Podcasts about Software engineering

As you grow as an engineer, something counterintuitive happens: the systems you build get simpler, not more complex. In this episode, I explore why senior engineers tend to collapse abstractions, accept certain risks, and reduce the surface area they're responsible for — and what drives that shift underneath the surface. Understanding the reasons behind the trend is what lets you get there on purpose instead of waiting a decade to arrive there accidentally. Early in your career, output goes up fast. You're building more than ever, especially with AI in the mix, and a lot of what you build carries real complexity. But watch engineers who have been doing this a long time and you'll notice the opposite of what you'd expect: as their craft improves, their code, their architectures, and their systems get simpler. In today's episode, I dig into why that happens — and why chasing simplicity directly is less useful than understanding the underlying forces that produce it. Simplicity Isn't the Goal — It's the Byproduct: Trying to "make things simpler" as a directive doesn't get you very far. If you can understand the core reasons complexity tends to fall as engineers mature, you can aim at those reasons instead and arrive at simplicity organically. Why Simpler Code Pays Off: Simpler things are easier to understand, which means junior engineers can pick up your code, and future-you can return to a project you've long since left and still make sense of it. Adapting a simple thing is almost always easier than adapting a complex one. The Pain of the Refactor Teaches You: A big part of this shift is scar tissue. Once you've lived through a massive refactor of a complex system, you start making different trade-offs — not from theory, but because you don't want to do that again. Collapsing Vertical Abstractions: One of the most common refactors senior engineers reach for is collapsing long chains of abstraction that only ever get used in one place. It's abstraction without reuse, and it forces you to re-load enormous context just to make a small change. Refining Your Risk Tolerance: Early on, we hedge against every possible risk. Later, we learn some risks are acceptable. Hedging is insurance, and sometimes it's very expensive insurance — paid in velocity, in onboarding difficulty, and in only being able to hire people who can hold all that complexity in their heads. Knowing When to Break Best Practices: Maybe the best practice says abstract this. But if it isn't that hard to understand, and you can get most of the benefit through better naming or tighter scoping, the "correct" move might be the wrong one. The Library Trap (In Both Directions): Seniors often take a trip through "let's not use external packages, we don't know what's in them" — and end up maintaining a shadow version of the thing they avoided. The more experienced call is often to accept the trade-off, adopt the well-documented dependency, and shrink what you are responsible for. Reducing Surface Area Creates Focus: The through-line in all of these trade-offs is a shrinking surface area. Fewer things to maintain means more focus, and more focus means the things you are responsible for get done very well. It's an open question whether those behaviors follow seniority or cause it. Drive to the Fundamentals: Think about a machine built from a ramp, a screw, a rubber band, and a motor. A more senior craftsperson recognizes the problem is fundamentally about conservation of energy, and reconfigures it down to two parts instead of eight. Slightly less efficient, maybe — but far less to teach, maintain, and break. Episode Homework: Ask yourself: what am I responsible for right now that I could simplify? Where can I get away from the tactics and the surface-level stuff, get down to the core of the thing, and focus on doing that core really well?

Sami is back this week with Mike McQuaid, CTPO and Homebrew Project Leader, as they take a deep dive into the importance of open source maintainers in modern software engineering. With 18 years of experience reducing developer friction and scaling open source software, Mike discusses the commonalities and differences between open source and proprietary software audiences, impact of large data centres on both the environment and women in the workplace, revealing what drove him to create Homebrew and where they are now, before taking Sami through burnout and why it's important to not be rude to your open source maintainers - you'll be surprised which national entities would fall apart if it weren't for their hard work and time. — Our guest for this episode has been Mike McQuaid. If you'd like to get in touch with Mike, or to keep up to date with his work, you can do so through his website. Also mentioned in today's episode: ‘Open Source Maintainers Owe You Nothing' blog post. Your host for this episode has been Sami Birnbaum. Sami can be found through his website or via LinkedIn. If you would like to support the show, head over to our GitHub page, or check out our website. Got a question or comment about the show? Why not write to our hosts: hosts@giantrobots.fm This has been a thoughtbot podcast. Stay up to date by following us on social media - LinkedIn - Mastodon - YouTube - Bluesky © 2026 Giant Robots Smashing Into Other Giant Robots Podcast

The fact that OpenAI has quickly adopted Temporal, a rapidly expanding AI ecosystem, and has even entered into a partnership with Crystal Palace FC shows that its business strategy is to pursue community-first innovation.For years, tech enterprises competed against each other on the grounds of innovative features. However, the competition has changed recently. Now the winners are those who build communities.In the recent episode of the Tech Transformed podcast, host Christina Stathopoulos, Founder of Dare to Data, is joined by Melissa Herrera, Senior Developer Advocate at Temporal, and Les Jackson, Staff Developer Advocate, to discuss the pivotal role of the community in technology. They further explore how Temporal's open-source philosophy fosters developer engagement, the impact of community feedback on product development, and the significance of partnerships, such as with OpenAI and Crystal Palace. The conversation emphasises the importance of authentic community relationships and the future direction of Temporal, highlighting the need for continuous integration and collaboration with developers.TakeawaysCommunity is a central part of Temporal's growth.Temporal's philosophy is rooted in open-source software.In-person community interactions are invaluable for developers.Feedback from the community directly shapes product direction.OpenAI's adoption of Temporal led to significant scaling.Partnerships should focus on community engagement, not just transactions.Temporal's collaboration with Crystal Palace merges tech and sports communities.Investing in community fosters trust and collaboration.Continuous integration with developer tools is essential for success.Authentic community relationships drive technology innovation.Chapters00:00 Introduction to Tech Transformed Podcast02:45 The Importance of Community in Technology05:13 Feedback from Developers: Shaping Product Direction07:51 OpenAI's Adoption of Temporal: A Case Study10:02 Unique Partnerships: Temporal and Crystal Palace15:12 Looking Ahead: Future of Temporal and Community Engagement19:38 Final Thoughts: Investing in CommunityTemporal, Enterprise AI, Developer Communities, Developer Advocacy, Open Source, OpenAI, AI Agents, AI Infrastructure, Durable Execution, AI Orchestration, Enterprise Software, Developer Experience, AI Workflows, AI Adoption, Community Led Growth, Developer Led Growth, Temporal SDK, Software Engineering, AI Engineering, Enterprise Technology, Crystal Palace, Tech Transformed

If AI can produce the code, what becomes more valuable for engineers?John Kuhn, CTO and cofounder of Integral, joins The Tech Trek to discuss how agentic development is changing engineering work, product ownership, experimentation, and hiring. Integral helps companies de identify and anonymize data for model training, including unstructured data.John argues that the value of an engineer is shifting away from simply writing code. As AI handles more implementation work, engineers need stronger product judgment, better systems thinking, and the ability to make decisions when requirements are incomplete. That means asking better questions, understanding customer problems more directly, and taking greater ownership of the outcome.The conversation also looks at what happens when software becomes cheaper to produce. Teams can prototype and experiment faster, but lower development costs do not eliminate the cost of building something customers do not want. Good product discovery still matters, especially when engineers are expected to operate with more autonomy.What You'll Take Away• Why engineers increasingly need to think like product managers• How agentic tools are changing the economics of prototyping and product experimentation• Why good product discovery requires questions that seek information instead of confirming an existing idea• Why engineering interviews may need to focus more on assumptions, constraints, systems thinking, and decision quality than manual coding speedA Moment Worth Pulling Out“Engineers are not meant to write code anymore. They're meant to solve problems.”John also raises an interesting idea for the future of technical hiring: instead of giving candidates only a time limit, give them a fixed AI compute budget and evaluate how efficiently they use it to reach a solution.Follow The Tech Trek for more conversations about AI, engineering, product, data, and technical leadership.

Does moving faster with AI mean you get to skip the laws that governed every software project before it? Milan Milanovic argues the opposite, the old laws of software engineering now apply twice as hard.In this episode, Milan Milanovic, CTO and author of Laws of Software Engineering, returns to unpack why the laws that have quietly governed software projects for decades are more relevant than ever in the AI era. He walks through Gall's Law and why AI lets teams generate complex systems on unvalidated assumptions faster than ever, Conway's Law and how it now runs twice, shaping both human organizations and agent topologies, and Goodhart's Law and the trap of “tokenmaxxing” as a metric. Milan also covers Hofstadter's Law and the 90-90 rule, explaining why the last 10% of a project still takes as long with AI in the loop, and the Dunning-Kruger effect, where vibe coders overestimate their skills while senior engineers underestimate theirs. The conversation closes with his advice for juniors entering the field, his top five must-read books, and how he personally uses AI for research and planning rather than implementation.Key topics discussed:Gall's Law: the trap of generating complex systems with AIConway's Law now runs twice, on humans and on AI agent topologyWhy software architects should help design your org chartGoodhart's Law and the danger of “tokenmaxxing” as a metricThe 90-90 rule: why AI's last 10% still costs half the projectDunning-Kruger effect on vibe coders and expertsWhy less code beats more, even with AI writing it for freeTimestamps:(00:00) Trailer & Intro(03:02) What Inspired Milan to Write Laws of Software Engineering?(05:32) How Did a Book on Software Laws Reach Such a Global Audience?(06:49) How Should You Read the Laws of Software Engineering Book?(08:51) Why Does the Book Cover People and Planning, Not Just Technical Laws?(11:07) What Is Gall's Law in Software Engineering?(14:46) How Can You Apply Gall's Law When Using AI?(17:34) What Is Conway's Law in Software Engineering?(19:34) How Should Big Corporations Build New Products Without Structural Reorganization?(21:26) How Does Conway's Law Apply to Small AI-Powered Product Teams?(23:25) What Is Goodhart's Law in Software Engineering?(25:52) What Are the Best Examples of Counterbalance Metrics for Leaders Today?(28:06) What Kind of Outcomes Should You Actually Measure?(31:01) What Is Hofstadter's Law in Software Engineering?(33:35) How Does Hofstadter's Law Apply in the Age of AI?(36:41) What Is the Dunning-Kruger Effect?(40:10) How Does the Dunning-Kruger Effect Apply to Experienced Engineers Learning AI?(42:46) Are Any of These Laws Becoming Less Relevant Because of AI?(44:32) What Does the Lindy Effect Say About the Fate of Software Developers?(47:49) What Is the Best Career Advice for Junior Developers Entering the AI Landscape?(50:34) What Are the Top Five Must-Read Books for Software Engineers?(55:46) How Can Software Engineers Apply These Laws in Their Daily Work?(57:26) 3 Tech Lead Wisdom_____Milan Milanovic's BioMilan Milanović is the CTO and the author of Laws of Software Engineering. He holds a PhD in Computer Science, has more than 20 years of experience across .NET, Azure, and mobile development, and is a Microsoft MVP. He runs Tech World With Milan, a software engineering newsletter and community followed by more than 400,000 engineers. He writes about architecture, engineering leadership, and how AI is changing the way software gets built.Follow Milan:LinkedIn – linkedin.com/in/milanmilanovicTwitter / X – @milan_milanovicPersonal website – milan.milanovic.org Book's website - lawsofsoftwareengineering.com Newsletter - newsletter.techworld-with-milan.comLike this episode?Show notes & transcript: techleadjournal.dev/episodes/266.Follow @techleadjournal on LinkedIn and Instagram.Buy me a coffee or become a patron.

This interview was recorded for GOTO State of the Art in April 2026.https://gotopia.techRead the full transcription of this interview here:https://gotopia.tech/articles/450Abby Bangser - Principal Engineer at Syntasso & Team Topologies AdvocateCharles Humble - Freelance Techie, Podcaster, Editor, Author & ConsultantRESOURCESAbbyhttps://bsky.app/profile/abangser.bsky.socialhttps://twitter.com/a_bangserhttps://github.com/abangserhttps://www.linkedin.com/in/abbybangserhttps://www.syntasso.io/members-area/abby/profileCharleshttps://bsky.app/profile/charleshumble.bsky.socialhttps://linkedin.com/in/charleshumblehttps://mastodon.social/@charleshumblehttps://conissaunce.comLinkshttps://blog.container-solutions.com/paula-kennedy-on-platform-team-responsibilities-patterns-and-anti-patternshttp://sites.libsyn.com/406853/syntasso-coo-paula-kennedy-on-platform-team-responsibilities-patterns-and-anti-patternshttps://www.oreilly.com/library/view/platform-as-a/0642572243777https://github.com/Cloud-Native-Platform-Engineering/cnpe-community/issues/79https://www.kratix.iohttps://leaddev.com/ai/nobody-knows-what-programming-will-look-like-in-two-yearshttps://leaddev.com/ai/shipping-faster-thinking-less-the-ai-code-verification-traphttps://www.cncf.io/blog/2023/11/20/announcing-the-platform-engineering-maturity-modelhttps://platformengineering.com/features/portals-and-pipelines-arent-enough-avoiding-the-platform-facadeDESCRIPTIONAbby Bangser opens with a clear-eyed status report on platform engineering: the concept of centralizing shared capabilities with self-service delivery is well understood, but the execution keeps going wrong in the same way. Organizations move from DevOps to platform engineering, but their platform teams end up becoming the new bottleneck — a centralized group drowning under the weight of the entire organization's requests, which is exactly what DevOps was supposed to fix. Abby traces this to an architectural problem: too many platforms are still built as centralized Terraform machines rather than as a marketplace of composable offerings. Her "platform as a product" test is blunt and useful: has the team ever said "no" to a feature request, or deprecated something?If not, they don't have a product — they have a request queue.The AI dimension is where the conversation gets most urgent. Abby's position is direct: AI agents are the new forcing function for platform maturity. The biggest misconception she wants to dismantle is the persistent equation of platform engineering with infrastructure-as-code: renaming your Terraform team doesn't count. Platform engineering is about building an experience — for human developers and increasingly for AI agents — that is self-service, compliant, and coherent at organizational scale. The Team Topologies model of interaction modes (from high-collaboration to fully automated on-demand APIs) gives a useful health check for where a platform actually sits on that maturity curve.RECOMMENDED BOOKSChankramath, Cheneweth, Oliver & Alvarez • Effective Platform Engineering • https://amzn.to/3OnxN8iGregor Hohpe • Platform Strategy • https://amzn.to/4cxfYdbBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

Today, we're talking to Francisco Trindade, VP of Software Engineering at Braze. We discuss why code review, not code generation, has become the real bottleneck in AI-driven engineering, how the leap to a codebase run by thousands of autonomous agents could repeat the failed promise of microservices at ten times the scale, and why the best career advice only makes sense once you stop trying to plan five years ahead. All of this right here, right now, on the Modern CTO Podcast!

How do you build the foundations for a secure Google-scale enterprise that stays secure even if an AI is writing the code and nobody has time to review it? In this episode, hosts Timothy Peacock and Anton Chuvakin sit down with Christoph Kern, Principal Security Engineer at Google, to look under the hood of "secure-by-design." They trace Google's 15-year engineering journey to fundamentally eliminate entire classes of vulnerabilities rather than just playing whack-a-mole with bugs after they are written.

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubCheck out more here:https://gotopia.tech/episodes/449Simon Brown - Author & Creator of "The C4 Model"Susanne Kaiser - Independent Tech Consultant & Author of "Architecture for Flow"RESOURCESSimonhttps://simonbrown.jehttps://bsky.app/profile/simonbrown.jehttps://twitter.com/simonbrownhttps://linkedin.com/in/simonbrownjerseyhttps://c4model.comSusannehttps://bsky.app/profile/suksr.bsky.socialhttps://mastodon.social/@suksrhttps://www.linkedin.com/in/susannekaiser1https://susannekaiser.netLinkshttps://structurizr.comDESCRIPTIONSimon Brown explains that the C4 Model started not as a grand design theory, but as a practical answer to an embarrassing problem: running a workshop on architecture diagramming, he realized he couldn't understand any of the diagrams being produced. The model formalized his own consulting practice of hierarchical handover documentation — context diagram at the top, containers below, components and code at the bottom — giving it a name and making it teachable. His core advice is to start with just the top two levels: context and container diagrams. These change infrequently, are quick to draw, and deliver immediate value. Levels three and four — components and code — are included for completeness but age rapidly with every commit, and in most cases aren't worth the maintenance overhead.Two especially practical insights emerge from the conversation. First, how to handle microservices: if your team owns all the services inside a system boundary, each service is a collection of C4 containers; if you depend on another team's service that you can't see inside, model it as an opaque software system. This maps naturally to how domain boundaries and team ownership actually work in practice. Second, the surprisingly important distinction between modeling and diagramming: C4's real value is the shared vocabulary — systems, containers, components, code — not the visual notation itself. Teams using entirely different tooling can communicate clearly because they've agreed on what the words mean. Simon is firm that there will be no C4 v2; the model is intentionally lightweight, and adding more would risk turning it into the next UML — comprehensive, heavy, and largely abandoned.RECOMMENDED BOOKSSimon Brown • The C4 Model • https://amzn.to/4xB9V33Susanne Kaiser • Adaptive Systems With Domain-Driven Design, Wardley Mapping & Team Topologies • https://amzn.to/3XTmNCcNick Rozanski &, Eóin Woods • Software Systems Architecture • https://amzn.to/4cOLtTvvan Kelle, Verschatse &Baas-Schwegler • Collaborative Software Design • https://amzn.to/4iv0N8IBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

The evening session of the ongoing three-day Summer Course featured an insightful panel discussion on ‘Inner Awakening and Righteous Living - Dharma as the Compass for Daily Life'. Moderated by Prof Rani P L (Dean, Faculty of Humanities & Social Sciences and Languages & Literature, and Director, Anantapur Campus), the panel brought together Ms Sai Swaroopa Iyer (Program Lead, INDICA) Ms Sai Geetha M N (Senior Director, Software Engineering, Lowe's India) Dr Lathasree (Senior Medical Officer, Sri Sathya Sai General Hospital) and Ms Vidyulatha Narayan (Manager, Education Division, East Sussex County, UK.)Drawing upon Bhagawan Sri Sathya Sai Baba's teachings, the speakers explained that Dharma is not merely a code of conduct but the very expression of one's innate divinity. They reiterated Swami's message that righteousness in the heart leads to peace in the world, while emphasising trikarana shuddhi—the unity of thought, word, and action—as the hallmark of a noble life. The panel elaborated on various dimensions of Dharma, including Swarupa Dharma, the realisation of one's true divine nature, and Deha Dharma, the responsibility of caring for the body and mind through pure food, conduct, thoughts, speech, and balanced living. Quoting examples from the Ramayana, Mahabharata, and Bhagawad Gita, the speakers explained that true Dharma lies in performing one's duties selflessly, with detachment, and offering every action to the Divine while upholding universal human values. Concluding the discussion, the panel affirmed that Dharma ultimately becomes the foundation for inner awakening, enabling one to listen to the voice of conscience, purify the mind, and manifest the divinity within. By making Dharma the natural way of life, they noted, humanity can move from individual transformation to universal harmony and lasting peace.

This interview was recorded at GOTO Copenhagen 2025.https://gotocph.comAdrian Mouat - Developer Relations at Chainguard & Author of "Using Docker"Kief Morris - Author of "Infrastructure as Code" & Distinguished Engineer at ThoughtworksSam Newman - Author of "Building Microservices" & "Monolith to Microservices"RESOURCESAdrianhttps://bsky.app/profile/adrianmouat.comhttps://twitter.com/adrianmouathttps://github.com/amouathttps://linkedin.com/in/adrianmouathttp://www.adrianmouat.comKiefhttps://bsky.app/profile/kief.comhttps://twitter.com/kiefhttps://github.com/kiefhttps://www.linkedin.com/in/kiefmorrishttps://infrastructure-as-code.comhttps://kief.comSamhttps://twitter.com/samnewmanhttps://www.linkedin.com/in/samnewmanhttp://samnewman.iohttp://samnewman.io/bloghttps://github.com/snewmanABSTRACTIn this session, Sam Newman will interview Kief Morris and Adrian Mouat, both experts in their field. We will explore the current reality of security in the container world, how infrastructure automation is impacted by AI, and whether platform teams are actually working. We'll also be taking lots of questions from the audience! [...]Read the full abstract here:https://gotocph.com/2025/sessions/3938RECOMMENDED BOOKSAdrian Mouat • Using Docker • https://amzn.to/3PEYIJLLiz Rice • Container Security • https://amzn.to/3oU4iJeKief Morris • Infrastructure as Code • https://amzn.to/4e6EBQcSam Newman • Building Resilient Distributed Systems • https://www.oreilly.com/library/view/building-resilient-distributed/9781098163532Sam Newman • Monolith to Microservices • https://amzn.to/2Nml96EBlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

In the final episode of Season 15, Charles Suggs and Emma Whamond are joined by James Gray, co-author of Designing Elixir Systems with OTP, to talk about what has changed in software development and what still holds true. Years after the book's release, many of its core ideas around architecture, boundaries, supervision, and system design remain deeply relevant, even as AI tools reshape how developers learn, build, and collaborate. James brings a unique perspective to the conversation: he is an experienced Elixir and OTP educator who only recently began using LLMs in his own workflow. He shares what surprised him, where the tools have been useful, where they have created risk, and what happened when Claude accidentally wiped his local development database. The conversation explores how AI can speed up parts of the work while making foundational knowledge, code review, testing, and clear system boundaries even more important. To close the season, James and the hosts reflect on what developers should hold onto as the stack continues to shift. They discuss responsible LLM usage, the changing role of pair programming, who owns the mental model when AI helps write code, and why Elixir's long-standing strengths may matter even more in an AI-assisted development world. James is scheduled to speak at ElixirConf 2026, September 10–11 in Chicago, and the Elixir Wizards will be there too! Join us and the broader Elixir community, and use promo code Elixirwizards for 10% off in-person or virtual tickets at https://elixirconf.com/ Key topics discussed in this episode: James Gray's work on Designing Elixir Systems with OTP What still holds true in Elixir system design Layered architecture and durable software fundamentals Functional core, imperative shell in modern applications Supervision trees as application lifecycle maps Why boundaries still matter in AI-assisted development What AI changes about learning and building software What AI does not change about software design James' first month using LLMs When Claude erased a local development database Trust, verification, and responsible LLM usage The risk of AI-generated boundary violations Pair programming, mental models, and developer judgment Ethical and legal questions around AI tools Why OTP concepts still matter in distributed systems How Elixir thinking applies to AI agent workflows What developers should preserve as the stack shifts James' upcoming ElixirConf talk Links mentioned: Java Programming Language https://www.java.com/en/ Perl Programming Language https://www.perl.org/ Ruby Programming Language https://www.ruby-lang.org/en/ Best of Ruby Quiz by James Gray https://www.google.com/books/edition/Best_of_Ruby_Quiz/bMggAQAAIAAJ Designing Elixir Systems with OTP https://pragprog.com/titles/jgotp/designing-elixir-systems-with-otp/ RubyConf talk: Boundaries by Gary Bernhardt https://youtu.be/yTkzNHF6rMs Book: Real-World Event Sourcing by Kevin Hoffman https://pragprog.com/titles/khpes/real-world-event-sourcing/ Broadway Library https://elixir-broadway.org/ Supervision Trees https://elixir.hexdocs.pm/supervisor-and-application.html PostgreSQL https://www.postgresql.org/ ClickHouse https://clickhouse.com/ GenServer https://elixir.hexdocs.pm/GenServer.html Programming as Theory Building by Peter Naur https://pages.cs.wisc.edu/~remzi/Naur.pdf “A computer can never be held accountable, therefore a computer must never make a management decision.” – IBM Training Manual, 1979 Oban https://oban.pro/ Anthropic Claude Fable https://www.anthropic.com/claude/fable Claude Design https://claude.com/product/design Tidewave Agentic Dev Environment for Phoenix and Rails https://tidewave.ai/ 2x – nine months later: We did it https://ideas.fin.ai/p/2x-nine-months-later James Gray's Blog https://programmersstone.blog/about/ ElixirConf https://elixirconf.com/ ExMex https://exmexconf.com/Special Guest: James Gray.

Having an AI policy is a great first step, but successful AI Governance Implementation goes much further. Governance isn't about creating documents that sit on a shelf—it's about building processes that become part of everyday development. As organizations continue integrating AI into products and workflows, the challenge shifts from whether to use AI to how to manage it responsibly. In Part 2 of our conversation with Dr. Latha Karthigaa, Co-Founder of the Global AI Certification Council (GAICC), we explored practical steps organizations of every size can take to introduce AI governance without slowing innovation. About Latha Karthigaa Dr. Latha Karthigaa is the Director & Head of AI Governance of the Global AI Certification Council (GAICC), where she helps organizations implement responsible AI through governance frameworks, certifications, and AI management systems. With a PhD in Software Engineering and experience in education, digital marketing, and AI strategy, she focuses on helping professionals and enterprises adopt AI responsibly. Learn more: GAICC: https://gaicc.org LinkedIn: https://www.linkedin.com/in/lathakarthigaa/ AI Governance Implementation Starts with Ownership One of the biggest risks organizations face isn't malicious AI—it's unmanaged AI. Many businesses experiment with AI tools, build internal assistants, or launch AI-powered features without assigning clear ownership. When something goes wrong, no one knows who is responsible for investigating, fixing, or improving the system. Every AI system should have a designated owner. That doesn't mean one person writes every line of code or monitors every prompt. It means someone is accountable for ensuring the system continues operating within acceptable boundaries as models evolve, data changes, and new risks emerge.

Artificial intelligence is advancing at an incredible pace, but without an AI Governance Framework, organizations risk creating systems that are difficult to trust, maintain, or scale. Many people assume that governance slows innovation, but in reality, the opposite is often true. The right guardrails allow teams to move faster by reducing uncertainty and preventing costly mistakes before they happen. In Part 1 of our conversation with Dr. Latha Karthigaa, Co-Founder of the Global AI Certification Council (GAICC), we explored why AI governance is becoming a business necessity and why developers should view it as an enabler rather than a barrier. About Latha Karthigaa Dr. Latha Karthigaa is the Director & Head of AI Governance of the Global AI Certification Council (GAICC), where she helps organizations implement responsible AI through governance frameworks, certifications, and AI management systems. With a PhD in Software Engineering and experience in education, digital marketing, and AI strategy, she focuses on helping professionals and enterprises adopt AI responsibly. Learn more: GAICC: https://gaicc.org LinkedIn: https://www.linkedin.com/in/lathakarthigaa/ AI Governance Framework Is Like Guardrails on a Highway One of the best analogies from the discussion compared AI governance to the guardrails along a highway. Drivers can safely travel at higher speeds because guardrails help keep them on course. Remove those barriers, and everyone naturally slows down because the consequences of a mistake become much greater. AI works the same way. Governance isn't about preventing innovation. It's about creating confidence that allows organizations to innovate responsibly. When developers know the boundaries, they spend less time reacting to unexpected issues such as biased outputs, hallucinations, data misuse, or compliance failures.

In this episode, Dave and Jamison answer these questions: I stayed too long at my first company and now my career is ruined? Hi both, long time listener. (Since I graduated university 4 years ago actually and felt I could never find a job but listening to you guys made me feel not like such an outcast in our field.) I graduated in 2022. Didn't really apply around much and almost immediately accepted the full time offer from the company I did my internship with during my degree. And fast forward to middle of 2026… I am still here. I've long stopped growing as an engineer and have actually been stuck on a simple CLI tool for almost 4 years now with little to no cloud/backend/etcetera experience to show in my 5 year career. I don't feel I am desirable in the job market at all due to this and waited way too long to look for a job because I was comfortable and happy. I feel paralyzed (no need to put on your space therapist hats BUT some career advice would be wonderful!) (Also did I mention I am in Canada and want to move to Europe and live the rest of my life there but visa sponsorship with my level of experience is impossible? Maybe that'll be for my next question…) Thanks for all you do, cheers! I have a non-CS engineering degree, and am looking to transition into Software Engineering. I have the option of pursuing a degree with Tuition Reimbursement from my job, but am in a bit of a strange situation deciding which degree. Basically, I've figured that with my current Engineering background I could pursue either a second Bachelor's degree through Universities offering a “Post-Baccalaureate” program in Computer Science, or a Master's degree, with maybe only a slightly longer program to get the Master's as opposed to the Bachelor's. Otherwise the total length/cost of the programs would be roughly the same. I have heard though that it might not be a great idea to go into a Master's program unless you know what specifically you want to study. I am curious what you think would be more “hire-able” between the two options for someone pursuing the career change into Software Engineering?

In this episode of Elixir Wizards, Charles Suggs and Emma Whamond are joined by Zach Daniel, creator of the Ash Framework and Igniter, VP of Engineering at Remedy Meds, and upcoming ElixirConf keynote speaker, to talk about what sits between an LLM and useful engineering work. Zach reflects on how much has changed since his last appearance on the podcast in October 2024, moving from Igniter, code generation, and project patching into AI agents, context layers, and custom engineering workflows. The conversation explores how deterministic tools and probabilistic LLMs can work together, and why the most useful AI systems often depend on the structure built around the model. We also discuss why teams should be careful about outsourcing the systems that hold their organizational knowledge and decision-making. He shares his perspective on owning the AI stack, building internal knowledge systems, training junior developers in an AI-augmented world, avoiding vendor lock-in, and why Elixir may be especially well-suited for safer agentic workflows. Zach will be a keynote speaker at ElixirConf 2026, September 10–11 in Chicago, and the Elixir Wizards will be there too! Join us and the broader Elixir community, and use promo code Elixirwizards for 10% off in-person or virtual tickets at https://elixirconf.com/ Key topics discussed in this episode: Zach Daniel's work with Ash, Igniter, and AI tooling How software development has changed since 2024 Deterministic code generation vs. LLM-generated code Combining structured tools with AI agents What it means to own your AI stack Organizational knowledge as an engineering “spinal column” Context layers, documentation, and internal workflows Building custom agentic systems Security, vendor lock-in, and open source LLMs Junior developers and apprenticeship in the AI era Why Elixir and the BEAM fit agentic workflows Links mentioned: Ash Framework https://ash-hq.org/ Igniter https://igniter.hexdocs.pm/ Phoenix Framework https://www.phoenixframework.org/ Remedy Meds https://remedymeds.com/ Keynote: Code Generators are Dead. Long Live Code Generators - Chris McCord | ElixirConf EU 2025 https://www.youtube.com/watch?v=ojL_VHc4gLk https://phoenix.hexdocs.pm/Mix.Tasks.Phx.Gen.Live.html LSP https://en.wikipedia.org/wiki/Language_Server_Protocol Claude Code https://claude.com/product/claude-code GitHub Actions https://github.com/features/actions Harness Engineering https://en.wikipedia.org/wiki/Agent_harness
Claude SDK https://code.claude.com/docs/en/agent-sdk/overview Zach's Twitter https://x.com/ZachSDaniel1 ElixirConf https://elixirconf.com/ AshConf https://luma.com/wz4z0iz6 Goatmire https://goatmire.com/Special Guest: Zach Daniel.

As artificial intelligence accelerates both innovation and cyber risk, organizations are facing unprecedented pressure to secure sensitive data while deploying AI at scale. In this episode of Cloud Wars Live, Bob Evans speaks with Vipin Samar, SVP, Software Engineering, Database Security, Oracle, about Oracle's expanded AI security strategy and how the company is helping customers defend against increasingly sophisticated AI-powered attacks. Samar explains Oracle's three-part security philosophy and why removing barriers to rapid patching and risk assessment has become essential in the emerging era of agentic AI. Winning the AI Security Race The Big Themes: AI Has Fundamentally Changed the Cybersecurity Landscape: Vipin Samar argues that artificial intelligence has dramatically shifted the balance between defenders and attackers. While organizations are rapidly adopting agentic AI to improve productivity and automate business processes, the same advances are empowering cybercriminals. Modern large language models can now write software, analyze applications, identify vulnerabilities, and even recommend methods for exploiting those weaknesses. Tasks that once required highly trained hackers and weeks of effort can now be completed in hours by individuals with far less technical expertise. Speed Has Become a Critical Security Requirement: One of the interview's strongest themes is that cybersecurity now operates on AI timelines rather than human timelines. Samar explains that attackers no longer wait weeks or months to exploit newly discovered vulnerabilities. AI allows them to identify weaknesses, analyze patches, and develop exploits almost immediately after updates become available. That makes rapid patch deployment essential. Oracle is responding by simplifying and accelerating the entire patching lifecycle through automation, database lifecycle management tools, application testing capabilities, and deployment technologies that reduce operational complexity. Oracle Is Removing Adoption Barriers: Oracle's strategy extends beyond developing new security technology. Samar explains that many organizations delay implementing security improvements because of procurement hurdles, lengthy approval processes, limited budgets, or concerns about operational disruption. Oracle is attempting to eliminate those obstacles by making several enterprise-grade security products available free for a limited time, including Oracle Data Safe, Database Security Assessment capabilities, Database Lifecycle Management Pack, and Exadata Management Pack. Visit Cloud Wars for more.

The skills you build and the tools you master matter, but they aren't your most important asset when things go wrong — and something eventually will. In this episode, I work through why our careers and lives are governed more by avoiding catastrophic downside than by chasing upside, and why the single best tool for surviving a bad event isn't testing, insurance, or money — it's genuine trust with the people around you. Here's a question to sit with: what is the most important tool you have as a software engineer and as a leader? Most of us reach for something technical, but the answer runs deeper than that. In this episode, I start with the humble premortem — the practice of assuming something has already gone wrong so we can pressure-test our plans — and use it to explore why so much of our work is really about predicting and mitigating risk. From there, I make the case that because we're all exposed to a far larger downside than upside on any given day, the tool that matters most is the one that helps you survive the bad event you couldn't prevent: your relationships with other people, built on real trust. The Premortem as a Risk Lens: Learn why assuming failure ahead of time is such a useful counter to our natural optimism. Our plans quietly assume everything will go right, and a premortem forces us to inspect the gaps our best-laid plans never covered. Life Is Already About Predicting Risk: Nearly every action we take — stepping forward, eating the sushi, merging into traffic — is a small bet on an outcome we can't prove in advance. Much of what we're managing isn't even our own behavior, but the risk other people put us through. Why the Downside Dwarfs the Upside: On a typical Monday, your potential gain is limited, but your potential loss is not. A single catastrophic event — a breached customer, untested code shipped, an injury for an athlete — can undo far more than any single good action could ever build. This is why avoiding failure, not chasing brilliance, quietly shapes most successful careers. Likelihood Times Impact: Even a one-in-a-hundred-days negative event can cost you your job or your company a fortune, while very few actions could produce a commensurate gain like doubling your salary. Our behavioral aversion to risk turns out to be rational. Mitigate the Blast Radius, Not Just the Incidence: You can never be 100% certain a bad event won't happen. Good people who show up, stay reliable, and grow their skills still get laid off. So beyond reducing the likelihood of harm, you have to reduce its impact when it lands. Relationships Are the Real Safety Net: The most important tool in your belt isn't technical — it's your relationships with other human beings. Invested in honestly, they pay you back forever, and they're the thing you fall back on when the negative event you tried to prevent happens anyway. Trust Is the Core Currency: Genuine relationships require reality — real curiosity and care, not performed name-remembering, which people can see through. Trust compounds like an asset, while money spent to buy loyalty is gone the moment it's paid. When you hit a hard deadline or discover something's broken, a reservoir of trust is what lets people extend their best effort without you having to throw more money on the table. Episode Homework: Go invest in your relationships regardless of your current risk profile. Spend extra time in your one-on-ones, with your team, and in your retros — and get curious about what the people around you actually want, instead of assuming you already know.

This is a recap of the top 10 posts on Hacker News on July 09, 2026. This podcast was generated by wondercraft.ai (00:30): GPT-5.6Original post: https://news.ycombinator.com/item?id=48849066&utm_source=wondercraft_ai(01:56): EU Parliament greenlights Chat Control 1.0Original post: https://news.ycombinator.com/item?id=48843923&utm_source=wondercraft_ai(03:23): Show HN: 18 WordsOriginal post: https://news.ycombinator.com/item?id=48845049&utm_source=wondercraft_ai(04:50): My thoughts on the Bun Rust rewriteOriginal post: https://news.ycombinator.com/item?id=48843352&utm_source=wondercraft_ai(06:17): Postgres rewritten in Rust, now passing 100% of the Postgres regression testsOriginal post: https://news.ycombinator.com/item?id=48841676&utm_source=wondercraft_ai(07:43): Show HN: Getting GLM 5.2 running on my slow computerOriginal post: https://news.ycombinator.com/item?id=48842459&utm_source=wondercraft_ai(09:10): Hy3Original post: https://news.ycombinator.com/item?id=48847552&utm_source=wondercraft_ai(10:37): I think I have LLM burnoutOriginal post: https://news.ycombinator.com/item?id=48839984&utm_source=wondercraft_ai(12:04): Why developers are ditching GitHub for Codeberg and self-hosting alternativesOriginal post: https://news.ycombinator.com/item?id=48842611&utm_source=wondercraft_ai(13:31): Muse Spark 1.1Original post: https://news.ycombinator.com/item?id=48846184&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Sejal Amin is the Chief Technology Officer at Priceline, where she leads product engineering, infrastructure, data, and technology operations. Pedro Gutierrez is Senior Director of Software Engineering, where he has helped drive developer experience initiatives and the adoption of Priceline's product operating model.In this episode of the Engineering Enablement podcast, Justin Reock talks with Sejal and Pedro about Priceline's journey from a project-based organization to a product operating model and the role developer experience played in making that transformation successful. They discuss how DX metrics and developer feedback helped identify organizational bottlenecks, guide structural changes, empower engineering managers, and build trust across teams. They also explore the importance of clear communication, creating a dedicated developer experience team, and how their operating model has helped prepare Priceline for AI-driven software development.Where to find Sejal Amin:  • LinkedIn: https://www.linkedin.com/in/sejal-aminWhere to find Pedro Gutierrez: • LinkedIn: https://www.linkedin.com/in/pedro-gutierrez-b6605422Where to find Justin Reock:• LinkedIn: https://www.linkedin.com/in/justinreock In this episode, we cover:(00:00) Intro(01:07) Meet Sejal Amin and Pedro Gutierrez(01:47) How Priceline's developer experience journey began(04:55) Lessons from Priceline's first developer experience surveys(06:55) How DX improved Priceline's developer experience surveys(09:47) Identifying the causes of organizational slowness(12:33) How the product operating model changed the way Priceline works(14:10) Priceline's phased rollout with DX(18:14) How DX insights drove organizational changes(19:33) Why Priceline improved developer experience before org change was complete(22:18) How clear communication builds trust(24:25) Early results from Priceline's Core Four(25:38) Creating a culture of continuous feedback to build trust(27:40) What has changed in the engineering manager role(30:10) Resources for learning about the product operating model(32:40) What Pedro learned from implementing DX(34:51) The developer experience team(35:59) How AI tools have impacted Priceline's teams (37:20) How the product operating model supports AI-driven development(39:13) Final advice for engineering leadersReferenced:• Measuring developer productivity with the DX Core 4• Transformed: Moving to the Product Operating Model (Silicon Valley Product Group) • Team Topologies• Flow Framework• Project to Product: How to Survive and Thrive in the Age of Digital Disruption with the Flow Framework

This interview was recorded for GOTO Unscripted.https://gotopia.techLuca Mezzalira - Solutions Architect, Consultant, International Speaker & Author of "Building Micro-Frontends"Teena Idnani - Senior Solutions Architect at Microsoft & Woman in Tech LeaderCheck out more here:https://gotopia.tech/articles/447RESOURCESLucahttps://www.buildingmicrofrontends.comhttps://twitter.com/lucamezzalirahttps://www.linkedin.com/in/lucamezzalirahttps://linktr.ee/lucamezzalirahttps://lucamezzalira.comTeenahttps://www.linkedin.com/in/teenaidnaniDESCRIPTIONLuca Mezzalira and Teena Idnani open with a clear framing: AI is accelerating certain tasks architects do, but the fundamentals — understanding context, connecting technical decisions to business capability, designing for evolutionary systems — are unchanged and arguably more important than ever. Luca identifies what he calls the shift from a T-shaped to an "M-shaped" architect: broader and deeper simultaneously, using AI as a research accelerator that collapses days of trade-off analysis into hours. Both agree that the biggest current risk isn't that AI will replace architects, but that AI-generated code can look convincingly correct and pass initial testing while concealing edge-case failures that only surface under load, regulatory audit, or upstream change — particularly dangerous in regulated industries like finance.The conversation sharpens around what architects must double down on to stay relevant. Luca advocates for merging deterministic and probabilistic systems through harness engineering — combining linters, static analysis, and deterministic guardrails with AI code assistants to produce more predictable outcomes. More broadly, both conclude that empathy is now a core technical skill: the architect's real job is riding the "elevator" between the engine room and the C-suite, translating freely in both directions. Luca's rule of thumb has shifted from 70% people and context, 30% technical — to nearly 90/10 in the AI era. The competitive edge for architects, they agree, is not in generating code, but in knowing which problems are worth solving and why.RECOMMENDED BOOKSLuca Mezzalira • Building Micro-Frontends • https://amzn.to/4ekqV5YLuca Mezzalira • Front-End Reactive Architectures • https://amzn.to/43a8VVJGregor Hohpe • The Software Architect Elevator • https://amzn.to/3F6d2axGregor Hohpe • Enterprise Integration Patterns, Vol 2 • https://amzn.to/3TNedQ3BlueskyInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

In this episode of Elixir Wizards, Charles Suggs and Emma Whamond are joined by Flora Petterson, a self-taught Elixir developer, speaker, and maker, to talk about what happens when software systems forget and how developers can design for recovery. Flora shares her unconventional path to programming, from puppetry and creative work to discovering Elixir through Nerves and hardware projects. The conversation centers on her talk, “After the Crash,” which uses forgetting as a way to think about failure in software systems. Flora and the hosts explore process crashes, lost state, GenServer lifecycle, supervision, “let it crash,” and the difference between preventing failure and building systems that know how to recover. The episode also expands from software memory to team memory. Flora discusses documentation, institutional knowledge, mentorship, storytelling, and the challenge of preserving both what a system does and why decisions were made. Along the way, she shares how creative, low-stakes projects can help developers build deeper technical understanding and how the Elixir community can continue supporting new and self-taught engineers. Key topics discussed in this episode: Learning through experimentation with Nerves, hardware, and animatronics What it means for software systems to “forget” Failure, recovery, and lost state in the BEAM GenServer lifecycle and process recovery The nuance behind “let it crash” What supervision trees can and cannot protect Designing systems to recover from failure Deciding when state should be recovered or abandoned Documentation as organizational memory Capturing the “why” behind technical decisions Tests, docs, and examples as living knowledge Mentorship for self-taught and junior developers Storytelling as technical communication Creative projects as a path to deeper learning AI's impact on new developers Community, curiosity, and growth in Elixir Join us and the broader Elixir community at ElixirConf in Chicago this September 10-11. Use promo code Elixirwizards for 10% off in-person or virtual tickets at https://elixirconf.com/ Links mentioned: Embedded Elixir Nerve https://nerves-project.org/ Gig City Elixir Conference https://www.gigcityelixir.com/ Strange Loop Conference https://www.thestrangeloop.com/ Elixir Wizards S15E05 The State of Code Quality with Saša Jurić http://bit.ly/EWS15E05 One Art by Elizabeth Bishop https://www.poetryfoundation.org/poems/47536/one-art "Systems that run forever self-heal and scale" by Joe Armstrong (2013) https://www.youtube.com/watch?v=cNICGEwmXLU Conway's Law https://en.wikipedia.org/wiki/Conway%27s_law Dunning–Kruger effect https://en.wikipedia.org/wiki/Dunning%E2%80%93Kruger_effect Elixir Wizards S15E10 Bruce Tate http://bit.ly/EWS15E10 Designing Elixir Systems with OTP by James Gray & Bruce Tate https://pragprog.com/titles/jgotp/designing-elixir-systems-with-otp/ GenServer Behavior https://elixir.hexdocs.pm/main/GenServer.html IEx https://iex.hexdocs.pm/IEx.html Domain Driven Design by Eric Evans https://www.domainlanguage.com/ GDPR General Data Protection Regulation https://gdpr-info.eu/ https://blog.cleancoder.com/uncle-bob/2017/02/23/NecessaryComments.html Living Documentation by Cyrille Matraire https://www.oreilly.com/library/view/living-documentation-continuous/9780134689418/ Exciting ExDoc talk by Flora Petterson 2025 CodeSync https://www.youtube.com/watch?v=tLr9FOARwzQ ExDoc https://ex-doc.hexdocs.pm/ The AI Coding Crisis Blog Series https://grox.io/blog/series/the-ai-coding-crisis/ Build Animatronics with Nerves talk https://www.youtube.com/watch?v=4Ulurej4NTg Sam Aaron Sonic Pi https://sonic-pi.net/ https://scratch.mit.edu/ https://www.scratchfoundation.org/learn Nerves LiveBook https://github.com/nerves-livebook/ Hardware for Embedded Systems https://www.raspberrypi.com/ Goatmire Conference https://goatmire.com/

At the end of June, Thoughtworks and Martin Fowler convened an unconference-style event in Switzerland with a range of industry leaders. The aim was to reflect on the current challenges and learning around AI assisted and agentic software engineering and discuss the implications for the future — and, most importantly, what actions need to be taken today. To review the event and dive into some of the topics that surfaced, host Ken Mugrage is joined by Thoughtworks colleagues Kief Morris (author of Infrastructure as Code) and Andrew Harmel-Law (author of Facilitating Software Architecture). They highlight some of the key ideas and issues that emerged from the event's conversations, ranging from developer identity to harness engineering. Learn more about the European edition of the Future of Software Engineering Retreat: https://www.thoughtworks.com/about-us/events/the-future-of-software-development-europe-2026 Read Andrew's recent blog post that asks whether non-functional requirements could be a vital guardrail for AI-generated code: https://www.thoughtworks.com/insights/blog/architecture/non-functional-requirements-missing-guardrail-ai-generated-code

Send us Fan MailJoey Pinz sits down with entrepreneur, software engineer, and AI strategist John Harden for a powerful conversation about curiosity, automation, SaaS innovation, and the future of human thinking in an AI-first world.

Episode 76: Featuring George Matelich, Co-Founder of Rely On this episode of The Boulos Beat, host Greg Boulos sits down with George Matelich, co-founder of Rely, to explore how artificial intelligence is reshaping the commercial real estate due diligence process.  George asserts his platform significantly accelerates due diligence, reducing a process that traditionally takes 60–90 days to as little as one day Throughout the conversation, Greg and George discuss how AI can help mitigate transaction risk, improve efficiency, and uncover opportunities that might otherwise be overlooked. 

Today's conversation features Brian McCarthy, President of Global Revenue and Field Operations at Cursor and former CRO at Rubrik, where he helped scale the business from $118 million to $1.5 billion in ARR. In this segment, Brian breaks down the rapid evolution of AI in software development, from simple code autocomplete to fully autonomous agent-driven environments, and what that shift means for competition, product strategy, and go-to-market execution. He also explains how Cursor's multi-model approach creates a unique advantage in a fast-moving market, and why aligning that innovation with focused sales execution is critical to winning. Brian McCarthy is President of Global Revenue and Field Operations at Cursor and former CRO at Rubrik, where he helped scale the company from $118M to $1.5B in ARR. He is known for building high-performance revenue organizations and execution-focused cultures in complex enterprise environments. Connect with Brian: LinkedIn Resources mentioned: Ep. 71 - What the Best Sales Leaders Do with Brian McCarthy All In Podcast with Chamath, Jason, Sacks & Friedberg Listen to the full episode: Why Sales Execution Wins in an AI-First World with Brian McCarthy, President of Global Revenue and Field Operations at Cursor Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

In this episode, Elixir Wizards Charles Suggs and Emma Whamond are joined by Bruce Tate, founder of Groxio and longtime Elixir educator, to discuss career growth and mentorship in the age of AI. Bruce reflects on Elixir's evolution and explores how AI coding agents are changing the way developers learn, work, and advance. He explains his “anti-vibe coding” philosophy and recommends practices for maintaining code quality, arguing that many struggles attributed to junior developers are really failures of training and mentorship. Rather than using AI productivity gains to eliminate entry-level roles, Bruce challenges teams to reinvest that time in educating juniors to support their long-term career path. Bruce will expand on this conversation at ElixirConf 2026, September 10–11 in Chicago, and the Elixir Wizards will be there too! Join us and the broader Elixir community, and use promo code Elixirwizards for 10% off in-person or virtual tickets at https://elixirconf.com/ Topics discussed in this episode: The evolution of the Elixir ecosystem from conception to now Rethinking career growth and mentorship in the age of AI What “anti-vibe coding” means Functional core and imperative shell design OTP fundamentals and architectural literacy Managing AI-generated code and pull requests Mentorship beyond code review Developing engineering judgment API design as a core skill AI as a force multiplier for junior developers Reinvesting AI productivity gains in people Links mentioned: https://grox.io/ Seven Languages in Seven Weeks by Bruce Tate https://pragprog.com/titles/btlang/seven-languages-in-seven-weeks/ Programming Phoenix LiveView https://pragprog.com/titles/liveview/programming-phoenix-liveview/ BASIC Programming Language https://www.purebasic.com/ C++ Programming Language https://cplusplus.com/ COBOL Programming Language https://www.ibm.com/think/topics/cobol Fortran Programming Language https://fortran-lang.org/ Bitter Java by Bruce Tate https://www.amazon.com/Bitter-Java-Bruce-Tate/dp/193011043X Slashdot Effect https://en.wikipedia.org/wiki/Slashdot_effect Slashdot https://slashdot.org/ Java Programming Language https://www.java.com/en/ Ruby Programming Language https://www.ruby-lang.org/en/ Lisp Programming Language https://lisp-lang.org/ Haskell Programming Language https://www.haskell.org/ Erlang Programming Language https://www.erlang.org/ Eric Meadows-Jönsson https://github.com/ericmj Machine Learning in Elixir by Sean Moriarity https://pragprog.com/titles/smelixir/machine-learning-in-elixir/ The AI Collective https://www.aicollective.com/ Elixir Pipe Operator https://operator.hexdocs.pm/readme.html Phoenix Framework https://www.phoenixframework.org/ Phoenix LiveView https://phoenix-live-view.hexdocs.pm/Phoenix.LiveView.html Broadway https://hex.pm/packages/broadway Designing Elixir Systems with OTP by James Edward Gray & Bruce Tate https://pragprog.com/titles/jgotp/designing-elixir-systems-with-otp/ The AI Coding Crisis Blog Series https://grox.io/blog/series/the-ai-coding-crisis/ Groxio Live https://grox.io/liveSpecial Guest: Bruce Tate.

This is one of my favorite conversations that I wanted to replay this week. If you are in any post sales Customer Success, or Account Management world, you have most likely heard of this week's guest. This week's guest started his journey studying Information Systems and Software Engineering before making the shift into Professional Services, Support, and Customer Success. At the time of recording, he was an Executive VP of Corporate Market and Chief Customer Officer at Higher Logic. Now, he is the CEO of Balboa Solutions, where they help their clients maximize the value of the Pendo platform to power adoption, enablement, and user analytics.This week's guest is the heart of Mount Pleasant, South Carolina, Mr. Jay Nathan. In this week's episode, we discussed:Customer Centric MindsetNatural Curiosity For CustomersLessons From Duke Energy (Large Enterprises and Heavy Process)The Start of The Largest CS CommunityUsing Your Own ProductMuch More! Please enjoy this week's episode with Jay Nathan.____________________________________________________________________________I am now in the early stages of writing my first book! In this book, I will be telling my story of getting into sales and the lessons I have learned so far, and intertwine stories, tips, and advice from the Top Sales Professionals In The World! As a first time author, I want to share these interviews with you all, and take you on this book writing journey with me! Like the show? Subscribe to the email: https://mailchi.mp/a71e58dacffb/welcome-to-the-20-podcast-communityI want your feedback!Reach out to 20percentpodcastquestions@gmail.com, or find me on LinkedIn

Few topics generate more debate than elections. But long before voters head to the polls, important decisions have already been made about how communities are grouped into districts and how representation is structured. While redistricting is often discussed as a political issue, it's also a fascinating operations research problem – one involving competing objectives and complex constraints such as geography, population, representation, and legal requirements, as well as competing definitions of fairness. My guest today, Ian Ludden, Assistant Professor of Computer Science and Software Engineering at Rose-Hulman Institute of Technology, will share how computational methods and OR/MS are helping researchers evaluate district maps and better understand the choices that shape our democracy. We'll explore questions of fairness, representation, and how data-driven approaches can help us better understand one of the most consequential – and often controversial – processes in American democracy.

In this episode, we explore how AI is transforming education, from classrooms to corporate training. What changes are needed in schools and universities? How does AI affect both students and educators? And how should companies rethink internal training and hiring to stay competitive?To answer these questions, we're joined by Rainer Stropek, CEO of Software Architects and Chairman of Coding Club Linz. With decades of experience teaching at high schools and universities—and helping organizations upskill their engineers—Rainer brings a unique perspective on how software engineering education is evolving.While many view AI as a threat, Rainer sees it as a “Christmas gift”—opening up endless opportunities to learn, adapt, and innovate.Tune in to hear why curiosity is more important than ever, how educational institutions can prepare future engineers, and why organizations must step up to ensure everyone has a fair chance to succeed in the age of AI.Links we discussedRainer's LinkedIn Profile: https://www.linkedin.com/in/rainerstropek/Rainer's Website: https://rainerstropek.me/CodeClub: https://codeclub.org/en/Coder DoJo Linz: https://linz.coderdojo.net/

As AI vendors hype “recursive self-improvement” and other scary features, we see more and more “mainstreaming” of AI technology in business. In other words, the AI does not solve problems by itself: we as HR and IT leaders need to clearly define our needs and then buy, build, and tune the technologies we buy. Some AI vendors (ie. Paradox, Radancy, Sana, Maki, others) are laser focused on very specific use-cases, and they are delivering solution-first offerings that really add value. The frontier vendors, however, are struggling to do this and much of their revenue still comes from “enabling others” to create solutions. And the new usage-based pricing is forcing this kind of pragmatic thinking. In this podcast I highlight this “commoditization” of core AI features and explain why your “problem identification” work is perhaps the biggest effort in the HR 2030 Agentic HR strategy. (Take our new HR 2030 course or sign up for our new Global HR Excellence Certification.) Additional Information Is AI A “Normal” Technology? The Rise of the Supermanager Our New Book Superpowered, Coming This Fall! New Course: Galileo Is Ready To Teach You What You Need to Know about HR 2030     Chapters (00:00:00) - A Lesson on AI in Software Engineering(00:09:44) - Bill Gates on the Need for People in AI

https://clearmeasure.com/developers/forums/ Tamir Dresher is a Principal Engineer at Microsoft Threat Protection, where he focuses on scaling AI agent systems and distributed architectures, bringing over 15 years of experience building large-scale distributed systems. He is the co-creator of Squad, an open-source multi-agent runtime for GitHub Copilot that orchestrates AI teams directly inside your repository. Tamir is the author of "Rx.NET in Action" (Manning) and "Hands-On Full-Stack Web Development with ASP.NET Core" (Packt), and has been a lecturer in Software Engineering at the Ruppin Academic Center since 2013. A prominent figure in the Israeli and international developer communities, he is a Microsoft MVP alumnus who speaks frequently at global conferences and writes actively on his blog at tamirdresher.com. Website / Blog - https://www.tamirdresher.com/  LinkedIn - https://www.linkedin.com/in/tamirdresher/ GitHub: https - //github.com/tamirdresher Twitter/X - @tamir_dresher Blog Post - https://www.tamirdresher.com/blog/2026/05/24/squad-watch-extensions-customer-success Github - https://github.com/bradygaster/squad Want to Learn More? Visit AzureDevOps.Show for show notes and additional episodes.

The skills problem isn't going anywhere — it's just wearing new clothes. In this episode, I unpack how the lessons we learned decades ago (limiting work in progress, the theory of constraints, test-driven development) are coming roaring back as the fundamentals that will carry you through the agentic shift. The bottleneck has moved, and knowing where it went changes how you should work. A lot of what we're learning about building with agentic tooling isn't new at all — it's a re-emphasis on lessons software engineers learned twenty years ago, just arriving in a new form. In today's episode, I walk through why the fundamentals are becoming more important than ever, why so many of us feel scattered despite having the most powerful tooling we've ever had, and where the real bottleneck in software delivery has quietly moved. My goal isn't to convince you that your job is now babysitting AI — it's to show you which parts of the work are still squarely yours, and how older principles can make you faster and more confident right now. Limiting Work in Progress Is Back: Just because you can spin up fifty agents doesn't mean you should split your focus across fifty things. Orchestrated fan-outs are powerful, but a human juggling agents across hiring, on-call, and a project all at once still pays the same old context-switching tax — and the quality drops while the speed never improves. Work Deeper, Not Wider: Instead of spreading yourself shallowly across more tickets, run multiple sessions on the same domain. Write a competing or adversarial version that critiques your assumptions, develop better documentation, or capture what you're learning as a reusable skill. Depth beats breadth. The Scattered-Engineer Epidemic: Engineers are burning out faster, not slower. We have the capacity to push more through the pipeline, so we're getting handed (or choosing) more than we can carry. Reducing parallelism often holds your delivery speed steady while dropping your cycle time and raising quality. The Theory of Constraints, Revisited: Treat your software development lifecycle as a pipeline with a bottleneck — and if you can't find one, you've optimized one part too far. Writing code used to be the choke point, so we spent enormous energy de-risking work before it ever reached an engineer. The Bottleneck Has Moved: When production gets cheap, it's no longer worth heavily de-risking upstream — which is why engineers are picking up more experimental, proof-of-concept, discovery work, and product folks are prototyping with these tools too. The new constraint isn't writing the code; it's verifying the agent didn't ship something broken. Verification Scales With Your Effort: The more an agent produces, the bigger the pile of PRs, MRs, and outputs waiting on human review. That backlog is the new bottleneck — and skepticism is creeping in because we're not even sure our tests are sufficient to verify what the agent built. Why TDD Fits This Moment: The honest question isn't "Can I trust the agent?" — it's "What verification loop do I need to build so I can trust it more?" Clear requirements feed a clear testing loop: write the failing test, let the agent write the code to turn it green, and you bridge the gap between requirements gathered and requirements met. It's not as simple as "go write a test," but it's a strong fit for where we are right now. Episode Homework: Go dig into the fundamentals — limiting WIP, the theory of constraints, test-driven development. Find the old lesson that still applies to your workflow today, bring it to your team's flow, and email me about what you discover.

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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/

This is a recap of the top 10 posts on Hacker News on June 15, 2026. This podcast was generated by wondercraft.ai (00:30): Iroh 1.0Original post: https://news.ycombinator.com/item?id=48542480&utm_source=wondercraft_ai(01:56): A backdoor in a LinkedIn job offerOriginal post: https://news.ycombinator.com/item?id=48546294&utm_source=wondercraft_ai(03:23): Ask HN: Has anyone replaced Claude/GPT with a local model for daily coding?Original post: https://news.ycombinator.com/item?id=48542100&utm_source=wondercraft_ai(04:50): Curl will not accept vulnerability reports during July 2026Original post: https://news.ycombinator.com/item?id=48537165&utm_source=wondercraft_ai(06:16): What happened to nerds?Original post: https://news.ycombinator.com/item?id=48538229&utm_source=wondercraft_ai(07:43): TinyWind: A pixel pirate sailing game with real wind physics (380k+ kms sailed)Original post: https://news.ycombinator.com/item?id=48543475&utm_source=wondercraft_ai(09:10): CrankGPTOriginal post: https://news.ycombinator.com/item?id=48540854&utm_source=wondercraft_ai(10:37): Apple Foundation ModelsOriginal post: https://news.ycombinator.com/item?id=48536776&utm_source=wondercraft_ai(12:03): Hetzner Price AdjustmentOriginal post: https://news.ycombinator.com/item?id=48540844&utm_source=wondercraft_ai(13:30): Even more batteries included with EmacsOriginal post: https://news.ycombinator.com/item?id=48535886&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

This is a recap of the top 10 posts on Hacker News on June 14, 2026. This podcast was generated by wondercraft.ai (00:30): How to earn a billion dollarsOriginal post: https://news.ycombinator.com/item?id=48526360&utm_source=wondercraft_ai(01:56): Show HN: Kage – Shadow any website to a single binary for offline viewingOriginal post: https://news.ycombinator.com/item?id=48529990&utm_source=wondercraft_ai(03:23): Not everyone is using AI for everythingOriginal post: https://news.ycombinator.com/item?id=48527700&utm_source=wondercraft_ai(04:50): Honda Civics and the Evil ValetOriginal post: https://news.ycombinator.com/item?id=48523080&utm_source=wondercraft_ai(06:17): Your ePub Is fineOriginal post: https://news.ycombinator.com/item?id=48533848&utm_source=wondercraft_ai(07:44): Free SQL→ER diagram tool, runs in the browser, nothing uploadedOriginal post: https://news.ycombinator.com/item?id=48523992&utm_source=wondercraft_ai(09:11): I indexed 669 GB of my GoPro videos using my M1 Max computer and local ML modelsOriginal post: https://news.ycombinator.com/item?id=48528029&utm_source=wondercraft_ai(10:38): Rio de Janeiro's "homegrown" LLM appears to be a merge of an existing modelOriginal post: https://news.ycombinator.com/item?id=48528371&utm_source=wondercraft_ai(12:05): Linux 7.1Original post: https://news.ycombinator.com/item?id=48528729&utm_source=wondercraft_ai(13:32): Don't trust large context windowsOriginal post: https://news.ycombinator.com/item?id=48524620&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Today's kindergartners will retire in 2082, but are we preparing them for that world or ours? In this episode, Dr. Michael Conner sits down with Stewart Brown, Director of Partnerships at Code4Kids, to challenge the "21st century" trap that's already widening the gap for Generation Alpha and Beta.Stewart and Michael dig into why coding is becoming as essential as cursive, a fundamental literacy every student needs, not just a technical skill for future developers. They explore how cross-curricular computer science can bridge math, science, arts, and social studies, and why the education system urgently needs to move beyond outdated frameworks and embrace what Stewart calls 22nd-century thinking.The conversation unpacks why understanding technology matters far more than becoming a software engineer, and how Code for Kids is already reshaping what digital literacy looks like in practice, through connective, curriculum-integrated education that gives students real agency in a technology-saturated world.Stewart Brown is a multiple founder of international EdTech companies and a trusted voice in AI literacy in education. Code for Kids, launched in 2018, integrates coding, robotics, digital literacy, and STEAM across the curriculum.This is essential listening for educators, administrators, and parents asking the question that matters most: how do we truly prepare students for their future? Subscribe to Voices for Excellence for conversations that challenge education's status quo.

This is a recap of the top 10 posts on Hacker News on June 13, 2026. This podcast was generated by wondercraft.ai (00:30): Statement on US government directive to suspend access to Fable 5 and Mythos 5Original post: https://news.ycombinator.com/item?id=48511072&utm_source=wondercraft_ai(01:56): Open source AI must winOriginal post: https://news.ycombinator.com/item?id=48511908&utm_source=wondercraft_ai(03:23): Noise infusion banned from statistical products published by Census BureauOriginal post: https://news.ycombinator.com/item?id=48517377&utm_source=wondercraft_ai(04:50): Every Frame PerfectOriginal post: https://news.ycombinator.com/item?id=48516251&utm_source=wondercraft_ai(06:16): Amazon CEO's talks with U.S. officials triggered crackdown on Anthropic modelsOriginal post: https://news.ycombinator.com/item?id=48519092&utm_source=wondercraft_ai(07:43): Israeli firm BlackCore suspected of meddling in New York and Scotland votesOriginal post: https://news.ycombinator.com/item?id=48514560&utm_source=wondercraft_ai(09:10): Leaving MozillaOriginal post: https://news.ycombinator.com/item?id=48513806&utm_source=wondercraft_ai(10:37): There is a shadow hanging over this Fable thingOriginal post: https://news.ycombinator.com/item?id=48513536&utm_source=wondercraft_ai(12:03): GLM 5.2 Is OutOriginal post: https://news.ycombinator.com/item?id=48518684&utm_source=wondercraft_ai(13:30): Treating pancreatic tumours may have revealed cancer's master switchOriginal post: https://news.ycombinator.com/item?id=48517199&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

This is a recap of the top 10 posts on Hacker News on June 12, 2026. This podcast was generated by wondercraft.ai (00:30): Statement on US government directive to suspend access to Fable 5 and Mythos 5Original post: https://news.ycombinator.com/item?id=48511072&utm_source=wondercraft_ai(01:58): AI agent bankrupted their operator while trying to scan DN42Original post: https://news.ycombinator.com/item?id=48500012&utm_source=wondercraft_ai(03:26): CRISPR tech selectively shreds cancer cells, including "undruggable" cancersOriginal post: https://news.ycombinator.com/item?id=48505231&utm_source=wondercraft_ai(04:55): Claude Fable is relentlessly proactiveOriginal post: https://news.ycombinator.com/item?id=48498573&utm_source=wondercraft_ai(06:23): Nobody ever gets credit for fixing problems that never happened (2001) [pdf]Original post: https://news.ycombinator.com/item?id=48498385&utm_source=wondercraft_ai(07:52): Open source AI must winOriginal post: https://news.ycombinator.com/item?id=48511908&utm_source=wondercraft_ai(09:20): Kimi K2.7-Code: open-source coding model with better token efficiencyOriginal post: https://news.ycombinator.com/item?id=48502347&utm_source=wondercraft_ai(10:49): "Don't You Just Upload It to ChatGPT?"Original post: https://news.ycombinator.com/item?id=48507278&utm_source=wondercraft_ai(12:17): Electric motors with no rare earthsOriginal post: https://news.ycombinator.com/item?id=48510010&utm_source=wondercraft_ai(13:46): How to setup a local coding agent on macOSOriginal post: https://news.ycombinator.com/item?id=48507020&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

This is a recap of the top 10 posts on Hacker News on June 11, 2026. This podcast was generated by wondercraft.ai (00:30): Show HN: Homebrew 6.0.0Original post: https://news.ycombinator.com/item?id=48490024&utm_source=wondercraft_ai(01:57): Pokémon Go Scans Trained the Navigation Tech for Military DronesOriginal post: https://news.ycombinator.com/item?id=48487029&utm_source=wondercraft_ai(03:24): AI agent runs amok in Fedora and elsewhereOriginal post: https://news.ycombinator.com/item?id=48484584&utm_source=wondercraft_ai(04:51): MiMo Code is now released and open-sourceOriginal post: https://news.ycombinator.com/item?id=48490826&utm_source=wondercraft_ai(06:18): If you are asking for human attention, demonstrate human effortOriginal post: https://news.ycombinator.com/item?id=48497609&utm_source=wondercraft_ai(07:45): Solar generates more energy in US than coal for first timeOriginal post: https://news.ycombinator.com/item?id=48492306&utm_source=wondercraft_ai(09:12): Petition to Withdraw Canada's Bill C-22Original post: https://news.ycombinator.com/item?id=48491830&utm_source=wondercraft_ai(10:39): Lines of code got a better publicistOriginal post: https://news.ycombinator.com/item?id=48489402&utm_source=wondercraft_ai(12:06): Anthropic apologizes for invisible Claude Fable guardrailsOriginal post: https://news.ycombinator.com/item?id=48489229&utm_source=wondercraft_ai(13:33): Show HN: FablePool – pool money behind a prompt, and Fable builds it in publicOriginal post: https://news.ycombinator.com/item?id=48496539&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

In this episode of Elixir Wizards, Charles Suggs and Emma Whamond are joined by Ellyse Cedeno, founder of Heuristic Salvo and a software engineer and product leader with more than 25 years of experience across early internet platforms, gaming, health tech, and distributed systems. Ellyse shares the winding path that took her from early search engines and Netscape to game development, medical research at Mount Sinai, and eventually to Elixir. Along the way, she talks about staying curious over a long technical career, rediscovering joy through side projects, and why being willing to feel like a beginner again can be one of the most useful skills a developer can build. The conversation explores what it means to grow as an engineer in a world where AI tooling is becoming part of the everyday workflow. Ellyse makes the case that technical skill still matters, but the human parts of software development (like judgment, curiosity, communication, trust, and influence) are becoming increasingly important. We also talk about soft influence and how developers can create change inside organizations without relying on hard authority. Key Topics Discussed in this Episode: Ellyse's career path through early internet platforms, gaming, health tech, and distributed systems Moving from Netscape and search engines to medical research and software consulting Discovering Elixir through an interest in concurrent and distributed systems Why beginner's mindset still matters after decades in tech How neurodivergence, curiosity, and deep focus shape Ellyse's approach to programming Rediscovering joy in programming through side projects and experimentation Building an MMORPG game server in Elixir Exploring hardware, Nerves, and live theremin demos The role of passion projects in professional growth Protecting time for learning in productivity-focused environments Work-life balance differences between the U.S. and Europe How AI tools are changing expectations for modern developers Why AI does not replace judgment, taste, or technical understanding Understanding business needs instead of only focusing on technical preferences Introducing Elixir into a TypeScript-heavy organization Using Elixir microservices to solve specific technical problems What “soft influence” looks like in engineering teams Building trust through one-on-one conversations Knowing when influence is working and when it is not Negotiating technical decisions without turning them into power struggles The relationship between technical competence and interpersonal skill Managing imposter syndrome during pair programming and collaborative work Documentation as a visibility and ownership tool Community involvement, conference speaking, and finding your people Staying curious without burning out Why the human side of software development still matters Links Mentioned: https://en.wikipedia.org/wiki/Netscape Icahn School of Medicine at Mt. Sinai https://icahn.mssm.edu/ Evernote https://evernote.com/ Joplin https://joplinapp.org/ Book: Elixir in Action by Saša Jurić https://www.manning.com/books/elixir-in-action-third-edition Book: The Little LISPer https://www.scribd.com/doc/263131641/The-Little-Lisper Ellyse's Goatmire Talk https://goatmire.com/speaker/ellyse-cedeno Nerves https://nerves-project.org/ xHain Hack & Makespace in Berlin https://x-hain.de/en/ https://cursor.com/ Haskell Programming Language https://www.haskell.org/ Java Programming Language https://www.java.com/en/ Clojure Programming Language https://clojure.org/ Scheme Programming Language https://www.scheme.org/ TypeScript Programming Language https://www.typescriptlang.org/ Nostrum Library https://hexdocs.pm/nostrum/intro.html Gleam Programming Language https://gleam.run/ Book: Getting Past No by William Ury https://www.williamury.com/getting-past-no/ “The Gambler” by Kenny Rogers https://www.youtube.com/watch?v=7hx4gdlfamo Ted Talk: Do schools kill creativity? | Sir Ken Robinson https://youtu.be/iG9CE55wbtY Ellyse's Codeberg https://codeberg.org/ellyxir Ellyse's Game Server Repo https://codeberg.org/ellyxir/gameserver Goatmire Elixir & NervesConf 2026 https://www.goatmire.com/

This is a recap of the top 10 posts on Hacker News on June 10, 2026. This podcast was generated by wondercraft.ai (00:30): macOS Container MachinesOriginal post: https://news.ycombinator.com/item?id=48469658&utm_source=wondercraft_ai(01:59): Building an HTML-first site doubled our users overnightOriginal post: https://news.ycombinator.com/item?id=48475483&utm_source=wondercraft_ai(03:28): German ruling declares Google liable for false answers in AI OverviewsOriginal post: https://news.ycombinator.com/item?id=48470248&utm_source=wondercraft_ai(04:57): πFSOriginal post: https://news.ycombinator.com/item?id=48480978&utm_source=wondercraft_ai(06:27): I'm Eric Ries, author of "The Lean Startup" and new book "Incorruptible" – AMAOriginal post: https://news.ycombinator.com/item?id=48477135&utm_source=wondercraft_ai(07:56): Mercedes‑Benz starts large‑scale production of electric axial flux motorOriginal post: https://news.ycombinator.com/item?id=48472877&utm_source=wondercraft_ai(09:25): PgDog is funded and coming to a database near youOriginal post: https://news.ycombinator.com/item?id=48476466&utm_source=wondercraft_ai(10:54): AWS Bedrock to require sharing data with Anthropic for Mythos and future modelsOriginal post: https://news.ycombinator.com/item?id=48473166&utm_source=wondercraft_ai(12:24): Chrome is looking to permanently drop MV2 extensionOriginal post: https://news.ycombinator.com/item?id=48471970&utm_source=wondercraft_ai(13:53): Claude Desktop spawns 1.8 GB Hyper-V VM on every launch, even for chat-only useOriginal post: https://news.ycombinator.com/item?id=48479452&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

The skills that survive every industry shakeup aren't the ones you can Google — they're softer, harder to name, and far more durable. In this episode, Jonathan explores principle-oriented thinking: the practice of stripping away the labels we attach to tools, roles, and even ourselves to see what something actually does at its core. It's the difference between handing your coding off to an agent and rethinking your entire workflow around what these new materials are truly capable of. If you've been following along with our recent focus on durable skills, you know we've been hunting for the abilities that translate beyond this month, this year, or whatever AI does to our industry next. Today's skill doesn't have a tidy name you can search for — it's softer than that. Jonathan calls it "principle-oriented thinking": the habit of deconstructing the labels we put on things to understand their core components, properties, and capabilities. It's how NASA engineers turned a sock into a water filter on Apollo 13, and it's how forward-thinking engineers are reframing what AI can actually do rather than jamming it into a predetermined slot. Labels Are Useful Shortcuts — Until They Aren't: Every label, from "software engineer" to "sock," carries baggage, heuristics, and presupposition. That's not a flaw — labels are how we move through the world quickly. But when a label is the only lens you have, it quietly caps how much value you can get out of the thing you're looking at. The Apollo 13 Sock: When the crew needed to fix a life-threatening problem with mismatched parts, the engineers on the ground had to forget what a sock was for and ask what it actually is — a piece of cloth with tensile strength, flexibility, and filtering properties. Strip the assumption that it goes on a foot, and a whole new set of uses opens up. Stop Slotting AI Into Old Roles: The common move is to take one responsibility — coding, debugging, refactoring — hand it to an agent, and keep everything else the same. That works, but it's low-leverage. The more powerful approach starts by asking what the agent is fundamentally capable of, then rebuilding the workflow around those raw materials. See Things as Materials, Not Fixed Functions: When you deconstruct out from under a label, tools and concepts start to look like craftable raw materials. You can then combine them in new, valuable ways they haven't been combined before — alloying old methods with new capabilities to create properties neither had on its own. Reason From Properties, Not Personas: Ask what the actual properties of an LLM are. Non-determinism isn't a bug to apologize for — it's a property you can exploit. The existence of many different models is a property too, which is exactly what makes adversarial review possible. That's principle-oriented thinking applied to agents. Extend the Latticework: Charlie Munger talked about a latticework of mental models that weave together rather than sit in isolation. The durable skill isn't quarantining your concept of "AI" off to the side — it's grafting a new section onto the existing tapestry and letting it reshape everything you already understood. Episode Takeaway: Look at how you spend your time and ask new questions of it. What is the material here? What kind of thinking does the agent actually do? What can a human do that an LLM can't — and the other way around? That's how you avoid believing a sock is only ever good for a foot.

This is a recap of the top 10 posts on Hacker News on June 09, 2026. This podcast was generated by wondercraft.ai (00:30): Claude Fable 5Original post: https://news.ycombinator.com/item?id=48463808&utm_source=wondercraft_ai(01:56): Making Graphics Like it's 1993Original post: https://news.ycombinator.com/item?id=48459294&utm_source=wondercraft_ai(03:22): If Claude Fable stops helping you, you'll never knowOriginal post: https://news.ycombinator.com/item?id=48467896&utm_source=wondercraft_ai(04:48): CEOs who think AI replaces their employees are just bad CEOsOriginal post: https://news.ycombinator.com/item?id=48465675&utm_source=wondercraft_ai(06:15): Microsoft's open source tools were hacked to steal passwords of AI developersOriginal post: https://news.ycombinator.com/item?id=48457830&utm_source=wondercraft_ai(07:41): FCC wants to kill burner phones by forcing telecoms to get all customers' IDsOriginal post: https://news.ycombinator.com/item?id=48462308&utm_source=wondercraft_ai(09:07): macOS Container MachinesOriginal post: https://news.ycombinator.com/item?id=48469658&utm_source=wondercraft_ai(10:34): Cleaning up after AI rockstar developersOriginal post: https://news.ycombinator.com/item?id=48458586&utm_source=wondercraft_ai(12:00): Albania Is Not for Sale: Kushner's $4B Resort Triggers'Flamingo Revolution'Original post: https://news.ycombinator.com/item?id=48461012&utm_source=wondercraft_ai(13:26): Apple decided not to roll out Siri in EU after denied request for exemptionOriginal post: https://news.ycombinator.com/item?id=48463024&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with software engineer and entrepreneur Arowolo Muritadhor for a wide-ranging conversation that moves from agriculture and manufacturing in Nigeria to the evolving role of crypto in the country's economy. They touch on how hyperinflation, particularly the naira's dramatic drop in 2023, pushed Nigerians toward stablecoins as a practical savings tool, and how informal kiosk networks have stepped in where traditional banking infrastructure falls short. The conversation also covers the tension between government regulation and the permissionless nature of blockchain technology, comparisons between the decline of the Roman Empire and current shifts in US economic dominance, the role of mobile payments in Africa, language learning, and whether AI agents have any real utility in crypto infrastructure yet. You can connect with Arowolo on LinkedIn and X at @armolas_06.Timestamps00:00 - Host welcomes Arowolo Muritadhor, introducing topics of software engineering and animal food production in Nigeria.05:00 - Discussion shifts to manufacturing, components assembly, and China's dominance in low-cost production globally.10:00 - Conversation explores crypto adoption in Nigeria as a network state phenomenon, separating informed users from mainstream population.15:00 - Mobile payments and kiosk ATM replacements emerge as critical financial infrastructure bridging unbanked Nigerians.20:00 - Roman Empire parallels drawn to modern crypto taxation, government control, and inevitable death-and-taxes reality.25:00 - Bitcoin and Ethereum permissionless nature debated against government wallet-level censorship vulnerabilities.30:00 - AI agents examined as crypto infrastructure tools, revealing mostly trading bots rather than foundational builders.35:00 - Nigeria's 2023 naira collapse compared to Argentina's hyperinflation, driving citizens toward stablecoin dollar savings.40:00 - US Treasury history unpacked through FDR gold confiscation and Nixon ending convertibility, paralleling empire decline.45:00 - Crypto reframed as anti-bank rather than purely anti-government, enabling freedom through immutable accountability.50:00 - Transparent blockchain ledgers discussed as potential government accountability tools across democracy, republic, and oligarchy structures.Key Insights1. Nigeria has a significant divide between its northern and southern regions in terms of economic activity. The north, centered around Abuja, is more agricultural with substantial cattle production, while Lagos in the south functions as a dense urban and commercial hub. This geographic and economic split shapes how different financial tools and technologies are adopted across the country.2. China's dominance in low-cost manufacturing has made it nearly impossible for countries like Nigeria, the United States, or Argentina to compete on price alone. The more realistic path for developing economies is to import components and focus on local assembly and creativity, which is where meaningful economic participation becomes possible.3. Crypto adoption in Nigeria accelerated dramatically around 2023 when the naira experienced a sharp devaluation against the US dollar. Before that point, saving in dollars was difficult for many Nigerians, especially those without formal bank accounts, making stablecoins like USDT an attractive and practical alternative for preserving wealth.4. Informal kiosk operators in Nigeria have organically become a substitute for ATMs, giving communities access to basic financial services where traditional banking infrastructure does not reach. This grassroots financial layer is now a key entry point for integrating crypto and stablecoin payments into everyday commerce.5. Governments are increasingly trying to regulate crypto at the wallet and centralized exchange level, using tax compliance as a primary mechanism. While Bitcoin and Ethereum remain largely permissionless, the practical chokepoints for most users remain centralized platforms where identity and transactions can be monitored.6. The historical parallel between the fall of the Roman Empire and current shifts in US economic and geopolitical power offers a useful frame for understanding why crypto matters. Just as Rome debased its currency and struggled to sustain imperial costs, the US faces mounting debt and a financialized economy that may accelerate dollar instability and push more people toward alternative stores of value.7. One genuinely constructive use case for blockchain beyond speculation is immutable accountability, particularly for public institutions and prediction markets. A transparent ledger that governments or officials voluntarily adopt could create verifiable records of decisions and promises, reducing corruption and increasing trust in ways that traditional governance structures have struggled to achieve.

Right now, the questions we have about our careers feel existential. We keep coming back to the same theme: how do you prepare for an industry that's changing this fast, and what mindset actually works in this new reality? One skill keeps surfacing as the answer — your ability to update your own mental models. In today's episode, I want to push on that further and put some of software engineering's most beloved thinking models under scrutiny. Some of these models served you well for years. Some of them now deserve to be challenged, replaced, or thrown out entirely — and learning how to tell the difference is itself the skill that will determine whether you hit a ceiling. Move Past "So What" Questions: The typical engineering objection to agentic coding is that it produces quality issues. But the people deciding to adopt these tools already accept that. Our job is to stop arguing the surface-level point and start asking the real one: so what do we actually do about this new economic reality? The Economics of Acceptable Loss: Abstraction always leaves something to be desired. An agent's code may not match what a staff engineer produces by hand over months — but that gap is usually an acceptable trade against shipping something two, three, or four times faster. Understand the cost-benefit picture instead of pretending the cost doesn't exist. Abstraction Has Always Done This: This isn't new. The calculator dissolved the specialization once required for complex math. Spreadsheets commoditized ledgering and accounting. Agentic coding is the same pattern arriving for our work — making something that required deep specialization suddenly far more accessible. Roles Are Blurring: As these generic tools raise everyone's ability to abstract, the boundaries soften. You're already seeing product managers open pull requests and engineers making product decisions. The neat lines around "what an engineer is" are not as fixed as they used to feel. Why Your Hard-Won Wisdom Is the Target: If you've spent years in this industry, your models were bought with blood, sweat, and failed projects. That experience is real wisdom — and it's exactly what I'm asking you to be willing to challenge, because the thing that always worked for you is the thing most likely to become a ceiling. This Skill Survives Either Way: Even if you think AI is mostly hype and I've been infected by it — fine. The ability to challenge your pre-existing models is a critical skill regardless. It's how you keep growing as you get more senior instead of repeating what used to work. Models Are Approximations: The whole point of a model is to approximate the reality around us. That's their value and their limitation. When the underlying reality shifts this dramatically, holding tightly to an old approximation stops being wisdom and starts being a liability.

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. For many of us, this career has always been engaging for deeply creative and intellectual reasons—and that excitement is still here. But our mental models of what it means to be a good engineer, and what it means to keep improving, have gone a little stale. In today's episode, I want to talk about a distinction that I believe will become the cornerstone mistake for seasoned engineers: confusing _practice_ with _adaptation_, and leaning on the wrong one at the worst possible moment. Two Surfaces Coming Into Contact: Picture your knowledge, skills, and toolset as one surface, and the actual state of the art as another. We've always known the surface area we could learn far exceeds what we can learn, which forces us to place bets on a learning strategy. What's changing is how fast that second surface is moving underneath us. Improvement by Practice vs. Improvement by Change: Practice is wielding what you've already adopted—smoothing out errors, building muscle memory, refining what you already know. Adaptation is fundamentally folding something new into your repertoire. Both are real forms of improvement, but they are not interchangeable. The Cornerstone Mistake for Senior Engineers: Later in your career, the time you spend adapting naturally goes down as you settle into practice. The biggest error I'm already watching engineers make is moving too quickly toward practice when the industry is loudly calling for adaptation instead. Inspect and Adapt—at the Right Altitude: Sprint retros were never really about getting marginally better at the thing you already do. The intent of "inspect and adapt" is to step up one level and examine the system. The trap is treating adaptation like a minor refinement—getting a little better at prompting—when it should mean asking whether you're thinking about prompting in the wrong way entirely. Question the Ratio, Not Just the Output: Real adaptation looks like asking whether you have the right mix of human and agent on a problem. Are you leaning on the agent for things you shouldn't, or failing to lean on it for the things you should? Have you genuinely thought about how sub-agents or an agent team are working the problem you're producing? A Spectrum, Not a Binary: On one end, you make micro-adjustments to your refinement process. On the other end of experimentation, you ask whether refinement—or even having engineers plan the work—is the right thing at all. The point isn't that practice is dead; it's that the industry is changing fast enough that the adaptive end of that spectrum deserves far more of your attention than it used to. Episode Homework: Take something you currently treat as a practice problem—"how do I refine tickets faster?"—and step up a level. Ask the adaptive version of the question instead: "Is refinement even the right thing anymore?"

If you've heard that your job in the agentic coding era is to "become a manager of agents," you may have noticed something doesn't quite fit. Most of us never trained to be managers, and frankly, that's not the role most engineers want. In today's episode, I unpack what that shift _actually_ means — it's closer to a tech lead or architect mindset — and zoom in on a specific interviewing and on-the-job skill that will help you stay employable: how you think about, talk about, and take ownership of failure. Don't Just Bring Star Stories — Bring Failure Stories: Interviewers don't only want to hear how you succeeded. They want to know what you do when the pressure's on and things fall apart. If every story you tell is a highlight reel, there's a built-in social signal that you're hiding something. Get comfortable telling the other kind of story. Identify the Real Problem, Not the Proximal One: The most common failure story I hear in interviews is "the knowledge transfer was bad" or "the docs weren't good." That's not wrong — it's just incomplete. The senior mindset asks why that happened. Why didn't we have docs? Why was context insufficient? Walk it back until you hit something actionable but not too abstract. The Systemic Diagnosis is the Leveled-Up Answer: Fixing the proximal cause fixes this instance. Fixing the root cause fixes the system that keeps producing instances like this. When you connect what you learned to a systemic adjustment, you stop sounding like someone who survived a bad project and start sounding like someone who improves the organization around them. Ownership Means Owning the Outcome, Not the Task: Use the homeowner metaphor. A homeowner doesn't personally fix every leaking pipe — but the outcome of the home is theirs. As an engineer, your scope of ownership has expanded dramatically in the agentic era. You're now responsible for outcomes of code you may not have even read, and the deciding skill is how you carry that responsibility. The Word to Pair With Ownership is Relentlessness: Not in an anxious, burn-yourself-out way. Relentlessness means following a thread to its natural end — through escalation, through asking the next question, through finding the right person if it's not you. It's the antidote to "I'll let someone else handle it" syndrome. You Don't Have to Do It All Yourself: Relentless ownership is not "carry every task across the finish line personally." If you're not qualified, the owner's job is to find who is, communicate risk to stakeholders, and keep the trail alive until the outcome is resolved. That's the differentiator between a senior thinking engineer and a junior one working through assigned tickets. Failure Is Usually a Lapse in Ownership: If you make a list of five things you've failed at (and you should), you'll often find the through-line isn't lack of skill — it's that you stopped escalating, stopped following up, stopped staying with the thing until it was actually resolved. Episode Homework: Write down five real failures. For each one, ask: where did I stop being relentless? What system produced this outcome — and what would I change upstream next time?