Brought to You By: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Companies like Graphite, Notion, and Brex rely on Statsig to measure the impact of the pace they ship. Get a 30-day enterprise trial here. •⁠ Linear – The system for modern product development. Linear is a heavy user of Swift: they just redesigned their native iOS app using their own take on Apple’s Liquid Glass design language. The new app is about speed and performance – just like Linear is. Check it out. — Chris Lattner is one of the most influential engineers of the past two decades. He created the LLVM compiler infrastructure and the Swift programming language – and Swift opened iOS development to a broader group of engineers. With Mojo, he’s now aiming to do the same for AI, by lowering the barrier to programming AI applications. I sat down with Chris in San Francisco, to talk language design, lessons on designing Swift and Mojo, and – of course! – compilers. It’s hard to find someone who is as enthusiastic and knowledgeable about compilers as Chris is! We also discussed why experts often resist change even when current tools slow them down, what he learned about AI and hardware from his time across both large and small engineering teams, and why compiler engineering remains one of the best ways to understand how software really works. — Timestamps (00:00) Intro (02:35) Compilers in the early 2000s (04:48) Why Chris built LLVM (08:24) GCC vs. LLVM (09:47) LLVM at Apple  (19:25) How Chris got support to go open source at Apple (20:28) The story of Swift  (24:32) The process for designing a language  (31:00) Learnings from launching Swift  (35:48) Swift Playgrounds: making coding accessible (40:23) What Swift solved and the technical debt it created (47:28) AI learnings from Google and Tesla  (51:23) SiFive: learning about hardware engineering (52:24) Mojo’s origin story (57:15) Modular’s bet on a two-level stack (1:01:49) Compiler shortcomings (1:09:11) Getting started with Mojo  (1:15:44) How big is Modular, as a company? (1:19:00) AI coding tools the Modular team uses  (1:22:59) What kind of software engineers Modular hires  (1:25:22) A programming language for LLMs? No thanks (1:29:06) Why you should study and understand compilers — The Pragmatic Engineer deepdives relevant for this episode: •⁠ AI Engineering in the real world • The AI Engineering stack • Uber's crazy YOLO app rewrite, from the front seat • Python, Go, Rust, TypeScript and AI with Armin Ronacher • Microsoft’s developer tools roots — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Brought to You By: •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. Most teams end up in this situation: ship a feature to 10% of users, wait a week, check three different tools, try to correlate the data, and you’re still unsure if it worked. The problem is that each tool has its own user identification and segmentation logic. Statsig solved this problem by building everything within a unified platform. Check out Statsig. •⁠ Linear – The system for modern product development. In the episode, Armin talks about how he uses an army of “AI interns” at his startup. With Linear, you can easily do the same: Linear’s Cursor integration lets you add Cursor as an agent to your workspace. This agent then works alongside you and your team to make code changes or answer questions. You’ve got to try it out: give Linear a spin and see how it integrates with Cursor. — Armin Ronacher is the creator of the Flask framework for Python, was one of the first engineers hired at Sentry, and now the co-founder of a new startup. He has spent his career thinking deeply about how tools shape the way we build software. In this episode of The Pragmatic Engineer Podcast, he joins me to talk about how programming languages compare, why Rust may not be ideal for early-stage startups, and how AI tools are transforming the way engineers work. Armin shares his view on what continues to make certain languages worth learning, and how agentic coding is driving people to work more, sometimes to their own detriment.  We also discuss:  • Why the Python 2 to 3 migration was more challenging than expected • How Python, Go, Rust, and TypeScript stack up for different kinds of work  • How AI tools are changing the need for unified codebases • What Armin learned about error handling from his time at Sentry • And much more  Jump to interesting parts: • (06:53) How Python, Go, and Rust stack up and when to use each one • (30:08) Why Armin has changed his mind about AI tools • (50:32) How important are language choices from an error-handling perspective? — Timestamps (00:00) Intro (01:34) Why the Python 2 to 3 migration created so many challenges (06:53) How Python, Go, and Rust stack up and when to use each one (08:35) The friction points that make Rust a bad fit for startups (12:28) How Armin thinks about choosing a language for building a startup (22:33) How AI is impacting the need for unified code bases (24:19) The use cases where AI coding tools excel  (30:08) Why Armin has changed his mind about AI tools (38:04) Why different programming languages still matter but may not in an AI-driven future (42:13) Why agentic coding is driving people to work more and why that’s not always good (47:41) Armin’s error-handling takeaways from working at Sentry  (50:32) How important is language choice from an error-handling perspective (56:02) Why the current SDLC still doesn’t prioritize error handling  (1:04:18) The challenges language designers face  (1:05:40) What Armin learned from working in startups and who thrives in that environment (1:11:39) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode:

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In this episode, Conor and Bryce chat with Sean Parent about Rust and AI! Link to Episode 252 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Socials ADSP: The Podcast: TwitterConor Hoekstra: Twitter | BlueSky | MastodonBryce Adelstein Lelbach: TwitterAbout the Guest: Sean Parent is a senior principal scientist and software architect managing Adobe's Software Technology Lab. Sean first joined Adobe in 1993 working on Photoshop and is one of the creators of Photoshop Mobile, Lightroom Mobile, and Lightroom Web. In 2009 Sean spent a year at Google working on Chrome OS before returning to Adobe. From 1988 through 1993 Sean worked at Apple, where he was part of the system software team that developed the technologies allowing Apple’s successful transition to PowerPC. Show Notes Date Recorded: 2025-08-21 Date Released: 2025-09-19 C++ Under the SeaBetter codeAdobe ASL Adam & Eve ArchitectureAdobe Software Technology LabASL LibrariesRust Programming LanguageIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

In this episode, Conor and Bryce chat with Jared Hoberock about the NVIDIA Thrust Parallel Algorithms Library, Rust vs C++, Python and more. Link to Episode 240 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Socials ADSP: The Podcast: TwitterConor Hoekstra: Twitter | BlueSky | MastodonBryce Adelstein Lelbach: TwitterAbout the Guest Jared Hoberock joined NVIDIA Research in October 2008. His interests include parallel programming models and physically-based rendering. Jared is the co-creator of Thrust, a high performance parallel algorithms library. While at NVIDIA, Jared has contributed to the DirectX graphics driver, Gelato, a final frame film renderer, and OptiX, a high-performance, programmable ray tracing engine. Jared received a Ph.D in computer science from the University of Illinois at Urbana-Champaign. He is a two-time recipient of the NVIDIA Graduate Research Fellowship. Show Notes Date Generated: 2025-05-21 Date Released: 2025-06-27 ThrustThrust Docsiota Algorithmthrust::counting_iteratorthrust::sequenceMLIRNumPyNumbaIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

Supported by Our Partners • Graphite — The AI developer productivity platform.  • Sentry — Error and performance monitoring for developers. — Reddit’s native mobile apps are more complex than most of us would assume: both the iOS and Android apps are about 2.5 million lines of code, have 500+ screens, and a total of around 200 native iOS and Android engineers work on them.  But it wasn’t always like this. In 2021, Reddit started to double down on hiring native mobile engineers, and they quietly rebuilt the Android and iOS apps from the ground up. The team introduced a new tech stack called the “Core Stack” – all the while users remained largely unaware of the changes. What drove this overhaul, and how did the team pull it off? In this episode of The Pragmatic Engineer, I’m joined by three engineers from Reddit’s mobile platform team who led this work: Lauren Darcey (Head of Mobile Platform), Brandon Kobilansky (iOS Platform Lead), and Eric Kuck (Principal Android Engineer). We discuss how the team transitioned to a modern architecture, revamped their testing strategy, improved developer experience – while they also greatly improved the app’s user experience.  We also get into:  • How Reddit structures its mobile teams—and why iOS and Android remain intentionally separate  • The scale of Reddit’s mobile codebase and how it affects compile time • The shift from MVP to MVVM architecture • Why Reddit took a bet on Jetpack Compose, but decided (initially) against using SwiftUI • How automated testing evolved at Reddit  • Reddit’s approach to server-driven-mobile-UI • What the mobile platforms team looks for in a new engineering hire • Reddit’s platform team’s culture of experimentation and embracing failure  • And much more! If you are interested in large-scale rewrites or native mobile engineering challenges: this episode is for you. — Timestamps (00:00) Intro (02:04) The scale of the Android code base (02:42) The scale of the iOS code base (03:26) What the compile time is for both Android and iOS (05:33) The size of the mobile platform teams  (09:00) Why Reddit has so many mobile engineers  (11:28) The different types of testing done in the mobile platform  (13:20) The benefits and drawbacks of testing  (17:00) How Eric, Brandon, and Lauren use AI in their workflows (20:50) Why Reddit grew its mobile teams in 2021 (26:50) Reddit’s modern tech stack, Corestack  (28:48) Why Reddit shifted from MVP architecture to MVVM (30:22) The architecture on the iOS side (32:08) The new design system (30:55) The impact of migrating from Rust to GraphQL (38:20) How the backend drove the GraphQL migration and why it was worth the pain (43:17) Why the iOS team is replacing SliceKit with SwiftUI (48:08) Why the Android team took a bet on Compose  (51:25) How teams experiment with server-driven UI—when it worked, and when it did not (54:30) Why server-driven UI isn’t taking off, and why Lauren still thinks it could work (59:25) The ways that Reddit’s modernization has paid off, both in DevX and UX (1:07:15) The overall modernization philosophy; fixing pain points  (1:09:10) What the mobile platforms team looks for in a new engineering hire  (1:16:00) Why startups may be the best place to get experience  (1:17:00) Why platform teams need to feel safe to fail  (1:20:30) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The platform and program split at Uber • Why and how Notion went native on iOS and Android • Paying down tech debt  • Cross-platform mobile development — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Summary In this episode of the Data Engineering Podcast Viktor Kessler, co-founder of Vakmo, talks about the architectural patterns in the lake house enabled by a fast and feature-rich Iceberg catalog. Viktor shares his journey from data warehouses to developing the open-source project, Lakekeeper, an Apache Iceberg REST catalog written in Rust that facilitates building lake houses with essential components like storage, compute, and catalog management. He discusses the importance of metadata in making data actionable, the evolution of data catalogs, and the challenges and innovations in the space, including integration with OpenFGA for fine-grained access control and managing data across formats and compute engines.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Viktor Kessler about architectural patterns in the lakehouse that are unlocked by a fast and feature-rich Iceberg catalogInterview IntroductionHow did you get involved in the area of data management?Can you describe what LakeKeeper is and the story behind it? What is the core of the problem that you are addressing?There has been a lot of activity in the catalog space recently. What are the driving forces that have highlighted the need for a better metadata catalog in the data lake/distributed data ecosystem?How would you characterize the feature sets/problem spaces that different entrants are focused on addressing?Iceberg as a table format has gained a lot of attention and adoption across the data ecosystem. The REST catalog format has opened the door for numerous implementations. What are the opportunities for innovation and improving user experience in that space?What is the role of the catalog in managing security and governance? (AuthZ, auditing, etc.)What are the channels for propagating identity and permissions to compute engines? (how do you avoid head-scratching about permission denied situations)Can you describe how LakeKeeper is implemented?How have the design and goals of the project changed since you first started working on it?For someone who has an existing set of Iceberg tables and catalog, what does the migration process look like?What new workflows or capabilities does LakeKeeper enable for data teams using Iceberg tables across one or more compute frameworks?What are the most interesting, innovative, or unexpected ways that you have seen LakeKeeper used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on LakeKeeper?When is LakeKeeper the wrong choice?What do you have planned for the future of LakeKeeper?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links LakeKeeperSAPMicrosoft AccessMicrosoft ExcelApache IcebergPodcast EpisodeIceberg REST CatalogPyIcebergSparkTrinoDremioHive MetastoreHadoopNATSPolarsDuckDBPodcast EpisodeDataFusionAtlanPodcast EpisodeOpen MetadataPodcast EpisodeApache AtlasOpenFGAHudiPodcast EpisodeDelta LakePodcast EpisodeLance Table FormatPodcast EpisodeUnity CatalogPolaris CatalogApache GravitinoPodcast Episode KeycloakOpen Policy Agent (OPA)Apache RangerApache NiFiThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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It’s time for another episode of the Data Engineering Central Podcast. In this episode, we cover … * Rust-based tool called UV to replace pip and poetry etc * Apache X-Table and the Future of the Lake House * How is AI going to affect you? Thanks for being a consumer of Data Engineering Central; your support means a lot. Please share this podcast with your friend…

Supported by Our Partners • WorkOS — The modern identity platform for B2B SaaS. • Vanta — Automate compliance and simplify security with Vanta. — Linux is the most widespread operating system, globally – but how is it built? Few people are better to answer this than Greg Kroah-Hartman: a Linux kernel maintainer for 25 years, and one of the 3 Linux Kernel Foundation Fellows (the other two are Linus Torvalds and Shuah Khan). Greg manages the Linux kernel’s stable releases, and is a maintainer of multiple kernel subsystems. We cover the inner workings of Linux kernel development, exploring everything from how changes get implemented to why its community-driven approach produces such reliable software. Greg shares insights about the kernel's unique trust model and makes a case for why engineers should contribute to open-source projects. We go into: • How widespread is Linux? • What is the Linux kernel responsible for – and why is it a monolith? • How does a kernel change get merged? A walkthrough • The 9-week development cycle for the Linux kernel • Testing the Linux kernel • Why is Linux so widespread? • The career benefits of open-source contribution • And much more! — Timestamps (00:00) Intro (02:23) How widespread is Linux? (06:00) The difference in complexity in different devices powered by Linux  (09:20) What is the Linux kernel? (14:00) Why trust is so important with the Linux kernel development (16:02) A walk-through of a kernel change (23:20) How Linux kernel development cycles work (29:55) The testing process at Kernel and Kernel CI  (31:55) A case for the open source development process (35:44) Linux kernel branches: Stable vs. development (38:32) Challenges of maintaining older Linux code  (40:30) How Linux handles bug fixes (44:40) The range of work Linux kernel engineers do  (48:33) Greg’s review process and its parallels with Uber’s RFC process (51:48) Linux kernel within companies like IBM (53:52) Why Linux is so widespread  (56:50) How Linux Kernel Institute runs without product managers  (1:02:01) The pros and cons of using Rust in Linux kernel  (1:09:55) How LLMs are utilized in bug fixes and coding in Linux  (1:12:13) The value of contributing to the Linux kernel or any open-source project  (1:16:40) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: What TPMs do and what software engineers can learn from them The past and future of modern backend practices Backstage: an open-source developer portal — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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In this episode, Conor and Ben chat with Tristan Brindle about plans for CppNorth 2025, plans for Flux, the slow death of Twitter and more! Link to Episode 225 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Socials ADSP: The Podcast: TwitterConor Hoekstra: Twitter | BlueSky | MastodonBen Deane: Twitter | BlueSkyAbout the Guest Tristan Brindle a freelance programmer and trainer based in London, mostly focussing on C++. He is a member of the UK national body (BSI) and ISO WG21. Occasionally I can be found at C++ conferences. He is also a director of C++ London Uni, a not-for-profit organisation offering free beginner programming classes in London and online. He has a few fun projects on GitHub that you can find out about here. Show Notes Date Generated: 2025-02-17 Date Released: 2025-03-14 CppNorth 2025FluxIteration Revisited: A Safer Iteration Model for C++ - Tristan Brindle - CppNorth 2023ADSP Episode 126: Flux (and Flow) with Tristan BrindleIterators and Ranges: Comparing C++ to D to Rust - Barry Revzin - [CppNow 2021]Keynote: Iterators and Ranges: Comparing C++ to D, Rust, and Others - Barry Revzin - CPPP 2021Iteration Inside and Out - Bob Nystrom BlogExpanding the internal iteration API #99std::distancestd::ranges::distanceC++ London MeetupDenver C++ MeetupIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

In this episode, Conor and Ben chat with Tristan Brindle about recent updates to Flux, internal iteration vs external iteration and more. Link to Episode 224 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Socials ADSP: The Podcast: TwitterConor Hoekstra: Twitter | BlueSky | MastodonBen Deane: Twitter | BlueSkyAbout the Guest Tristan Brindle a freelance programmer and trainer based in London, mostly focussing on C++. He is a member of the UK national body (BSI) and ISO WG21. Occasionally I can be found at C++ conferences. He is also a director of C++ London Uni, a not-for-profit organisation offering free beginner programming classes in London and online. He has a few fun projects on GitHub that you can find out about here. Show Notes Date Generated: 2025-02-17 Date Released: 2025-03-07 FluxLightning Talk: Faster Filtering with Flux - Tristan Brindle - CppNorth 2023Arrays, Fusion & CPUs vs GPUs.pdfIteration Revisited: A Safer Iteration Model for C++ - Tristan Brindle - CppNorth 2023ADSP Episode 126: Flux (and Flow) with Tristan BrindleIterators and Ranges: Comparing C++ to D to Rust - Barry Revzin - [CppNow 2021]Keynote: Iterators and Ranges: Comparing C++ to D, Rust, and Others - Barry Revzin - CPPP 2021Iteration Inside and Out - Bob Nystrom BlogExpanding the internal iteration API #99Intro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don’t), where industry insights meet laid-back banter. Whether you’re a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let’s get into the heart of data, unplugged style! This week, we dive into: The creative future with AI: is generative AI helping or hurting creators? Environmental concerns of AI: the hidden costs of AI’s growing capabilities—how much energy do these models actually consume, and is it worth it?AI copyright controversies: Mark Zuckerberg’s LLaMA model faces criticism for using copyrighted materials like content from the notorious LibGen database.Trump vs. AI regulation: The former president repeals Biden’s AI executive order, creating a Wild West approach to AI development in the U.S. How will this impact innovation and global competition?Search reimagined with Perplexity AI: A new era of search blending conversational AI and personalized data unification. Could this be the future of information retrieval?Apple Intelligence on pause: Apple's AI-generated news alerts face a bumpy road. For more laughs, check out the dedicated subreddit AppleIntelligenceFail.Rhai scripting for Rust: Empowering Rust developers with an intuitive embedded scripting language to make extensibility a breeze.Poisoned text for scrapers: Exploring creative ways to protect web content from unauthorized scraping by AI systems.The rise of the AI Data Engineer: Is this a new role in data science, or are we just rebranding existing skills?

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Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don’t), where industry insights meet laid-back banter. Whether you’re a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let’s get into the heart of data, unplugged style! In this episode, we are joined by special guest Nico for a lively and wide-ranging tech chat. Grab your headphones and prepare for: Strava’s ‘Athlete Intelligence’ feature: A humorous dive into how workout apps are getting smarter—and a little sassier.Frontend frameworks: HTMX is a tough choice: A candid discussion on using React versus emerging alternatives like HTMX and when to keep things lightweight.Octoverse 2024 trends and language wars: Python takes the lead over JavaScript as the top GitHub language, and we dissect why Go, TypeScript, and Rust are getting love too.GenAI meets Minecraft: Imagine procedurally generated worlds and dreamlike coherence breaks—Minecraft-style. How GenAI could redefine gameplay narratives and NPC behavior.OpenAI’s O1 model leak: Insights on the recent leak, what’s new, and its implications for the future of AI.Tiger Beetle’s transactional databases and testing tales: Nico walks us through Tiger Style, deterministic simulation testing, and why it’s a game changer for distributed databases.Automated testing for LLMOps: A quick overview of automated testing for large language models and its role in modern AI workflows.DeepLearning.ai’s short courses: Quick, impactful learning to level up your AI skills.

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that flow as smoothly as your morning coffee, where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style! In today's episode: Remote work and hybrid challenges: Insights from the IMF on remote productivity, plus the challenges of work-life balance and Amazon’s office return with other companies' strategies for bringing employees back to the office.The fall of Zapata AI: A look at the shutdown of Zapata AI and the struggles in building successful quantum computing ventures.WTF Python: Exploring Python’s type hints, overloads, and those confusing "WTF" moments. Check out WTFPython.Data profiling tools: A dive into YData Profiling and Sweetviz for detailed data analysis.GifCities and personal websites: Reflecting on the fall of GifCities, the retro GIF hub, and discussing Murilo’s blog journey.Rust’s complexity debate: Discussing the blog post My Negative Views on Rust and whether Rust is too complex or simply misunderstood..io domain controversy: Examining the future of the .io domain as the British Indian Ocean Territory transfers sovereignty. Read more on Every.to and MIT Technology Review.Ducks or AI? A fun challenge to distinguish real ducks from AI-generated ones in the Duck Imposter Game.Adobe's AI video generator: A discussion on Adobe Firefly’s AI-powered video generator and its potential impact on content creation.

Welcome to the Data Engineering Central Podcast —— a no-holds-barred discussion on the Data Landscape. Welcome to Episode 02 In today’s episode, we will talk about the following topics from the Data Engineering perspective … * Using OpenAI’s o1 Model to do Data Engineering work * Lord Save us from more ETL tools * Rust for the small things * Hosted (SaaS) vs Build

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Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. We dive into conversations smoother than your morning coffee (but let’s be honest, just as caffeinated) where industry insights meet light-hearted banter. Whether you’re a data wizard or just curious about the digital chaos around us, kick back and get ready to talk shop—unplugged style! In this episode: Farewell Pandas, Hello Future: Pandas is out, and Ibis is in. We're talking faster, smarter data processing—featuring the rise of DuckDB and the powerhouse that is Polars. Is this the end of an era for Pandas?UV vs. Rye: Forget pip—are these new Python package managers built in Rust the future? We break down UV, Rye, and what it all means for your next Python project.AI-Generated Podcasts: Is AI about to take over your favorite podcasts? We explore the potential of Google’s Notebook LM to transform content into audio gold.When AI Steals Your Voice: Jeff Geerling’s voice gets cloned by AI—without his consent. We dive into the wild world of voice cloning, the ethics, and the future of AI-generated media.Hacking AI with Prompt Injection: Could you outsmart AI? We share some wild strategies from the game Gandalf that challenge your prompt injection skills and teach you how to jailbreak even the toughest guardrails.Jony Ive’s New Gadget Rumor: Is Jony Ive plotting an Apple killer? Rumors are swirling about a new AI-powered handheld device that could shake up the smartphone market.Zero-Downtime Deployments with Kamal Proxy: No more downtime! We geek out over Kamal Proxy, the sleek HTTP tool designed for effortless Docker deployments.Function Calling and LLMs: Get ready for the next evolution in AI—function calling. We discuss its rise in LLMs and dive into the Gorilla project, the leaderboard testing the future of smart APIs.

The first episode of The Pragmatic Engineer Podcast is out. Expect similar episodes every other Wednesday. You can add the podcast in your favorite podcast player, and have future episodes downloaded automatically. Listen now on Apple, Spotify, and YouTube. Brought to you by: • Codeium: ​​Join the 700K+ developers using the IT-approved AI-powered code assistant. • TLDR: Keep up with tech in 5 minutes — On the first episode of the Pragmatic Engineer Podcast, I am joined by Simon Willison. Simon is one of the best-known software engineers experimenting with LLMs to boost his own productivity: he’s been doing this for more than three years, blogging about it in the open. Simon is the creator of Datasette, an open-source tool for exploring and publishing data. He works full-time developing open-source tools for data journalism, centered on Datasette and SQLite. Previously, he was an engineering director at Eventbrite, joining through the acquisition of Lanyrd, a Y Combinator startup he co-founded in 2010. Simon is also a co-creator of the Django Web Framework. He has been blogging about web development since the early 2000s. In today’s conversation, we dive deep into the realm of Gen AI and talk about the following:  • Simon’s initial experiments with LLMs and coding tools • Why fine-tuning is generally a waste of time—and when it’s not • RAG: an overview • Interacting with GPTs voice mode • Simon’s day-to-day LLM stack • Common misconceptions about LLMs and ethical gray areas  • How Simon’s productivity has increased and his generally optimistic view on these tools • Tips, tricks, and hacks for interacting with GenAI tools • And more! I hope you enjoy this episode. — In this episode, we cover: (02:15) Welcome (05:28) Simon’s ‘scary’ experience with ChatGPT (10:58) Simon’s initial experiments with LLMs and coding tools (12:21) The languages that LLMs excel at (14:50) To start LLMs by understanding the theory, or by playing around? (16:35) Fine-tuning: what it is, and why it’s mostly a waste of time (18:03) Where fine-tuning works (18:31) RAG: an explanation (21:34) The expense of running testing on AI (23:15) Simon’s current AI stack  (29:55) Common misconceptions about using LLM tools (30:09) Simon’s stack – continued  (32:51) Learnings from running local models (33:56) The impact of Firebug and the introduction of open-source  (39:42) How Simon’s productivity has increased using LLM tools (41:55) Why most people should limit themselves to 3-4 programming languages (45:18) Addressing ethical issues and resistance to using generative AI (49:11) Are LLMs are plateauing? Is AGI overhyped? (55:45) Coding vs. professional coding, looking ahead (57:27) The importance of systems thinking for software engineers  (1:01:00) Simon’s advice for experienced engineers (1:06:29) Rapid-fire questions — Where to find Simon Willison: • X: https://x.com/simonw • LinkedIn: https://www.linkedin.com/in/simonwillison/ • Website: https://simonwillison.net/ • Mastodon: https://fedi.simonwillison.net/@simon — Referenced: • Simon’s LLM project: https://github.com/simonw/llm • Jeremy Howard’s Fast Ai: https://www.fast.ai/ • jq programming language: https://en.wikipedia.org/wiki/Jq_(programming_language) • Datasette: https://datasette.io/ • GPT Code Interpreter: https://platform.openai.com/docs/assistants/tools/code-interpreter • Open Ai Playground: https://platform.openai.com/playground/chat • Advent of Code: https://adventofcode.com/ • Rust programming language: https://www.rust-lang.org/ • Applied AI Software Engineering: RAG: https://newsletter.pragmaticengineer.com/p/rag • Claude: https://claude.ai/ • Claude 3.5 sonnet: https://www.anthropic.com/news/claude-3-5-sonnet • ChatGPT can now see, hear, and speak: https://openai.com/index/chatgpt-can-now-see-hear-and-speak/ • GitHub Copilot: https://github.com/features/copilot • What are Artifacts and how do I use them?: https://support.anthropic.com/en/articles/9487310-what-are-artifacts-and-how-do-i-use-them • Large Language Models on the command line: https://simonwillison.net/2024/Jun/17/cli-language-models/ • Llama: https://www.llama.com/ • MLC chat on the app store: https://apps.apple.com/us/app/mlc-chat/id6448482937 • Firebug: https://en.wikipedia.org/wiki/Firebug_(software)# • NPM: https://www.npmjs.com/ • Django: https://www.djangoproject.com/ • Sourceforge: https://sourceforge.net/ • CPAN: https://www.cpan.org/ • OOP: https://en.wikipedia.org/wiki/Object-oriented_programming • Prolog: https://en.wikipedia.org/wiki/Prolog • SML: https://en.wikipedia.org/wiki/Standard_ML • Stabile Diffusion: https://stability.ai/ • Chain of thought prompting: https://www.promptingguide.ai/techniques/cot • Cognition AI: https://www.cognition.ai/ • In the Race to Artificial General Intelligence, Where’s the Finish Line?: https://www.scientificamerican.com/article/what-does-artificial-general-intelligence-actually-mean/ • Black swan theory: https://en.wikipedia.org/wiki/Black_swan_theory • Copilot workspace: https://githubnext.com/projects/copilot-workspace • Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems: https://www.amazon.com/Designing-Data-Intensive-Applications-Reliable-Maintainable/dp/1449373321 • Bluesky Global: https://www.blueskyglobal.org/ • The Atrocity Archives (Laundry Files #1): https://www.amazon.com/Atrocity-Archives-Laundry-Files/dp/0441013651 • Rivers of London: https://www.amazon.com/Rivers-London-Ben-Aaronovitch/dp/1625676158/ • Vanilla JavaScript: http://vanilla-js.com/ • jQuery: https://jquery.com/ • Fly.io: https://fly.io/ — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

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In this episode, Conor and Bryce chat with Doug Gregor from Apple about the Swift programming language! Link to Episode 184 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest: Douglas Gregor is is a Distinguished Engineer at Apple working on the Swift programming language, compiler, and related libraries and tools. He is code owner emeritus of the Clang compiler (part of the LLVM project), a former member of the ISO C++ committee, and a co-author on the second edition of C++ Templates: The Complete Guide. He holds a Ph.D. in computer science from Rensselaer Polytechnic Institute. Show Notes Date Recorded: 2024-04-29 Date Released: 2024-05-31 Swift Programming LanguageSwift ActorsD Programming LanguageRust Programming LanguageFearless Concurrency? Understanding Concurrent Programming Safety in Real-World Rust SoftwareSwift Protocols2022 LLVM Dev Mtg: Implementing Language Support for ABI-Stable Software Evolution in Swift and LLVMOxide Episode - Discovering the XZ Backdoor with Andres FreundSwift Algorithms LibraryIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that flow like your morning coffee, where industry insights meet laid-back banter. Whether you're a data aficionado or just curious about the digital age, pull up a chair and let's explore the heart of data, unplugged style!

Stack Overflow and OpenAI Deal Controversy: Discussing the partnership controversy, with users protesting the lack of an opt-out option and how this could reshape the platform. Look into Phind here.Apple and OpenAI Rumors - could ChatGPT be the new Siri? Examining rumors of ChatGPT potentially replacing Siri, and Apple's AI strategy compared to Microsoft’s MAI-1. Check out more community opinions here.Hello GPT-4o: Exploring the new era with OpenAI's GPT-4o that blends video, text, and audio for more dynamic human-AI interactions. Discussing AI's challenges under the European AI Act and chatgpt’s use in daily life and dating apps like Bumble.Claude Takes Europe: Claude 3 now available in the EU. How does it compare to ChatGPT in coding and conversation?ElevenLabs' Music Generation AI: A look at ElevenLabs' AI for generating music and the broader AI music landscape. How are these algorithms transforming music creation? Check out the AI Song Contest here.Google Cloud’s Big Oops with UniSuper: Unpack the shocking story of how Google Cloud accidentally wiped out UniSuper’s account. What does this mean for data security and redundancy strategies?The Great CLI Debate: Is Python really the right choice for CLI tools? We spark the debate over Python vs. Go and Rust in building efficient CLI tools.

In this episode, Conor and Bryce chat with Doug Gregor from Apple about the history of C++0x Concepts (part 2). Link to Episode 181 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest: Douglas Gregor is is a Distinguished Engineer at Apple working on the Swift programming language, compiler, and related libraries and tools. He is code owner emeritus of the Clang compiler (part of the LLVM project), a former member of the ISO C++ committee, and a co-author on the second edition of C++ Templates: The Complete Guide. He holds a Ph.D. in computer science from Rensselaer Polytechnic Institute.

Show Notes

Date Recorded: 2024-04-29 Date Released: 2024-05-10 C++20 ConceptsSwift Programming LanguageElements of ProgrammingTecton: A Language for Manipulating Generic ObjectsGeneric Programming by David Musser and Alexander StepanovOriginal paper on concepts for C++0x (Stroustrup and Dos Reis)C++ Concepts vs Rust Traits vs Haskell Typeclasses vs Swift Protocols - Conor Hoekstra - ACCU 2021Paper on the implementation of concepts in ConceptGCC (Gregor, Siek)C++0x Concepts proposal that explains the model (Gregor, Stroustrup)Language wording for concepts that went into C++0xDoug’s last-ditch effort to bring back a simpler C++0x Concepts model using archetypes for type checkingJeremy Siek’s extensive C++0x Concepts writeupType-Soundness and Optimization in the Concepts ProposalIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

In this episode, Conor and Bryce chat with Doug Gregor from Apple about the history of C++0x Concepts. Link to Episode 180 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest: Douglas Gregor is is a Distinguished Engineer at Apple working on the Swift programming language, compiler, and related libraries and tools. He is code owner emeritus of the Clang compiler (part of the LLVM project), a former member of the ISO C++ committee, and a co-author on the second edition of C++ Templates: The Complete Guide. He holds a Ph.D. in computer science from Rensselaer Polytechnic Institute.

Show Notes

Date Recorded: 2024-04-29 Date Released: 2024-05-03 C++20 ConceptsSwift Programming LanguageElements of ProgrammingTecton: A Language for Manipulating Generic ObjectsGeneric Programming by David Musser and Alexander StepanovOriginal paper on concepts for C++0x (Stroustrup and Dos Reis)C++ Concepts vs Rust Traits vs Haskell Typeclasses vs Swift Protocols - Conor Hoekstra - ACCU 2021Paper on the implementation of concepts in ConceptGCC (Gregor, Siek)C++0x Concepts proposal that explains the model (Gregor, Stroustrup)Language wording for concepts that went into C++0xDoug’s last-ditch effort to bring back a simpler C++0x Concepts model using archetypes for type checkingJeremy Siek’s extensive C++0x Concepts writeupIntro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8