Podcasts about software engineers

Anish Acharya speaks with Microsoft VP of Design John Maeda and Impeccable founder and CEO Paul Bakaus about how AI is changing the practice of design. The conversation explores the relationship between design and technology, the rise of AI-powered creative tools, and whether automation raises the floor, the ceiling, or both. Maeda and Bakaus discuss software craftsmanship, taste, creative judgment, and why some aspects of design may become increasingly automated while others become more valuable. They also examine agentic workflows, the future of user experience, the role of designers in an AI-native world, and how new tools may reshape the relationship between designers, engineers, and software itself.   Resources: Follow Anish Acharya on X: https://x.com/illscience Follow John Maeda on X: https://x.com/johnmaeda Follow Paul Bakaus on X: https://x.com/pbakaus Get the GitHub Copilot app: gh.io/app Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

シリコンバレーファウンダーのヘルスハックについて話しました。クレアチンの話 https://t.co/8E5FPn1M1f起業家のポスト https://x.com/denk_tweets/status/2000958854666813556睡眠とクレアチンの関係 https://x.com/newstart_2024/status/2057873475721732527NAD+ https://amzn.to/4uKnaf5NAC https://amzn.to/3SkLV45Omega3 https://amzn.to/4w3gaLv感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

Your Java AI application is live in production. But have you tested whether it can be jailbroken, manipulated into revealing its system prompt, or tricked into printing content it should never output?In this episode, Iryna Dohndorf, Software Engineer at Karakun Group and creator of Tiberius, explains how to bring security testing to LLM-powered Java applications. We cover why traditional unit tests break down with non-deterministic systems, how the Scan-Fixture-Validate workflow works, what buff mutation testing is, and why even well-trained models can be cracked with something as simple as the grandmother attack.Topics include:Why LLM non-determinism breaks the classic input/output test modelThe Scan-Fixture-Validate principle and sharing test artifacts across teamsPrompt injection, jailbreaks, and emotional manipulation attacksBuff mutation: testing linguistic surface coverageProbabilistic security contracts and multi-trial scansFingerprinting and why your model choice should not be detectableLLM as a judge: using a second model as a guardrailGetting started with Tiberius in Spring Boot and LangChain4jGuestIryna Dohndorf - Software Engineer at Karakun GroupLinkedInLinksArticle on FoojayTiberius on GitHubSecurity Testing GuideTimestamps00:00 Introduction of topic and guest01:05 The problem Tiberius wants to solve06:39 How "traditional" unit tests don't work for LLM integrations10:23 Scan-Fixture-Validate principle and sharing artifacts15:15 Using different skills, for example, the grandmother skill17:33 Testing for required versus forbidden bias19:35 The probes across nine attack categories used by Tiberius20:44 Buff mutation testing26:55 Using Tiberius in your pipelines and when to fail29:35 Using multi-trial scans31:14 Fingerprinting: which model you use, should not be detectable32:55 Combining multiple models, model as a judge34:41 Sharing JSON models to improve tests36:05 How to get started with Tiberius in Spring and with LangChain4j36:41 Quarkus not supported yet, plans for the future39:07 Conclusions and a call out to everyone to become a Foojay author

Danny Yang and Sam Goldman, both Software Engineers at Meta, speak with host Gregory M. Kapfhammer about the Rust-based Pyrefly type checker for Python. After a look at the foundational concepts for annotating and checking types for Python programs, Danny and Sam present a deep dive of the implementation of Pyrefly. While comparing and contrasting against various type checkers, they also describe how Pyrefly implements the language server protocol (LSP) for Python. The episode explores a range of other topics, including how to balance the features, performance, and language integrations of a type checker.

Zipline Roundtable episode: Building Real-Time ML Systems with Zipline + ChrononJoin the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguideBig shout-out to ZiplineAI for the collaboration!// AbstractReal-time ML use cases like personalization and risk decisioning come with a unique set of challenges: serving fresh feature values at low latency for inference, generating temporally consistent backfills for training, and building complex chains of on-demand, batch, and streaming transformations. In this roundtable, practitioners from Intuit, CreditKarma, Depop, and OpenAI share how they use Zipline and the OSS Chronon project to solve these challenges and deploy real-time ML use cases in production.// BioGerman KrikorianGerman is a Software Engineer on the Feature Platform team at Credit Karma. Since joining the company during the early development of its recommendation system, they have played a key role in building and scaling the platform over the years. Their work focuses on feature pipelines and the feature store, which serves as critical infrastructure supporting numerous teams and business verticals across the organization.Ben MagyarBen is an engineer at Depop working on ML and data systems. Before Depop, he worked on Search at Etsy. Most of his work is around the infrastructure and operational problems that come with running ML systems at scale.Raj KatakamRaj architects ML Infrastructure at Credit Karma (Intuit). He holds a Master's in Software Engineering from Carnegie Mellon and a B.Tech in EECE from IIT Kharagpur. His interests include ML Infrastructure, Distributed Systems, Real-Time Data Processing, and Generative AI. His current focus is on providing feature engineering platforms, production GenAI infrastructure, vector databases, ML model serving, and MLOps pipelines for fraud detection, personalized recommendations, financial insights, and model explainability.Mick JermsurawongLed Flyte ML training/experimentation at Stripe, and now led Chronon for ML features at OpenAIHosted by Demetrios// Related LinksWebsite: https://zipline.ai/https://chronon.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/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with German on LinkedIn: /e2zdkwh8cxghydg/Connect with Raj on LinkedIn: /rajkiran2190Connect with Mick on LinkedIn:/mick-jermsurawong/

In this episode a regular guest joins me to give an inside view of Data Centers.  Ben, The Christian Libertarian is a Software Engineer that works at them.  He gives some very unique perspectives, facts, how they are structured and built, and how long they have been around.  Do we have to worry about them?  Are they taking over?  How can we make them partners in our environment and communities?  Seriously, we can't condemn them.  Every electronic we use is hooked up some way to a data center.  So if we don't want to stop using computers, electronics, Apps, phones, etc. we are stuck with them, so wouldn't it be better to learn more and work with them to get them to do the right things in our communities, respect our land, water, and environment.  And in the meantime, get our politicians to stop giving away the farm and get some legislation to protect us from being being over surveilled and having our data collected 24/7.

シリコンバレーで起きているAIネイティブな組織づくりについて話しました。Coinbase のレイオフ https://x.com/brian_armstrong/status/2051616759145185723Anthropicで働くことにした理由 https://henrythe9th.substack.com/p/whats-it-really-like-to-work-insideAIネイティブインタビュー https://sierra.ai/blog/the-ai-native-interview感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

WeAreDevelopers, the Berlin-based developer conference founded in 2015, has grown into a major global event, attracting 15,000 developers from over 70 countries each year. In 2026, it expands beyond Europe with new editions in San Jose, California, and Bengaluru, India. Co-founder and CEO Sead Ahmetovic says the conference was created to give developers a stronger voice in an industry where marketers, salespeople, and entrepreneurs often receive more recognition.  He believes developers, despite being less vocal, build the products that power the modern world. The event began as a small meetup that quickly gained popularity, filling a gap between highly specialized technical gatherings and broader business-focused conferences. Former GitHub CEO Thomas Dohmke highlights another benefit: giving developers a platform to share the stories behind their work and inspire peers.  Discussing the future of software development, Dohmke predicts AI agents will handle much of the coding, while developers focus on managing ideas, prompts, and workflows. Ahmetovic agrees, arguing that developers will remain essential, spending less time typing code and more time thinking, orchestrating, and creating new solutions.  Learn more from The New Stack around the latest in developer community growth:  How Community Helps Developers Grow  Empowering Developers Is Critical to Drive AI Innovation  3 Ways Organizations Can Redefine the Developer Experience  Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

"C'est important que je comprenne le code qui a été généré." Le D.E.V. de la semaine est Guillaume Lours, Software Engineer chez Docker. Avec lui, on plonge dans les coulisses de Docker Sandbox, cette solution pensée pour sécuriser l'automatisation et l'exécution de code par IA. Guillaume nous explique comment limiter les risques sans sacrifier l'efficacité, et détaille l'architecture technique : micro VM légère, man-in-the-middle pour la gestion des credentials, profils de sécurité personnalisables, le tout pour une expérience fluide. Il partage aussi des astuces d'usage au quotidien pour tester, reviewer ou itérer plus sereinement. Un éclairage concret sur le futur du développement outillé par l'IA.Chapitrages00:00:53 : Docker et Magie Noire00:01:32 : Présentation de Guillaume00:03:15 : Docker et les Générations00:04:50 : Résumé de Docker00:07:12 : Utilisation de Docker pour Tous00:08:51 : Écosystèmes et Docker00:12:43 : Complexité vs Simplicité00:17:20 : Introduction à Docker Compose00:20:23 : Fonctionnement de Docker Compose00:22:43 : Responsabilités de Compose et Engine00:28:19 : Ordonnancement et Réconciliation00:32:45 : Conteneurisation vs Virtualisation00:37:31 : Introduction à Docker Sandbox00:40:04 : Fonctionnement de Docker Sandbox00:45:58 : Usages de Docker Sandbox00:52:28 : Philosophie de Docker sur le Code00:58:44 : Recommandations et Conclusion Liens évoqués pendant l'émission YT Devoxx France Construire une application indépendante de la tech US en 2025 | Eventuallycoding2025, Europe Vs USA : la tech à l'heure des choix | EventuallycodingLe mythe de la neutralité : quand la tech devient politique

名著『ザ・ゴール』の制約理論を指針にして、AI時代のソフトウェア開発における真のボトルネックを論理的に洗い出し、コーティングv.s.レビュー速度のような表面的な制約ではなくて、人間の集中力や周辺調整、破滅的失敗の頻度上昇こそが新たな制約に移行しているのではないかという仮説と、その上で、予測不能な環境下でエンジニアが淘汰されないために不可欠となる、破滅的失敗を防ぐ強力な「ブレーキ」の構築と、時代に左右されない不変の原理について考察します。関連エピソード- 212. ハーネスエンジニアリング- 210. AI時代のコードの書き方- 205. ザ・ゴールについてソフトウェアエンジニアが熱く語る- 164. Vibe Coding は開発者の仕事を奪うか?- 106. トヨタ生産方式の教科書から学ぶSRE感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

For pt 3 Chad speaks to Jesse who is a Software Engineer based in Massachusetts. Outside of work, Jesse contributes to Web Standards work, and small web projects. For the past three game jams Jesse has worked on three separate games as well as accessibility libraries and templates to enable frameworks like PICO-8 and Love2d to be screen-reader friendly. The past projects include Lunch Gambit (Games for Blind Gamers 3), and LadyBud Roll (Games for Blind Gamers 4). And this year for Games For Blind Gamers 5, Jesse created the wonderful game Toasty.Check out the amazing Toasty Game on the link belowhttps://jrjurman.itch.io/toasty

Link Up w/The Morning Sickness Digitally All Over:Instagram: @hms_98_official, @bosskupd, @bretvesely, @dickToledoX/Twitter: @HMSon98, @DickToledo, @bretveselyFacebook: @HMSKUPDYouTube: @hmspodcast9320, @98kupdRequest/Call in/Wakeup Song line:(IN AZ) 602.585.9800More HMS: holmbergpodcast.com, 98kupd.comEmail: dtoledo@98kupd.com, bvesely@98kupd.com, bbogen@98kupd.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Link Up w/The Morning Sickness Digitally All Over:Instagram: @hms_98_official, @bosskupd, @bretvesely, @dickToledoX/Twitter: @HMSon98, @DickToledo, @bretveselyFacebook: @HMSKUPDYouTube: @hmspodcast9320, @98kupdRequest/Call in/Wakeup Song line:(IN AZ) 602.585.9800More HMS: holmbergpodcast.com, 98kupd.comEmail: dtoledo@98kupd.com, bvesely@98kupd.com, bbogen@98kupd.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today I learnedではカリフォルニア、ベイエリアで働くソフトウェアエンジニアが気になったトピックを紹介しながら、トレンドを追っかけていきます。今回は ながらAIのホストをゲストにおよびしました。ながらAI https://nagara.ai/ハヤカワ五味 https://x.com/hayakawagomiusutaku https://x.com/usutaku_channelながらAIはじめたきっかけながらAIの情報収集Claude Codeを使っている層って?おじさんxTiktokはブルーオーシャン組織におけるAI導入のハードルサンフランシスコのテックにはびこる実存危機https://x.com/deedydas/status/2055491938464489888 これから起きるAI格差をどう乗り越えるのか転職するなら今年次第?AIの先のトレンドはベイエリアのテックで働く人が子供に何を教育するか感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

As AI agents transform software engineering, how do you leverage them without losing your coding skills or risking production disasters? In this episode, Google Cloud AI Director Addy Osmani breaks down the shift from babysitting basic models to mastering advanced agent harnesses.Discover how to safely delegate complex technical tasks while maintaining your human engineering identity and setting up secure boundaries for your AI.In this episode, we cover:Human Identity vs. Machine Identity: How to avoid the trap of "cognitive surrender" and keep your critical thinking sharp.Stopping the AI "Babysitting" Cycle: How to transition from constant manual oversight to secure agent governance.Rising Abstractions: Why agent harnesses (like Claude Code and Antigravity) are changing how software is built.The Verification Bottleneck: Why coding is easy, but verifying that your agent didn't ruin production is the real challenge.This episode is a must-watch for software engineers and tech leaders looking to integrate AI agents into their workflows safely and effectively. You'll walk away with actionable frameworks to boost your development velocity without letting your own technical edge rot.Guest:Addy Osmani is a Director at Google Cloud AI, famous for his work on Google Chrome and focused on AI agents in software engineering.Timestamps:00:00:00 - Intro 00:00:45 - The Reality of "Babysitting" Your AI Agent Setup 00:01:16 - How to Stop Babysitting and Build Secure AI Agents 00:02:36 - The Dangerous Mistakes of Uncontrolled AI Experiments 00:03:39 - Rising Abstractions: From Code to Agent Harnesses 00:05:18 - Why You Should Delegate Technical Tasks to AI 00:07:05 - How to Choose the Best AI Agent Harness 00:08:31 - How to Manage Your Developer Innovation Budget 00:10:17 - Are We Losing Pair Programming to AI Agents? 00:12:14 - Cognitive Surrender: The Hidden Threat of Generated Code 00:13:40 - The Verification Bottleneck: How to Trust AI Code 00:15:59 - How to Safely Scale Your Personal AI Bandwidth#AIAgents #SoftwareEngineering #DeveloperProductivity

What does it actually mean to be AI native? Not the buzzword — the real thing. Host Bobby Brill brings together seven ServiceNow experts across six conversations for a complete picture of what AI native thinking, building, and working looks like right now.━━━━━━━━━━━━━━━━━━━━━━━━WHAT WE COVER━━━━━━━━━━━━━━━━━━━━━━━━DI LE — AI Ethicist & Human-Centered AI Strategist, ServiceNowThe clearest definitions you'll find anywhere of responsible AI, ethical AI, and human-centered AI — and why all three are required if you're going to do this right. Plus: why AI native means AI as the operating system, not a feature.DR. ALAINA BEAVER — Global Head of Accessibility Customer Engagement, ServiceNowServiceNow built the world's first AI model accessibility checker with the Global Accessibility Awareness Day Foundation — and open-sourced it on GitHub for free. Because responsible AI native behavior means holding AI itself accountable.ANAND THARANATHAN — Research Leader, ServiceNowA framework from cognitive science every AI builder needs: use, disuse, misuse, and abuse. The four modes of AI interaction — and why proper use is the only one that delivers.TARA BOGAVELLI & KATRINA STANKIEWICZ — Voice AI Research Team, ServiceNowHow ServiceNow built a rigorous open-source evaluation framework for voice agents from scratch — and what cascade failures, transcription errors, and prosody failures actually sound like in practice.IAN THURLOW & ANDREW YAN — Software Engineering Manager & Software Engineer, ServiceNowThe daily ground-floor reality of being AI native: AI as accelerator, AI as the new Stack Overflow, the calculator analogy, and why fundamentals matter more than ever.━━━━━━━━━━━━━━━━━━━━━━LEARN MORE━━━━━━━━━━━━━━━━━━━━━━━━ServiceNow Responsible AI: https://www.servicenow.com/responsible-aiAI Model Accessibility Checker: https://www.servicenow.com/accessibility-statement.htmlServiceNow AI: https://www.servicenow.com/artificial-intelligence━━━━━━━━━━━━━━━━━━━━━━━━ABOUT THIS PODCAST━━━━━━━━━━━━━━━━━━━━━━━━Hosted by Bobby Brill. A ServiceNow podcast exploring the people, technology, and ideas shaping the future of work.#AINative #ServiceNow #ResponsibleAI #HumanCenteredAI #AIEthics #EnterpriseAI #FutureOfWork #NowAssist #ArtificialIntelligence #PodcastSee omnystudio.com/listener for privacy information.

At a recent MCP developer summit, The New Stack spoke with Till Döhmen, AI lead atMotherDuck, about the company's growing role in the evolving DuckDB ecosystem. Backed by investors includingTomasz Tunguz, MotherDuck is commercializing the open-source analytical databaseDuckDBwhile also expanding how employees interact with data through AI agents rather than traditional dashboards. Döhmen emphasized the company's close collaboration withDuckDB FoundationandDuckDB Labs. Because MotherDuck operates what he described as the world's largest fleet of DuckDB databases, the startup regularly pushes the database to its limits and feeds insights back to the core maintainers. Rather than forking DuckDB to create proprietary advantages, MotherDuck instead extends the platform through its existing architecture while contributing core improvements upstream when needed. The conversation highlighted the delicate but productive relationship between venture-backed companies and the open-source projects they commercialize, positioning MotherDuck as another example of startups driving both OSS adoption and strong business growth simultaneously. Learn more from The New Stack around the latest in DuckDB: DuckDB: Query Processing Is King DuckDB: In-Process Python Analytics for Not-Quite-Big Data Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

What does it actually mean to be AI native? Not the buzzword — the real thing. Host Bobby Brill brings together seven ServiceNow experts across six conversations for a complete picture of what AI native thinking, building, and working looks like right now.━━━━━━━━━━━━━━━━━━━━━━━━WHAT WE COVER━━━━━━━━━━━━━━━━━━━━━━━━DI LE — AI Ethicist & Human-Centered AI Strategist, ServiceNowThe clearest definitions you'll find anywhere of responsible AI, ethical AI, and human-centered AI — and why all three are required if you're going to do this right. Plus: why AI native means AI as the operating system, not a feature.DR. ALAINA BEAVER — Global Head of Accessibility Customer Engagement, ServiceNowServiceNow built the world's first AI model accessibility checker with the Global Accessibility Awareness Day Foundation — and open-sourced it on GitHub for free. Because responsible AI native behavior means holding AI itself accountable.ANAND THARANATHAN — Research Leader, ServiceNowA framework from cognitive science every AI builder needs: use, disuse, misuse, and abuse. The four modes of AI interaction — and why proper use is the only one that delivers.TARA BOGAVELLI & KATRINA STANKIEWICZ — Voice AI Research Team, ServiceNowHow ServiceNow built a rigorous open-source evaluation framework for voice agents from scratch — and what cascade failures, transcription errors, and prosody failures actually sound like in practice.IAN THURLOW & ANDREW YAN — Software Engineering Manager & Software Engineer, ServiceNowThe daily ground-floor reality of being AI native: AI as accelerator, AI as the new Stack Overflow, the calculator analogy, and why fundamentals matter more than ever.━━━━━━━━━━━━━━━━━━━━━━LEARN MORE━━━━━━━━━━━━━━━━━━━━━━━━ServiceNow Responsible AI: https://www.servicenow.com/responsible-aiAI Model Accessibility Checker: https://www.servicenow.com/accessibility-statement.htmlServiceNow AI: https://www.servicenow.com/artificial-intelligence━━━━━━━━━━━━━━━━━━━━━━━━ABOUT THIS PODCAST━━━━━━━━━━━━━━━━━━━━━━━━Hosted by Bobby Brill. A ServiceNow podcast exploring the people, technology, and ideas shaping the future of work.#AINative #ServiceNow #ResponsibleAI #HumanCenteredAI #AIEthics #EnterpriseAI #FutureOfWork #NowAssist #ArtificialIntelligence #PodcastSee omnystudio.com/listener for privacy information.

05/26/26: Matt Evans is one of the endorsed Republican candidates for State Legislature running from District 25. He has served on the Walcott Township Board of Supervisors, as the finance deacon for his church, and as the chairman of the North Dakota District 25 Republican Party. He and his wife home-school their four children. Matt also had a 22 year career at Microsoft as a Software Engineer and Engineering Manager. Learn more about Matt and his campaign and views at evans4nd.com. (Joel Heitkamp is a talk show host on the Mighty 790 KFGO in Fargo-Moorhead. His award-winning program, “News & Views,” can be heard weekdays from 8 – 11 a.m. Follow Joel on X/Twitter @JoelKFGO.)See omnystudio.com/listener for privacy information.

Episode 180: rightFolds in an AI world? rightFolds as a pun on Mark's recent right vocal fold surgery, healing means we're good to record again, plus IA celebrates 17 years of existence, even if episodes have seriously lacked of late. Last episode Aug 27, 2025 - it's been a while. Does language theory and evolution have a place/need in an AI world? New JVM language features vs Syntactic sugar ala Clojure/Scala features Bun's recent zig->rust total AI rewrite Vercel engineer built Zero, a programming Language for AI Agents | Yeamt Why Did They Build This? jank now has its own custom IR Do any of these funky languages matter in an AI world? Is 'Good Enough' Good Enough: Mindsets and Behaviors for Sales Excellence Is "good enough" good enough?!. A common misunderstanding of the… | by Ted Rau Is Good Enough, Good Enough? (Part 1) AI and the increased threat of Supply Chain attacks How We Got a CISA GitHub Leak Taken Down in Under a Day NPM and its recent attacks Package Managers are Evil - gingerBill The Aesthetic Problem of Namespacing - gingerBill Tooling Highlights from Git 2.54 "Git history" FTW, unless you're using Jujitsu

「Tidy first? 個人で実践する経験主義的ソフトウェア設計 」 オライリー・ジャパン https://amzn.to/4mSa6SK関連エピソード- 195. ソフトウェア設計の結合バランス https://open.spotify.com/episode/6DUL1T5RsfWB8OIWLsj01f- 193. Thinking in Systems https://open.spotify.com/episode/3XRi3HEyVxRbhzhJJV1YEq感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social関連エピソード

Featuring: Ernest Koe, CEO of Proof Todd Geist, CTO of ProofKyle Duval, Software Engineer at ProofDescription:In this episode of the Context Podcast, join Ernest Koe, Todd Geist, and Kyle Duval in another AI conversation. This week, they discuss the “slop cannon”, and all the reasons that SaaS isn't dead. From more advanced security threats, to the skills needed to manage a complex code base, there's still plenty of reasons why engineers are needed. In the second part of the episode, they discuss the newest AI-related changes to the Ottomatic platform. OttoFMS supports MCP servers, and documentation now supports AI-assisted chat. Got a question? Just type it in, and find what you're looking for fast with doc-based querying.Featured Links:Mo Bitar's video on AIMCP Server Documentation

JetBrains is positioning itself as the last major independent AI coding-tool vendor in a market increasingly tied to hyperscalers and foundation model labs. Speaking at Google Cloud Next, JetBrains VP of business developmentMikhail Vink argued that competitors such as Microsoft Copilot, Anysphere Cursor, and Windsurfare all tied to either AI labs or cloud providers. By contrast, JetBrains says its independence allows customers to switch freely between models fromOpenAI,Anthropic, andGoogle Cloudwithout being locked into one ecosystem. That flexibility underpins JetBrains' broader AI strategy. Rather than building its own foundation model, the company is focusing on orchestration and governance through JetBrains Central, announced in March as a management layer for AI agents, usage controls, analytics, and consumption-based billing. Vink said the company's profitability, 16 million users, and 300,000 commercial customers from its long-running IDE business have allowed it to remain venture-free and model-neutral. JetBrains argues that as developers increasingly swap between AI models, neutrality may become more valuable than owning the models themselves. Learn more from The New Stack around the latest in AI coding-tools:  JetBrains ‘Agentic' AI Agent Helps Automate Coding Tasks JetBrains: AI agents are about to repeat the cloud ROI crisis  JetBrains names the debt AI agents leave behind Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

After 250 episodes of Beyond Coding, a pattern shows up again and again: the engineers who thrive aren't the ones chasing the newest tool or the cleanest code. They're the ones who learn fast, keep things simple, and understand the business they're building for. This special pulls the sharpest moments from recent guests into one conversation about what actually makes a great software engineer in 2026.We cover:Why learning is the only skill that outlives every tool, language, and platformHow the best architects act more like scouts than cartographersWhy "simple is complicated enough" beats clean code dogma at scaleHow to design systems that evolve instead of trying to predict 10 years outWhat junior engineers should actually do in the age of AI agentsFor software engineers who want to think clearer, build better, and grow into the kind of engineer companies can't replace.Timestamps:00:00:00 - Intro00:00:17 - Why You Should Increase Your Breadth, Not Just Focus00:02:16 - The Only Skill That Survives Every Tech Cycle00:04:14 - Buzzwords Are Just Old Ideas in New Clothes00:05:26 - What Clients Say vs What They Actually Want00:06:45 - The Bad Architects Are Easier to Spot00:08:50 - Why Good Engineers Use Boring Technology00:11:40 - Stop Building for 100x Scale on Day One00:13:13 - The Dogma of Clean Code Is Hurting You00:15:15 - Simple Is Complicated Enough at Scale00:16:28 - Design Only for the Next Order of Magnitude00:18:19 - How to Talk Tech with Non-Technical Stakeholders00:19:30 - The $50,000-Per-Hour Container Terminal Lesson00:22:11 - Architects Are No Longer Cartographers, They're Scouts00:25:18 - Start with a Question, Not an Answer00:26:49 - Junior to Senior in the Age of AI Agents00:27:29 - Don't Be a Fool with a Tool00:29:43 - From Explicit to Implicit Knowledge Economy00:30:38 - Use AI to Validate, Not to Generate#softwareengineering #engineeringcareer #softwarearchitecture

Show NoteHarness engineering https://martinfowler.com/articles/harness-engineering.html感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

What began as an internal developer tool atBlockhas evolved into a broader open-source initiative with industry backing. Goose, Block's AI coding agent, followed a path similar to Amazon's transformation of internal infrastructure intoAmazon Web Services. After deploying Goose companywide, Block open-sourced the tool under a permissive license, leading to rapid adoption across the developer community. But according to Manik Surtani, Office of the CTO, Block and Co Founder of Agentic AI Foundation, early momentum exposed governance challenges. Although Goose was technically open source, Block retained trademark ownership, creating concerns for enterprises seeking truly independent governance. To address this, the team partnered with the creators ofAnthropicand the Model Context Protocol community to establish theAgentic AI Foundationunder the umbrella of theLinux Foundation. Goose, MCP, and Agents.MD became the foundation's initial projects, chosen largely to accelerate the launch of the new organization and create a collaborative ecosystem around agentic AI development. Learn more from The New Stack around the latest in open-source AI:  Anthropic extends MCP with a UI framework Why the Linux Foundation adopted MCP, with Jim Zemlin and Mazin Gilbert Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

At Google Cloud Next 2026, Fivetran Chief Product Officer Anjan Kundavaram argued that enterprise data systems are unprepared for the scale of AI-driven analytics. Unlike humans, AI agents can generate exponentially more queries, often routing them through the same expensive compute infrastructure. Kundavaram compared it to “using a Lamborghini to mow the lawn.” To address this, Fivetran introduced its “Open Data Infrastructure” vision and a benchmark designed to expose hidden AI workload costs in closed ecosystems. Kundavaram said agents can optimize for cost instead of speed, choosing cheaper compute engines when appropriate — but only in open architectures with multiple options. Closed systems force every query through high-cost paths. He also warned that fragmented data and weak context create a “triple whammy” of poor AI responses, soaring analytics bills, and wasted compute. While many organizations respond by tightening controls, Kundavaram argued the better path is investing in open infrastructure, interoperability, and strong semantic data practices before AI costs spiral further.   Learn more from The New Stack around the latest in enterprise data systems:  Enterprise AI Success Demands Real-Time Data Platforms AI Agents Are Morphing Into the 'Enterprise Operating System' Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

At Google Cloud Next 2026, Finout co-founder and CEO Roi Ravhon and Google Cloud FinOps lead Pathik Sharma discussed how FinOps is rapidly evolving for the AI era. Ravhon argued that while cloud FinOps had a decade to mature, AI economics are forcing the industry to adapt within a year. Unlike traditional cloud workloads, AI costs are unpredictable because token usage varies even for identical prompts, while advanced reasoning models consume significantly more tokens despite falling prices. Both emphasized that effective AI FinOps requires intelligent orchestration, routing workloads to the cheapest capable models instead of defaulting to expensive frontier models. Sharma noted that AI costs extend beyond APIs to GPUs, storage, training, and organizational adoption. They also cautioned against relying solely on LLMs for operational automation. Deterministic systems, observability metrics, and human approvals remain essential guardrails. Ultimately, both stressed that FinOps is primarily an organizational and cultural discipline, recommending newcomers start with the FinOps Foundation before investing in tools. Learn more from The New Stack around the latest in FinOps:  Why FinOps Isn't About Saving Money  FinOps Foundation's FOCUS 1.2 Expands to SaaS, PaaS  Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   

「技術さえあれば評価されるはずだ」——そう信じて、理不尽な評価や組織の壁に突き当たったことはありませんか？ 今日は、Amazonで約15年間、800人以上のエンジニアを率いる副社長まで上り詰めたEthan Evans氏の、合計3時間半のインタビューをもとに、キャリア構築と社内政治の極意を30分に凝縮してお届けします。特に技術力に自信がありながら、組織の力学に悩む日本の技術者や、外資系企業での文化の違いに悩むひとに、実践的な指針となる内容だと思います。Amazon VP: Stack Ranking & PIPs, Working With Bezos, His Promotions https://youtu.be/40-ENZmqcz0 (2h50m)Retired Amazon VP: How Corporate Politics Work And How To Win https://youtu.be/6WaeGfLnRvc (53m)感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

Managing Kubernetes at fleet scale introduces significant complexity, especially as organizations expand from a few clusters to hundreds or thousands across cloud, on-premises, and edge environments. While GitOps remains the dominant model for declarative management, its traditional one-to-one repository-to-cluster approach struggles to handle multi-cluster realities such as global traffic routing, shared secrets, and unified observability. AsStephane Erbrech, Principal Software Engineer at Microsoftexplains, the challenge shifts from deployment to governance—maintaining consistency, security, and compliance across a vast distributed system without manual intervention. This need is amplified by the rise of AI workloads at the edge, where inference is increasingly decentralized. To address these challenges,Microsoft Azure Kubernetes Fleet Managerenables coordinated, staged rollouts across clusters, allowing teams to validate updates in lower-risk environments before production. Supporting this,Cilium Cluster Meshprovides seamless cross-cluster connectivity, enabling workload mobility and efficient resource use, especially for scarce GPU capacity. Together, these tools help modern platform teams manage lifecycle, networking, and orchestration at scale.  Learn more from The New Stack around managing Kubernetes at fleet scale:  KubeFleet: The Future of Multicluster Kubernetes App Management Why Microsoft is betting on temporary identities to stop autonomous agents from going rogue Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Agentic AI is advancing rapidly, with open-source projects racing to keep pace with real-world deployment. To accelerate progress, the Linux Foundation consolidated key technologies—Model Context Protocol (MCP), Goose, and AGENTS.md—under the newly formed Agentic AI Foundation (AAIF) in late 2025. At the MCP Dev Summit in New York City, Linux Foundation CEO Jim Zemlin and newly appointed AAIF executive director Mazin Gilbert discussed this transition. Zemlin explained that leading both organizations was unsustainable, prompting a careful search for a leader with both technical expertise and collaborative leadership skills. Gilbert now takes on the challenge of guiding AAIF as it shapes the emerging agentic AI ecosystem. While the foundation currently oversees three projects, its broader mission involves defining the future architecture of agent-driven systems—deciding what to build, when, and why. These decisions will influence the trajectory of open-source AI development. The conversation also highlights the importance of open collaboration, funding dynamics, and early adopters in shaping the agentic stack's evolution.   Learn more from The New Stack around the latest in open-source projects and The Linux Foundation:  Anthropic Donates the MCP Protocol to the Agentic AI Foundation SAFE-MCP, a Community-Built Framework for AI Agent Security Google Donates the Agent2Agent Protocol to the Linux Foundation Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

In this episode ofThe New Stack Makers, Matthew O'Riordan, CEO of Ably, explains how infrastructure originally built for human collaboration is now well-suited for long-running AI agents. While Ably initially resisted positioning itself as an AI company, the rise of agents that reason, call tools, and operate over extended periods revealed a natural fit for its real-time communication platform. O'Riordan highlights the limitations of HTTP for these use cases. While effective for short, request-response interactions, HTTP struggles with persistent, stateful experiences—such as handling dropped connections, multi-device usage, or mid-task interruptions. To address this, a new “durable session” layer is emerging, enabling continuous synchronization between agents and users through shared state, presence, and recovery mechanisms. Ably's solution, AI Transport, augments existing architectures by keeping HTTP for requests while shifting responses to durable sessions. Features like mutable message streams and “live objects” allow seamless reconnection and collaboration. The goal is to provide a drop-in layer that developers can adopt without rethinking their stack—moving beyond traditional pub/sub models. Learn more from The New Stack around Ably and AI Transport:  How MCP Uses Streamable HTTP for Real-Time AI Tool Interaction Ably Touts Real-Time Starter Kits for Vercel and Netlify AI Agents Need Help. Here's 4 Ways To Ship Software Reliably Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

In this week's First $1,000 segment, we hear from an engineer who takes out his daytime frustrations on a series of side hustle balloon sculptures.Side Hustle School features a new episode EVERY DAY, featuring detailed case studies of people who earn extra money without quitting their job. This year, the show includes free guided lessons and listener Q&A several days each week.Show notes: SideHustleSchool.comEmail: team@sidehustleschool.comBe on the show: SideHustleSchool.com/questionsConnect on Instagram: @193countriesVisit Chris's main site: ChrisGuillebeau.comRead A Year of Mental Health: yearofmentalhealth.comIf you're enjoying the show, please pass it along! It's free and has been published every single day since January 1, 2017. We're also very grateful for your five-star ratings—it shows that people are listening and looking forward to new episodes.

Mitchell Hashimoto 氏へのインタビューをベースにオープンソース、Git、AI開発ついて話しました。ファウンダーCEOから平社員に戻ったSWEが語るパッションドリブンなキャリアパス https://open.spotify.com/episode/0ROQTmAq7wTHDNbQPNHRyD?si=sRzMIp-5R9WHt52GrP92pg感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

Kubernetes is rapidly emerging as the de facto operating system for AI, with two-thirds of organizations using it for generative AI inference and 82% adopting it in production. Its ecosystem — including tools like Kubeflow — enables organizations to build, scale, and retain control of AI systems through open, community-driven infrastructure. Bob Killen of CNCF and Liam Bollmann-Dodd of SlashData shared insights from recent reports showing that AI success still hinges on strong engineering fundamentals—especially internal developer platforms and overall developer experience. While AI-generated code accelerates development, it shifts bottlenecks to DevOps, reliability, and security, increasing operational complexity. As a result, operator experience and well-defined guardrails have become critical to safely scaling AI. These controls help constrain both human and AI developers, reducing risk while enabling speed. At the same time, organizations are evolving team structures, expanding platform engineering groups to support internal users more effectively. Despite growing complexity, the core lesson remains consistent: open source innovation thrives on people, processes, and collaboration as much as on technology itself. Learn more from The New Stack around the latest in Kubernetes and its emergence as an operating system for AI:  Kubernetes and AI: Are They a Fit? How AI Is Pushing Kubernetes Storage Beyond Its Limits Kubernetes and AI Are Shaping the Next Generation of Platforms Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

In this episode of The New Stack Makers, AWS developer advocate Morgan Willis demonstrates Strands Agents, an open source agentic framework with rapid adoption since its launch. Using a simple accounting API, she walks through three approaches to retrieving a customer's latest invoice, highlighting how design choices dramatically impact efficiency. The initial method maps each API endpoint to a separate tool, requiring five chained calls and consuming about 52,000 tokens. By shifting to intent-based tools—focused on outcomes rather than individual data operations—the same task is completed in a single call using just 2,000 tokens, improving both efficiency and reasoning. In a third iteration, tools are hosted on a remote MCP server via AWS Agent Core Gateway, with semantic search limiting the agent's toolset to only what's relevant per query, further reducing token usage. Willis emphasizes that narrowly scoped agents outperform general-purpose ones, delivering better speed, accuracy, and context efficiency. Designing smaller, specialized agents with tailored tools is key as tool ecosystems expand. Learn more from The New Stack around the latest with Strands and MCP: AWS Launches Its Take on an Open Source AI Agents SDK What Is MCP? Game Changer or Just More Hype? MCP's biggest growing pains for production use will soon be solved Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Most engineers are using AI coding tools without understanding what they actually are and it's costing them. Microsoft Certified Trainer Rob Bos has trained thousands of engineers on AI tooling, and he sees the same gaps in fundamentals show up again and again, regardless of seniority. This is what you need to know:What an LLM actually is (and why understanding this changes how you use it)Why prompt engineering isn't optionalHow AI magnifies your existing technical debt instead of fixing itThe 6-month learning curve nobody warns you aboutWhy your role as an engineer was never about writing codeThe environmental cost behind every promptWhether you're skeptical of AI tools or already living in agent mode, these are the fundamentals that separate engineers who get real value from those who get burned by the hype.Connect with Rob:https://www.linkedin.com/in/bosrobReferences:Token tracker: https://marketplace.visualstudio.com/items?itemName=RobBos.copilot-token-trackerDev survey: https://www.activestate.com/wp-content/uploads/2019/05/ActiveState-Developer-Survey-2019-Open-Source-Runtime-Pains.pdfTimestamps:00:00:00 - Intro00:00:43 - The #1 Thing Engineers Get Wrong About AI00:02:09 - How Much LLM Theory Do You Actually Need?00:03:58 - Why Pair Programming Is Still the Best Way to Learn AI00:05:26 - Why Rob Skips Tab Completion and Lives in Agent Mode00:07:03 - The "AI Doesn't Increase Productivity" Debate00:08:29 - Why Your Real Job Was Never Writing Code00:09:14 - The 2-Hours-of-Coding Problem No One Talks About00:11:02 - More Code = More Pressure on Your Review Process00:12:21 - Why AI Magnifies Existing Technical Debt00:13:39 - The Customer Who Couldn't Start AI With Developers Yet00:15:11 - The Future Engineer: Reviewer, Not Writer00:17:00 - Convincing the AI Skeptic Who Tried It Years Ago00:19:17 - LLMs Explained Without Visuals (Attention & Semantics)00:22:41 - Why Prompt Engineering Actually Matters00:24:20 - From Zero to Hero: The 6-Month Learning Curve00:26:18 - Is This Confrontational for 20-Year Veterans?00:29:30 - Becoming a Better Engineer by Thinking in Systems00:31:26 - Will AI Stop Working as Innovation Slows?00:34:26 - The Lost Art of Pair Programming with AI00:35:44 - Tribalism in AI Tools (And Why It's Pointless)00:37:33 - Tool Agnostic: Start With the Foundations00:39:40 - Is the IDE Still Relevant?00:40:50 - The Bluescreen Story That Changed His Mind00:41:47 - The Hidden Environmental Cost of AI Coding00:44:15 - 36 Million Tokens in 30 Days: What Does It Mean?00:45:47 - Running LLMs at the Edge to Cut the Footprint00:46:48 - Why You Should Be Allowed to Wait Five Minutes Longer00:47:05 - Outro#githubcopilot #aicoding #softwareengineering

Broadcom's VMware Cloud Foundation (VCF) is evolving from a turnkey infrastructure stack into a modern application platform, balancing simplicity with the flexibility demanded by Kubernetes-driven environments. AtKubeCon + CloudNativeCon Europe 2026, Broadcom leaders highlighted how VCF is adapting to support platform engineering teams, cloud-native workloads, and large-scale operations. A key industry shift is the return to private cloud, driven by data sovereignty concerns and the growing impact of AI. Enterprises are bringing workloads back on-premises while still expecting a cloud-like operating model. Broadcom is responding by prioritizing on-prem stability and aligning closely with open source, reflecting its strong contributions toKubernetesand related projects. Kubernetes is no longer a bolt-on but the core control plane of VCF, enabling unified management of compute, storage, and networking through declarative APIs. At the same time, the distinction between virtual machines and containers is fading. The focus is shifting toward application-centric platforms, where developers interact through consistent abstractions, allowing infrastructure to be provisioned seamlessly behind the scenes. Learn more from The New Stack around the latest around Broadcom:  Broadcom ‘Doubles Down' on Open Source, Donates Kubernetes Tool to CNCF Why Broadcom gave Velero to the CNCF Sandbox — and what it means for Kubernetes data protection Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Unser heutiger Gast ist jemand, der ein Unternehmen von innen heraus erlebt und geprägt hat – über mehr als zwei Jahrzehnte hinweg. Er hat Informatik studiert und ist direkt nach dem Studium bei BSI Software eingestiegen, zunächst als Software Engineer, später als Projektmanager und über viele Jahre hinweg in unterschiedlichen Rollen gewachsen. Seit 2014 ist er CEO. BSI Software ist heute eines der spannendsten Tech-Unternehmen im deutschsprachigen Raum – über 600 Mitarbeitende, mehr als die Hälfte davon beteiligt am Unternehmen, aktiv in der gesamten DACH-Region und Italien. Und gleichzeitig ein Unternehmen, das viele Dinge bewusst anders macht: keine klassischen Hierarchien, kein Organigramm, stattdessen ein Netzwerk von Menschen, die eigenverantwortlich arbeiten und auf Augenhöhe miteinander und mit ihren Kunden agieren. Was besonders auffällt: Hier wird Unternehmenskultur nicht nur beschrieben, sondern konsequent gelebt. Themen wie fairer Lohn, transparente Bewertungssysteme und echte Mitverantwortung werden nicht delegiert, sondern gemeinsam ausgehandelt – oft in intensiven Diskussionen, getragen von vielen Perspektiven. Seit über acht Jahren beschäftigen wir uns in diesem Podcast mit der Frage, wie Arbeit den Menschen stärkt, statt ihn zu schwächen. Wir haben in mehr als 500 Episoden mit fast 700 Persönlichkeiten darüber gesprochen, was sich für sie verändert hat – und was sich noch verändern muss. Heute fragen wir: Was passiert, wenn man ein Unternehmen konsequent als Netzwerk organisiert – ohne klassische Hierarchien und ohne Organigramm? Wie gelingt es, Leistung fair zu bewerten und zu vergüten, wenn nicht Verhandlungsgeschick, sondern der Beitrag zum gemeinsamen Erfolg zählen soll? Und was braucht es, damit Verantwortung wirklich im Unternehmen verteilt wird – und nicht nur auf dem Papier steht? Fest steht: Für die Lösung unserer aktuellen Herausforderungen brauchen wir neue Impulse. Daher suchen wir weiter nach Methoden, Vorbildern, Erfahrungen, Tools und Ideen, die uns dem Kern von New Work näherbringen. Darüber hinaus beschäftigt uns von Anfang an die Frage, ob wirklich alle Menschen das finden und leben können, was sie im Innersten wirklich, wirklich wollen. Ihr seid bei On the Way to New Work, heute mit Markus Brunold. [Hier](https://linktr.ee/onthewaytonewwork) findet ihr alle Links zum Podcast und unseren aktuellen Werbepartnern

What Every Programmer Should Know About Memory「プログラマーなら知っておくべき、メモリのすべて」https://people.freebsd.org/~lstewart/articles/cpumemory.pdf感想をぜひハッシュタグ #tilfm でつぶやいてください!お便りフォーム https://forms.gle/J2ioXHS98dYNoMbq5Your co-hosts:Tomoaki Imai, Noxx CTO https://x.com/tomoaki_imai bsky: https://bsky.app/profile/tomoaki-imai.bsky.socialRyoichi Kato, Software Engineer ⁠https://x.com/ryo1kato bsky: https://bsky.app/profile/ryo1kato.bsky.social

In this episode of The New Stack Makers, Nimisha Asthagiri of Thoughtworks explores why many AI initiatives stall between proof of concept and production. A key issue is that organizations focus on speed—asking how to move faster—rather than rethinking what new capabilities AI actually enables. Successful companies take a systems-thinking approach, investing in organizational literacy and aligning teams around meaningful use cases instead of retrofitting AI into existing workflows. Asthagiri highlights that core engineering practices are ফিরে to prominence. As AI-generated code increases, so does the risk of “cognitive debt,” where developers lose understanding of their own systems. To counter this, teams are reviving fundamentals like test-driven development, mutation testing, observability, and zero-trust security, especially as autonomous agents contribute to production code. She also introduces the concept of “dark code”—AI-generated code that may never be used—and argues for more intentional lifecycle management, including ephemeral code. Ultimately, the focus shifts from code itself to specifications, context management, and disciplined engineering practices.   Learn more from The New Stack around the latest about system-thinking approaches:  System Two AI: The Dawn of Reasoning Agents in Business  A practical systems engineering guide: Architecting AI-ready infrastructure for the agentic era  Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

Graduating within the CNCF marks a major milestone for an open source project, signaling not just technical maturity but strong governance, security practices, and widespread adoption. Kyverno, a Kubernetes policy engine, reached this stage after five years — becoming only the 35th project to progress from sandbox to graduation. As co-founder Jim Bugwadia explains, incubation reflects production readiness and adoption, while graduation validates the project's long-term sustainability and governance rigor. Originally built to help teams manage Kubernetes complexity through declarative policies, Kyverno has evolved alongside the ecosystem. Its shift to the Kubernetes-native Common Expression Language (CEL) and rising demand driven by AI workloads have expanded its user base beyond regulated industries to mainstream enterprises. With over three billion downloads, it underscores the growing need for automated policy enforcement across development, security, and operations teams. Commercially, Nirmata maintains a clear boundary between open source and enterprise offerings, focusing on remediation and advanced management. While only 2–5% of users convert, that small percentage becomes meaningful at Kyverno's scale. Learn more from The New Stack around the latest about Kyverno: Simplify Kubernetes Security With Kyverno and OPA Gatekeeper Using the Kyverno CLI to Write Policy Test Cases Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

It's YOUR time to #EdUp with John Leech, Deputy Registrar & Director of Operations, Virginia Tech, & Zach Lamphere, Senior Manager Software Engineer, Ellucian⁠In this episode, recorded LIVE from the Ellucian Live 2026 conference in Denver, Colorado,YOUR cohost is Gretchen Fricke, Higher Education Strategic Consultant, EllucianYOUR host is Dr. Jodi BlincoListen in to #EdUpThank YOU so much for tuning in. Join us on the next episode for YOUR time to EdUp!Connect with YOUR EdUp Team - ⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠Elvin Freytes⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ & ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Dr. Joe Sallustio⁠⁠⁠⁠⁠● Join YOUR EdUp community at ⁠The EdUp Experience⁠We make education YOUR business!P.S. Want access to the only intelligence platform built exclusively from presidential conversations in higher education? Join EdUp Leadership!

What if vibe coding is the worst thing you could do with AI agents? The developers seeing the biggest gains aren't prompting harder. They're planning smarter, spec-first, and treating AI as a facilitator rather than a code generation engine.In this episode, Brian Madison, creator of the BMad Method, shares how a year of late-night AI experiments led him to a structured, Agile-inspired approach to building software with AI agents. Brian explains why jumping straight into agent mode without upfront planning (what most people call vibe coding) reliably hits a wall, and how a disciplined spec-first workflow breaks through that ceiling.He walks through the BMad Method's core workflow: brainstorming, PRD, architecture, UX design, and context-rich user stories, each feeding into the next so the agent always has exactly what it needs. Brian also recounts a transformative two-week sprint he ran with his team where engineers were given permission to fail, and how that single experiment changed the way his entire organisation works with AI.Finally, he reflects on what this shift means for the future of software engineering — where the unit of work is moving from tasks and stories to full features and epics, and every engineer can operate more like a tech lead.Key topics discussed:Why vibe coding hits a wall and how spec-driven dev fixes itUsing AI as a facilitator, not just a code generatorThe BMad Method: PRD → architecture → context-rich storiesHow a 2-week “no typing” sprint transformed his engineering teamGiving teams permission to fail as a leadership toolThe shift from user stories to epics as the unit of workWhy problem decomposition is engineers' biggest AI superpowerTimestamps:(00:00:00) Trailer & Intro(00:02:44) How Did the US Army Shape Brian's Journey into Software Engineering?(00:06:35) How Can Engineers Overcome Imposter Syndrome and Build Self-Confidence?(00:10:23) What Does BMad Actually Stand For?(00:13:49) What Is the BMad Method?(00:22:11) How Does BMad Approach Context and Spec Engineering?(00:29:02) What Sparked the Creation of the BMad Method?(00:44:55) What Productivity Gains Has the BMad Method Produced?(00:48:36) How Will AI Change the Unit of Work for Software Engineers?(00:55:51) How Does BMad Keep Specs and Code in Sync Over Time?(01:01:01) What Is the Best Way to Get Started with the BMad Workflow?(01:05:00) Which AI Models and Tools Does the BMad Method Support?(01:08:21) 4 Tech Lead Wisdom_____Brian Madison's BioBrian Madison is the creator of the BMad Method, an open-source framework that treats AI as a facilitator for workflows across any domain—software development, product management, operations, and beyond. Used globally, the BMad Method helps people work through complex processes using AI personas, from engineers driving spec-driven development to product managers crafting better PRDs and requirements.Currently a Senior Engineering Manager at Extend, Brian led product engineering teams toward becoming an AI-native organization and now leads the entire AI SDLC transformation for the company, using the BMad Method as a framework, reimagining how AI flows through the full software development lifecycle.Brian's approach to leadership was forged during his service in the U.S. Army, where he learned the values of servant leadership, discipline, and mission-first execution.Follow Brian:LinkedIn – linkedin.com/in/bmadcodeBMadWebsite – bmadcode.comDocs – docs.bmad-method.orgGitHub – github.com/bmad-code-org/BMAD-METHODDiscord – discord.gg/gk8jAdXWmjYouTube – youtube.com/@BMadCodeX – x.com/BMadCodeFacebook – facebook.com/@BMadCodeLike this episode?Show notes & transcript: techleadjournal.dev/episodes/255.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

This episode is brought to you by the MLflow team. Check out more information at MLflow.org.Mihail Eric is Head of AI at Monaco and Adjunct Lecturer at Stanford University, where he teaches CS146S: "The Modern Software Developer" — the first course in the world dedicated to how AI is transforming every stage of the software development lifecycle. With 12+ years building production AI systems at Amazon Alexa, Storia AI (YC S24), and early-stage startups, Mihail has one of the most grounded, practitioner-level takes on what it actually means to be a software engineer in 2026.The Modern Software Engineer // MLOps Podcast #370 with Mihail Eric, Head of AI at Monaco

Matt Watson sits down with Mohan Reddy, serial entrepreneur and Chief Scientist at Cornerstone AI Labs, to explore how AI is fundamentally reshaping the way we think about work, skills, and human potential. Mohan shares the origin story of Skyhive—a workforce intelligence platform built to reskill and upskill people at a global scale—and how its acquisition by Cornerstone brought that mission to a larger stage.The conversation digs into why AI doesn't eliminate skills but transforms them, the distinction between tasks that can be automated versus those that require human judgment, and why "vibe coding" is both a breakthrough and a danger. Mohan also makes the case for reverse engineering as the most critical skill in an AI-driven world, and why sandbox environments will be essential for building trust in AI-assisted workflows.Whether you're a founder, engineer, or business leader trying to navigate the AI transition, this episode offers a grounded, optimistic perspective from someone who has spent decades at the intersection of human potential and machine intelligence.If you enjoyed today's episode, subscribe to the Starter Hustle podcast and leave us a review!⏱️ Episode Breakdown00:30 The Journey of Mohan Reddy and Skyhive03:34 Transition to Cornerstone AI Labs06:22 AI's Impact on Skills and Workforce09:33 The Evolution of Software Engineering12:29 The Future of Coding and AI Collaboration15:33 Upskilling in the Age of AI18:30 Curiosity and Learning in Tech21:34 Final Thoughts and AdviceLinks & ResourcesConnect with Mohan Reddy on LinkedInWhat Smart CTOs Are Doing Differently With Offshore Teams in 2025Subscribe to the Global Talent SprintFull Scale – Build your dev team quickly and affordablyIf you're trying to get your team out of the basement and into real product ownership, this episode is your playbook. Stop being a ticket factory. Build teams that think, create, and lead.Follow the show, rate it, and send this to someone who's still trying to do “real Scrum.” They need it more than you do.

In episode 334 of the Glass and Out Podcast we're pleased to be joined by Matt Mueller, Hudl's Chief Operating Office. If you're a member of The Coaches Site or have attended one of our events, then you are likely aware that we, as in The Coaches Site, are proud partners with Hudl. Their sports technology software, video capture and data insights helps coaches, athletes, teams and organizations succeed at every level.  Today in hockey, Hudl serves coaches of all levels. From grassroots coaches all the way to the National Hockey League where 19 of the 32 teams rely on Hudl to provide them transparency into all facets of their team's performance. Mueller has been with Hudl since day one and has worn many different hats during his time with the company. He started as a Software Engineer and then became the company's first Support Manager. Mueller built Hudl's global sales, marketing and operations teams, and eventually became the General Manager of Hudl's core subscription business, Coaching Tools. In his current role, Mueller is focused on helping Hudl build and achieve its long-term strategies and overall, enhancing the experience enjoyed by coaches and athletes. Listen as he shares the origins story of the company, why you have to be ruthless with team culture and Hudl's goal of making every athlete get their shot. Watch on YouTube: https://youtu.be/1grgL03sJ24 Secure your TCS Live ticket: https://thecoachessitelive.com/ Download the TCS app: https://www.thecoachessite.com/app Learn more about our presenting sponsors: Hudl: hudl.com/tcs Biosteel: BioSteelTeams.com/Glassandout  

After decades in IT production support, Darren didn't just retire — he escaped. At 54, the constant pings, midnight pages, and responsibility without authority finally ended, and what showed up in their place was one word he never expected: relief.In this episode of Retirement Reality, Darren shares how early retirement reshaped everything from his stress levels to his daily rhythm. He talks about the first months of decompression, learning to live without a schedule, how $6,000 a month comfortably covers their lifestyle, and why healthcare planning through the ACA was worth the effort. He also opens up about identity, hobbies, renovating the home they almost sold, and discovering he's officially “unemployable” in the best way.If you're dreaming of getting out of a high-stress job, especially in IT, this episode shows how retirement can feel less like stopping work and more like finally breathing. If it helps you picture your own next chapter, consider subscribing so you don't miss the next story.--Darren is not a client of Root Financial Partners, LLC and received no compensation for participating in this video. His statements reflect his own opinions and experience and are not indicative of any specific client's experience and are not a guarantee of results. No cash or non-cash compensation was provided, and no material conflicts are known.Advisory services are offered through Root Financial Partners, LLC, an SEC-registered investment adviser. This content is intended for informational and educational purposes only and should not be considered personalized investment, tax, or legal advice. Viewing this content does not create an advisory relationship. We do not provide tax preparation or legal services. Always consult an investment, tax or legal professional regarding your specific situation.Create Your Custom Early Retirement Strategy HereGet access to the same software I use for my clients and join the Early Retirement Academy hereAri Taublieb, CFP ®, MBA  is the Chief Growth Officer of Root Financial Partners and a Fiduciary Financial Planner specializing in helping clients retire early with confidence.

Mark. Hamilton sits down with Nathan Cinnamond to do a fun and easy-to-understand breakdown of how the 2026 F1 Power Units operate. -How does the internal combustion engine work and how does it compare to a road car? -What is a turbocharger for and how does it work? -How do F1 cars "harvest" electricity and what is the MGU-K? -How do F1 cars utilize electrical energy to create propulsion and how does this impact race strategy in 2026? -Beano or Dandy comics? The most important UK question of all. A graduate of Cornell University, Nathan is an accomplished Software Engineer in an incredibly complex professional field by day and an F1 data blogger by night. Nathan fell in love with F1 during his youth in the UK and is passionate about utilizing his technical expertise to help others improve their comprehension of the sport's most technical details. You can (and should) check out Nathan's Substack ... The Overcut Hit that subscribe button and tune in for the full, unfiltered breakdown! You don't wanna miss this!

If you're a software engineer right now, you likely feel like your world is changing overnight. We are writing half or less the amount of code that we wrote even a year ago, which represents a seismic, groundbreaking shift in our industry. However, the rapid introduction of new tools can slide quickly from exciting to purely chaotic, leaving you feeling like you are falling behind. In today's episode, I explore how this changes the nature of our day-to-day work, and why the key to surviving this transition is shifting your mindset from a traditional "Software Engineer" to an "Agent Manager". The Illusion of Velocity vs. Actual Chaos: While the big-picture promise of AI is that the software development pipeline will move exponentially faster, the reality on the ground often feels like unadulterated chaos. Trying to adopt every new tool while spinning up multiple agents to work on parallel tickets introduces a massive new cognitive burden. The Context-Switching Trap: Understand why parallelizing agent workflows fundamentally changes your context-switching overhead. You are no longer just reloading context to build something yourself; you are reloading it to manage, review, and validate a building agent, which rapidly drains your cognitive ability and leads to burnout. The "Agent Manager" Mindset: Treating AI as just a "smart autocomplete" while you try to do the same old job will not work. You need to start viewing your role more like assembly line or process management, focusing on facilitating the system rather than typing every line of syntax. Adopt Old-School Quality Control Tactics: Discover how traditional management methods are becoming essential for individual contributors. Just like a factory manager doesn't inspect every single item off the line, you must develop methods for spot checks, anomaly detection, and standardizing outputs to evaluate the quality and quantity of your agents' work. Shift Your Work Upfront: Recognize that your core effort must move to the specification and planning phases. Your job is increasingly about setting the context, defining the prompt, and establishing strict guardrails before the agent begins its work. Redefining Your Work in Progress (WIP): Proven principles like limiting WIP and focusing on finishing rather than starting are more important than ever to reduce cognitive burden. However, you must adapt these principles to fit a workflow where you are managing processes rather than manually coding. Episode Homework: Take a step back and ask yourself: "What is my true work in progress? Am I actually manually doing these tickets, or am I managing the processes that produce quality ticket work?".