Podcasts about software engineers

Show NoteAI NativeなSoftware engineerになる方法について話しました。https://addyo.substack.com/p/the-ai-native-software-engineer生産性のはてに失われるもの https://tomoima525.hatenablog.com/entry/2025/05/28/050000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

AI can scaffold an app in seconds, but can it refactor that thousand-line React file when the first bug hits production? In this episode, I sit down with Brian Jenney software engineer and program owner of the coding bootcamp Parsity, to draw a hard line between “code that runs” and “code that lasts.” From mentoring career-switchers to stress-testing AI in real-world pipelines, Brian shares why craftsmanship and product judgment still beat copy-paste prompts.

Nvidia Distinguished Engineer Kevin Klues noted that low-level systems work is invisible when done well and highly visible when it fails — a dynamic that frames current Kubernetes innovations for AI. At KubeCon + CloudNativeCon North America 2025, Klues and AWS product manager Jesse Butler discussed two emerging capabilities: dynamic resource allocation (DRA) and a new workload abstraction designed for sophisticated AI scheduling.DRA, now generally available in Kubernetes 1.34, fixes long-standing limitations in GPU requests. Instead of simply asking for a number of GPUs, users can specify types and configurations. Modeled after persistent volumes, DRA allows any specialized hardware to be exposed through standardized interfaces, enabling vendors to deliver custom device drivers cleanly. Butler called it one of the most elegant designs in Kubernetes.Yet complex AI workloads require more coordination. A forthcoming workload abstraction, debuting in Kubernetes 1.35, will let users define pod groups with strict scheduling and topology rules — ensuring multi-node jobs start fully or not at all. Klues emphasized that this abstraction will shape Kubernetes' AI trajectory for the next decade and encouraged community involvement.Learn more from The New Stack about dynamic resource allocation: Kubernetes Primer: Dynamic Resource Allocation (DRA) for GPU WorkloadsKubernetes v1.34 Introduces Benefits but Also New Blind SpotsJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

At KubeCon North America 2025, GitLab's Emilio Salvador outlined how developers are shifting from individual coders to leaders of hybrid human–AI teams. He envisions developers evolving into “cognitive architects,” responsible for breaking down large, complex problems and distributing work across both AI agents and humans. Complementing this is the emerging role of the “AI guardian,” reflecting growing skepticism around AI-generated code. Even as AI produces more code, humans remain accountable for reviewing quality, security, and compliance.Salvador also described GitLab's “AI paradox”: developers may code faster with AI, but overall productivity stalls because testing, security, and compliance processes haven't kept pace. To fix this, he argues organizations must apply AI across the entire development lifecycle, not just in coding. GitLab's Duo Agent Platform aims to support that end-to-end transformation.Looking ahead, Salvador predicts the rise of a proactive “meta agent” that functions like a full team member. Still, he warns that enterprise adoption remains slow and advises organizations to start small, build skills, and scale gradually.Learn more from The New Stack about the evolving role of "cognitive architects":The Engineer in the AI Age: The Orchestrator and ArchitectThe New Role of Enterprise Architecture in the AI EraThe Architect's Guide to Understanding Agentic AIJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Jonathan Bryce, the new CNCF executive director, argues that inference—not model training—will define the next decade of computing. Speaking at KubeCon North America 2025, he emphasized that while the industry obsesses over massive LLM training runs, the real opportunity lies in efficiently serving these models at scale. Cloud-native infrastructure, he says, is uniquely suited to this shift because inference requires real-time deployment, security, scaling, and observability—strengths of the CNCF ecosystem. Bryce believes Kubernetes is already central to modern inference stacks, with projects like Ray, KServe, and emerging GPU-oriented tooling enabling teams to deploy and operationalize models. To bring consistency to this fast-moving space, the CNCF launched a Kubernetes AI Conformance Program, ensuring environments support GPU workloads and Dynamic Resource Allocation. With AI agents poised to multiply inference demand by executing parallel, multi-step tasks, efficiency becomes essential. Bryce predicts that smaller, task-specific models and cloud-native routing optimizations will drive major performance gains. Ultimately, he sees CNCF technologies forming the foundation for what he calls “the biggest workload mankind will ever have.” Learn more from The New Stack about inference: Confronting AI's Next Big Challenge: Inference Compute Deep Infra Is Building an AI Inference Cloud for Developers Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Episode 406 of The VentureFizz Podcast features Bill Simmons, serial entrepreneur and Co-Founder of Orbit.me and DataXu. I'm going to use the cliché: Bill actually is a rocket scientist. His background is in aerospace engineering, he holds a PhD from MIT, and he worked on 13 space missions. In addition, he was part of a major Government competition for simulating options for space travel to Mars. His team simulated 35 billion possible options to generate 1,100 different Mars missions that were all feasible. This groundbreaking technology that leverage Big Data, which we now recognize as AI and machine learning, launched his first company, DataXu, in 2008. DataXu became a pioneer in the programmatic ad platform category and raised over $87M in funding. The company scaled as a major player in the Boston tech scene, and was acquired by Roku in 2019. Now, Bill is tackling a challenge we all likely face with his new startup, Orbit.me. Information is scattered across texts, multiple email inboxes, LinkedIn, WhatsApp, and social apps—it's impossible to keep track of what matters. Orbit.me is a perfect use case for AI, organizing your scattered messages into “Orbits” which are dedicated spaces built around the real contexts of your life, like parenting, work, or other important matters. Chapters: 00:00 Introduction 02:41 Current Status of Space Travel & Mars 08:34 Bill Simmons Background Story 10:21 Academic Experience 13:12 Space Missions including Mars Research 17:19 How DataXu Came to Fruition & Focus on AdTech 20:03 Scaling DataXu & Market Strategies 23:15 The Competitive Landscape of AdTech 26:20 The Technology Behind Real-Time Bidding 29:49 Building DataXu's Culture During Growth 32:51 DataXu Acquisition by Roku 35:54 The Transition to Product Management & Experience at The Trade Desk 37:13 The Details of Orbit.me 43:07 The Team Behind Orbit.me 48:58 The Evolving Role of Software Engineers in the AI Era 52:01 Lightening Round Questions

Fixify, a leading provider in AI-driven IT support automation, has selected Cork City as the home of its new EU Centre of Excellence, creating 50 skilled jobs in the region over the next 18 months. The new facility will serve as a regional base for Fixify's development, support, and customer success for worldwide operations. This project is supported by the Irish Government through IDA Ireland. Attending the event, Taoiseach Micheál Martin TD said: "This announcement from Fixify to select Cork as the home of its new EU Centre of Excellence demonstrates a deep commitment to the region and creates 50 high-tech jobs in an exciting and growing sector. I have no doubt that these highly skilled jobs in IT, software engineering and data analysis will be a further boost to the workforce in the region. I want to acknowledge the role of IDA Ireland in supporting this project and I look forward to seeing the continued growth of Fixify in Cork over the coming years." Minister for Enterprise Tourism & Employment Peter Burke TD said: "Fixify's decision to establish its EU Centre of Excellence in Cork is very welcome news and is a strong endorsement of Ireland's position as a global leader in technology and innovation. This investment will bring 50 high-quality jobs to the region and further strengthen our thriving digital ecosystem. Cork's deep talent pool, supported by world-class institutions like UCC and MTU, and its proven track record in attracting and sustaining high-value FDI, make it ideally placed to support Fixify's growth. I wish the Fixify team in Cork the very best for the future." Fixify is now hiring in roles including IT Helpdesk Analysts, Software Engineers, Data Engineers, and Data Scientists. To explore career opportunities with Fixify, please visit Fixify careers. "We chose Cork for Fixify's European base - a city that brings together deep technical expertise, quality of life and community spirit - the conditions that make great work last," said Matt Peters, CEO Fixify. "Establishing our base here enables Fixify to tap into Ireland's exceptional talent and contribute to its thriving tech ecosystem as we scale automation and support that remains genuinely human worldwide." "Our investment in Cork is a strong vote of confidence in Ireland's technology talent and infrastructure," added Caroline Coughlan, Director, Employee Experience & People Operations at Fixify "Over the next 18 months, we will be scaling our presence here in parallel with delivering outstanding value to our customers across EMEA." IDA Ireland CEO Michael Lohan said: "I am very pleased that Fixify has chosen Cork as home to its EU Centre of Excellence as it recognises the quality and depth of the South West region's talent pool, Ireland's vibrant culture, and our pro-business environment. I wish to congratulate Fixify on this expansion and look forward to supporting them as they enhance Ireland's reputation as home to a thriving technology sector" See more stories here. More about Irish Tech News Irish Tech News are Ireland's No. 1 Online Tech Publication and often Ireland's No.1 Tech Podcast too. You can find hundreds of fantastic previous episodes and subscribe using whatever platform you like via our Anchor.fm page here: https://anchor.fm/irish-tech-news If you'd like to be featured in an upcoming Podcast email us at Simon@IrishTechNews.ie now to discuss. Irish Tech News have a range of services available to help promote your business. Why not drop us a line at Info@IrishTechNews.ie now to find out more about how we can help you reach our audience. You can also find and follow us on Twitter, LinkedIn, Facebook, Instagram, TikTok and Snapchat.

早期教育を施した子供がどのように成長したかについてインタビューした記事について話しました。https://chrislakin.blog/p/spaced-repetition-for-teaching-two動画 https://youtu.be/8U-Lza__Kko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, I speak with Tarek Ziadé, a renowned Software Engineer, currently working at Mozilla building AI in Firefox, author, open-source contributor, and long-time community leader. Tarek has built large-scale distributed systems, written influential books about Python, and contributed to foundational open-source projects.

Thinking of building your own AI security tool? In this episode, Santiago Castiñeira, CTO of Maze, breaks down the realities of the "Build vs. Buy" debate for AI-first vulnerability management.While building a prototype script is easy, scaling it into a maintainable, audit-proof system is a massive undertaking requiring specialized skills often missing in security teams. The "RAG drug" relies too heavily on Retrieval-Augmented Generation for precise technical data like version numbers, which often fails .The conversation gets into the architecture required for a true AI-first system, moving beyond simple chatbots to complex multi-agent workflows that can reason about context and risk . We also cover the critical importance of rigorous "evals" over "vibe checks" to ensure AI reliability, the hidden costs of LLM inference at scale, and why well-crafted agents might soon be indistinguishable from super-intelligence .Guest Socials -⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Santiago's LinkedinPodcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Cybersecurity, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions asked:(00:00) Introduction(02:00) Who is Santiago Castiñeira?(02:40) What is "AI-First" Vulnerability Management? (Rules vs. Reasoning)(04:55) The "Build vs. Buy" Debate: Can I Just Use ChatGPT?(07:30) The "Bus Factor" Risk of Internal Tools(08:30) Why MCP (Model Context Protocol) Struggles at Scale(10:15) The Architecture of an AI-First Security System(13:45) The Problem with "Vibe Checks": Why You Need Proper Evals(17:20) Where to Start if You Must Build Internally(19:00) The Hidden Need for Data & Software Engineers in Security Teams(21:50) Managing Prompt Drift and Consistency(27:30) The Challenge of Changing LLM Models (Claude vs. Gemini)(30:20) Rethinking Vulnerability Management Metrics in the AI Era(33:30) Surprises in AI Agent Behavior: "Let's Get Back on Topic"(35:30) The Hidden Cost of AI: Token Usage at Scale(37:15) Multi-Agent Governance: Preventing Rogue Agents(41:15) The Future: Semi-Autonomous Security Fleets(45:30) Why RAG Fails for Precise Technical Data (The "RAG Drug")(47:30) How to Evaluate AI Vendors: Is it AI-First or AI-Sprinkled?(50:20) Common Architectural Mistakes: Vibe Evals & Cost Ignorance(56:00) Unpopular Opinion: Well-Crafted Agents vs. Super Intelligence(58:15) Final Questions: Kids, Argentine Steak, and Closing

In this episode, I sit down with Mark Freeman and Chad Sanderson (Gable.ai) to discuss the release of their new O'Reilly book, Data Contracts: Developing Production-Grade Pipelines at Scale. They dive deep into the chaotic journey of writing a 350-page book while simultaneously building a venture-backed startup.The conversation takes a sharp turn into the evolution of Data Contracts. While the concept started with data engineers, Mark and Chad explain why they pivoted their focus to software engineers. They argue that software engineers are facing a "Data Lake Moment, "prioritizing speed over craftsmanship, resulting in massive technical debt and integration failures.Gable: https://www.gable.ai/

Helm — originally a hackathon project called Kate's Place — turned 10 in 2025, marking the milestone with the release of Helm 4, its first major update in six years. Created by Matt Butcher and colleagues as a playful take on “K8s,” the early project won a small prize but quickly grew into a serious effort when Deus leadership recognized the need for a Kubernetes package manager. Renamed Helm, it rapidly expanded with community contributors and became one of the first CNCF graduating projects.Helm 4 reflects years of accumulated design debt and evolving use cases. After the rapid iterations of Helm 1, 2, and 3, the latest version modernizes logging, improves dependency management, and introduces WebAssembly-based plugins for cross-platform portability—addressing the growing diversity of operating systems and architectures. Beyond headline features, maintainers emphasize that mature projects increasingly deliver “boring” but essential improvements, such as better logging, which simplify workflows and integrate more cleanly with other tools. Helm's re-architected internals also lay the foundation for new chart and package capabilities in upcoming 4.x releases. Learn more from The New Stack about Helm: The Super Helm Chart: To Deploy or Not To Deploy?Kubernetes Gets a New Resource Orchestrator in the Form of KroJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

If you think your value as a software engineer comes just from writing code, you're already at risk.In this episode, Outsystems CEO Woodson Martin reveals why AI isn't the real threat to your career. Irrelevance is. He explains that writing code is now only 20% of the job, and the engineers who thrive are the ones who master the other "80% that matters."We cover:The billions of lines of ungoverned code AI is creatingWhy the "Forward Deployed Engineer" model is changing team structuresThe 80% of engineering work that AI cannot replaceHow to shift from coder to problem solver who drives business revenueA CEO's advice for building a lasting engineering careerThis is a reality check for developers, tech leads, and architects who want to stay relevant as agentic AI reshapes the industry.Connect with Woodson:https://www.linkedin.com/in/woodsonmartinTimestamps:00:00:00 - Intro00:00:56 - How Agentic AI keeps the human in the loop00:01:55 - Real-world example: Automating the grunt work00:04:17 - How engineers are using agents internally00:05:52 - Blending Low-Code and High-Code for complex systems00:08:28 - Is a Low-Code career a trap for engineers?00:10:50 - Will AI make software engineering obsolete?00:12:09 - The 80/20 Rule: Why code is only 20% of your job00:13:14 - Layoffs vs. the rise of the solo entrepreneur00:15:18 - Career advice for a volatile tech market00:17:02 - How to retain top talent and keep them happy00:20:10 - Why we radically changed our engineering team structure00:24:33 - The "Forward Deployed Engineer" model explained00:27:08 - Outsystems vs. OpenAI: The future of platform building00:31:45 - The tech debt problem no one's talking about00:34:23 - The one thing that keeps you from becoming irrelevant#SoftwareEngineering #CareerAdvice #AIAgents

Kubernetes has relied on role-based access control (RBAC) since 2017, but its simplicity limits what developers can express, said Micah Hausler, principal engineer at AWS, on The New Stack Makers. RBAC only allows actions; it can't enforce conditions, denials, or attribute-based rules. Seeking a more expressive authorization model for Kubernetes, Hausler explored Cedar, an authorization engine and policy language created at AWS in 2022 and later open-sourced. Although not designed specifically for Kubernetes, Cedar proved capable of modeling its authorization needs in a concise, readable way. Hausler highlighted Cedar's clarity—nontechnical users can often understand policies at a glance—as well as its schema validation, autocomplete support, and formal verification, which ensures policies are correct and produce only allow or deny outcomes.Now onboarding to the CNCF sandbox, Cedar is used by companies like Cloudflare and MongoDB and offers language-agnostic tooling, including a Go implementation donated by StrongDM. The project is actively seeking contributors, especially to expand bindings for languages like TypeScript, JavaScript, and Python.Learn more from The New Stack about Cedar:Ceph: 20 Years of Cutting-Edge Storage at the Edge The Cedar Programming Language: Authorization SimplifiedJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

From GaTech Students to Young Leaders in Tech: A Combined Interview with former colleagues: Omar Sharifali (Senior Software Engineer, Meta) and Malcolm Danmola (Senior Product Manager, Amazon)---In this re-aired interview we discuss with two colleagues, Omar and Malcom their career trajectory and story to developing from a student to a young leader in Tech. This interview is definitely worth listening to and learning from.---Please note the views of this podcast represent those of my guest and I, and do not constitute medical or professional advice. We disclaim any loss in any way.

医師システム安全、ヒューマンエラー、そして信頼性工学の分野における世界的な第一人者として知られるRichart Cook氏の講演をもとに「なぜ複雑なシステムは滅多に故障しないのか」という視点からシステムの新しい信頼性の考え方、弾力性とはなにかを紐解いていきます。Velocity 2012 「なぜ複雑なシステムは故障するのか」 https://www.youtube.com/watch?v=2S0k12uZR14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

Google presenta Gemini 3, un modello che segna un salto netto rispetto alla generazione precedente: finestra di contesto fino a 1 milione di token, integrazione multimodale completa (testo, immagini, video, audio, codice) e punteggi record nei benchmark più difficili, da LMArena a GPQA Diamond.Accanto a questo, Google lancia Nano Banana Pro, il modello ottimizzato per infografiche, slide e contenuti professionali.E le prime generazioni pubblicate? A dir poco incredibili! Le trovate qui → https://x.com/icreatelife/status/1992123987128955219Intanto Yann LeCun annuncia l'uscita da Meta per fondare una startup di Advanced Machine Intelligence, basata su modelli del mondo e ragionamento causale: una direzione alternativa agli LLM classici.Questo episodio di Algoritmi è offerto da Shop Circle, uno dei software group più importanti d'Europa!E c'è di più, Shop Circle sta assumendo un Director of AI Enablement e un/una Software Engineer.

Join career coach and former finance professional turned software engineer, JC Clark, as she shares hard-won insights from her journey of 1800+ job applications. Discover insider tips to land high-paying remote jobs, build powerful professional networks, and navigate career changes. Learn how to thrive in virtual workplaces while maintaining work-life balance, especially for working parents.

AWS's approach to Elastic Kubernetes Service has evolved significantly since its 2018 launch. According to Mike Stefanik, Senior Manager of Product Management for EKS and ECR, today's users increasingly represent the late majority—teams that want Kubernetes without managing every component themselves. In a conversation onThe New Stack Makers, Stefanik described how AI workloads are reshaping Kubernetes operations and why AWS open-sourced an MCP server for EKS. Early feedback showed that meaningful, task-oriented tool names—not simple API mirrors—made MCP servers more effective for LLMs, prompting AWS to design tools focused on troubleshooting, runbooks, and full application workflows. AWS also introduced a hosted knowledge base built from years of support cases to power more capable agents.While “agentic AI” gets plenty of buzz, most customers still rely on human-in-the-loop workflows. Stefanik expects that to shift, predicting 2026 as the year agentic workloads move into production. For experimentation, he recommends the open-source Strands SDK. Internally, he has already seen major productivity gains from BI agents that automate complex data analysis tasks.Learn more from The New Stack about Amazon Web Services' approach to Elastic Kubernetes ServiceHow Amazon EKS Auto Mode Simplifies Kubernetes Cluster Management (Part 1)A Deep Dive Into Amazon EKS Auto (Part 2)Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AWS re:Invent has long featured CTO Werner Vogels' closing keynote, but this year he signaled it may be his last, emphasizing it's time for “younger voices” at Amazon. After 21 years with the company, Vogels reflected on arriving as an academic and being stunned by Amazon's technical scale—an energy that still drives him today. He released his annual predictions ahead of re:Invent, with this year's five themes focused heavily on AI and broader societal impacts.Vogels highlights technology's growing role in addressing loneliness, noting how devices like Alexa can offer comfort to those who feel isolated. He foresees a “Renaissance developer,” where engineers must pair deep expertise with broad business and creative awareness. He warns quantum-safe encryption is becoming urgent as data harvested today may be decrypted within five years. Military innovations, he notes, continue to influence civilian tech, for better and worse. Finally, he argues personalized learning can preserve children's curiosity and better support teachers, which he views as essential for future education.Learn more from The New Stack about evolving role of technology systems from past to future: Werner Vogels' 6 Lessons for Keeping Systems Simple50 Years Later: Remembering How the Future Looked in 1974Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

先延ばしグセを構造的に活用する方法について話しました。https://structuredprocrastination.com/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

DevOps practitioners — whether developers, operators, SREs or business stakeholders — increasingly rely on telemetry to guide decisions, yet face growing complexity, siloed teams and rising observability costs. In a conversation at KubeCon + CloudNativeCon North America, IBM's Jacob Yackenovich emphasized the importance of collecting high-granularity, full-capture data to avoid missing critical performance signals across hybrid application stacks that blend legacy and cloud-native components. He argued that observability must evolve to serve both technical and nontechnical users, enabling teams to focus on issues based on real business impact rather than subjective judgment.AI's rapid integration into applications introduces new observability challenges. Yackenovich described two patterns: add-on AI services, such as chatbots, whose failures don't disrupt core workflows, and blocking-style AI components embedded in essential processes like fraud detection, where errors directly affect application function.Rising cloud and ingestion costs further complicate telemetry strategies. Yackenovich cautioned against limiting visibility for budget reasons, advocating instead for predictable, fixed-price observability models that let organizations innovate without financial uncertainty.Learn more from The New Stack about the latest in observability: Introduction to ObservabilityObservability 2.0? Or Just Logs All Over Again?Building an Observability Culture: Getting Everyone OnboardJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of Resilient Cyber, I'm joined by Jesus Alejandro Cardenes Cabre, SVP of Product Architecture and John Xiaremba, Software Engineer, both from the VIA Knowledge Hub team to dig into all things post-quantum cryptography (PQC). This includes PQC standards, as well as practical steps developers must take today to mitigate future risks.

Major banks once built their own Linux kernels because no distributions existed, but today commercial distros — and Kubernetes — are universal. At KubeCon + CloudNativeCon North America, AWS's Jesse Butler noted that Kubernetes has reached the same maturity Linux once did: organizations no longer build bespoke control planes but rely on shared standards. That shift influences how AWS contributes to open source, emphasizing community-wide solutions rather than AWS-specific products.Butler highlighted two AWS EKS projects donated to Kubernetes SIGs: KRO and Karpenter. KRO addresses the proliferation of custom controllers that emerged once CRDs made everything representable as Kubernetes resources. By generating CRDs and microcontrollers from simple YAML schemas, KRO transforms “glue code” into an automated service within Kubernetes itself. Karpenter tackles the limits of traditional autoscaling by delivering just-in-time, cost-optimized node provisioning with a flexible, intuitive API. Both projects embody AWS's evolving philosophy: building features that serve the entire Kubernetes ecosystem as it matures into a true enterprise standard.Learn more from The New Stack about the latest in Kube Resource Orchestrator and Karpenter:  Migrating From Cluster Autoscaler to Karpenter v0.32How Amazon EKS Auto Mode Simplifies Kubernetes Cluster Management (Part 1) Kubernetes Gets a New Resource Orchestrator in the Form of KroJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Clockwork began with a narrow goal—keeping clocks synchronized across servers—but soon realized that its precise latency measurements could reveal deeper data center networking issues. This insight led the company to build a hardware-agnostic monitoring and remediation platform capable of automatically routing around faults. Today, Clockwork's technology is especially valuable for large GPU clusters used in training LLMs, where communication efficiency and reliability are critical. CEO Suresh Vasudevan explains that AI workloads are among the most demanding distributed applications ever, and Clockwork provides building blocks that improve visibility, performance and fault tolerance. Its flagship feature, FleetIQ, can reroute traffic around failing switches, preventing costly interruptions that might otherwise force teams to restart training from hours-old checkpoints. Although the company originated from Stanford research focused on clock synchronization for financial institutions, the team eventually recognized that packet-timing data could underpin powerful network telemetry and dynamic traffic control. By integrating with NVIDIA NCCL, TCP and RDMA libraries, Clockwork can not only measure congestion but also actively manage GPU communication to enhance both uptime and training efficiency. Learn more from The New Stack about the latest in Clockwork: Clockwork's FleetIQ Aims To Fix AI's Costly Network Bottleneck What Happens When 116 Makers Reimagine the Clock? Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

James Shore氏による2025年10月のAgile Cambridge カンファレンスでの講演「The Accountability Problem」についてとりあげました。講演の書き起こし: https://www.jamesshore.com/v2/blog/2025/the-accountability-problem象の絵: https://2.media.letscodejavascript.com/www.jamesshore.com/images/accountability-problem/014.jpeg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

At JupyterCon 2025, Jupyter Deploy was introduced as an open source command-line tool designed to make cloud-based Jupyter deployments quick and accessible for small teams, educators, and researchers who lack cloud engineering expertise. As described by AWS engineer Jonathan Guinegagne, these users often struggle in an “in-between” space—needing more computing power and collaboration features than a laptop offers, but without the resources for complex cloud setups. Jupyter Deploy simplifies this by orchestrating an entire encrypted stack—using Docker, Terraform, OAuth2, and Let's Encrypt—with minimal setup, removing the need to manually manage 15–20 cloud components. While it offers an easy on-ramp, Guinegagne notes that long-term use still requires some cloud understanding. Built by AWS's AI Open Source team but deliberately vendor-neutral, it uses a template-based approach, enabling community-contributed deployment recipes for any cloud. Led by Brian Granger, the project aims to join the official Jupyter ecosystem, with future plans including Kubernetes integration for enterprise scalability. Learn more from The New Stack about the latest in Jupyter AI development: Introduction to Jupyter Notebooks for DevelopersDisplay AI-Generated Images in a Jupyter Notebook Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In an interview at JupyterCon, Brian Granger — co-creator of Project Jupyter and senior principal technologist at AWS — reflected on Jupyter's evolution and how AI is redefining open source sustainability. Originally inspired by physics' modular principles, Granger and co-founder Fernando Pérez designed Jupyter with flexible, extensible components like the notebook format and kernel message protocol. This architecture has endured as the ecosystem expanded from data science into AI and machine learning. Now, AI is accelerating development itself: Granger described rewriting Jupyter Server in Go, complete with tests, in just 30 minutes using an AI coding agent — a task once considered impossible. This shift challenges traditional notions of technical debt and could reshape how large open source projects evolve. Jupyter's 2017 ACM Software System Award placed it among computing's greats, but also underscored its global responsibility. Granger emphasized that sustaining Jupyter's mission — empowering human reasoning, collaboration, and innovation — remains the team's top priority in the AI era. Learn more from The New Stack about the latest in Jupyter AI development: Introduction to Jupyter Notebooks for Developers Display AI-Generated Images in a Jupyter Notebook  Join our community of newsletter subscribers to stay on top of the news and at the top of your game.    Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Sabrina Goldfarb works at GitHub on billing systems and the Copilot AI tool. She made a career change from video editing to software engineering and started her tech journey with Frontend Masters' HTML/CSS bootcamp about eight years ago, which sparked her passion for coding.Sabrina co-founded Circulart, a fintech startup collecting data from emerging market central banks. She balances her GitHub work with managing the startup's technical architecture, crediting AI tools for making this possible by condensing what used to take a week into a day.She's passionate about teaching and giving back, driven by remembering her own eight-year struggle to break into tech. She advocates for creating your own opportunities and being persistent. Learn AI alongside coding fundamentals. Security and code understanding remain important, but AI proficiency is now essential. She encourages automating repetitive tasks and staying open to new technology.Check out Sabrina's Practical Prompt Engineering Course: https://frontendmasters.com/courses/prompt-engineering/Frontend Masters Online:Twitter: https://twitter.com/FrontendMastersLinkedIn: https://www.linkedin.com/company/frontend-masters/Facebook: https://www.facebook.com/FrontendMastersInstagram: https://instagram.com/FrontendMastersAbout Us:Advance your skills with in-depth, modern front-end engineering courses — our 150+ high-quality courses and 18 curated learning paths will guide you from mid-level to senior developer!frontendmasters.com

Jennings Anderson, a Software Engineer with Meta Platforms, and Amy Rose, the Chief Technology Officer at Overture Maps Foundation, speak with host Gregory M. Kapfhammer about the Overture Maps project, which creates reliable, easy-to-use, and interoperable open map data. After exploring the foundations of geospatial information systems, Gregory and his guests dive deep into the implementation of Overture Maps through features like the Global Entity Reference System (GERS). In addition to discussing the organizational structure of the Overture Maps Foundation and the need for a unified database of geospatial data, Jennings and Amy explain how to implement applications using data from Overture Maps. Brought to you by IEEE Computer Society and IEEE Software magazine.

James Reggio (CTO @ Brex) shares the story of "Brex 3.0", an 18-month journey behind their operational evolution. We explore how they rewound their org from a Series E to a Series C mindset, and replaced siloed OKRs with seasonal "marquee initiatives." James deconstructs the “Brex Hacker House”, an AI-focused startup within a startup experiment aimed to disrupt their core business. This conversation is all about evolving operational rhythms, layers of management, product building, and culture change! ABOUT JAMES REGGIOJames Reggio is Brex's Chief Technology Officer. James is a forward thinking technology leader who currently oversees Brex's entire Engineering org. James joined Brex in 2020 as Principal Engineer and has played a vital role in building the company's mobile app and AI capabilities. Prior to Brex, James had an extensive career as a Software Engineer at leading companies such as Microsoft, Salesforce, AirBnB, Stripe and more. Additionally, James founded two companies: Altair Management and Banter, a social discovery platform for podcasts that was later acquired by Convoy in 2018. James received his B.A. of Science from The University of Texas Austin. SHOW NOTES:The birth of Brex 3.0: Using a layoff as a "moment to refound the company" (3:38)Moving from a Series E to a Series C operational mindset (5:28)The problem with a GM model: How siloed OKRs and roadmaps created "deadlock" (6:07)New rituals: Why the CEO became "chief editor of the roadmap" (8:16)The impact on morale: "Folks just knew how their work fit into the bigger picture" (11:16)The challenge of the new model: Who do you hold accountable when you "win and lose as a team"? (13:43)The lesson for reintroducing systems: "Less is more" (15:43)The "Startup within a Startup": Launching an internal team to disrupt Brex (16:49)“What if we were founding Brex again today?” The 4 constraints for the "Hacker House" experiment (17:58)Questions eng leaders should ask when running a similar experiment to Brex (21:02)Aha moment: "With agentic coating, code is so cheap" (22:35)Managing the two narratives: "compounding" the core biz vs. “innovating" with AI (26:01)A surprising dynamic: Why the AI team struggled to see their impact (while the core team didn't) (29:38)Building alongside your customer to iterate / experiment faster (36:06)The turnaround is over: Brex hits 50% YoY growth and cash-flow positive (38:45)Rapid fire questions (42:10) This episode wouldn't have been possible without the help of our incredible production team:Patrick Gallagher - Producer & Co-HostJerry Li - Co-HostNoah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Jupyter AI v3 marks a major step forward in integrating intelligent coding assistance directly into JupyterLab. Discussed by AWS engineers David Qiu and Piyush Jain at JupyterCon, the new release introduces AI personas— customizable, specialized assistants that users can configure to perform tasks such as coding help, debugging, or analysis. Unlike other AI tools, Jupyter AI allows multiple named agents, such as “Claude Code” or “OpenAI Codex,” to coexist in one chat. Developers can even build and share their own personas as local or pip-installable packages. This flexibility was enabled by splitting Jupyter AI's previously large, complex codebase into smaller, modular packages, allowing users to install or replace components as needed. Looking ahead, Qiu envisions Jupyter AI as an “ecosystem of AI personas,” enabling multi-agent collaboration where different personas handle roles like data science, engineering, and testing. With contributors from AWS, Apple, Quansight, and others, the project is poised to expand into a diverse, community-driven AI ecosystem.Learn more from The New Stack about the latest in Jupyter AI development: Introduction to Jupyter Notebooks for DevelopersDisplay AI-Generated Images in a Jupyter NotebookJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Why does an AI that brilliantly generates code suddenly fail at basic math? The answer explains why your LLM will fail when you least expect it.In this episode, Emmanuel Maggiori, author of “Smart Until It's Dumb” and “The AI Pocket Book,” cuts through the AI hype to reveal what LLMs actually do and, more importantly, what they can't. Drawing from his experience building AI systems and witnessing multiple AI booms and busts, Emmanuel explains why machine learning works brilliantly until it makes mistakes no human would ever make.He shares why businesses repeatedly fail at AI adoption, how hallucinations are baked into the technology, and what developers need to know about building reliable AI products.Whether you're implementing AI at work or concerned about your career, this conversation offers a grounded perspective on navigating the current AI wave without getting swept away by unrealistic promises.Key topics discussed:Why AI projects fail the same way repeatedlyHow LLMs work and why they brilliantly failWhy hallucinations can't be fixed with better promptsWhy self-driving cars still need human operatorsAdopting AI without falling into hype trapsHow engineers stay relevant in the AI eraWhy AGI predictions are mostly marketingBuilding valuable products in boring industriesTimestamps:(00:00:00) Trailer & Intro(00:02:32) Career Turning Points(00:06:41) Writing “Smart Until It's Dumb” and “The AI Pocket Book”(00:08:14) The History of AI Booms & Winters(00:11:34) Why Generative AI Hype is Different Than the Past AI Waves(00:13:26) AI is Smart Until It's Dumb(00:16:45) How LLM and Generative AI Actually Work(00:22:53) What Makes LLMs Smart(00:27:25) Foundational Model(00:30:01) RAG and Agentic AI(00:34:09) Tips on How to Adopt AI Within Companies(00:37:56) How to Reduce & Avoid AI Hallucination Problem(00:45:49) The Important Role of Benchmarks When Building AI Products(00:50:57) Advice for Software Engineers to Deal With AI Concerns(00:56:49) Advice for Junior Developers(00:59:34) Vibe Coders and Prompt Engineers: New Jobs or Just Hype?(01:01:55) The AGI Possibility(01:07:23) Three Tech Lead Wisdom_____Emmanuel Maggiori's BioEmmanuel Maggiori, PhD, is a software engineer and 10-year AI industry insider. He has developed AI for a variety of applications, from processing satellite images to packaging deals for holiday travelers. He is the author of the books Smart Until It's Dumb, Siliconned, and The AI Pocket Book.Follow Emmanuel:LinkedIn – linkedin.com/in/emaggioriWebsite – emaggiori.comLike this episode?Show notes & transcript: techleadjournal.dev/episodes/238.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

Tiny teamsという少数精鋭組織の作り方について話します。The tiny teams playbook https://www.latent.space/p/tinyTiny Teams ― AI 時代は少数精鋭が最強? https://note.com/tomoima525/n/n043022fd958d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

Agentic coding isn't software engineering. If you've been coding for a while, it doesn't give you the same rewards. Yet some developers are all too eager to let their skills atrophy and outsource the fun parts of being a developer to an AI, instead of fixing the root issue - their skill issue. This is mainly targeted towards junior developers, but a lot of this applies to more experienced developers as well. Or it will, once they let their skills atrophy.---------------------------------------------------

書籍「バイリンガル教育の方法」2016年完全改訂版 https://amzn.to/3X1x3Xm関連エピソードep176. 上達の法則ep150. 「科学的根拠に基づく最高の勉強法」ep115. 英語学習についてやってきたことと AI が出てきた今英語学習をどうするべき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

Today, we're talking about building real AI products with foundation models. Not toy demos, not vibes. We'll get into the boring dashboards that save launches, evals that change your mind, and the shift from analyst to AI app builder. Our guide is Hugo Bowne-Anderson, educator, podcaster, and data scientist, who's been in the trenches from scalable Python to LLM apps. If you care about shipping LLM features without burning the house down, stick around. Episode sponsors Posit NordStellar Talk Python Courses Links from the show Hugo Bowne-Anderson: x.com Vanishing Gradients Podcast: vanishinggradients.fireside.fm Fundamentals of Dask: High Performance Data Science Course: training.talkpython.fm Building LLM Applications for Data Scientists and Software Engineers: maven.com marimo: a next-generation Python notebook: marimo.io DevDocs (Offline aggregated docs): devdocs.io Elgato Stream Deck: elgato.com Sentry's Seer: talkpython.fm The End of Programming as We Know It: oreilly.com LorikeetCX AI Concierge: lorikeetcx.ai Text to SQL & AI Query Generator: text2sql.ai Inverse relationship enthusiasm for AI and traditional projects: oreilly.com Watch this episode on YouTube: youtube.com Episode #526 deep-dive: talkpython.fm/526 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Many leaders are trapped between chasing ambitious, ill-defined AI projects and the paralysis of not knowing where to start. Dr. Randall Olson argues that the real opportunity isn't in moonshots, but in the "trillions of dollars of business value" available right now. As co-founder of Wyrd Studios, he bridges the gap between data science, AI engineering, and executive strategy to deliver a practical framework for execution. In this episode, Randy and Hugo lay out how to find and solve what might be considered "boring but valuable" problems, like an EdTech company automating 20% of its support tickets with a simple retrieval bot instead of a complex AI tutor. They discuss how to move incrementally along the "agentic spectrum" and why treating AI evaluation with the same rigor as software engineering is non-negotiable for building a disciplined, high-impact AI strategy. They talk through: How a non-technical leader can prototype a complex insurance claim classifier using just photos and a ChatGPT subscription. The agentic spectrum: Why you should start by automating meeting summaries before attempting to build fully autonomous agents. The practical first step for any executive: Building a personal knowledge base with meeting transcripts and strategy docs to get tailored AI advice. Why treating AI evaluation with the same rigor as unit testing is essential for shipping reliable products. The organizational shift required to unlock long-term AI gains, even if it means a short-term productivity dip. LINKS Randy on LinkedIn (https://www.zenml.io/llmops-database) Wyrd Studios (https://thewyrdstudios.com/) Stop Building AI Agents (https://www.decodingai.com/p/stop-building-ai-agents) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/-YQjKH3wRvc)

Are you struggling with motivation on your learning to code journey? It could be poor goal setting. It could be that trying to find a job is suppressing your curiosity and excitement for coding. It could very well be that software engineering isn't what you initially thought it was. But almost certainly, what I share in this has or is hurting your motivation more than you think.---------------------------------------------------

If you struggle to talk to women or feel stuck in the friend zone, you need to hear Tony's story. A shy, single dad and software engineer, Tony was dateless when he began working with Connell. By learning how to flirt with women and bring confidence to every date, Tony met his dream girlfriend, Jennifer. In this episode, he shares the exact shifts and flirting techniques that helped him succeed—so you can find love, too.Episode Highlights:01:54: Why Tony Felt Women Didn't Want Him03:31: The Self-Doubt Voice that Keeps Men Single—and How to Silence It12:36: Steal the “Time Traveler” Online Dating Opener that Got Jennifer's Attention17:36: How Tony Escaped the Friend Zone on His First Date21:20: The First Date that Changed Everything27:53: Tony's Take—the One Move Every Shy, Single Guy Should MakeTO TAKE YOUR DATING RESULTS TO A WHOLE NEW LEVEL, BOOK A FREE CALL WITH CONNELL TO LEARN ABOUT 1-1 COACHING: http://www.DatingTransformation.comEMAIL CONNELL FOR A FREE COPY OF HIS NO. 1 AMAZON BESTSELLING BOOK, “DATING SUCKS BUT YOU DON'T”: Connell@datingtransformation.com

In this conversation, Dave Prior and Hugo Bowne-Anderson discuss the evolving landscape of AI and data science, focusing on the role of AI agents in solving business problems. Hugo shares insights on how to effectively implement AI solutions, the importance of understanding the underlying data, and the need for continuous improvement in AI systems. They also touch on the skills necessary for navigating the AI landscape, the value of collaboration between technical and non-technical teams, and the importance of assessing the value of AI projects. Hugo concludes by offering a course on building AI applications, emphasizing the iterative nature of AI development. Takeaways - Hugo emphasizes the importance of data in AI applications. - AI agents can automate tasks but require human oversight. - Understanding the problem is crucial before implementing AI solutions. - Prompt engineering remains a valuable skill alongside learning about agents. - Consultants should educate clients on practical AI applications. - AI systems should be built incrementally and iteratively. - Value assessment in AI projects should focus on efficiency and cost savings. - Continuous improvement is essential for AI systems to remain effective. - Experimentation with AI tools can lead to innovative solutions. - Collaboration between technical and non-technical teams is vital for successful AI implementation. Chapters 00:00 Introduction to Data and AI Literacy 06:14 Understanding AI Agents vs. LLMs 09:18 The Role of Agents in Business Solutions 12:21 Navigating the Future of AI and Agents 15:24 Consulting and Client Education in AI 18:37 Building Incremental AI Solutions 21:29 The Future of AI Coding and Debugging 24:32 Prototyping with AI: Challenges and Solutions 25:32 Leveraging AI for User Insights and Competitive Analysis 27:29 Understanding Value in AI Development 32:05 The Role of Product Managers in AI Integration 33:00 AI as an Instrument: The Human Element 35:33 Getting Started with AI: Practical Steps for Teams 38:51 Building AI Applications: Course Overview and Insights Links from the Podcast: Stop Building AI Agents - Here's what you should build instead (Article) https://www.decodingai.com/p/stop-building-ai-agents Anthropic https://www.anthropic.com/engineering/multi-agent-research-system The Colgate Study https://www.pymc-labs.com/blog-posts/AI-based-Customer-Research Hugo's Course (Starts November 3, 2025) Building AI Applications for Data Scientists and Software Engineers (with a 25% discount) https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=drunkenpm (You can use the discount code drunkenpm to get 25% off) How To Be A Podcast Guest with Jay Hrcsko https://youtu.be/vkNbgwcolIM Contacting Hugo LinkedIn https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/ Substack https://hugobowne.substack.com/ Contacting Dave Linktree: https://linktr.ee/mrsungo Dave's Classes: https://www.eventbrite.com/cc/dave-prior-classes-4758623

音声AIカンファレンスに参加して見えた音声AIの現在地について話しました。- Vapicon https://vapi.ai/vapicon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 talk, Hugo Bowne-Anderson, an independent data and AI consultant, educator, and host of the podcasts Vanishing Gradients and High Signal, shares his journey from academic research and curriculum design at DataCamp to advising teams at Netflix, Meta, and the US Air Force. Together, we explore how to build reliable, production-ready AI systems—from prompt evaluation and dataset design to embedding agents into everyday workflows.You'll learn about: How to structure teams and incentives for successful AI adoptionPractical prompting techniques for accurate timestamp and data generationBuilding and maintaining evaluation sets to avoid “prompt overfitting”- Cost-effective methods for LLM evaluation and monitoringTools and frameworks for debugging and observing AI behavior (Logfire, Braintrust, Phoenix Arise)The evolution of AI agents—from simple RAG systems to proactive, embedded assistantsHow to escape “proof of concept purgatory” and prioritize AI projects that drive business valueStep-by-step guidance for building reliable, evaluable AI agentsThis session is ideal for AI engineers, data scientists, ML product managers, and startup founders looking to move beyond experimentation into robust, scalable AI systems. Whether you're optimizing RAG pipelines, evaluating prompts, or embedding AI into products, this talk offers actionable frameworks to guide you from concept to production.LINKSEscaping POC Purgatory: Evaluation-Driven Development for AI Systems - https://www.oreilly.com/radar/escaping-poc-purgatory-evaluation-driven-development-for-ai-systems/Stop Building AI Agents - https://www.decodingai.com/p/stop-building-ai-agentsHow to Evaluate LLM Apps Before You Launch - https://www.youtube.com/watch?si=90fXJJQThSwGCaYv&v=TTr7zPLoTJI&feature=youtu.beMy Vanishing Gradients Substack - https://hugobowne.substack.com/Building LLM Applications for Data Scientists and Software Engineers https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=datatalksclubTIMECODES:00:00 Introduction and Expertise04:04 Transition to Freelance Consulting and Advising08:49 Restructuring Teams and Incentivizing AI Adoption12:22 Improving Prompting for Timestamp Generation17:38 Evaluation Sets and Failure Analysis for Reliable Software23:00 Evaluating Prompts: The Cost and Size of Gold Test Sets27:38 Software Tools for Evaluation and Monitoring33:14 Evolution of AI Tools: Proactivity and Embedded Agents40:12 The Future of AI is Not Just Chat44:38 Avoiding Proof of Concept Purgatory: Prioritizing RAG for Business Value50:19 RAG vs. Agents: Complexity and Power Trade-Offs56:21 Recommended Steps for Building Agents59:57 Defining Memory in Multi-Turn ConversationsConnect with HugoTwitter - https://x.com/hugobowneLinkedin - https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/Github - https://github.com/hugobowneWebsite - https://hugobowne.github.io/Connect with DataTalks.Club:Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQCheck other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

Episode 497 / Gretchen AndrewGretchen Andrew is an artist born in Los Angeles, United States, 1988 who lives and Works in London and Park City, Utah. She studied Information Systems and got a BS from Boston College, and worked for Intuit as a Software Engineer, Google as a People Technology Manager, and apprenticed with Billy Childish at his studio.She's had shows at Gray Area, San Francisco, Heft Gallery, NYC, Hope 93, London. FxHash, Berlin Art Week, Galloire, Dubai UAE,  Falko Alexander, Cologne, Germany, Annka Kultys Gallery, London, United Kingdom and many others.She's shown at fairs including 2025 Expo Chicago, 2024 Untitled Miami, Paris Photo (21C Award, solo presentation) and the 2022 Vienna Contemporary (solo presentation).She has lectured at the Tate Modern, the Luma Foundation in Zurich, the Mia Foundation in Dubai and the University of Chicago.

Most AI teams find their multi-agent systems devolving into chaos, but ML Engineer Alex Strick van Linschoten argues they are ignoring the production reality. In this episode, he draws on insights from the LLM Ops Database (750+ real-world deployments then; now nearly 1,000!) to systematically measure and engineer constraint, turning unreliable prototypes into robust, enterprise-ready AI. Drawing from his work at Zen ML, Alex details why success requires scaling down and enforcing MLOps discipline to navigate the unpredictable "Agent Reliability Cliff". He provides the essential architectural shifts, evaluation hygiene techniques, and practical steps needed to move beyond guesswork and build scalable, trustworthy AI products. We talk through: - Why "shoving a thousand agents" into an app is the fastest route to unmanageable chaos - The essential MLOps hygiene (tracing and continuous evals) that most teams skip - The optimal (and very low) limit for the number of tools an agent can reliably use - How to use human-in-the-loop strategies to manage the risk of autonomous failure in high-sensitivity domains - The principle of using simple Python/RegEx before resorting to costly LLM judges LINKS The LLMOps Database: 925 entries as of today....submit a use case to help it get to 1K! (https://www.zenml.io/llmops-database) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/-YQjKH3wRvc)

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Hosts Tor and Chet are joined by Adarsh Fernando, a Product Manager, and Ray Buse, a Software Engineer, to discuss Journeys for Android Studio. Powered by Gemini's vision and reasoning, Journeys aims to simplify end-to-end test creation and maintenance by converting the natural language you provide to describe the steps and assertions for each test, resulting in actions and evaluations performed directly on your app. Chapters: 0:00 - Intro 1:46 - Journeys: New AI-powered testing approach 3:40 - How Journeys Works with Gemini 4:27 - The natural language advantage 5:49 - Real-world use case: Google Maps 6:53 - Debugging with AI reasoning 8:08 - Why Journeys is important: Bridging the testing gap 9:56 - Journeys and End-to-End Testing 12:18 - Performance and Cached Journeys 24:14 - Android Studio and Firebase integration 25:27 - The development workflow 31:22 - AI for everyone: Beyond end-to-end testing 33:28 - Looking ahead: Feedback and the future  Resources: Journeys for Android Studio → https://goo.gle/4m9YOr3 App Testing (Android) → https://goo.gle/3HVKTqB   Tor on Bluesky → https://goo.gle/3ViCAYS Chet on Bluesky → https://goo.gle/4gzpccM Ardash on Bluesky → https://goo.gle/47JGNw9

Can you navigate AI disruption without understanding your landscape? Discover how to gain true situational awareness.The rise of AI has exposed a fundamental problem in how organizations make decisions. Most leaders operate using stories and graphs, not actual maps of their landscape. This leaves them vulnerable to disruption and unable to make informed choices about where to apply new technologies. The result is chaos, waste, and strategic mistakes that could have been avoided.In this episode, Simon Wardley, creator of Wardley Mapping, explains how to build true situational awareness in your organization. He shares why most business “maps” aren't really maps at all, how to understand the landscape before making decisions, and what leaders need to know about AI adoption beyond the current hype.Key topics discussed:Why leading with stories instead of maps creates fake CEOsThe critical difference between graphs and maps in business strategyWhat Wardley mapping is and the three pattern types leaders must understandHow to identify where human decision-making adds value in your AI adoptionWhy vibe coding is powerful but dangerous without proper code reviewsWhy software development is still a craft, not engineeringHow Jevons Paradox means AI won't eliminate jobs but expand codebasesThe hidden dangers of AI hallucinations and the need for critical thinkingTimestamps:(00:00:00) Trailer & Intro(00:02:59) Career Turning Points(00:06:45) Importance of Understanding Landscape for Leaders(00:10:42) The Problem of Leading with Stories(00:12:49) Wardley Maps vs Other Types of Business Maps/Analysis(00:17:32) Wardley Map Overview(00:23:54) Why Mapping is Not a Common Industry Practice(00:26:23) Climatic Patterns, Doctrines, and Gameplay(00:30:51) Understanding Disruption by Using a Map(00:33:17) Navigating the Recent AI Disruption(00:39:37) A Leader's Guide to Adopting AI(00:42:49) Turning Coding From a Craft Into Engineering(00:48:05) Simon's AI & Vibe Coding Experiments(00:55:28) The Importance of Critical Thinking for Software Engineers(01:03:49) Navigating Career Anxiety Due to AI Fear(01:08:56) Tech Lead Wisdom_____Simon Wardley's BioSimon Wardley is a researcher, former CEO, and the creator of Wardley Mapping, a powerful method for visualizing and developing business strategy. His journey began accidentally after a bookseller recommended Sun Tzu's The Art of War, which sparked a fascination with understanding the competitive “landscape.”As the former CEO of an online photo service acquired by Canon, he felt like a “fake CEO,” leading with stories while lacking true situational awareness. This led him to discover that almost all business “maps” were merely graphs, prompting him to develop his own mapping technique. Today, his work is used by organizations like NASA and taught at multiple MBA programs, helping leaders to “look before they leap” and navigate complex technological and market shifts, including the current disruption caused by AI.Follow Simon:LinkedIn – linkedin.com/in/simonwardleyTwitter – x.com/swardleyWebsite – www.swardleymaps.comLike this episode?Show notes & transcript: techleadjournal.dev/episodes/235.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

As AI tools race into every corner of software development, a simple question keeps coming back to me. Will AI replace human testers, or will it force us to rethink what great testing looks like in the first place. In today's conversation, I talk with Santiago Komadina Geffroy, a Software Engineer at Jalasoft and an educator with Jala University, about what changes, what stays, and what teams should do next. Santiago shares how his day job and teaching intersect. He points to a gap he sees often. Engineers are experimenting with large language models without fully understanding how they work, which leads to overconfidence and avoidable rework. He argues for clearer interaction patterns between tools and people. Think less about magic prompts and more about protocols, context sharing, and agent to agent collaboration. That shift frees testers to do the thinking work that AI still struggles with, from exploratory testing and usability judgment to spotting the weird edge cases that only show up when real humans use real products. We also get into bias and ethics. AI is only as fair as the data it learns from, and that matters in healthcare, finance, and hiring where a mistake can carry life changing consequences. Santiago calls for stronger education around data quality, authorship, privacy, and environmental impact, not as a side note but as part of how engineers are trained. He believes governance helps teams move faster with fewer regrets when they take AI into production. Security sits in the mix too. Many AI tools need deep system access. If compromised, they can distort results or leak sensitive information. Santiago is candid about the limits of any single safeguard. He recommends a culture of shared responsibility where engineers understand when to call in security specialists and how to design workflows that keep humans in the loop for consequential decisions. We close with what Jalasoft has learned from building with AI inside a nearshore model in South America. More thinking time. Smaller, controllable scopes. Clear lines between routine automation and human judgment. The headline is simple. AI will change testing. Human testers will remain at the heart of quality.

In this episode of the Get Creative Podcast, host Justin Tuminowski sits down with Stella Han, co-founder and CEO of Fractional, to explore how anyone—from seasoned investors to complete beginners—can start building wealth in real estate with as little as $5,000. Stella shares her inspiring journey from software engineer to real estate entrepreneur with a portfolio of 150+ units, and the breakthrough moment that led her to co-create Fractional: a platform designed to make capital raising and community-driven investing accessible for everyone—not just accredited investors. Follow Stella: https://www.instagram.com/hellastellah/ ➡️ Get the CRM that will take you further: https://www.gohighlevel.com/pace ➡️ Use Creative Listing for FREE to buy and sell creatively: https://bit.ly/CreativeListing ➡️ Join the SubTo Community: https://subto.sjv.io/RG6EDb ➡️ Become a Top Tier Transaction Coordinator: https://toptiertc.pxf.io/yqmoxW ➡️ Discover the Gator Method: https://gator.sjv.io/Z6qOyX ➡️ Get to the SquadUp Summit Conference: https://bit.ly/GetToSquadUpSummit COMMUNITY MEMBERS! ➡️ Get Featured on the Get Creative Podcast: https://bit.ly/GetCreativeGuestForm Refer a Friend to SubTo: refer.nre.ai/subto Refer a Friend to TTTC: refer.nre.ai/tttc Refer a Friend to Gator: refer.nre.ai/gator PLUG IN & SUBSCRIBE Creative Real Estate Facebook Group: https://www.facebook.com/groups/creativefinancewithpacemorby Instagram: https://www.instagram.com/pacemorby/  YouTube: https://www.youtube.com/@PaceMorby TikTok: https://www.tiktok.com/@pacemorby  X: https://x.com/PaceJordanMorby The Pace Morby Show: https://www.youtube.com/@thepacemorbyshow