heavyweight lock trees (guide by Nikolay) https://gitlab.com/postgres-ai/postgresql-consulting/postgres-howtos/-/blob/main/0042_how_to_analyze_heavyweight_locks_part_2.mdLock management (docs) https://www.postgresql.org/docs/current/runtime-config-locks.htmlOur episode on zero-downtime migrations https://postgres.fm/episodes/zero-downtime-migrations~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Highlights from this week's conversation include:Pranav's Background and Journey in Data (1:10)Backstory of Mooncake Labs (2:05)PostgreSQL as a Force (4:47)Curiosity in Product Management (7:33)Challenges with Iceberg (11:12)Go-to-Market Strategy (13:52)Building Community Engagement (15:56)Importance of Feedback (18:26)AI Integration in Mooncake Labs (21:29)Innovation in data interaction (23:49)PostgreSQL and startup growth (28:41)Core component of business strategy (31:20)The Origin of the name Mooncake Labs (34:12)Upcoming Product Release (38:40)Connecting with Mooncake Labs and Parting Thoughts (42:49)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Highlights from this week's conversation include:Background of ClickHouse (1:14)PostgreSQL Data Replication Tool (3:19)Emerging Technologies Observations (7:25)Observability and Market Dynamics (11:26)Product Development Challenges (12:39)Challenges with PostgreSQL Performance (15:30)Philosophy of Open Source (18:01)Open Source Advantages (22:56)Simplified Stack Vision (24:48)End-to-End Use Cases (28:13)Migration Strategies (30:21)Final Thoughts and Takeaways (33:29)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Databricks just snatched up another AI company. This week, data analytics giant announced a $1 billion acquisition of Neon, a startup building an open-source alternative to AWS Aurora Postgres. It's the latest in a spree of high-profile buys, joining MosaicML and Tabular, as Databricks positions itself as the place to build, deploy, and scale AI-native applications.  Today, on TechCrunch's Equity podcast, hosts Kirsten Korosec, Max Zeff, and Anthony Ha unpack the Databricks–Neon deal, where Neon's serverless Postgres tech fits into the larger vision, and whether $1 billion still counts as “a lot of money” these days (spoiler: Kirsten and Anthony are on the fence). Listen to the full episode to hear about: Chime's long-awaited IPO plans and what the neobank's S-1 did (and didn't) reveal. AWS entering a ‘strategic partnership' that could shake up cloud infrastructure, especially as the Middle East ramps up its AI ambitions The return of the web series. Yes, really. Short-form scripted content is back, and investors are placing big bets on nostalgic trend Equity will be back next week, so don't miss it! Equity is TechCrunch's flagship podcast, produced by Theresa Loconsolo, and posts every Wednesday and Friday.  Subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. For the full episode transcript, for those who prefer reading over listening, check out our full archive of episodes here. Credits: Equity is produced by Theresa Loconsolo with editing by Kell. We'd also like to thank TechCrunch's audience development team. Thank you so much for listening, and we'll talk to you next time. Learn more about your ad choices. Visit megaphone.fm/adchoices

Today's show: Chime is finally going public with strong financials and a shot at matching its $25B 2021 valuation, signaling real momentum in the IPO market. Databricks just made a $1B bet on agentic AI by acquiring Neon, a Postgres-as-a-service startup riding the new database wave. Then, Dave Rubin joins to share how he built and sold Locals, his uncancellable creator platform, all while navigating the intense media landscape.Timestamps:(0:00) Episode Teaser(1:14) Jason and Alex open the show(1:42) Why Chime's IPO is such a promising sign(7:22) Chime's financials and valuation(10:12) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(12:17) So why did Databricks buy Neon?(13:22) Where is the AI Integration Desktop App?(17:29) Jason's plan to bring Americans back to the movies(20:10) Northwest Registered Agent. Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit northwestregisteredagent.com/twist today!(21:58) Make movies All-you-can-eat!(26:26) Special Guest: Dave Rubin(30:39) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist(31:41) Why Dave Rubin goes phone free for weeks at a time(45:24) Why Identity politics is killing business and sports(49:23) Can Locals reinvent subscription models?Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.comCheck out the TWIST500: https://www.twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcpLinks from episode:Rubin Report on Locals: https://rubinreport.locals.com/Follow Dave:X: https://x.com/RubinReportYouTube: https://www.youtube.com/channel/UCJdKr0Bgd_5saZYqLCa9mngFollow Lon:X: https://x.com/lonsFollow Alex:X: https://x.com/alexLinkedIn: ⁠https://www.linkedin.com/in/alexwilhelmFollow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanisThank you to our partners:(10:12) Squarespace - Use offer code TWIST to save 10% off your first purchase of a website or domain at https://www.Squarespace.com/TWIST(20:10) Northwest Registered Agent. Form your entire business identity in just 10 clicks and 10 minutes. Get more privacy, more options, and more done—visit northwestregisteredagent.com/twist today!(30:39) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twistGreat TWIST interviews: Will Guidara, Eoghan McCabe, Steve Huffman, Brian Chesky, Bob Moesta, Aaron Levie, Sophia Amoruso, Reid Hoffman, Frank Slootman, Billy McFarlandCheck out Jason's suite of newsletters: https://substack.com/@calacanisFollow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.comSubscribe to the Founder University Podcast: https://www.youtube.com/@founderuniversity1916

Nikolay and Michael discuss ten dangerous Postgres related issues — ones that might be painful enough to get onto the CTO and even CEOs desk, and then what you can do proactively. The ten issues discussed are:Heavy lock contentionBloat control and index maintenance  Lightweight lock contentionTransaction ID wraparound4-byte integer PKs hitting the limitReplication limitsHard limitsData lossPoor HA choice (split brain)Corruption of various kindsSome previous episodes they mentioned that cover the issues in more detail: PgDog https://postgres.fm/episodes/pgdogPerformance cliffs https://postgres.fm/episodes/performance-cliffsZero-downtime migrations https://postgres.fm/episodes/zero-downtime-migrations Queues in Postgres https://postgres.fm/episodes/queues-in-postgresBloat https://postgres.fm/episodes/bloatIndex maintenance https://postgres.fm/episodes/index-maintenanceSubtransactions https://postgres.fm/episodes/subtransactionsFour million TPS https://postgres.fm/episodes/four-million-tpsTransaction ID wraparound https://postgres.fm/episodes/transaction-id-wraparoundpg_squeeze https://postgres.fm/episodes/pg_squeeze synchronous_commit https://postgres.fm/episodes/synchronous_commitManaged service support https://postgres.fm/episodes/managed-service-support And finally, some other things they mentioned: A great recent SQL Server-related podcast episode on tuning techniques https://kendralittle.com/2024/05/20/erik-darling-and-kendra-little-rate-sql-server-performance-tuning-techniques/Postgres Indexes, Partitioning and LWLock:LockManager Scalability (blog post by Jeremy Schneider) https://ardentperf.com/2024/03/03/postgres-indexes-partitioning-and-lwlocklockmanager-scalability/Do you vacuum everyday? (talk by Hannu Krosing) https://www.youtube.com/watch?v=JcRi8Z7rkPgpg_stat_wal https://pgpedia.info/p/pg_stat_wal.htmlThe benefit of lz4 and zstd for Postgres WAL compression (Small Datum blog, Mark Callaghan) https://smalldatum.blogspot.com/2022/05/the-benefit-of-lz4-and-zstd-for.htmlSplit-brain in case of network partition (CloudNativePG issue/discussion) https://github.com/cloudnative-pg/cloudnative-pg/discussions/7462 ~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

How does a trek to K2 base camp in the Himalayas spark the idea for a database company? In Episode 27 of Talking Postgres with Claire Giordano, guest Peter Farkas—CEO and co-founder of FerretDB—shares the origin story of this open source MongoDB alternative. (Spoiler: “Ferret” wasn't the original name). We dig into why Postgres was the obvious choice, what “true open source” means to Peter, and how FerretDB is now powered by the open source DocumentDB extension from Microsoft. Plus, why Hungarian Trappist cheese might deserve a footnote in database history. Links mentioned in this episode:GitHub: FerretDB/FerretDB repoBlog: FerretDB 2.0 GA: Open Source MongoDB alternative, ready for productionACM SIGMOD: The Design of Postgres, published 15 June 1986Postgres Weekly: Issue 591 featuring FerretDBGitHub: Microsoft/DocumentDB open source repoConference talk: From MongoDB to Postgres: Building an Open Standard for Document Databases at POSETTE 2025OSI Blog: The SSL is Not an Open Source LicenseRedMonk Blog: OSS: Two Steps Forward, One Step Back, by Stephen O'GradyTalking Postgres Ep18: How I got started as a developer (& in Postgres) with David RowleyOpenDocDB: initiative to define an open standardWikipedia: K2 (yes, the mountain)Go Blog: The Go Gopherxkcd: webcomic 927 on StandardsWikipedia: Trappista cheeseCal invite: LIVE recording of Ep28 of Talking Postgres to happen on Wed Jun 18, 2025 

Heads-up: I use some salty language. Nothing hateful, just passionate about this topic. Skip if that's not your vibe.It's 2 AM, your side-project just went viral, and the signup flow is on fire. Do you keep “vibe-coding” blind prompts, or step up as the conductor who actually knows the score? In this first-ever solo episode, I unpack why “anyone can code with AI” is 2025's biggest myth and show you how to turn large language models into the ultimate co-pilot instead of a ticking time-bomb.Key TakeawaysIllusions break at scale. Vibe-coding can get you an MVP, but you'll pay interest when production fires start.Your new super-power isn't “no knowledge,” it's “faster knowledge.” LLMs shrink the gap between “I don't know” and “I can ship.”Learning beats prompting. Prompting is great, prompt-and-probe is better. Use back-and-forth to understand, not just generate.Career moat = curiosity. The people who thrive next year aren't the ones with the fanciest prompts; they're the ones who ask better questions and close their gaps daily.7-Day Knowledge-Gap ChallengePick one concept you avoid (CSS Flexbox? Indexing in Postgres?).Spend 15 min/day grilling an LLM: “Explain it like I'm 7… now show real-world code… now debug this snippet…”Log what surprised you, then share your aha momentsCall to ActionTry the challenge. Tag me with your progress by next week.Rate & Review. If this episode saved you from a 2 AM meltdown, drop a ⭐⭐⭐⭐⭐ on your favorite app.Share. Forward the LinkedIn post or the episode link to one builder who still thinks vibe-coding is a strategy.---If you enjoyed the episode, please share it with a friend!No Hacks websiteYouTubeLinkedInInstagram

Highlights from this week's conversation include:Recording from a Waymo (0:54)Future of Data Technology (2:45)AI Integration in Data Work (4:20)Speeding Up Data Experiences (5:29)Snapshot Conversations with Founders (9:52)Diversity of Perspectives on Postgres (12:37)Cultural Significance of Database Mascots (14:09)Incubation and Success of Open Source Projects (16:43)Final Thoughts and Takeaways (17:34)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

In this episode of The Data Engineering Show, the bros sit with Yingjun Wu, founder and CEO of Rising Wave, to explore the innovative world of stream processing systems. Yingjun shares his journey from academic research to creating a Postgres-compatible streaming system that drastically reduces resource usage. They discuss how Rising Wave's S3-based architecture and Postgres compatibility provide advantages over traditional systems like Flink, and explore the increasing role of Apache Iceberg in data pipelines.

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

Prequel is launching a new developer-focused service aimed at democratizing software error detection—an area typically dominated by large cloud providers. Co-founded by Lyndon Brown and Tony Meehan, both former NSA engineers, Prequel introduces a community-driven observability approach centered on Common Reliability Enumerations (CREs). CREs categorize recurring production issues, helping engineers detect, understand, and communicate problems without reinventing solutions or working in isolation. Their open-source tools, cre and prereq, allow teams to build and share detectors that catch bugs and anti-patterns in real time—without exposing sensitive data, thanks to edge processing using WebAssembly.The urgency behind Prequel's mission stems from the rapid pace of AI-driven development, increased third-party code usage, and rising infrastructure costs. Traditional observability tools may surface symptoms, but Prequel aims to provide precise problem definitions and actionable insights. While observability giants like Datadog and Splunk dominate the market, Brown and Meehan argue that engineers still feel overwhelmed by data and underpowered in diagnostics—something they believe CREs can finally change.Learn more from The New Stack about the latest Observability insights Why Consolidating Observability Tools Is a Smart MoveBuilding an Observability Culture: Getting Everyone Onboard Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Nikolay and Michael discuss synchronous_commit — what it means on single node setups, for synchronous replication setups, and the pros and cons of the different options for each. Here are some links to things they mentioned:synchronous_commit https://www.postgresql.org/docs/current/runtime-config-wal.html#GUC-SYNCHRONOUS-COMMITsynchronous_commit history on pgPedia https://pgpedia.info/s/synchronous_commit.htmlPatroni's maximum_lag_on_failover setting https://patroni.readthedocs.io/en/master/replication_modes.html#asynchronous-mode-durabilitywal_writer_delay https://www.postgresql.org/docs/current/runtime-config-wal.html#GUC-WAL-WRITER-DELAYSelective asynchronous commits in PostgreSQL - balancing durability and performance (blog post by Shayon Mukherjee) https://www.shayon.dev/post/2025/75/selective-asynchronous-commits-in-postgresql-balancing-durability-and-performance/Asynchronous Commit https://www.postgresql.org/docs/current/wal-async-commit.htmlsynchronous_standby_names https://www.postgresql.org/docs/current/runtime-config-replication.html#GUC-SYNCHRONOUS-STANDBY-NAMESJepson article about Amazon RDS multi-AZ clusters (by Kyle Kingsbury, aka "Aphyr”) https://jepsen.io/analyses/amazon-rds-for-postgresql-17.4~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Nikolay and Michael discuss managed service support — some tips on how to handle cases that aren't going well, tips for requesting features, whether to factor in support when choosing service provider, and whether to use one at all. Here are some links to things they mentioned:YugabyteDB's new upgrade framework https://www.yugabyte.com/blog/postgresql-upgrade-frameworkEpisode on Blue-green deployments https://postgres.fm/episodes/blue-green-deploymentspg_createsubscriber https://www.postgresql.org/docs/current/app-pgcreatesubscriber.html~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

Today we have Søren from Prisma on the show. Prisma has been the most popular ORM in the TypeScript world for a while, and now they're moving more into hosted infrastructure. We spend a lot of time talking about their new offering called Prisma Postgres, which is this unikernel-based Postgres offering. It's a really unique offering from both a technical and a product perspective. On the technical side, they're doing some interesting work compared to other Postgres providers. They're running on bare metal in a colocation facility rather than the default public clouds like AWS, GCP, and Azure. Further, they're using unikernels in a Firecracker VM, giving them unique startup and security characteristics. These technical decisions give them unique economics compared to standard providers, so they're able to have a generous free tier and a unique billing model that works great for serverless applications with spiky workloads. Around all of this, it's very interesting to see a company with such a unique spread of products — a popular, mature open-source library paired with a mission-critical infrastructure service offering. We talked about the difficulties in building a company that accommodates these two very different products. Timestamps 01:51 Start 06:08 Prisma Postgres 09:10 Accelerate 11:39 Why Postgres 17:32 How Prisma Postgres Works 21:32 Colocation Facility 22:05 Unikernels 27:56 CoLo vs Public Cloud 29:11 Building the team 31:46 Missing Features that are being worked on 32:31 Use Cases 33:37 Colo Locations 34:53 Cloudflare 35:42 Biggest surprises since release 37:34 More Unikernel adoption? 39:08 Supporting Prisma ORM 46:43 Mongo 47:51 Life as A CEO 53:04 MCP 57:23 Søren Questions Alex Software Huddle ⤵︎ X: https://twitter.com/SoftwareHuddle Substack: https://softwarehuddle.substack.com

This new PostgreSQL 17 feature is game changer. They know can combine IOs when performing sequential scan. Grab my database coursehttps://courses.husseinnasser.com

Scott and Wes break down the Model Context Protocol (MCP), a new open standard that gives AI agents secure, tool-like access to your dev environment. They cover how it works, why it's a big deal for AI coding workflows, and real-world use cases like GitHub, Sentry, and YouTube. Show Notes 00:00 Welcome to Syntax! 00:49 The lore of ICP. Wes MCP Shirt. 03:09 Brought to you by Sentry.io. 03:33 What is MCP? 05:06 The steps of AI coding. 07:11 MCP hosts. 07:28 MCP clients. 07:35 MCP servers. 08:24 Why you might want to do this. 10:39 How this works in VS Code. 14:10 Wes built an MCP server. SVGL. 14:57 Playwright. 17:24 Sentry's implementation. Building Sentry's MCP with David Cramer. 18:54 YouTube implementation. 21:19 DaVinci Resolve implementation. Smithery. 23:02 Postgres. 24:40 Transport protocols. 24:49 STDIO. 25:19 SSE. 25:32 Streaming. 26:24 Writing you own MCP server. 26:28 FastMCP. 27:00 Cloudflare. 28:01 Data validation. 28:47 Standard schema. Episode 873. 29:27 Other parts of MCP. 29:35 MCP resources. 30:37 MCP prompts. 30:48 MCP roots. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Tool Use and Model Context Protocol (MCP) Notes and resources at  ocdevel.com/mlg/mla-24 Try a walking desk to stay healthy while you study or work! Tool Use in Vibe Coding Agents File Operations: Agents can read, edit, and search files using sophisticated regular expressions. Executable Commands: They can recommend and perform installations like pip or npm installs, with user approval. Browser Integration: Allows agents to perform actions and verify outcomes through browser interactions. Model Context Protocol (MCP) Standardization: MCP was created by Anthropic to standardize how AI tools and agents communicate with each other and with external tools. Implementation: MCP Client: Converts AI agent requests into structured commands. MCP Server: Executes commands and sends structured responses back to the client. Local and Cloud Frameworks: Local (S-T-D-I-O MCP): Examples include utilizing Playwright for local browser automation and connecting to local databases like Postgres. Cloud (SSE MCP): SaaS providers offer cloud-hosted MCPs to enhance external integrations. Expanding AI Capabilities with MCP Servers Directories: Various directories exist listing MCP servers for diverse functions beyond programming. modelcontextprotocol/servers Use Cases: Automation Beyond Coding: Implementing MCPs that extend automation into non-programming tasks like sales, marketing, or personal project management. Creative Solutions: Encourages innovation in automating routine tasks by integrating diverse MCP functionalities. AI Tools in Machine Learning Automating ML Process: Auto ML and Feature Engineering: AI tools assist in transforming raw data, optimizing hyperparameters, and inventing new ML solutions. Pipeline Construction and Deployment: Facilitates the use of infrastructure as code for deploying ML models efficiently. Active Experimentation: Jupyter Integration Challenges: While integrations are possible, they often lag and may not support the latest models. Practical Strategies: Suggests alternating between Jupyter and traditional Python files to maximize tool efficiency. Conclusion Action Plan for ML Engineers: Setup structured folders and documentation to leverage AI tools effectively. Encourage systematic exploration of MCPs to enhance both direct programming tasks and associated workflows.

Nikolay and Michael discuss time-series considerations for Postgres — including when it matters, some tips for avoiding issues, performance considerations, and more. Here are some links to things they mentioned:Time series data https://en.wikipedia.org/wiki/Time_seriesTimescaleDB https://github.com/timescale/timescaledb13 Tips to Improve PostgreSQL Insert Performance https://www.timescale.com/blog/13-tips-to-improve-postgresql-insert-performanceWhy we're leaving the cloud (37 Signals / Basecamp / David Heinemeier Hansson) https://world.hey.com/dhh/why-we-re-leaving-the-cloud-654b47e0UUID v7 and partitioning (“how to” by Nikolay) https://gitlab.com/postgres-ai/postgresql-consulting/postgres-howtos/-/blob/main/0065_uuid_v7_and_partitioning_timescaledb.mdpg_cron https://github.com/citusdata/pg_cronpg_partman https://github.com/pgpartman/pg_partmanOur episode on BRIN indexes https://postgres.fm/episodes/brin-indexesTutorial from Citus (Andres Freund and Marco Slot) including rollups https://www.youtube.com/watch?v=0ybz6zuXCPoIoT with PostgreSQL (talk by Chris Ellis) https://youtube.com/watch?v=KnUoDBGv4aw&t=58pg_timeseries https://github.com/tembo-io/pg_timeseriesDuckDB https://duckdb.org~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith credit to:Jessie Draws for the elephant artwork

It's YOUR time to #EdUpIn this episode, recorded LIVE from Ellucian LIVE 2025 in Orlando, Florida,YOUR guests are Mike Stone, Systems Manager, Lois Kellermann, Programmer/Analyst, & George Kriss, CIO, Kaskaskia CollegeYOUR host is ⁠⁠⁠⁠⁠⁠⁠⁠⁠Dr. Joe Sallustio⁠⁠⁠⁠⁠How did Kaskaskia College complete their SAS modernization in just 12 months?What challenges did they face during their on-premise to SAS transition?How did they manage change across the institution during implementation?What benefits have they seen from their Ellucian Colleague modernization?Why was their financial aid implementation surprisingly smooth?Topics include:Winning the Ellucian Impact Award for SAS modernizationMoving from SQL to Postgres database seamlesslyCreating an inclusive college-wide project rather than just an IT initiativeUsing effective communication strategies for change managementFreeing up IT resources to focus on strategic student-facing initiativesListen in to #EdUpDo YOU want to accelerate YOUR professional development?Do YOU want to get exclusive early access to ad-free episodes, extended episodes, bonus episodes, original content, invites to special events, & more?Then ⁠⁠⁠⁠⁠⁠BECOME AN #EdUp PREMIUM SUBSCRIBER TODAY⁠⁠ - $19.99/month or $199.99/year (Save 17%)!Want YOUR org to cover costs? Email: EdUp@edupexperience.comThank YOU so much for tuning in. Join us on the next episode for YOUR time to EdUp!Connect with YOUR EdUp Team - ⁠⁠⁠⁠⁠⁠⁠⁠⁠Elvin Freytes⁠⁠⁠⁠⁠⁠⁠⁠⁠ & ⁠⁠⁠⁠⁠⁠⁠⁠⁠Dr. Joe Sallustio⁠⁠⁠⁠● Join YOUR EdUp community at ⁠⁠⁠⁠⁠⁠⁠⁠⁠The EdUp Experience⁠⁠⁠⁠⁠⁠⁠⁠⁠!We make education YOUR business!

News includes a new Elixir case study about Cyanview's camera shading technology used at major events like the Olympics and Super Bowl, Oban Pro 1.6 with 20x faster queue partitioning, the openid_connect package reaching version 1.0, Supabase's new Postgres Language Server for developer tooling, and ElixirEvents.net as a community resource. Plus, we interview Michael Lubas, founder of Paraxial.io, about web application security in Elixir, what's involved in a security audit, and how his Elixir-focused security company is helping teams and businesses in the community. Show Notes online - http://podcast.thinkingelixir.com/248 (http://podcast.thinkingelixir.com/248) Elixir Community News https://elixir-lang.org/blog/2025/03/25/cyanview-elixir-case/ (https://elixir-lang.org/blog/2025/03/25/cyanview-elixir-case/?utm_source=thinkingelixir&utm_medium=shownotes) – New Elixir case study about Cyanview, a Belgian company whose Remote Control Panel for camera shading is used at major events like the Olympics and Super Bowl. Their Elixir-powered solution enables remote camera control across challenging network conditions. https://oban.pro/docs/pro/1.6.0-rc.1/changelog.html (https://oban.pro/docs/pro/1.6.0-rc.1/changelog.html?utm_source=thinkingelixir&utm_medium=shownotes) – Oban Pro 1.6 released with subworkflows, improved queue partitioning (20x faster), and a new guide explaining different job composition approaches. https://oban.pro/docs/pro/1.6.0-rc.1/composition.html (https://oban.pro/docs/pro/1.6.0-rc.1/composition.html?utm_source=thinkingelixir&utm_medium=shownotes) – New Oban Pro guide explaining when to use chains, workflows, chunks, or batches for job composition. https://github.com/DockYard/openid_connect (https://github.com/DockYard/openid_connect?utm_source=thinkingelixir&utm_medium=shownotes) – The Elixir package 'openid_connect' reached version 1.0, providing client library support for working with various OpenID Connect providers like Google, Microsoft Azure AD, Auth0, and others. https://hexdocs.pm/openid_connect/readme.html (https://hexdocs.pm/openid_connect/readme.html?utm_source=thinkingelixir&utm_medium=shownotes) – Documentation for the newly released openid_connect 1.0 package. https://bsky.app/profile/davelucia.com/post/3llqwsbyutc2z (https://bsky.app/profile/davelucia.com/post/3llqwsbyutc2z?utm_source=thinkingelixir&utm_medium=shownotes) – Announcement that openid_connect is maintained by tvlabs. https://bsky.app/profile/germsvel.com/post/3llee5lyerk2b (https://bsky.app/profile/germsvel.com/post/3llee5lyerk2b?utm_source=thinkingelixir&utm_medium=shownotes) – PhoenixTest v0.6.0 has been released with significant changes, including a breaking change. https://github.com/germsvel/phoenix_test (https://github.com/germsvel/phoenix_test?utm_source=thinkingelixir&utm_medium=shownotes) – GitHub repository for PhoenixTest. https://hexdocs.pm/phoenixtest/upgradeguides.html#upgrading-to-0-6-0 (https://hexdocs.pm/phoenix_test/upgrade_guides.html#upgrading-to-0-6-0?utm_source=thinkingelixir&utm_medium=shownotes) – Upgrade guide for updating to PhoenixTest v0.6.0 with its breaking change. https://hexdocs.pm/phoenix_test/changelog.html#0-6-0 (https://hexdocs.pm/phoenix_test/changelog.html#0-6-0?utm_source=thinkingelixir&utm_medium=shownotes) – Changelog for PhoenixTest v0.6.0. https://supabase.com/blog/postgres-language-server (https://supabase.com/blog/postgres-language-server?utm_source=thinkingelixir&utm_medium=shownotes) – Supabase has released a new Postgres Language Server for developers, providing IDE intellisense and autocomplete for PostgreSQL. https://marketplace.visualstudio.com/items?itemName=Supabase.postgrestools (https://marketplace.visualstudio.com/items?itemName=Supabase.postgrestools?utm_source=thinkingelixir&utm_medium=shownotes) – VSCode extension for Supabase's new Postgres developer tools. https://github.com/supabase-community/postgres-language-server (https://github.com/supabase-community/postgres-language-server?utm_source=thinkingelixir&utm_medium=shownotes) – GitHub repository for Supabase's Postgres Language Server. https://pgtools.dev/ (https://pgtools.dev/?utm_source=thinkingelixir&utm_medium=shownotes) – Official website for Postgres Tools with documentation and features. https://pgtools.dev/checking_migrations/ (https://pgtools.dev/checking_migrations/?utm_source=thinkingelixir&utm_medium=shownotes) – Feature in Postgres Tools that lints database migrations to check for problematic schema changes. https://github.com/fly-apps/safe-ecto-migrations (https://github.com/fly-apps/safe-ecto-migrations?utm_source=thinkingelixir&utm_medium=shownotes) – Resource for ensuring safe Ecto migrations. https://fly.io/phoenix-files/safe-ecto-migrations/ (https://fly.io/phoenix-files/safe-ecto-migrations/?utm_source=thinkingelixir&utm_medium=shownotes) – Article about safe Ecto migrations posted on Fly.io. https://elixirevents.net/ (https://elixirevents.net/?utm_source=thinkingelixir&utm_medium=shownotes) – Community resource created by Johanna Larsson for tracking, sharing, and learning about Elixir events worldwide. https://bsky.app/profile/elixirevents.net (https://bsky.app/profile/elixirevents.net?utm_source=thinkingelixir&utm_medium=shownotes) – Bluesky account for ElixirEvents.net for following Elixir community events. 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) Discussion Resources https://paraxial.io/ (https://paraxial.io/?utm_source=thinkingelixir&utm_medium=shownotes) https://paraxial.io/blog/index (https://paraxial.io/blog/index?utm_source=thinkingelixir&utm_medium=shownotes) – Blog with posts about security for Elixir, Rails, and the Paraxial service https://www.cnn.com/2025/03/18/tech/google-wiz-acquisition/index.html (https://www.cnn.com/2025/03/18/tech/google-wiz-acquisition/index.html?utm_source=thinkingelixir&utm_medium=shownotes) https://podcast.thinkingelixir.com/93 (https://podcast.thinkingelixir.com/93?utm_source=thinkingelixir&utm_medium=shownotes) – Our last discussion was 3 years ago in episode 93! Titled "Preventing Service Abuse with Michael Lubas" https://www.amazon.com/Innovators-Dilemma-Revolutionary-Change-Business/dp/0062060244 (https://www.amazon.com/Innovators-Dilemma-Revolutionary-Change-Business/dp/0062060244?utm_source=thinkingelixir&utm_medium=shownotes) https://www.merriam-webster.com/dictionary/Kafkaesque - having a nightmarishly complex, bizarre, or illogical quality (https://www.merriam-webster.com/dictionary/Kafkaesque - having a nightmarishly complex, bizarre, or illogical quality?utm_source=thinkingelixir&utm_medium=shownotes) https://paraxial.io/blog/oban-pentest (https://paraxial.io/blog/oban-pentest?utm_source=thinkingelixir&utm_medium=shownotes) – Completed a Security Audit of Oban Pro - this is after ObanPro went free and OpenSource https://paraxial.io/blog/elixir-best (https://paraxial.io/blog/elixir-best?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir and Phoenix Security Checklist: 11 Best Practices https://paraxial.io/blog/rails-command-injection (https://paraxial.io/blog/rails-command-injection?utm_source=thinkingelixir&utm_medium=shownotes) – Ruby on Rails Security: Preventing Command Injection https://paraxial.io/blog/paraxial-three (https://paraxial.io/blog/paraxial-three?utm_source=thinkingelixir&utm_medium=shownotes) – Paraxial.io v3 blog post Guest Information - Michael Lubas, Paraxial.io Founder - michael@paraxial.io - https://x.com/paraxialio (https://x.com/paraxialio?utm_source=thinkingelixir&utm_medium=shownotes) – on Twitter/X - https://x.com/paraxialio (https://x.com/paraxialio?utm_source=thinkingelixir&utm_medium=shownotes) – on Twitter/X - https://github.com/paraxialio/ (https://github.com/paraxialio/?utm_source=thinkingelixir&utm_medium=shownotes) – on Github - https://www.youtube.com/@paraxial5874 (https://www.youtube.com/@paraxial5874?utm_source=thinkingelixir&utm_medium=shownotes) – Paraxial.io channel on YouTube - https://genserver.social/paraxial (https://genserver.social/paraxial?utm_source=thinkingelixir&utm_medium=shownotes) – on Fediverse - https://paraxial.io/ (https://paraxial.io/?utm_source=thinkingelixir&utm_medium=shownotes) – Blog 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)

Nikolay and Michael are joined by Tomas Vondra to discuss single query performance cliffs — what they are, why they happen, some things we can do to make them less likely or less severe, and some potential improvements to Postgres that could help. Here are some links to things they mentioned:Tomas Vondra https://postgres.fm/people/tomas-vondraWhere do performance cliffs come from? (Talk by Tomas) https://www.youtube.com/watch?v=UzdAelm-QSYWhere do performance cliffs come from? (Slides) https://vondra.me/pdf/performance-cliffs-posette-2024.pdfIncrease the number of fast-path lock slots (committed for Postgres 18) https://www.postgresql.org/message-id/flat/E1ss4gX-000IvX-63%40gemulon.postgresql.org San Francisco Bay Area Postgres meet-up with Tomas on 8th April (online) https://www.meetup.com/postgresql-1/events/306484787Our episode on Extended Statistics https://postgres.fm/episodes/extended-statisticsLogging plan of the currently running query (proposed patch by Rafael Thofehrn Castro and Atsushi Torikoshi) https://commitfest.postgresql.org/patch/5330Our episode with Peter Geoghegan on Skip Scan https://postgres.fm/episodes/skip-scanIndex Prefetching patch that Tomas is collaborating with Peter Geoghegan on https://commitfest.postgresql.org/patch/4351A generalized join algorithm, G-Join (paper by Goetz Graefe) https://dl.gi.de/server/api/core/bitstreams/ce8e3fab-0bac-45fc-a6d4-66edaa52d574/content Smooth Scan: Robust Access Path Selection without Cardinality Estimation (paper by R. Borovica, S. Idreos, A. Ailamaki, M. Zukowski, C. Fraser) https://stratos.seas.harvard.edu/sites/g/files/omnuum4611/files/stratos/files/smoothscan.pdfJust-in-Time Compilation (JIT) https://www.postgresql.org/docs/current/jit.htmlNotes from a pgconf.dev unconference session in 2024 about JIT (discusses issues) https://wiki.postgresql.org/wiki/PGConf.dev_2024_Developer_Unconference#JIT_compilationImplementing an alternative JIT provider for PostgreSQL (by Xing Guo) https://higuoxing.com/archives/implementing-jit-provider-for-pgsqlTomas' Office Hours https://vondra.me/posts/office-hours-experiment ~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith special thanks to:Jessie Draws for the elephant artwork 

Apple Patches Everything Apple released updates for all of its operating systems. Most were released on Monday with WatchOS patches released today on Tuesday. Two already exploited vulnerabilities, which were already patched in the latest iOS and macOS versions, are now patched for older operating systems as well. A total of 145 vulnerabilities were patched. https://isc.sans.edu/diary/Apple%20Patches%20Everything%3A%20March%2031st%202025%20Edition/31816 VMWare Workstation and Fusion update check broken VMWare s automatic update check in its Workstation and Fusion products is currently broken due to a redirect added as part of the Broadcom transition https://community.broadcom.com/vmware-cloud-foundation/question/certificate-error-is-occured-during-connecting-update-server NIM Postgres Vulnerability NIM Developers using prepared statements to send SQL queries to Postgres may expose themselves to a SQL injection vulnerability. NIM s Postgres library does not appear to use actual prepared statements; instead, it assembles the code and the user data as a string and passes them on to the database. This may lead to a SQL injection vulnerability https://blog.nns.ee/2025/03/28/nim-postgres-vulnerability/

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

Nikolay and Michael are joined by Lev Kokotov to discuss PgDog — including whether or when sharding is needed, the origin story (via PgCat), what's already supported, and what's coming next.    Here are some links to things they mentioned:Lev Kokotov https://postgres.fm/people/lev-kokotovPgDog https://github.com/pgdogdev/pgdogPgCat https://github.com/postgresml/pgcatAdopting PgCat (Instacart blog post) https://www.instacart.com/company/how-its-made/adopting-pgcat-a-nextgen-postgres-proxyPgDog discussion on Hacker News https://news.ycombinator.com/item?id=43364668Citus https://github.com/citusdata/citusSharding & IDs at Instagram (blog post) https://instagram-engineering.com/sharding-ids-at-instagram-1cf5a71e5a5cSharding pgvector (blog post by Lev) https://pgdog.dev/blog/sharding-pgvector~~~What did you like or not like? What should we discuss next time? Let us know via a YouTube comment, on social media, or by commenting on our Google doc!~~~Postgres FM is produced by:Michael Christofides, founder of pgMustardNikolay Samokhvalov, founder of Postgres.aiWith special thanks to:Jessie Draws for the elephant artwork 

Steve Yegge's latest rant about the future of "coding", Ethan McCue shares some life altering Postgres patterns, Hillel Wayne makes the case for Verification-First Development, Gerd Zellweger experienced lots of pain setting up GitHub Actions & Cascii is a web-based ASCII diagram builder.

News includes a new library called phoenix_sync for real-time sync in Postgres-backed Phoenix applications, Peter Solnica released a Text Parser for extracting structured data from text, a useful tip on finding Hex package versions locally with mix hex.info, Wasmex updated to v0.10 with WebAssembly component support, and Chrome introduces a new browser feature similar to LiveView.JS. We also talked with Alistair Woodman and Jonatan Männchen from the EEF about Jonatan's role as CISO, the Security Working Group, and their work on OpenChain compliance for supply-chain security, Software Bill of Materials (SBoMs), and what these initiatives mean for the Elixir community, and more! Show Notes online - http://podcast.thinkingelixir.com/245 (http://podcast.thinkingelixir.com/245) Elixir Community News https://gigalixir.com/thinking (https://gigalixir.com/thinking?utm_source=thinkingelixir&utm_medium=shownotes) – Gigalixir is sponsoring the show, offering 20% off standard tier prices for a year with promo code "Thinking". https://github.com/electric-sql/phoenix_sync (https://github.com/electric-sql/phoenix_sync?utm_source=thinkingelixir&utm_medium=shownotes) – New library called phoenix_sync providing real-time sync for Postgres-backed Phoenix applications. https://hexdocs.pm/phoenix_sync/readme.html (https://hexdocs.pm/phoenix_sync/readme.html?utm_source=thinkingelixir&utm_medium=shownotes) – Documentation for phoenix_sync, a solution for building modern, real-time apps with local-first/sync in Elixir. https://github.com/josevalim/sync (https://github.com/josevalim/sync?utm_source=thinkingelixir&utm_medium=shownotes) – José Valim's original proof of concept repo that was promptly archived. https://electric-sql.com/ (https://electric-sql.com/?utm_source=thinkingelixir&utm_medium=shownotes) – Electric SQL's platform that syncs subsets of Postgres data into local apps and services, allowing data to be available offline and in-sync. https://solnic.dev/posts/announcing-textparser-for-elixir/ (https://solnic.dev/posts/announcing-textparser-for-elixir/?utm_source=thinkingelixir&utm_medium=shownotes) – Peter Solnica released TextParser, a library for extracting interesting parts of text like hashtags and links. https://hexdocs.pm/text_parser/readme.html (https://hexdocs.pm/text_parser/readme.html?utm_source=thinkingelixir&utm_medium=shownotes) – Documentation for the Text Parser library that helps parse text into structured data. https://www.elixirstreams.com/tips/mix-hex-info (https://www.elixirstreams.com/tips/mix-hex-info?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir stream tip on using mix hex.info to find the latest package version for a Hex package locally, without needing to search on hex.pm or GitHub. https://github.com/phoenixframework/tailwind/blob/main/README.md#updating-from-tailwind-v3-to-v4 (https://github.com/phoenixframework/tailwind/blob/main/README.md#updating-from-tailwind-v3-to-v4?utm_source=thinkingelixir&utm_medium=shownotes) – Guide for upgrading Tailwind to V4 in existing Phoenix applications using Tailwind's automatic upgrade helper. https://gleam.run/news/hello-echo-hello-git/ (https://gleam.run/news/hello-echo-hello-git/?utm_source=thinkingelixir&utm_medium=shownotes) – Gleam 1.9.0 release with searchability on hexdocs, Echo debug printing for improved debugging, and ability to depend on Git-hosted dependencies. https://d-gate.io/blog/everything-i-was-lied-to-about-node-came-true-with-elixir (https://d-gate.io/blog/everything-i-was-lied-to-about-node-came-true-with-elixir?utm_source=thinkingelixir&utm_medium=shownotes) – Blog post discussing how promises made about NodeJS actually came true with Elixir. https://hexdocs.pm/wasmex/Wasmex.Components.html (https://hexdocs.pm/wasmex/Wasmex.Components.html?utm_source=thinkingelixir&utm_medium=shownotes) – Wasmex updated to v0.10 with support for WebAssembly components, enabling applications and components to work together regardless of original programming language. https://ashweekly.substack.com/p/ash-weekly-issue-8 (https://ashweekly.substack.com/p/ash-weekly-issue-8?utm_source=thinkingelixir&utm_medium=shownotes) – AshWeekly Issue 8 covering AshOps with mix task capabilities for CRUD operations and BeaconCMS being included in the Ash HQ installer script. https://developer.chrome.com/blog/command-and-commandfor (https://developer.chrome.com/blog/command-and-commandfor?utm_source=thinkingelixir&utm_medium=shownotes) – Chrome update brings new browser feature with commandfor and command attributes, similar to Phoenix LiveView.JS but native to browsers. https://codebeamstockholm.com/ (https://codebeamstockholm.com/?utm_source=thinkingelixir&utm_medium=shownotes) – Code BEAM Lite announced for Stockholm on June 2, 2025 with keynote speaker Björn Gustavsson, the "B" in BEAM. https://alchemyconf.com/ (https://alchemyconf.com/?utm_source=thinkingelixir&utm_medium=shownotes) – AlchemyConf coming up March 31-April 3 in Braga, Portugal. Use discount code THINKINGELIXIR for 10% off. https://www.gigcityelixir.com/ (https://www.gigcityelixir.com/?utm_source=thinkingelixir&utm_medium=shownotes) – GigCity Elixir and NervesConf on May 8-10, 2025 in Chattanooga, TN, USA. https://www.elixirconf.eu/ (https://www.elixirconf.eu/?utm_source=thinkingelixir&utm_medium=shownotes) – ElixirConf EU on May 15-16, 2025 in Kraków & Virtual. https://goatmire.com/#tickets (https://goatmire.com/#tickets?utm_source=thinkingelixir&utm_medium=shownotes) – Goatmire tickets are on sale now for the conference on September 10-12, 2025 in Varberg, Sweden. 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) Discussion Resources https://elixir-lang.org/blog/2025/02/26/elixir-openchain-certification/ (https://elixir-lang.org/blog/2025/02/26/elixir-openchain-certification/?utm_source=thinkingelixir&utm_medium=shownotes) https://cna.erlef.org/ (https://cna.erlef.org/?utm_source=thinkingelixir&utm_medium=shownotes) – EEF CVE Numbering Authority https://erlangforums.com/t/security-working-group-minutes/3451/22 (https://erlangforums.com/t/security-working-group-minutes/3451/22?utm_source=thinkingelixir&utm_medium=shownotes) https://podcast.thinkingelixir.com/220 (https://podcast.thinkingelixir.com/220?utm_source=thinkingelixir&utm_medium=shownotes) – previous interview with Alistair https://digital-strategy.ec.europa.eu/en/policies/cyber-resilience-act (https://digital-strategy.ec.europa.eu/en/policies/cyber-resilience-act?utm_source=thinkingelixir&utm_medium=shownotes) – CRA - Cyber Resilience Act https://www.cisa.gov/ (https://www.cisa.gov/?utm_source=thinkingelixir&utm_medium=shownotes) – CISA US Government Agency https://www.cisa.gov/sbom (https://www.cisa.gov/sbom?utm_source=thinkingelixir&utm_medium=shownotes) – Software Bill of Materials https://oss-review-toolkit.org/ort/ (https://oss-review-toolkit.org/ort/?utm_source=thinkingelixir&utm_medium=shownotes) – Desire to integrate with tooling outside the Elixir ecosystem like OSS Review Toolkit https://github.com/voltone/rebar3_sbom (https://github.com/voltone/rebar3_sbom?utm_source=thinkingelixir&utm_medium=shownotes) https://cve.mitre.org/ (https://cve.mitre.org/?utm_source=thinkingelixir&utm_medium=shownotes) https://openssf.org/projects/guac/ (https://openssf.org/projects/guac/?utm_source=thinkingelixir&utm_medium=shownotes) https://erlef.github.io/security-wg/securityvulnerabilitydisclosure/ (https://erlef.github.io/security-wg/security_vulnerability_disclosure/?utm_source=thinkingelixir&utm_medium=shownotes) – EEF Security WG Vulnerability Disclosure Guide Guest Information - https://x.com/maennchen_ (https://x.com/maennchen_?utm_source=thinkingelixir&utm_medium=shownotes) – Jonatan on Twitter/X - https://bsky.app/profile/maennchen.dev (https://bsky.app/profile/maennchen.dev?utm_source=thinkingelixir&utm_medium=shownotes) – Jonatan on Bluesky - https://github.com/maennchen/ (https://github.com/maennchen/?utm_source=thinkingelixir&utm_medium=shownotes) – Jonatan on Github - https://maennchen.dev (https://maennchen.dev?utm_source=thinkingelixir&utm_medium=shownotes) – Jonatan's Blog - https://www.linkedin.com/in/alistair-woodman-51934433 (https://www.linkedin.com/in/alistair-woodman-51934433?utm_source=thinkingelixir&utm_medium=shownotes) – Alistair Woodman on LinkedIn - awoodman@erlef.org - https://github.com/ahw59/ (https://github.com/ahw59/?utm_source=thinkingelixir&utm_medium=shownotes) – Alistair on Github - http://erlef.org/ (http://erlef.org/?utm_source=thinkingelixir&utm_medium=shownotes) – Erlang Ecosystem Foundation Website 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)

Wes and Scott talk with Aaron Francis about Fusion for Laravel, a new way to seamlessly integrate PHP into JavaScript. They discuss how Fusion expands on Inertia, its potential for React support, and how it simplifies full-stack development. Show Notes 00:00 Welcome to Syntax! 01:22 Aaron's background in PHP Yii Laravel 02:27 What is Fusion for Laravel? Fusion for Laravel 09:14 How Fusion works 13:57 The benefits of Laravel 19:18 Invalidation and caching 25:20 Brought to you by Sentry.io 25:32 Optimistic UI 28:28 React integration? 31:44 Fusion's original name (and the naming process) 33:30 Laravel's approach to frontend frameworks Livewire 37:32 Databases and scaling 41:27 Postgres extensibility and hosting options Crunchy Data Xata 47:44 The vision for Fusion 48:31 Sick Picks + Shameless Plugs Sick Picks Aaron: Better Display CLI Shameless Plugs Aaron: High Performance SQLite Mastering Postgres Screencasting.com Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads