Podcasts about Git

#342: Most companies have plenty of documentation. The problem is almost none of it is findable, current, or true. Between what's documented, what's actually true, and what people actually do, there are gaps wide enough to kill any AI initiative before it starts. Viktor makes a distinction that reframes the whole problem: there are two types of documentation. Why something was done -- that's eternal. How something works -- that's outdated the moment someone changes a config and forgets to update the wiki. The information about that change probably exists somewhere -- in a Zoom recording, a Slack thread, somebody's head -- but it's not where anyone would think to look for it. The running system itself is the most accurate documentation any company has. Your Kubernetes cluster tells you how many pods are running right now. Git tells you how many you wished you had. Those aren't the same thing, and pretending Git is the source of truth is a comfortable lie most teams tell themselves daily. RAG won't save this. Not the way most people imagine it -- point an agent at your docs and let it answer questions. That fails for the same reason Google's old enterprise search appliance failed. What could work is a continuous process that watches every information source, extracts what matters, and updates a central location intelligently. We have the pieces for this. Nobody's built it yet. The practical path forward: audit what you have before building anything new. Instrument your documentation the way you instrument applications -- find out what people search for and can't find. Design for retrieval, not storage. Build feedback loops. And stop treating documentation as a project with an end date. The companies that treat this as a strategic advantage instead of a chore are the ones that will actually make AI work for them.   YouTube channel: https://youtube.com/devopsparadox   Review the podcast on Apple Podcasts: https://www.devopsparadox.com/review-podcast/   Slack: https://www.devopsparadox.com/slack/   Connect with us at: https://www.devopsparadox.com/contact/

Security professional Mason Moser joins The PowerShell Podcast to share his journey from discovering PowerShell through Learn PowerShell in a Month of Lunches to building real-world automation tools in a security environment. Mason talks about how starting slowly, returning to PowerShell after a break, and consistently building small tools helped him gain confidence and deepen his skills. The conversation also explores the value of community involvement, overcoming imposter syndrome, presenting technical topics publicly, and practical workflows for security and scripting. Mason discusses using Git with AI-assisted coding, building internal PowerShell tools for teams, and how small daily automation tasks can steadily build long-term PowerShell expertise. Key Takeaways: • Start small and stay consistent — even simple scripts like cleaning up files or automating routine tasks build real PowerShell confidence over time. • Community involvement accelerates learning — asking thoughtful questions, sharing tools, and participating in discussions can dramatically improve your growth. • Git is essential when working with AI-generated code — committing changes frequently makes it easier to review, rollback, and understand modifications AI tools produce. Guest Bio: Mason Moser is a security professional based in Oklahoma who focuses on automation, governance, and risk within the electric utility industry. With a background in programming and security operations, Mason uses PowerShell to build internal tooling, streamline security workflows, and improve operational efficiency. He is an active participant in the PowerShell community and recently presented a PowerShell Wednesday session on Vim and keyboard-driven development workflows. Resource Links: Learn PowerShell in a Month of Lunches – https://www.manning.com/books/learn-powershell-in-a-month-of-lunches PDQ Discord – https://discord.gg/PDQ Connect with Andrew - https://andrewpla.tech/links PowerShell Wednesdays – https://www.youtube.com/@PDQ Vim Editor – https://www.vim.org The PowerShell Podcast on YouTube: https://youtu.be/7EtWrrblKMw

Walt explains his mysterious nature, Bry rants, Tax mascots, Airport Plaza robbery, Prank gone wrong, Git ‘em is a narc.https://public.liveread.io/media-kit/tesd

This is episode 319 recorded on February 6th, 2026, where John and Jason break down the Microsoft Fabric January 2026 Feature Summary — including the Osmos acquisition for AI-ready data engineering, Git branch improvements and Python SDK support for the Fabric API, expanded OneLake security, and new Real-Time Intelligence enhancements. For show notes please visit www.bifocal.show

Sandra und Daniel treffen sich auf einen kurzen Kaffee und lassen ihre letzte Woche Revue passieren.

In this episode of Manufacturing Hub, Vlad and Dave sit down with Travis Cox and Kevin McCluskey from Inductive Automation to unpack what was actually proven at ProveIt and why it matters for teams trying to modernize plants without building a fragile mess of point to point integrations. If you have ever looked at a shiny demo and wondered what the real architecture looks like, how it scales beyond a single line, and what it takes to roll out across multiple sites without turning every change into a high risk event, this conversation is for you.Travis and Kevin walk through their ProveIt Enterprise B build and the thinking behind it. The core idea is simple but powerful: treat the factory like a system that needs a shared digital infrastructure, built on open standards, where data is contextualized and reusable. They break down how they used Ignition Edge close to PLCs for resiliency, local HMIs, and disciplined data modeling, then moved data through MQTT into a Unified Namespace so multiple applications can consume the same trusted signals and context. This is the difference between “we can connect to anything” and “we can scale without rewriting everything every time the business changes.” Open standards show up repeatedly in the conversation because ProveIt is specifically designed to force interoperability and practical implementation tradeoffs. Inductive Automation has also written about ProveIt as a place where MQTT, OPC UA, and SQL show up as real foundations rather than slogans.From there, the episode gets into the part that should make both OT and IT teams pay attention: modern deployment practices applied to industrial applications. Kevin outlines a clear maturity path from a single designer workflow to version control, then to containerized deployments, and finally to full GitOps style promotion across dev, staging, and production using tools like Argo CD, Helm, Kubernetes, and release promotion concepts that look like what the software world has used for years. Argo CD is explicitly built around Git repositories as the source of truth for desired state, which is exactly why it fits this style of deployment. The live portion of the conversation demonstrates how fast this can get when the infrastructure is treated as code: they spin up a brand new “site four” by submitting a form, generating a pull request, merging it, and letting the pipeline do the rest.Timestamps00:00 Welcome back and why this ProveIt recap matters01:35 Meet Travis Cox and Kevin McCluskey from Inductive Automation03:10 What ProveIt is and the key vendor questions it forces05:20 Enterprise B architecture overview from PLC to Edge to site to enterprise07:30 HMI walkthrough across liquid processing, filling, packaging, palletizing09:05 Why deploy Ignition Edge instead of only a centralized site gateway12:05 Design once, reuse everywhere and what that means for scaling quickly14:35 On prem realities versus cloud infrastructure in the ProveIt environment17:10 MCP, n8n workflows, and bringing live operational context into AI20:40 i3X style API access to models, history, and alarms for interoperability23:15 GitHub, Docker Compose, Helm, Kubernetes, Argo CD, Cargo and GitOps promotion36:55 Spinning up a new site live and what it changes for multi site rolloutsAbout the hostsVlad Romanov is an electrical engineer and MBA who has spent over a decade building and modernizing manufacturing systems across industrial automation, controls, and plant operations. Through Joltek, Vlad works with manufacturers to assess current state OT foundations, reduce modernization risk, improve reliability, and build internal capability through practical training and standards that stick.Dave Griffith co hosts Manufacturing Hub and brings a practitioner lens focused on what works on the plant floor, how architectures survive real constraints, and how industrial teams can modernize without breaking production.About the guestsTravis Cox is Chief Technology Evangelist at Inductive Automation and has spent over two decades helping customers and partners design scalable architectures, apply best practices, and deliver real solutions with Ignition.Kevin McCluskey is Chief Technology Architect at Inductive Automation and works with organizations on architecture decisions, platform direction, and enabling the next generation of industrial applications.Learn more about Joltekhttps://www.joltek.com/serviceshttps://www.joltek.com/book-a-modernization-consultation

Choice is good, but sometimes you may want a little help! Our first two highlights showcase approaches you can take to inform your next LLM for analyses and open-source license. Plus how to make your mark(s) within your version control history. Episode Links This week's curator: Sam Parmar - @parmsam@fosstodon.org (Mastodon) & @parmsam_ (X/Twitter)How to choose the best LLM using R and vitalsPick a License, Not Any LicenseGit commits: please mark your stitches!Entire issue available at rweekly.org/2026-W09Supplement Resourcesrollama - R wrapper to Ollama https://jbgruber.github.io/rollama/Opps, Git! How to recover from common mistakes workshop https://r-posts.com/oops-git-how-to-recover-from-common-mistakes-workshop/Supporting the showUse the contact page at https://serve.podhome.fm/custompage/r-weekly-highlights/contact to send us your feedbackR-Weekly Highlights on the Podcastindex.org - You can send a boost into the show directly in the Podcast Index. First, top-up with Alby, and then head over to the R-Weekly Highlights podcast entry on the index.A new way to think about value: https://value4value.infoGet in touch with us on social mediaEric Nantz: @rpodcast@podcastindex.social (Mastodon), @rpodcast.bsky.social (BlueSky) and @theRcast (X/Twitter)Mike Thomas: @mike_thomas@fosstodon.org (Mastodon), @mike-thomas.bsky.social (BlueSky), and @mike_ketchbrook (X/Twitter) Music credits powered by OCRemixSeven Pipes to Heaven - Super Mario Land - Nostalvania - https://ocremix.org/remix/OCR03256You Are Not Confined - Final Fantasy 9 - Sonicade - https://ocremix.org/remix/OCR01064

OpenClaw is a self-hosted AI agent daemon that executes autonomous tasks through messaging apps like WhatsApp and Telegram using persistent memory. It integrates with Claude Code to enable software development and administrative automation directly from mobile devices. Links Notes and resources at ocdevel.com/mlg/mla-29 Try a walking desk - stay healthy & sharp while you learn & code Generate a podcast - use my voice to listen to any AI generated content you want OpenClaw is a self-hosted AI agent daemon (Node.js, port 18789) that executes autonomous tasks via messaging apps like WhatsApp or Telegram. Developed by Peter Steinberger in November 2025, the project reached 196,000 GitHub stars in three months. Architecture and Persistent Memory Operational Loop: Gateway receives message, loads SOUL.md (personality), USER.md (user context), and MEMORY.md (persistent history), calls LLM for tool execution, streams response, and logs data. Memory System: Compounds context over months. Users should prompt the agent to remember specific preferences to update MEMORY.md. Heartbeats: Proactive cron-style triggers for automated actions, such as 6:30 AM briefings or inbox triage. Skills: 5,705+ community plugins via ClawHub. The agent can author its own skills by reading API documentation and writing TypeScript scripts. Claude Code Integration Mobile to Deploy Workflow: The claude-code-skill bridge provides OpenClaw access to Bash, Read, Edit, and Git tools via Telegram. Agent Teams: claude-team manages multiple workers in isolated git worktrees to perform parallel refactors or issue resolution. Interoperability: Use mcporter to share MCP servers between Claude Code and OpenClaw. Industry Comparisons vs n8n: Use n8n for deterministic, zero-variance pipelines. Use OpenClaw for reasoning and ambiguous natural language tasks. vs Claude Cowork: Cowork is a sandboxed, desktop-only proprietary app. OpenClaw is an open-source, mobile-first, 24/7 daemon with full system access. Professional Applications Therapy: Voice to SOAP note transcription. PHI requires local Ollama models due to a lack of encryption at rest in OpenClaw. Marketing: claw-ads for multi-platform ad management, Mixpost for scheduling, and SearXNG for search. Finance: Receipt OCR and Google Drive filing. Requires human review to mitigate non-deterministic LLM errors. Real Estate: Proactive transaction deadline monitoring and memory-driven buyer matching. Security and Operations Hardening: Bind to localhost, set auth tokens, and use Tailscale for remote access. Default settings are unsafe, exposing over 135,000 instances. Injection Defense: Add instructions to SOUL.md to treat external emails and web pages as hostile. Costs: Software is MIT-licensed. API costs are paid per-token or bundled via a Claude subscription key. Onboarding: Run the BOOTSTRAP.md flow immediately after installation to define agent personality before requesting tasks.

This is a recap of the top 10 posts on Hacker News on February 20, 2026. This podcast was generated by wondercraft.ai (00:30): Trump's global tariffs struck down by US Supreme CourtOriginal post: https://news.ycombinator.com/item?id=47089213&utm_source=wondercraft_ai(01:56): Keep Android OpenOriginal post: https://news.ycombinator.com/item?id=47091419&utm_source=wondercraft_ai(03:23): Facebook is cookedOriginal post: https://news.ycombinator.com/item?id=47091748&utm_source=wondercraft_ai(04:50): The path to ubiquitous AI (17k tokens/sec)Original post: https://news.ycombinator.com/item?id=47086181&utm_source=wondercraft_ai(06:16): I tried building my startup entirely on European infrastructureOriginal post: https://news.ycombinator.com/item?id=47085483&utm_source=wondercraft_ai(07:43): Ggml.ai joins Hugging Face to ensure the long-term progress of Local AIOriginal post: https://news.ycombinator.com/item?id=47088037&utm_source=wondercraft_ai(09:10): I found a useful Git one liner buried in leaked CIA developer docsOriginal post: https://news.ycombinator.com/item?id=47088181&utm_source=wondercraft_ai(10:36): An AI Agent Published a Hit Piece on Me – The Operator Came ForwardOriginal post: https://news.ycombinator.com/item?id=47083145&utm_source=wondercraft_ai(12:03): I found a Vulnerability. They found a LawyerOriginal post: https://news.ycombinator.com/item?id=47092578&utm_source=wondercraft_ai(13:30): Wikipedia deprecates Archive.today, starts removing archive linksOriginal post: https://news.ycombinator.com/item?id=47092006&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Как справляться с потоком информации, не забывать детали рабочих созвонов и превращать хаос в структуру? Виктор делится своей системой Personal Knowledge Management (PKM) в Obsidian. В этом выпуске разбираем теорию «Второго мозга»: от пирамиды знаний до метода Zettelkasten и системы организации папок Johnny Decimal. Саша скептически ищет практическую пользу, а Виктор показывает свой граф заметок. Также внутри — анонс нашей книги про Kubernetes интервью и список мастхэв плагинов. О чём выпуск: - Пирамида DIKW: Чем данные отличаются от мудрости и как это процессить. - Методологии: Zettelkasten (связи) и Johnny Decimal (структура папок). - AI и Obsidian: Как сделать RAG по своим заметкам с помощью Copilot и локальных моделей. - Синхронизация: Git, S3, WebDAV или платные сервисы — что выбрать. - Плагины: Обзор базового набора (Dataview, Excalidraw, Templater и др.). - Анонс книги: Как мы 2 года писали «Cracking the Kubernetes Interview».   ССЫЛКИ

Vincent Heuschling reçoit Hayssam Saleh, créateur de **Starlake**, une plateforme data open source française née de la factorisation de projets clients depuis 2017-2018. L'épisode intervient dans un contexte de consolidation du marché (rachat de DBT et de SQLMesh par Fivetran), qui invite à challenger les solutions établies.Starlake se distingue par une approche **entièrement déclarative** (YAML + SQL natif, sans Jinja) couvrant toute la chaîne data engineering : ingestion, transformation, orchestration et qualité des données. L'outil s'appuie sur les moteurs sous-jacents des plateformes cibles (Snowflake, BigQuery, Spark) et génère automatiquement les DAGs pour les orchestrateurs du marché (Airflow, Dagster, Snowflake Tasks).Parmi les fonctionnalités marquantes : le **data branching** (branches de données à la manière de Git), l'inférence automatique de schémas YAML à partir de fichiers sources, un **transpiler SQL** multi-plateformes, et l'extraction du lineage depuis du SQL brut sans annotation. L'intégration récente de **DuckLake** ouvre la voie à des architectures on-premise souveraines à coût maîtrisé (sous 300 €/mois sur OVH, Scaleway, Clever Cloud).Le modèle économique repose sur le support, la formation, et le consulting : Starlake s'installe dans le cloud du client, avec mise à jour automatique gérée par l'équipe, sans accès aux données.**Chapitres****00:00:27** – Introduction : consolidation du marché data (rachat de DBT et SQLMesh par Fivetran) et présentation de l'épisode**00:03:13** – Hayssam et la genèse de Starlake : parcours Spark/Scala, POC à 4 000 formats de fichiers (2017-2018)**00:09:51** – Architecture et philosophie : load, transform, orchestration unifiés en déclaratif (YAML + SQL natif, pas de Jinja)**00:00:18:18** – Starlake vs DBT : différences philosophiques, composabilité, fonctionnalités 100 % open source**00:00:22:20** – Data branching, Starlake Labs (pipe syntax, transpiler SQL, lineage) et expérience développeur (DuckDB local, UI point-and-click)**00:36:35** – Modèle open source et économique : licence Apache, support, formation, marketplace cloud souveraine**00:43:42** – DuckLake : alternative on-premise/cloud souverain (OVH, Scaleway, Clever Cloud) et comment contribuer / démarrer**Le BigdataHebdo**Le BigdataHebdo est le podcast Francophone de la Data et de l'IA.Retrouvez plus de 200 épisodes https://bigdatahebdo.comRejoignez la communauté sur le Slack https://join.slack.com/t/bigdatahebdo/shared_invite/zt-a931fdhj-8ICbl9dbsZZbTcze61rr~Q

This week we did what every developer swears they won't do - we broke our own Git workflow.Join us as we chat about what went wrong, what we learned, and where we're heading next. This conversation is a reminder that even experienced developers get rusty without practice. If you can relate, or just simply enjoy Julian's git puns, leave a comment!And while you're at it, check out our Pybites Books platform - https://pybitesbooks.com/ - Loads of new updates coming soon!___

The force is strong with this episode. The Voice of The Smokin' Hot Toddcast, Doc Summit, recently posted an A.I. photo of himself as a grizzled, hermit like Jedi that he called G'won Git. Well you know how Hot Toddy's brain works, he immediately wrote an entire story around this mythical being. This week, we bring to you our own Star Wars story on an all new episode of The Smokin' Hot Toddcast!

We are at a unique point in history where there is finally an alternative to human coding. If AI can write the code effectively, what is left for the software engineer?In this episode, Joris Conijn (AWS CTO at Xebia) argues that the era of "just coding" is over. We discuss why senior developers are safe (for now), why juniors are at risk of never learning the fundamentals, and how "Shadow AI" is forcing companies to change their security strategies.Most importantly, we break down the difference between a "Programmer" and a "Software Engineer" with the introduction of agentic tools. If you want to future-proof your career and move from writing lines of code to designing systems, this conversation is for you.In this episode, we cover:Why banning AI at work actually increases your security riskHow to use AI to automate the boring parts of the SDLC (requirements & user stories)The critical difference between "Coding" and "System Architecture"Why you should check your AI Agents into your Git repositoryThe 20-year problem: what happens when engineers never learn the fundamentals?Connect with Joris Conijn:https://www.linkedin.com/in/jorisconijnTIMESTAMPS00:00:00 - Intro 00:01:11 - What Keeps a CTO Excited About Tech? 00:02:58 - Stop Being the "Department of No" in Security 00:05:28 - The Real Risk of Banning AI at Work 00:06:32 - When Developers Hold the Organization Hostage 00:08:14 - The Hidden Dangers of Instant AI Code Fixes 00:09:50 - Will Future Devs Understand Object Oriented Programming? 00:11:36 - Using AI to Accelerate Learning vs Copy-Pasting 00:13:17 - Why Testing Matters More When AI Writes Code 00:16:42 - Automating the Boring Parts of the SDLC 00:19:06 - How to Turn Meeting Transcripts into User Stories 00:21:36 - The Critical Skill of Making Implicit Knowledge Explicit 00:23:10 - Why You Should Stop Obsessing Over Story Points 00:27:46 - The "A-Team" Approach to High-Trust Development 00:29:54 - Running Parallel Workflows with AI Agents 00:33:34 - Pro Tip: Check Your AI Agents into Git 00:35:52 - Balancing Autonomy and Governance in Large Teams 00:39:19 - There Is Finally an Alternative to Human Coders 00:41:07 - Programmer vs Software Engineer: What is the Difference? 00:44:45 - How to Teach Software Engineering in the AI Era#SoftwareEngineering #SystemDesign #AIAgents

Nach den ausschweifenden Jubiläumsfeiern finden Sylvester und Christopher zurück zum gewohnten Rhythmus. Zunächst schauen sie auf ein System zur Geräteverwaltung (MDM), das in den letzten Wochen bei verschiedenen europäischen Regierungen angegriffen wurde - der Hersteller war bereits mehrfach Thema im Podcast. Dann geht's allerdings weiter mit einem kurzen Abriß zu OpenClaw, dem gehypten KI-Assistenten, und seinen vielen Unsicherheiten. Sylvester kann dem Helferlein eine gewisse Faszination abgewinnen, warnt jedoch vor seinem unreflektierten Einsatz. Und Christopher erzählt, wie das Bundesamt für Sicherheit in der Informationstechnik die Verschlüsselung in Deutschland quantensicher machen will und dazu seine Richtlinien modernisiert. Betrachtungen zu unabsichtlichen Kommandos bei der Softwareentwicklung und zu Problemen verschiedener Texteditoren runden die Folge ab und entlassen Sylvester in den wohlverdienten Urlaub. Leider gibt es auf der Tonspur in dieser Folge einen leichten Hall von Christophers Stimme. Wir bitten das zu entschuldigen.

In this episode, Jeff Mains sits down with Michael Ferranti, a veteran of developer tools and cloud-native infrastructure with over a decade of experience at companies like PortWorks, Teleport, and Unleash. Michael shares insights on feature management, the critical role of feature flags in modern software delivery, and how to effectively market to developers. The conversation explores why "friends don't let friends build their own feature flag system," the evolving landscape of product-led growth, and how AI is reshaping go-to-market strategies for developer tools.Key Takeaways[5:27] - The Common Thread in Category Creation[7:17] - What is Feature Management?[11:56] - The Cost of Downtime[18:28] - The Race Car Analogy[19:59] - Marketing to Developers[24:18] - User vs. Buyer[30:30] - Easy to Try is Essential[35:30] - Organic Search is Declining[36:29] - AIO (AI Optimization)[40:26] - The PLG Myth[44:17] - The AI ShiftTweetable Quotes"The thing that makes product development and success in SaaS really easy is when you have a product that solves real problems in a market that's big enough.""Friends don't let friends build their own feature flag system. You're not writing your own version of Git—feature management is no different.""Feature flags are like brakes on a race car. They don't slow you down—they let you go faster by allowing you to take turns safely and accelerate out of them.""Marketing to developers is no more complicated than marketing to dentists. People are people—they respond to emotion, logic, and pain.""The biggest objection to feature flags is that people think it's gonna slow them down, when in fact it's all about speeding them up.""If you're doing go-to-market the same way you were doing it 12 months ago, you're probably doing it wrong. Now it's six months. Now it's three months."SaaS Leadership Lessons1. Market Size Trumps Perfect Execution Even with the best product and conversion rates, growth will plateau if your addressable market isn't large enough. Evaluate market size as rigorously as you evaluate product-market fit.2. Speed Requires Safety Mechanisms The fastest-moving teams aren't reckless—they've invested in systems (like feature flags) that allow them to ship confidently and recover instantly. Build your "brakes" before you try to accelerate.3. Know Your User vs. Your Buyer Developer tools require a dual strategy: serve the hands-on-keyboard users who will love (or hate) your product, while convincing budget holders of business value. Neglect either and you'll struggle.4. Friction is the Enemy of Adoption In developer tools, the ability to try your product without a sales conversation isn't optional—it's existential. Whether through open source, free trials, or freemium models, eliminate barriers to first value.5. Proprietary Data is Your AI Moat As AI reshapes discovery, the companies that win will be those with unique data sources that LLMs cite as authoritative. Think "Zillow for home prices" in your category.6. Adaptability is the New Competitive Advantage The pace of change has accelerated to the point where strategies have a 3-6 month shelf life. Build a culture of curiosity, experimentation, and rapid learning rather...

Vincent Warmerdam is a Founding Engineer at marimo, working on reinventing Python notebooks as reactive, reproducible, interactive, and Git-friendly environments for data workflows and AI prototyping. He helps build the core marimo notebook platform, pushing its reactive execution model, UI interactivity, and integration with modern development and AI tooling so that notebooks behave like dependable, shareable programs and apps rather than error-prone scratchpads.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractVincent Warmerdam joins Demetrios fresh off marimo's acquisition by Weights & Biases—and makes a bold claim: notebooks as we know them are outdated.They talk Molab (GPU-backed, cloud-hosted notebooks), LLMs that don't just chat but actually fix your SQL and debug your code, and why most data folks are consuming tools instead of experimenting. Vincent argues we should stop treating notebooks like static scratchpads and start treating them like dynamic apps powered by AI.It's a conversation about rethinking workflows, reclaiming creativity, and not outsourcing your brain to the model.// BioVincent is a senior data professional who worked as an engineer, researcher, team lead, and educator in the past. You might know him from tech talks with an attempt to defend common sense over hype in the data space. He is especially interested in understanding algorithmic systems so that one may prevent failure. As such, he has always had a preference to keep calm and check the dataset before flowing tonnes of tensors. He currently works at marimo, where he spends his time rethinking everything related to Python notebooks.// Related LinksWebsite: https://marimo.io/Coding Agent Conference: https://luma.com/codingagentsHyperbolic GPU Cloud: app.hyperbolic.ai~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]MLOps GPU Guide: https://go.mlops.community/gpuguideConnect with Demetrios on LinkedIn: /dpbrinkmConnect with Vincent on LinkedIn: /vincentwarmerdam/Timestamps:[00:00] Context in Notebooks[00:24] Acquisition and Team Continuity[04:43] Coding Agent Conference Announcement![05:56] Hyperbolic GPU Cloud Ad[06:54] marimo and W&B Synergies[09:31] marimo Cloud Code Support[12:59] Hardest Code to Generate[16:22] Trough of Disillusionment[20:38] Agent Interaction in Notebooks[25:41] Wrap up

¿Te preocupa tener tus claves y contraseñas en texto plano? En este episodio 770 de Atareao con Linux, te explico por qué deberías dejar de usar variables de entorno tradicionales y cómo Podman Secrets puede salvarte el día. Yo mismo he pasado años ignorando este problema en Docker por la pereza de configurar Swarm, pero con Podman, la seguridad viene de serie.Hablaremos en profundidad sobre el ciclo de vida de los secretos: cómo crearlos, listarlos, inspeccionarlos y borrarlos. Te mostraré cómo Podman gestiona estos datos sensibles fuera de las imágenes y fuera del alcance de miradas indiscretas en el historial de Bash. Es un cambio de paradigma para cualquier SysAdmin o entusiasta del Self-hosting.Pero no nos quedamos ahí. Te presento Crypta, mi nueva herramienta escrita en Rust que integra SOPS, Age y Git para que puedas gestionar tus secretos de forma profesional, permitiendo incluso la sincronización con repositorios remotos. Veremos cómo configurar drivers personalizados y cómo usar secretos en tus despliegues con MariaDB y Quadlets.Capítulos destacados:00:00:00 El peligro de las contraseñas en texto plano00:01:23 El problema con Docker Swarm y por qué elegir Podman00:03:16 ¿Qué es realmente un Secreto en Podman?00:04:22 Ciclo de vida: Creación y muerte de un secreto00:08:10 Implementación práctica en MariaDB y Quadlets00:12:04 Presentando Crypta: Gestión con SOPS, Age y Rust00:19:40 Ventajas de usar secretos en modo RootlessSi quieres que tu infraestructura sea realmente segura y coherente, este episodio es una hoja de ruta esencial. Aprende a ocultar lo que debe estar oculto y a dormir tranquilo sabiendo que tus tokens de API no están al alcance de cualquiera.Más información y enlaces en las notas del episodio

This show has been flagged as Clean by the host. These are the commands mentioned in the You may need to use "sudo" to run these commands depending on how your system is configured. strace uptime strace ls 2>&1 | grep open strace -e openat ls / strace ls /does/not/exist strace -o ls-trace.log ls strace -ff -o pid12345-trace.log -p 12345 HISTORY The original strace was written by Paul Kranenburg for SunOS and was inspired by its trace utility. The SunOS version of strace was ported to Linux and enhanced by Branko Lankester, who also wrote the Linux kernel support. Even though Paul released strace 2.5 in 1992, Branko's work was based on Paul's strace 1.5 release from 1991. In 1993, Rick Sladkey took on the project. He merged strace 2.5 for SunOS with the second release of strace for Linux, added many features from SVR4's truss(1), and produced a ver‐ sion of strace that worked on both platforms. In 1994 Rick ported strace to SVR4 and Solaris and wrote the automatic configuration support. In 1995 he ported strace to Irix (and became tired of writing about himself in the third person). Beginning with 1996, strace was maintained by Wichert Akkerman. During his tenure, strace development migrated to CVS; ports to FreeBSD and many architectures on Linux (including ARM, IA-64, MIPS, PA-RISC, PowerPC, s390, SPARC) were introduced. In 2002, responsibility for strace maintenance was transferred to Roland McGrath. Since then, strace gained support for several new Linux architectures (AMD64, s390x, SuperH), bi- architecture support for some of them, and received numerous additions and improvements in system calls decoders on Linux; strace development migrated to Git during that period. Since 2009, strace has been actively maintained by Dmitry Levin. During this period, strace has gained support for the AArch64, ARC, AVR32, Blackfin, C-SKY, LoongArch, Meta, Nios II, OpenRISC 1000, RISC-V, Tile/TileGx, and Xtensa architectures. In 2012, unmaintained and apparently broken support for non-Linux operating systems was removed. Also, in 2012 strace gained support for path tracing and file descriptor path decoding. In 2014, support for stack trace printing was added. In 2016, system call tampering was implemented. For the additional information, please refer to the NEWS file and strace repository commit log. Links https://strace.io https://en.wikipedia.org/wiki/Strace https://www.man7.org/linux/man-pages/man1/strace.1.html Provide feedback on this episode.

The news this week highlights shifts in Linux from multiple angles. What's evolving, why it matters, and that moment where the future actually works.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free! Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love. Support LINUX UnpluggedLinks:

Today on the Newcomer Podcast, we're joined by Yoni Rechtman, a partner at Slow Ventures and one of the most outspoken voices in venture capital right now. Rechtman doesn't hold back on Trump, the tech industry's political reckoning, or where the real opportunities in AI actually are.We talk about why Slow Ventures deliberately avoided foundation models despite the massive returns, where the second-order effects of AI create better investment opportunities, and how the firm is using "growth by buyout" to build billion-dollar companies in unsexy industries like parking lots.We also discuss Silicon Valley's response to the Trump administration, the Alex Pretti shooting, why Rechtman believes most venture capitalists are "amoral financiers" chasing momentum rather than principles, and what happens when the institutions that underpin entrepreneurial capitalism start to erode under authoritarian pressure.Rechtman shares his contrarian investment philosophy of finding "weird takes on important stories," why he thinks Git is structurally broken in the age of AI-generated code, and how AI is inverting every system built on scarce production and abundant attention.This conversation goes beyond typical VC talking points, addressing the uncomfortable questions about what the industry stands for when democracy itself is at stake.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop explores the complex world of context and knowledge graphs with guest Youssef Tharwat, the founder of NoodlBox who is building dot get for context. Their conversation spans from the philosophical nature of context and its crucial role in AI development, to the technical challenges of creating deterministic tools for software development. Tharwat explains how his product creates portable, versionable knowledge graphs from code repositories, leveraging the semantic relationships already present in programming languages to provide agents with better contextual understanding. They discuss the limitations of large context windows, the advantages of Rust for AI-assisted development, the recent Claude/Bun acquisition, and the broader geopolitical implications of the AI race between big tech companies and open-source alternatives. The conversation also touches on the sustainability of current AI business models and the potential for more efficient, locally-run solutions to challenge the dominance of compute-heavy approaches.For more information about NoodlBox and to join the beta, visit NoodlBox.io.Timestamps00:00 Stewart introduces Youssef Tharwat, founder of NoodlBox, building context management tools for programming05:00 Context as relevant information for reasoning; importance when hitting coding barriers10:00 Knowledge graphs enable semantic traversal through meaning vs keywords/files15:00 Deterministic vs probabilistic systems; why critical applications need 100% reliability20:00 CLI tool makes knowledge graphs portable, versionable artifacts with code repos25:00 Compiler front-ends, syntax trees, and Rust's superior feedback for AI-assisted coding30:00 Claude's Bun acquisition signals potential shift toward runtime compilation and graph-based context35:00 Open source vs proprietary models; user frustration with rate limits and subscription tactics40:00 Singularity path vs distributed sovereignty of developers building alternative architectures45:00 Global economics and why brute force compute isn't sustainable worldwide50:00 Corporate inefficiencies vs independent engineering; changing workplace dynamics55:00 February open beta for NoodlBox.io; vision for new development tool standardsKey Insights1. Context is semantic information that enables proper reasoning, and traditional LLM approaches miss the mark. Youssef defines context as the information you need to reason correctly about something. He argues that larger context windows don't scale because quality degrades with more input, similar to human cognitive limitations. This insight challenges the Silicon Valley approach of throwing more compute at the problem and suggests that semantic separation of information is more optimal than brute force methods.2. Code naturally contains semantic boundaries that can be modeled into knowledge graphs without LLM intervention. Unlike other domains where knowledge graphs require complex labeling, code already has inherent relationships like function calls, imports, and dependencies. Youssef leverages these existing semantic structures to automatically build knowledge graphs, making his approach deterministic rather than probabilistic. This provides the reliability that software development has historically required.3. Knowledge graphs can be made portable, versionable, and shareable as artifacts alongside code repositories. Youssef's vision treats context as a first-class citizen in version control, similar to how Git manages code. Each commit gets a knowledge graph snapshot, allowing developers to see conceptual changes over time and share semantic understanding with collaborators. This transforms context from an ephemeral concept into a concrete, manageable asset.4. The dependency problem in modern development can be solved through pre-indexed knowledge graphs of popular packages. Rather than agents struggling with outdated API documentation, Youssef pre-indexes popular npm packages into knowledge graphs that automatically integrate with developers' projects. This federated approach ensures agents understand exact APIs and current versions, eliminating common frustrations with deprecated methods and unclear documentation.5. Rust provides superior feedback loops for AI-assisted programming due to its explicit compiler constraints. Youssef rebuilt his tool multiple times in different languages, ultimately settling on Rust because its picky compiler provides constant feedback to LLMs about subtle issues. This creates a natural quality control mechanism that helps AI generate more reliable code, making Rust an ideal candidate for AI-assisted development workflows.6. The current AI landscape faces a fundamental tension between expensive centralized models and the need for global accessibility. The conversation reveals growing frustration with rate limiting and subscription costs from major providers like Claude and Google. Youssef believes something must fundamentally change because $200-300 monthly plans only serve a fraction of the world's developers, creating pressure for more efficient architectures and open alternatives.7. Deterministic tooling built on semantic understanding may provide a competitive advantage against probabilistic AI monopolies. While big tech companies pursue brute force scaling with massive data centers, Youssef's approach suggests that clever architecture using existing semantic structures could level the playing field. This represents a broader philosophical divide between the "singularity" path of infinite compute and the "disagreeably autistic engineer" path of elegant solutions that work locally and affordably.

Join us LIVE on Mondays, 4:30pm EST.A weekly Podcast with BHIS and Friends. We discuss notable Infosec, and infosec-adjacent news stories gathered by our community news team.https://www.youtube.com/@BlackHillsInformationSecurityChat with us on Discord! - https://discord.gg/bhis

¿Sigues usando Docker porque te da pereza el cambio? En este episodio de Atareao con Linux te voy a demostrar por qué los Quadlets son la razón definitiva para que te decantes por Podman de una vez por todas. Si ya te hablé de los Pods y te pareció interesante, lo de hoy es llevar la gestión de contenedores al siguiente nivel: la integración TOTAL con SystemD.En el episodio 768, te explico cómo los Quadlets permiten gestionar tus contenedores, volúmenes y redes exactamente como si fueran servicios nativos de tu sistema operativo. Olvídate de scripts extraños o de depender de herramientas externas; aquí todo se define con archivos de configuración sencillos (.container, .network, .volume) que SystemD entiende a la perfección.Te cuento mi experiencia real migrando mis proyectos actuales. Ya tengo bases de datos PostgreSQL funcionando bajo este modelo y la estabilidad es, simplemente, de otro planeta. Veremos cómo levantar un stack completo de WordPress con MariaDB y Redis utilizando esta tecnología, gestionando las dependencias entre ellos con las directivas 'After' y 'Requires' de SystemD. ¡Se acabó el que un contenedor intente arrancar antes de que la base de datos esté lista!Capítulos del episodio: 00:00:00 Introducción y el adiós definitivo a Docker 00:01:33 ¿Qué es un Quadlet y por qué revoluciona Linux? 00:03:22 Los 6 tipos de Quadlets disponibles 00:05:12 Cómo gestionar un Quadlet de tipo contenedor 00:06:46 Definiendo Redes y Volúmenes como servicios 00:08:13 El flujo de trabajo: Git, secretos y portabilidad 00:11:22 Integración con SystemD: Nombres y prefijos 00:13:42 Desplegando un Stack completo: WordPress, MariaDB y Redis 00:16:02 Modificando contenedores y recarga de SystemD (Daemon-reload) 00:17:50 Logs con JournalCTL y mantenimiento simplificado 00:19:33 Auto-update: Olvídate de Watchtower para siempre 00:20:33 Conclusiones y próximos pasos en la migraciónAdemás, exploramos ventajas brutales como el control de versiones mediante Git, la gestión de logs centralizada con JournalCTL y las actualizaciones automáticas nativas que harán que te olvides de Watchtower. Si quieres que tu servidor Linux sea más profesional, robusto y fácil de mantener, no puedes perderte este audio.Más información y enlaces en las notas del episodio

In today's Cloud Wars Minute, I look at how Microsoft is helping developers build and scale AI agents safely inside Visual Studio Code.Highlights00:10 — The Microsoft Copilot Studio extension for Visual Studio Code is now generally available, providing developers with the ability to build and manage Copilot Studio agents directly within the IDE. This extension is designed for developers and integrates seamlessly into their workflows.00:28 — It includes standard Git integration, request-based pull reviews, auditability, and is tailored to the VS Code UX. The new extension reflects the growing complexity of agents and equips developers with the same best practices they use for app development, including, as Microsoft puts it, source control, pull requests, change history, and repeatable deployments.01:02 — This extension really benefits developers when they need to manage complex agents, collaborate with multiple stakeholders, and ensure that any changes made are done so safely. It's ideal for developers who prefer to build within their IDE while also having an AI assistant available to help them iterate more quickly and productively.01:30 — The extension introduces important structural support for the development of AI agents. By integrating Copilot Studio directly into VS Code, Microsoft is empowering developers to build more efficiently, without compromising control, access to collaborators, or safety. This is a critical combination as AI agents become increasingly more powerful and complex.02:00 — As these agents continue to evolve, they require the same stringent checks and balances as traditional software. Microsoft's Copilot Studio extension addresses this by giving developers the tools they need to scale agents responsibly while maintaining performance. Visit Cloud Wars for more.

A prominent leader at the intersection of data science and data engineering across multiple languages shares her insights, how a recent Git workshop tailored for data science de-mystifies common pitfalls, and for the second straight episode a new transpiler bringing dplyr syntax to databases (quite literally). Episode Links This week's curator: Jon Carroll - @jonocarroll@fosstodon.org (Mastodon) & @jonocarroll.fosstodon.org.ap.brid.gy (Bluesky) & @carroll_jono (X/Twitter)The Test Set Pod - Column selectors, data quality, and learning in public (Episode link)Git & GitHub: Practical Version Control for Data Workdplyr comes to duckdbEntire issue available at rweekly.org/2026-W06Supplement Resources Risk Conference 2026 Agenda (Mike is presenting!) https://rconsortium.github.io/Risk_website/program.htmllibdplyr https://github.com/mrchypark/libdplyrSupporting the show Use the contact page at https://serve.podhome.fm/custompage/r-weekly-highlights/contact to send us your feedbackR-Weekly Highlights on the Podcastindex.org - You can send a boost into the show directly in the Podcast Index. First, top-up with Alby, and then head over to the R-Weekly Highlights podcast entry on the index.A new way to think about value: https://value4value.infoGet in touch with us on social mediaEric Nantz: @rpodcast@podcastindex.social (Mastodon), @rpodcast.bsky.social (BlueSky) and @theRcast (X/Twitter)Mike Thomas: @mike_thomas@fosstodon.org (Mastodon), @mike-thomas.bsky.social (BlueSky), and @mike_ketchbrook (X/Twitter) Music credits powered by OCRemix Lost Woods Inglewood - Legend of Zelda: Link's Awakening - BenCousins - https://ocremix.org/remix/OCR00853Aerobotics - Mega Man 8 - Just Coffee - https://ocremix.org/remix/OCR03323

Danny and Ritu dive deep into Anthropic's new Cowork feature in Claude Desktop - the good, the bad, and the ugly. Ritu shares a cautionary tale about file deletion gone wrong, while Danny demonstrates his custom skills pack that protects users from common pitfalls. What You'll Learn: What Cowork is and how it differs from Claude Code and Claude Desktop Why the rm -rf command can permanently delete your files (and how to prevent it) How to set up deny lists in your Claude settings to protect critical files The power of skills and bootstrap files for consistent, reliable outputs Decision panels: letting Claude guide you through complex choices Cascade skills: research to article to slides in one automated flow Key Timestamps: 00:00 - Introduction to Cowork 02:13 - Ritu's file deletion disaster story 08:37 - Understanding rm vs rm -rf commands 10:01 - Setting up deny lists for protection 16:00 - Evolution from Claude Desktop to Claude Code to Cowork 25:55 - Skills deep dive: orchestrator, quality gate, flow state 39:48 - Research cascade skill demonstration 43:18 - Decision panels walkthrough 48:58 - 2026 predictions: Ambient AI and pixel-free interfaces Resources Mentioned: Danny's Skills Pack (available to listeners) Typora - Markdown editor ($14 lifetime) Time Machine backup for Mac users Git for version control Connect with Ritu Java: LinkedIn Connect with Danny McMillan: LinkedIn | Seller Sessions

Editor's note: Welcome to our new AI for Science pod, with your new hosts RJ and Brandon! See the writeup on Latent.Space (https://Latent.Space) for more details on why we're launching 2 new pods this year. RJ Honicky is a co-founder and CTO at MiraOmics (https://miraomics.bio/), building AI models and services for single cell, spatial transcriptomics and pathology slide analysis. Brandon Anderson builds AI systems for RNA drug discovery at Atomic AI (https://atomic.ai). Anything said on this podcast is his personal take — not Atomic's.—From building molecular dynamics simulations at the University of Washington to red-teaming GPT-4 for chemistry applications and co-founding Future House (a focused research organization) and Edison Scientific (a venture-backed startup automating science at scale)—Andrew White has spent the last five years living through the full arc of AI's transformation of scientific discovery, from ChemCrow (the first Chemistry LLM agent) triggering White House briefings and three-letter agency meetings, to shipping Kosmos, an end-to-end autonomous research system that generates hypotheses, runs experiments, analyzes data, and updates its world model to accelerate the scientific method itself.* The ChemCrow story: GPT-4 + React + cloud lab automation, released March 2023, set off a storm of anxiety about AI-accelerated bioweapons/chemical weapons, led to a White House briefing (Jake Sullivan presented the paper to the president in a 30-minute block), and meetings with three-letter agencies asking “how does this change breakout time for nuclear weapons research?”* Why scientific taste is the frontier: RLHF on hypotheses didn't work (humans pay attention to tone, actionability, and specific facts, not “if this hypothesis is true/false, how does it change the world?”), so they shifted to end-to-end feedback loops where humans click/download discoveries and that signal rolls up to hypothesis quality* Cosmos: the full scientific agent with a world model (distilled memory system, like a Git repo for scientific knowledge) that iterates on hypotheses via literature search, data analysis, and experiment design—built by Ludo after weeks of failed attempts, the breakthrough was putting data analysis in the loop (literature alone didn't work)* Why molecular dynamics and DFT are overrated: “MD and DFT have consumed an enormous number of PhDs at the altar of beautiful simulation, but they don't model the world correctly—you simulate water at 330 Kelvin to get room temperature, you overfit to validation data with GGA/B3LYP functionals, and real catalysts (grain boundaries, dopants) are too complicated for DFT”* The AlphaFold vs. DE Shaw Research counterfactual: DE Shaw built custom silicon, taped out chips with MD algorithms burned in, ran MD at massive scale in a special room in Times Square, and David Shaw flew in by helicopter to present—Andrew thought protein folding would require special machines to fold one protein per day, then AlphaFold solved it in Google Colab on a desktop GPU* The E3 Zero reward hacking saga: trained a model to generate molecules with specific atom counts (verifiable reward), but it kept exploiting loopholes, then a Nature paper came out that year proving six-nitrogen compounds are possible under extreme conditions, then it started adding nitrogen gas (purchasable, doesn't participate in reactions), then acid-base chemistry to move one atom, and Andrew ended up “building a ridiculous catalog of purchasable compounds in a Bloom filter” to close the loopAndrew White* FutureHouse: http://futurehouse.org/* Edison Scientific: http://edisonscientific.com/* X: https://x.com/andrewwhite01* Cosmos paper: https://futurediscovery.org/cosmosFull Video EpisodeTimestamps00:00:00 Introduction: Andrew White on Automating Science with Future House and Edison Scientific00:02:22 The Academic to Startup Journey: Red Teaming GPT-4 and the ChemCrow Paper00:11:35 Future House Origins: The FRO Model and Mission to Automate Science00:12:32 Resigning Tenure: Why Leave Academia for AI Science00:15:54 What Does ‘Automating Science' Actually Mean?00:17:30 The Lab-in-the-Loop Bottleneck: Why Intelligence Isn't Enough00:18:39 Scientific Taste and Human Preferences: The 52% Agreement Problem00:20:05 Paper QA, Robin, and the Road to Cosmos00:21:57 World Models as Scientific Memory: The GitHub Analogy00:40:20 The Bitter Lesson for Biology: Why Molecular Dynamics and DFT Are Overrated00:43:22 AlphaFold's Shock: When First Principles Lost to Machine Learning00:46:25 Enumeration and Filtration: How AI Scientists Generate Hypotheses00:48:15 CBRN Safety and Dual-Use AI: Lessons from Red Teaming01:00:40 The Future of Chemistry is Language: Multimodal Debate01:08:15 Ether Zero: The Hilarious Reward Hacking Adventures01:10:12 Will Scientists Be Displaced? Jevons Paradox and Infinite Discovery01:13:46 Cosmos in Practice: Open Access and Enterprise Partnerships Get full access to Latent.Space at www.latent.space/subscribe

EP 276. In this week's update:Ireland has enacted sweeping new lawful interception powers, granting law enforcement expanded access to encrypted communications and raising fresh concerns among privacy advocates and tech companies.TikTok's latest U.S. privacy policy update expands location tracking, AI interaction logging, and cross-platform ad targeting, marking a significant escalation in data collection under its new American ownership structure.The newly released OWASP Top 10 (2025 edition) highlights the most critical web application security risks, providing developers and organizations with an updated roadmap to prioritize defenses against evolving threats.Security researchers have uncovered a critical bypass in NPM's post-Shai-Hulud supply-chain protections, allowing malicious code execution via Git dependencies in multiple JavaScript package managers.As Artemis II approaches, NASA defends the Orion spacecraft's unchanged heat shield design despite persistent cracking concerns from its uncrewed predecessor, while some former engineers warn the risk remains unacceptably high.Anthropic has significantly revised Claude's governing “constitution,” shifting from strict rules to high-level ethical principles while explicitly addressing the hypothetical possibility of AI consciousness and moral status.The European Parliament has adopted a strongly worded resolution urging the EU to reduce strategic dependence on American tech giants through aggressive investment in sovereign cloud, AI, and open digital infrastructure.This one's a good'n.  Let's get to it!Find the full transcript here.

This show has been flagged as Clean by the host. Development isn't over until it's packaged Most software development I've done has been utilities for highly specific workflows. I've written code to ensure that metadata for a company's custom file format gets copied along with the rest of the data when the file gets archived, code that ensures a search field doesn't mangle input, lots of Git hooks, file converters, parsers, and of course my fair share of dirty hacks. Because most software projects I work on are designed for a specific task, very few of them have required packaging. My utilities have been either integrated into a larger code base I'm not responsible for, or else distributed across an infrastructure by an admin. It's like a magic trick, which has made my life conveniently easier but, as magic does, it has also tricked me into thinking that my development work is done once I can prove that my code does its job. The reality is that code development isn't actually done until you can deliver it to your users in a format they can install. I don't think I'm alone in forgetting that software delivery is the real final product. There are many reasons some developers stop short of providing an installable package for the code they've worked on for weeks or months or years. First of all, packaging is work, and after writing and troubleshooting code for months, sometimes you just want your work to be over just as soon as everything functions as expected. Secondly, there are a lot of software package formats out there, regardless of what platform you're delivering to. However, I view packaging as part of quality assurance. There are lots of benefits you gain by packaging your code into an installer, and you don't have to target every package format. In fact, you get the benefits of packaging by creating just one package. Checking for consistency When you package your code as an installable file, whether it's an RPM file or a Bash script or a Flatpak or AppImage or EXE or MSI or anything else, you are checking your code base for consistency. Pick whatever package format you're most comfortable with, or the one you think represents the bulk of your target audience, and you're sure to find that the package tooling expects to be automated. Nobody wants to start packaging from scratch every time they update code, so naturally packaging tools are designed to be configured once for a specific code base and then to create updated packages each time the code base is updated. If you're building a package for your project and discover that you have to manually intervene, then you've discovered a bug in your code. Imagine that you've got a project repository with a name in camel-case. You hadn't noticed before, but your code refers to itself in a mix of lowercase and camel-case. Your package build grinds to a halt because a variable used by the packaging tools suddenly can't find your code base because it was set to a lowercase title but the archive of your code uses camel-case. If this happens to you, it's also going to happen for every software packager trying to help you deliver your project to their users. Fix it for yourself, and you've fixed it for everyone. Discover surprise dependencies For decades, one of the most common problems of software troubleshooting has been the phrase “well, it works on my machine.” No matter how many tools we developers have at our disposal to make it easy to build and run software on a clean system, it's still common to accidentally deliver software with surprise dependencies. It's easy to forget to revert to a clean snapshot in a virtual machine, or to use a container that just happens to have a more recent version of a library than you'd realised, or to get the path of an important executable wrong in a script, or to forget that not all computers ship with a thing you take for granted. Not all packaging tools are immune to this problem, but very robust ones (like RPM and DEB, Flatpak, and AppImage) are. I can't count the times I've tried to deliver an RPM only to be reminded by rpmbuild that I haven't included the -devel version of a dependency (many Linux distributions separate development libraries from binaries.) You may not literally fix every problem with dependency management by building a single package, but you can clearly identify what your code requires. It only takes a single warning from your packaging tool for you to add a note to other packagers about what they must include in their own builds. As an additional bonus, it's also a good reminder to double check the licenses your project is using. In the haze of desperate hacking to get something to just-work-already, it's helpful to get a gentle reminder that you've linked to a library with a different license than everything else. Few packaging tools (if any?) detect licensing requirements directly, but sometimes all it takes is a reminder that you're using a library that comes from a non-standard repo for you to remember to review licensing. Every package is an example package Once you've packaged your code once, you create an example for everyone coming to your project to turn it into a package of their own. It doesn't matter whether your example package is an RPM or a DEB or just a TGZ for a front-end like SlackBuild or Arch's AUR, it's the interaction between a packaging system and the input script that counts. Even a novice package maintainer is likely to be able to reverse engineer a packaging script enough to reuse the same logic for their own package. Here's the build and install section of the RPM for GNU Hello: %prep %autosetup %build %configure make %{?_smp_mflags} %install %make_install %find_lang %{name} rm -f %{buildroot}/%{_infodir}/dir %post /sbin/install-info %{_infodir}/%{name}.info %{_infodir}/dir || : Here's the GNU Hello build script for Arch Linux: source=(https://ftp.gnu.org/gnu/hello/$pkgname-$pkgver.tar.gz) md5sums=('5cf598783b9541527e17c9b5e525b7eb') build(){ cd "$pkgname-$pkgver" ./configure --prefix=/usr make } package(){ cd "$pkgname-$pkgver" make DESTDIR="$pkgdir/" install } There are differences, but you can see the shared logic. There are macros or functions that abstract some common steps of the build process, there are variables to ensure consistency, and they both benefit from using automake as provided by the source code. Armed with these examples, you could probably write a DEB package or Flatpak ref for GNU Hello in an afternoon. Package your code at least once Packaging is quality assurance. Even though a packaging system is really just a front-end for whatever build system your code uses anyway, the rigour of creating a repeatable and automated process for delivering your project is a helpful exercise. It benefits your project, and it benefits the people eager to deliver your project to other users. Software development isn't over until it's packaged.Shownotes taken from https://www.both.org/?p=13264Provide feedback on this episode.

On this episode of The SaaS CFO Podcast, Ben Murray welcomes Nick Holzherr, serial tech founder and the driving force behind GitLaw—an innovative AI-powered legal platform. Nick Holzherr shares his entrepreneurial journey, from exiting previous ventures like Whisk and Air HR, to launching GitLaw earlier this year with $3 million in backing. The conversation goes deep into the frustrations of traditional legal services, how GitLaw leverages trusted templates and advanced AI orchestration for SMBs, and what sets their product apart from simply using ChatGPT for contracts. You'll hear about go-to-market growth loops, the challenges of scaling in the rapidly evolving AI landscape, and Nick Holzherr's focus on building a product that customers love and trust. Whether you're interested in SaaS metrics, team dynamics, or the future of AI in legal tech, this episode is packed with insights from a founder who's in the thick of it. Show Notes: 00:00 "Revolutionizing Legal Services with AI" 04:09 "Contract Review and Market Standards" 09:10 "Building Success with Trusted Team" 11:45 AI-Powered Legal Document Collaboration 15:19 "Startup Uncertainty Amid Rapid Growth" 18:35 "Challenges in Marketing Metrics Transparency" 21:24 Retention and User Feedback Focus 22:51 "Balancing SaaS Margins and Costs" 26:33 "Making AI Trustworthy and Useful" 29:03 Git.law Service Overview Links: SaaS Fundraising Stories: https://www.thesaasnews.com/news/gitlaw-raises-3-million-in-funding Nick Holzherr's LinkedIn: https://www.linkedin.com/in/nickholzherr/ GitLaw's LinkedIn: https://www.linkedin.com/company/gitlawco/ GitLaw's Website: https://git.law/ To learn more about Ben check out the links below:Subscribe to Ben's daily metrics newsletter: https://saasmetricsschool.beehiiv.com/subscribe Subscribe to Ben's SaaS newsletter: https://mailchi.mp/df1db6bf8bca/the-saas-cfo-sign-up-landing-page SaaS Metrics courses here: https://www.thesaasacademy.com/ Join Ben's SaaS community here: https://www.thesaasacademy.com/offers/ivNjwYDx/checkout Follow Ben on LinkedIn: https://www.linkedin.com/in/benrmurray

Microsoft granted the FBI access to laptops encrypted with BitLocker. The EU opens an investigation into Grok's creation of sexually explicit images. Glimmers of access pierce Iran's internet blackout. Koi Security warns npm fixes fall short against PackageGate exploits. Some Windows 11 devices fail to boot after installing the January Patch Tuesday updates. CISA warns of active exploitation of  multiple vulnerabilities across widely used enterprise and developer software. ESET researchers have attributed the cyberattack on Poland's energy sector to Russia's Sandworm. This week's business breakdown. Brandon Karpf joins us to talk space and cyber. CISA sits out RSAC.  Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Our guest today is cybersecurity executive and friend of the show Brandon Karpf with Dave Bittner and T-Minus Space Daily host Maria Varmazis, for our monthly space and cyber segment. Brandon, Maria and Dave discuss “No more free rides: it's time to pay for space safety.” Selected Reading FBI Accessed Windows Laptops After Microsoft Shared BitLocker Recovery Keys (Hackread) European Commission opens new investigation into X's Grok (The Register) Amid Two-Week Internet Blackout, Some Iranians Are Getting Back Online (New York Times) Hackers can bypass npm's Shai-Hulud defenses via Git dependencies (Bleeping Computer) Microsoft investigates Windows 11 boot failures after January updates (Bleeping Computer) CISA says critical VMware RCE flaw now actively exploited (Bleeping Computer) CISA confirms active exploitation of four enterprise software bugs (Bleeping Computer) ESET Research: Sandworm behind cyberattack on Poland's power grid in late 2025 (ESET)  Aikido secures $60 million in Series B funding. (N2K Pro Business Briefing) CISA won't attend infosec industry's biggest conference (The Register) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show.   Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Rewrites are seductive. Clean slates promise clarity, speed, and “doing it right this time.” In practice, they're often late, over budget, and quietly demoralizing.In this episode of Maintainable, Robby sits down with Brittany Ellich, a Senior Software Engineer at GitHub, to talk about a different path. One rooted in stewardship, readability, and resisting the urge to start over.Brittany's career began with a long string of rebuild projects. Over time, she noticed a pattern. The estimates were wrong. Feature development stalled. Teams burned energy reaching parity with systems they'd already had. That experience pushed her toward a strong belief: if software is in production and serving users, it's usually worth maintaining.[00:00:57] What well-maintained software actually looks likeFor Brittany, readability is the first signal. If code can't be understood, it can't be changed safely. Maintenance begins with making systems approachable for the next person.[00:01:42] Rethinking technical debtShe explains how her understanding of technical debt has evolved. Rather than a fixed category of work, it's often anything that doesn't map directly to new features. Bugs, reliability issues, and long-term risks frequently get lumped together, making prioritization harder than it needs to be.[00:05:49] Why AI changes the maintenance equationBrittany describes how coding agents have made it easier to tackle small, previously ignored maintenance tasks. Instead of waiting for debt to accumulate into massive projects, teams can chip away incrementally. (Related: GitHub Copilot and the Copilot coding agent workflow she's explored.)[00:07:16] Context from GitHub's billing systemsWorking on metered billing at GitHub means correctness and reliability matter more than flash. Billing should be boring. When it's not, customers notice quickly.[00:11:43] Navigating a multi-era codebaseGitHub's original Rails codebase is still in active use. Brittany relies heavily on Git blame and old pull requests to understand why decisions were made, treating them as a form of living documentation.[00:25:27] Treating coding agents like teammatesRather than delegating massive changes, Brittany assigns agents small, well-scoped tasks. She approaches them the same way she would a new engineer: clear instructions, limited scope, and careful review.[00:36:00] Structuring the day to avoid cognitive overloadShe breaks agent interaction into focused windows, checking in a few times a day instead of constantly monitoring progress. This keeps deep work intact while still moving maintenance forward.[00:40:24] Low-risk ways to experimentImproving test coverage and generating repository instructions are safe entry points. These changes add value without risking production behavior.[00:54:10] Navigating team resistance and ethicsBrittany acknowledges skepticism around AI and encourages teams to start with existing backlog problems rather than selling AI as a feature factory.[00:57:57] Books, habits, and staying balancedOutside of software, Brittany recommends Atomic Habits by James Clear, sharing how small routines help her stay focused.The takeaway is clear. AI doesn't replace engineering judgment. Used thoughtfully, it can support the unglamorous work that keeps software alive.Good software doesn't need a rewrite.It needs caretakers.References MentionedGitHub – Brittany's current role and the primary environment discussedGitHub Universe – Where Brittany presented her coding agent workflowAtomic Habits by James Clear – Brittany's recommended book outside of techOvercommitted - Podcast Brittany co-hostsThe Balanced Engineer Newsletter – Brittany's monthly newsletter on engineering, leadership, and balanceBrittany Ellich's website – Central hub for her writing and linksGitHub Copilot – The AI tooling discussed throughout the episodeHow the GitHub billing team uses the coding agent in GitHub Copilot to continuously burn down technical debt – GitHub blog post referencedThanks to Our Sponsor!Turn hours of debugging into just minutes! AppSignal is a performance monitoring and error-tracking tool designed for Ruby, Elixir, Python, Node.js, Javascript, and other frameworks.It offers six powerful features with one simple interface, providing developers with real-time insights into the performance and health of web applications.Keep your coding cool and error-free, one line at a time! Use the code maintainable to get a 10% discount for your first year. Check them out! Subscribe to Maintainable on:Apple PodcastsSpotifyOr search "Maintainable" wherever you stream your podcasts.Keep up to date with the Maintainable Podcast by joining the newsletter.

Stolen Target source code looks real. CISA pulls the plug on Gogs. SAP rushes patches for critical flaws. A suspected Russian spy emerges in Sweden, while Cloudflare threatens to walk away from Italy. Researchers flag a Wi-Fi chipset bug, a long-running Magecart skimming campaign, and a surge in browser-in-the-browser phishing against Facebook users. Mandiant releases a new Salesforce defense tool, and NIST asks how to secure agentic AI before it secures itself. Our guests are Christine Blake and Madison Farabaugh from Inside the Media Minds. Plus, a Dutch court says seven years is still the going rate for a USB-powered cocaine plot. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Christine Blake and Madison Farabaugh from W2 Communications and hosts of Inside the Media Minds podcast on their show joining the N2K CyberWire network. You can listen to the latest episode of Inside the Media Minds today and catch new installments every month on your favorite podcast app. Selected Reading Target employees confirm leaked code after ‘accelerated' Git lockdown (Bleeping Computer) Fed agencies urged to ditch Gogs as zero-day makes CISA list (The Register) SAP's January 2026 Security Updates Patch Critical Vulnerabilities (SecurityWeek) Sweden detains ex-military IT consultant suspected of spying for Russia (The Record) Cloudflare CEO threatens to pull out of Italy  (The Register) One Simple Trick to Knock Out the Wi-Fi Network (GovInfo Security) Google's Mandiant releases free Salesforce access control checker (iTnews) Global Magecart Campaign Targets Six Card Networks (Infosecurity Magazine) Facebook login thieves now using browser-in-browser trick (Bleeping Computer) NIST Calls for Public to Help Better Secure AI Agents (GovInfo Security) Appeal fails for hacker who opened port to coke smugglers (The Register) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this explosive first installment of TESD in 2026, Git ‘em comes back from the brink of death and involuntary urination. Ming joins the boys to explain his apology to Mike. Spoiler alert: It doesn't go well!

Mauer mugs Evette. Git yer ugly mug over to PayPal and Patreon and donate to wildbow! Crypto Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 3.0 Licensehttp://creativecommons.org/licenses/by/3.0/The post Thicker Than Water 14.10 first appeared on Twig Audiobook.

Sam Partee started out his love for tech/engineering by working on cars. After many y ears of working on cars, and even starting his own car stereo installation business, he decided that cards were finite and moved onto computers. He fell in love with the space, and the rest is history, filled with super computers, AI, distributed training, Redis and the lot. Outside of tech, he loves to take long hikes with his snowy husky.Sam and his team built a prior solution, an agent to solve bugs for you. They ran into a litany of problems, but eventually figured out that there was a dire need for an authorization for the activities that agents wanted to do on your behalf. Fast forward, and they are working with Anthropic to define these auth protocols.This is the creation story of Arcade.SponsorsIncogniNordProtectVentionCodeCrafters helps you become a better engineer by building real-world, production-grade projects. Learn hands-on by creating your own Git, Redis, HTTP server, SQLite, or DNS server from scratch. Sign up for free today using this link and enjoy 40% off.Full ScalePaddle.comSema SoftwarePropelAuthPostmanMeilisearchLinkshttps://www.arcade.dev/https://www.linkedin.com/in/sampartee/Support this podcast at — https://redcircle.com/code-story-insights-from-startup-tech-leaders/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy