Podcasts about Software development

Carson Gross, computer science professor at Montana State and creator of htmx, joins the show to cut through the noise around AI and programming. He explains why the jump from high-level languages to LLMs is fundamentally different from past transitions, why junior developers who skip writing code risk being at the mercy of a stochastic system, and why systems architecture and managing code complexity are the skills that will matter most. A grounded, rational take on the future of software development jobs. Resources Yes,and...: https://htmx.org/essays/yes-and/ We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. Chapters 00:00 Introduction — Carson Gross and the "Yes, And…" Blog Post 01:45 Why Carson Felt Compelled to Write About AI and Coding 03:30 The Assembly-to-High-Level Analogy — and Why It Falls Apart 06:00 Juniors Must Write Code to Be Able to Read Code 08:15 The Sorcerer's Apprentice Trap 10:30 Could AI Actually Increase Demand for Programmers? 12:45 Why "SaaS Is Dead" Is Shortsighted 15:00 Systems Architecture as the High-Value Skill Going Forward 17:30 Essential vs Accidental Complexity — The No Silver Bullet Framework 20:00 How LLMs Break the Natural Feedback Loop of Bad Code 23:00 Will AI Change How We Think About Testing? 26:30 Abstraction, Paradigms, and Human-Readable Code 29:00 How Much Has AI Actually Boosted Carson's Own Productivity? 32:00 The Mental Health Cost of the AI Hype Cycle 35:30 Final Thoughts — Give Yourself (and Others) a BreakSpecial Guest: Carson Gross.

Always Be Prospecting Today on THINK Business, I sat down with Gabe Lullo, CEO of Alio, a company that makes 11 million cold calls a year. Yes, 11 million. We dug into something every business needs to hear: Momentum is not a strategy. Prospecting is. Too many teams slow down once the pipeline gets full. They rely on inbound. They wait. They hope. Whether business is good, bad, or booming, the companies that keep hunting stay relevant, stay sharp, and stay growing. We also talked first impressions, SDR training, the power of simple questions, and why the best salespeople never sound "salesy"—they sound human. AI is accelerating the game, no doubt. But one thing still separates leaders from laggards: Pick. Up. The. Phone. People are getting fewer calls today because everyone else is afraid to make them. That's an advantage for anyone willing to act. 3 Takeaways for Your Business Outbound is not optional. First impressions fuel conversions. The phone is your unfair advantage. People buy from people. A call breaks through the noise faster than any email sequence ever will. --- Gabe Lullo's expertise in sales, marketing, recruiting, and management began when he started his own business after graduation from the Barney School of Business at the University of Hartford. He owned and operated his own sales, training, and marketing firm for more than a decade. Gabe excelled in training sales and marketing professionals, and additionally, has had a successful career in executive recruiting. Gabe has been instrumental in expanding the company's search and placement for IT, Software Development, Sales, Customer Success, Marketing, and Executive leaders. Gabe's most recent success has been with us here at Alleyoop. For many years, he has been working to build and grow the company by focusing on its culture, environment, customer success, and sales. Connect with Jon Dwoskin: Twitter: @jdwoskin Facebook: https://www.facebook.com/jonathan.dwoskin Instagram: https://www.instagram.com/thejondwoskinexperience/ Website: https://jondwoskin.com/LinkedIn: https://www.linkedin.com/in/jondwoskin/ Email: jon@jondwoskin.com Get Jon's Book: The Think Big Movement: Grow your business big. Very Big! Connect with Gabe Lullo:Website: https://alleyoop.io LinkedIn: https://www.linkedin.com/in/lullo *E - explicit language may be used in this podcast.  

AI can generate working code quickly. Building reliable software to run infrastructure platforms is still a multi-year engineering challenge. In this sponsored episode, BlueCat chief strategy officer Andrew Wertkin joins John Burke and Scott Robohn to talk through the difference between code generation and enterprise software development, and the challenges and opportunities of engineering reliability... Read more »

AI can generate working code quickly. Building reliable software to run infrastructure platforms is still a multi-year engineering challenge. In this sponsored episode, BlueCat chief strategy officer Andrew Wertkin joins John Burke and Scott Robohn to talk through the difference between code generation and enterprise software development, and the challenges and opportunities of engineering reliability... Read more »

It's 2026. You're the CEO of a global bank or the Head of Surgery at a major hospital. An AI system looks at a complex set of volatile market data, or a patient's decade-long medical history, and gives you a directive. It doesn't just give you a percentage of probability. It tells you exactly what to do. But here is the catch: If that decision fails, "the machine told me so" won't hold up in court, in the boardroom, or at a patient's bedside. For a few years now, we've played a game of ‘black box' roulette, using AI that predicts the future based on the past. But in a world of sudden market shifts and unprecedented global change, the past is no longer a reliable map. We are entering the era of Automated Reasoning. This is the shift from AI that guesses to AI that proves. It's a market for explainable systems that is set to hit nearly ten billion dollars this year, doubling by 2032. Because today, leaders don't just need an answer; they need the logic behind it. They need a ‘glass box' they can trust. Scott Wiltamuth, Director of Software Development for Agentic AI and Automated Reasoning at AWS, alongside Mary Martin, Managing Director and Senior Partner at BCG and Varun Chitkara, Senior Vice President of Global Product & Technology at ADP join host Tom Parker. Sources: FT ResourcesThis content is paid for by AWS and is produced in partnership with the Financial Times' Commercial Department. The views and claims expressed are those of the guests alone and have not been independently verified by The Financial Times. Hosted on Acast. See acast.com/privacy for more information.

What can small language models teach us that the largest AI models cannot? Kelly and Julian are joined by Microsoft Cloud Advocate Gwyneth Peña-Sigüenza to explore why working with small language models (SLMs) may be one of the best ways to understand AI. Rather than relying on increasingly capable models that hide complexity, Gwyneth argues that constraints build stronger fundamentals. From prompt engineering and context management to deployment and security, SLMs force learners to think more carefully about how AI actually works. The conversation extends beyond AI models into learning itself. Gwyneth shares her self-taught journey from growing up on a remote farm in Ecuador with limited internet access to becoming a Microsoft Cloud Advocate and creator of the Learn to Cloud platform. Along the way, the group discusses productive struggle, mentorship, cloud engineering, Python, security, and what educators should prioritize as AI becomes part of every student's learning experience. The episode closes with a thoughtful discussion about AI dependency, judgment, and whether we would actually flip the switch and turn AI off if given the choice. Show Notes Wins of the Week Gwyneth celebrates the New York Knicks reaching the NBA Finals after more than 50 years. Julian shares that he has accepted a new role as a Fractional CTO. Kelly reflects on taking her first real vacation in over a year—and how stepping away from work sparked unexpected ideas. Small Language Models Why SLMs are valuable teaching tools Learning prompt engineering through constraints Running models locally on everyday hardware When local AI makes sense for classrooms Understanding tokens, context windows, and model limitations Why bigger models can sometimes hide important lessons Learning Through Constraints Learning to drive in an old manual pickup truck as a metaphor for learning AI fundamentals Why difficult learning experiences often create lasting understanding Building strong habits before relying on more capable tools Consistency versus constantly chasing the newest resource Self-Taught Learning Growing up without reliable internet in rural Ecuador Downloading YouTube playlists to learn programming offline Developing discipline through limited access The value of repetition and focused practice Why mentorship accelerates learning Python Journey Transitioning from cloud engineering to Python advocacy Learning Python beyond scripting Discovering what "Pythonic" really means Wrestling with list comprehensions and other advanced syntax Favorite learning resources: Fluent Python Effective Python Learn to Cloud Building an open-source cloud engineering curriculum Hands-on labs and automated verification AI-assisted assessment Supporting self-taught learners around the world Creating accessible technical education Cloud, AI, and Security Deploying AI applications to the cloud Containers, virtual machines, and serverless deployments Why operations and security deserve more classroom attention Introducing secure development practices early The importance of authentication, secrets management, and responsible deployment Teaching in the AI Era Helping students understand how AI works instead of simply using it Why productive struggle still matters The changing role of educators Balancing AI assistance with independent thinking Preparing students for a future where AI is always available Final Thoughts AI dependency versus capability Judgment as the skill that matters most Human connection in an AI-driven world Would we actually turn AI off? Finding balance between technological progress and intentional learning

Architectural Integrity in the Age of Algorithmic Code: Software Engineering and QA Governance with Konstantin KlyaginIn a recent episode of The Thoughtful Entrepreneur Podcast, host Josh Elledge sat down with Konstantin Klyagin, the Founder of Redwerk, to deconstruct the operational liabilities introduced by the rapid adoption of AI-assisted coding tools. Konstantin, a veteran software architect with more than two decades of global technology experience spanning Ukraine, Western Europe, and the United States, shares a critical perspective on why speed should never be mistaken for stability in software delivery. This conversation provides an essential, engineering-focused blueprint for mid-market founders, enterprise product owners, and technical leaders who want to leverage advanced software automation while aggressively protecting their products against technical debt, architectural breakdown, and security vulnerabilities.The Code Optimization Paradigm: Mitigating Technical Debt with Rigorous Quality AssuranceThe widespread corporate directive to accelerate release cycles through generative artificial intelligence has inadvertently created an environment where companies routinely exchange long-term structural stability for immediate development speed. Konstantin Klyagin cautions that while algorithmic coding tools are highly effective for rapid prototyping and generating initial Minimum Viable Products (MVPs), they frequently output thousands of lines of bloated, inefficient, and brittle syntax that lacks any context regarding an enterprise's scaling requirements. When product teams deploy this machine-generated code directly into production environments without strict human review, they inherit severe administrative and technical debt that complicates future software updates and compromises system security. True enterprise scalability is achieved not by handing over core development to complete automation, but by enforcing rigid software architecture guardrails and treating artificial intelligence strictly as a baseline productivity assistant overseen by seasoned human engineers.Transitioning an organization out of reactive code patching requires an absolute commitment to formal Quality Assurance (QA) governance rather than treating software testing as a post-development afterthought. Many founders commit the costly mistake of using their own end-user base as the primary line of bug discovery, which introduces significant friction into customer-facing operations and quietly erodes long-term brand equity. Real-world capital optimization demands that software organizations build sophisticated internal or external manual and automated testing pipelines to evaluate edge cases, business logic compliance, and real-time drop-off analytics long before new features hit the market. For instance, rather than accepting automated outputs at face value, professional engineering teams systematically refactor code lines—frequently condensing massive, AI-generated structures into a few dozen clean, optimized scripts—ensuring the application remains stable under high user loads and protects its core margins.Furthermore, maintaining a premium digital footprint in a highly competitive market demands that corporate leaders balance software innovation with deliberate strategic focus and lifestyle resilience. Drawing from his global journey and personal dedication to demanding outdoor sports like kite surfing, Konstantin highlights that clear executive decision-making relies heavily on maintaining cognitive agility outside the office. When a technology enterprise pairs an advanced, multi-model tech stack with a transparent workplace culture and external diagnostic assessments—such as comprehensive, unbiased software audits—it successfully insulates its bottom line against changing algorithmic trends. Ultimately, permanent industry authority belongs to the organizations that treat software engineering as a strict corporate discipline, balancing backend automation loops with definitive human oversight to predictably scale enterprise value.About Konstantin KlyaginKonstantin Klyagin is the Founder of Redwerk and a premier global technology strategist with over 21 years of specialized experience in software architecture and legacy system modernization. Having successfully scaled complex development structures for international government agencies and award-winning enterprise clients, Konstantin now advises mid-market companies on technical execution and product management. He is a passionate advocate for continuous technical education and high-accountability engineering standards within the global developer ecosystem.About RedwerkRedwerk is an elite, full-service software development agency and technical advisory firm specializing in product engineering, legacy maintenance, and professional quality assurance for mid-market businesses. Operating with a dedicated team of over 90 technical professionals, the company bridges the gap between high-level business goals and technical execution across diverse markets. Through structured implementation playbooks and specialized software bug audits, Redwerk enables organizations to eliminate technical debt and predictably scale their digital infrastructure.Links Mentioned in This EpisodeRedwerk Official Website: redwerk.comKonstantin Klyagin on LinkedIn: linkedin.com/in/thekonstKey Episode HighlightsThe AI Bloat Trap: Understanding why over-reliance on generative coding tools introduces thousands of lines of brittle, unoptimized syntax into production environments.The Architecture Ownership Mandate: Why experienced human engineers must guide all core structural and scalability decisions independent of automated recommendations.The Failure of User-Led QA: Transitioning away from using your active client base as bug testers by installing internal manual and automated verification pipelines.Data-Driven Drop-Off Analytics: Utilizing behavioral tracking tools to precisely map user journeys and resolve technical bottlenecks within the application funnel.The Long-Tail Software Audit: Leveraging objective, third-party code reviews to identify hidden operational vulnerabilities and build credibility with investors.ConclusionThe conversation with Konstantin Klyagin reinforces that true software optimization is an intentional discipline built on clean engineering principles rather than automated volume. By standardizing internal corporate tech governance, enforcing rigorous human-in-the-loop quality assurance, and focusing ruthlessly on long-term architectural health, business leaders can transform a volatile software setup into a highly structured, self-sustaining corporate asset.More from The Thoughtful Entrepreneur

Proposal declined: drop gccgogaal — Keep skills in sync across AI agentsvscode-go 0.54.0 releasedgeneric methods in Go have been implementedNot everybody is happy about itInterview with Louis CeronaGenetec ★ Support this podcast on Patreon ★

Today our hosts tackle an all-timer of a question: what's the right number of knobs on a pedal? Stay tuned until the end to find out if they arrive at a definitive, unanimously agreed upon answer! They also talk a lot about Andy's exploration of the Chase Bliss Big Time, and take some listener calls. There's product development chat, time signature chat, listen it's just more chat from your favorite chatterboxes. Buy some Old Blood: https://oldbloodnoise.com/ Join the conversation in Discord: https://discord.com/invite/PhpA5MbN5u Follow us all on the socials: @danfromdsf, @andyothling, @oldbloodnoise Subscribe to OBNE on Twitch: https://www.twitch.tv/oldbloodnoise Leave us a voicemail at 505-633-4647!

Robin and Mazen unpack the rise of AI-powered security threats, from the TanStack breach to compromised React Native packages and GitHub supply chain attacks. Learn practical ways to secure your React Native apps, manage dependencies safely, and reduce risk in modern mobile development.   Show Notes Snyk: TanStack Compromised Wiz: Mini Shai-Hulud Strikes Again TanStack: Hardening Followup TanStack: Full Postmortem StepSecurity: Malicious RN Packages Metro4Shell CVE-2025-11953 JFrog: CVE-2025-11953 Deep Dive ReactCon Talk: Aleksandra Desmurs-Linczewska Matteo Collina: Why Trusted Publishing Can't Save Us npm Security Best Practices React Native Security Docs pull_request vs pull_request_target explained   Connect With Us! Robin Heinze: @robinheinze Mazen Chami: @mazenchami React Native Radio: @ReactNativeRdio   This episode is brought to you by Infinite Red! Infinite Red is a premier mobile app consultancy, especially focused on Expo and React Native, located fully remote in the US. We're a team of 30 with highly experienced mobile app developers and have been doing this for over a decade. We are also one of the first development teams to adopt agentic coding in a way that keeps high quality standards and aren't afraid to do things the old school way if we need to. If you're looking for mobile app or React Native or Expo expertise for your next project, hit us up at infinite.red/radio.

In episode 294 of our SAP on Azure video podcast we talk about Software Development Life Cycle at Microsoft with AI. I have always been pretty impressed by the way how Microsoft pushes the way how to develop forward. Especially when we look at the SAP world Microsoft is actually quite a big customer with hundreds of internal ABAP developers. In the past we already talked about how we do Software Development Life Cycle at Microsoft, but obviously AI is also becoming more and more important. So for today I am really happy to have Barb, Ryan, Tom and Joseph joining us today to tell us more how we do AI Powered SDLC at Microsoft!Find all the links mentioned here: https://www.saponazurepodcast.de/episode294Reach out to us for any feedback / questions:* Goran Condric: https://www.linkedin.com/in/gorancondric/* Holger Bruchelt: https://www.linkedin.com/in/holger-bruchelt/ #Microsoft #SAP #Azure #SAPonAzure #Copilot #ABAP #SDLC

Gusto is betting that small businesses need more than another AI assistant. The company's new product, Gusto Cofounder, is designed to act as a proactive business partner that helps owners manage and grow their companies, drawing inspiration from the traditional mom-and-pop partnership that co-founder and CTOEddie Kimwitnessed growing up. Unlike reactive chatbots, Cofounder can take action across payroll, HR, benefits, scheduling, insurance, and accounting workflows by leveraging data already stored within Gusto. Users interact with the platform through text messages or Slack, while a consent framework ensures access to sensitive payroll and employee data remains tightly controlled. Businesses can grant explicit permissions and gradually increase autonomy as trust is established. The platform also integrates with third-party tools such as Google Workspace, enabling it to gather data, perform calculations, run payroll, and communicate results automatically. Kim said the product was built by a five-person team in just eight weeks using Claude Code, which he believes demonstrates how AI is expanding software creation beyond traditional engineering roles. Looking ahead, Gusto plans to add more integrations and eventually enable customers and developers to share reusable, industry-specific business automations. Learn more from The New Stack around how AI is expanding software creation beyond traditional engineering roles: How AI Is Reshaping Software Engineering: Key Takeaways From DeveloperWeek 2025 AI and the Future of Code: Developers Are Key The Engineer in the AI Age: The Orchestrator and Architect Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   

In this episode of Software People Stories, Gayatri Kalyanaraman sits down with Rajshri Kidambi, CEO of Radus Software LLC, for an inspiring and deeply personal conversation. Rajshri shares her journey from arriving in the United States as an H4 dependent to building a successful software company serving federal agencies for over two decades. Together, they discuss entrepreneurship, motherhood, Agile transformation, government technology, resilience, AI, and the power of taking calculated risks. A thoughtful conversation about leadership, growth, and creating impact that lasts.Timestamped Show Notes00:00 – Welcome to Software People Stories - Gayatri introduces Rajshri Kidambi, CEO of Radus Software LLC, and sets the stage for a conversation spanning technology, entrepreneurship, leadership, and life.00:57 – From Electronics Engineering to Software Development. Rajshri shares her early journey—from studying Electronics & Telecommunications Engineering in India to arriving in the U.S. on an H4 visa and transitioning into software development during the tech talent boom of the 1990s.02:38 – The Birth of Radus Software. After nearly a decade at CACI Federal, Rajshri realized she could build a company of her own. She discusses the decision to launch Radus Software and the motivation behind becoming an entrepreneur.05:43 – Discovering a Passion Beyond Programming. Moving from coding to customer interactions, Rajshri explains how becoming a subject matter expert and presenting software solutions to military stakeholders revealed her aptitude for business development and sales.07:18 – Overcoming Language and Confidence Barriers. Growing up in a Kannada-medium school, Rajshri initially struggled with confidence in professional communication. She shares how experience helped her overcome those challenges.09:11 – The Turning Point: Why She Started Her Own Company. Rajshri discusses seeing the value she created for her employer, understanding government contracting economics, and making the leap into entrepreneurship while raising young twins.11:28 – Motherhood, Career Pivots, and Personal Choices. An honest discussion on balancing career ambitions with family priorities, and why every woman's professional journey follows a different path.14:49 – Wearing Every Hat in a Startup. Rajshri reflects on the early years of Radus Software, where she managed accounting, invoicing, operations, contracts, and business development while learning entrepreneurship from the ground up.17:31 – Building Software for the U.S. Federal Government. Rajshri discusses her work across agencies including the Department of Defense, FAA, GSA, and NNSA, and explains how government technology modernization differs from the commercial sector.19:19 – Digital Transformation, Data Governance & SAM.gov. A look into large-scale government modernization initiatives, Agile adoption, business process automation, and the evolution of digital government platforms.22:48 – Agile, SAFE Frameworks, and Government Transformation. Gayatri and Rajshri compare experiences implementing Agile practices in government organizations and discuss the challenges of cultural transformation.26:01 – Working with Multiple Vendors on Large Government Programs. Rajshri explains the realities of delivering federal programs alongside large consulting firms, balancing collaboration, competition, and customer expectations.37:19 – Building Credibility as a Small Business. The journey from subcontractor to trusted prime contractor and the importance of past performance, reputation, and persistence in federal procurement.38:45 – The Highs of a 25-Year Entrepreneurial Journey. Rajshri shares one of her proudest moments—co-presenting with SAFE creator Dean Leffingwell and showcasing Radus Software's Agile product innovation.41:18 – The Lowest Point: Losing a Contract She Loved A deeply personal reflection on losing a major FAA contract after years of investment and how family support and lessons from the Bhagavad Gita helped her recover.45:06 – The One CEO Responsibility She Will Never Delegate. Why customer relationships remain the foundation of business growth and long-term success.47:15 – Looking Ahead: AI, Automation, and Agentic Systems. Rajshri discusses the future of AI, how tools like ChatGPT and Gemini are already changing the way people work, and the importance of governance and guardrails.50:52 – Human Intelligence in an AI-Powered World. A conversation about preserving critical thinking while embracing AI-assisted productivity.53:30 – Final Advice for Aspiring Entrepreneurs. Rajshri's closing message: take calculated risks, build something you care about, stay resilient, and enjoy the journey.55:44 – Closing Remarks. Gayatri wraps up the conversation and thanks Rajshri for sharing her story and insights.Memorable Quotes"If I can do it, anybody can do it.""Take calculated risks.""Relationships are what give you business.""You should be passionate, but not too attached to the outcome.""The future belongs to people who can work with AI, not compete against it." (paraphrased from discussion)Rajashri (“Raj”) Sankavaram, CEO Radus Software LLC, is the driving force behind the company's culture of excellence, integrity, and continuous innovation. With a strong background in quality management, program delivery, and organizational leadership, she has built Radus into a trusted federal and commercial partner known for precision, transparency, and reliability. Raj is the evangelist behind the creation of Metronome Orchestrated Agile®️, the company's flagship platform that unites human-centric design, compliance automation, and AI-assisted orchestration to simplify complex software delivery. Under her leadership, Radus has achieved recognition for its federal contracting excellence, delivering mission-critical solutions for agencies such as the IRS, FAA, NNSA, and DHA. Her emphasis on collaboration, mentorship, and quality-driven growth continues to shape Radus as a company that not only delivers outcomes but also builds enduring relationships grounded in trust and accountability. can be contacted at https://www.linkedin.com/in/raj-sankavaram-0947427/ 

OpenChoreo is an opinionated, “batteries included”, AI-native Kubernetes platform stack for Platform Engineers that combines GitOps, Observability, AI Agents, and Workflows into a custom K8s distribution “super pack” that is managed via Backstage, CLI, API, or MCP. Now a CNCF project.Check out the video podcast version here: 

What makes Chain React more than just another tech conference? Robin, Mazen, and Gant Laborde of Infinite Red share behind-the-scenes stories, community highlights, AI trends, and what React Native developers can expect at Chain React 2026.   Show Notes Chain React Conf 2026 (use code FRIENDS15) Food spots in Portland, OR   Connect With Us! Gant Laborde: @gantlaborde Robin Heinze: @robinheinze Mazen Chami: @mazenchami React Native Radio: @ReactNativeRdio   This episode is brought to you by Infinite Red! Infinite Red is a premier mobile app consultancy, especially focused on Expo and React Native, located fully remote in the US. We're a team of 30 with highly experienced mobile app developers and have been doing this for over a decade. We are also one of the first development teams to adopt agentic coding in a way that keeps high quality standards and aren't afraid to do things the old school way if we need to. If you're looking for mobile app or React Native or Expo expertise for your next project, hit us up at infinite.red/radio.

In this episode, Python Developer Advocate and author Will Vincent joins the hosts to discuss the lasting appeal of Django, changes in how people learn web development, and the ways AI is reshaping software engineering. While modern AI tools can generate working code in seconds, Django's opinionated design and emphasis on maintainability help developers avoid many of the security and architectural problems that often emerge as projects grow. Drawing on his background as an educator, author, and Developer Advocate at JetBrains, Will shares his perspective on the challenges facing today's developers and computer science students. The conversation touches on "vibe coding," the misconception that a successful prototype automatically translates into a production-ready application, and the increasing burden AI-generated content is placing on open-source maintainers. Will also discusses the rise of specialized AI models, the importance of human trust in technical communities, and why foundational software engineering skills remain valuable despite rapid advances in AI tooling. Key Topics Covered Why Django Still Matters A look at why Django continues to be a strong choice for building production applications, even if it doesn't receive the same level of attention as newer frameworks. The Reality Behind "Vibe Coding" Exploring the gap between generating code with AI and understanding the systems, tradeoffs, and architecture required to build reliable software. Learning to Program as an Adult Will reflects on his path from book editing and startup leadership to becoming a self-taught programmer, educator, and author. AI and Programming Education A discussion about how AI changes the learning process, why fundamentals still matter, and how concepts like music theory can help explain the value of understanding code beneath the surface. The Growing Burden on Open Source How maintainers are dealing with an influx of low-quality AI-generated issues, pull requests, and content, and what that means for community-driven projects. Local and Specialized AI Models Why privacy concerns, lower inference costs, and better hardware may drive adoption of smaller, task-focused models rather than ever-larger general systems. Developer Concerns in the AI Era How engineers are responding to growing pressure from leadership teams eager to adopt AI, and what trends JetBrains is seeing across the developer ecosystem. Resources Mentioned LearnDjango, Will Vincent's platform for learning Django and web development. Hello World 5 Different Ways, a Django tutorial that introduces key concepts through practical examples. Django Chat, the podcast Will co-hosts covering the Django ecosystem and web development. Django News, a weekly newsletter highlighting updates from the Django community. JetBrains, the software development company behind tools such as PyCharm and IntelliJ IDEA.Special Guest: Will Vincent.

In this Mob Mentality Show episode, we join James Herr and Woody Zuill for a one-of-a-kind session James calls the "Hot Sauce Ensemble" — mob programming a video game from scratch in the Godot engine using AI, while eating escalating hot sauces every three-minute rotation. Fair warning to podcast listeners: this episode has a strong YouTube component. If things start sounding chaotic and spicy, that's because they are — jump over to YouTube to see what's happening on screen. James set up the session with Claude Code in VS Code (backed by Amazon Bedrock) and a blank Godot project containing only one asset: a hot sauce sprite generated by ChatGPT. From there, the mob navigated an AI coding agent through a real-time game build — adding player movement, landing explosions, and physics-based bell pepper enemies that scatter when stomped. The enemies were bell peppers specifically because Chris despises them. The hero is hot sauce. The logic is sound. Along the way, James introduced the "plate spinning" technique: opening multiple AI agents in parallel terminals so one prompt cooks while the mob drives another, keeping momentum even when AI responses run long. We dig into: How "Hot Sauce Ensemble" combines traditional mob rotations with escalating spicy food — and why it works as a team-building format Using Claude Code in VS Code with Godot to build a playable game from a blank project in real time The plate spinning technique: running multiple AI coding agents in parallel terminals to maintain flow Why the goal should be "effective," not "productive" — and how mob programming and AI tools both support that shift How AI procedurally generates game art assets (bell pepper sprites built from polygon shapes and shading) without any image generation tools Navigating an unfamiliar codebase and engine as a mob, using an AI agent as the technical guide What happens to your prompting quality when habaneros and The Last Dab are involved Hot sauce as a hero, bell peppers as villains: designing game mechanics around personal taste (literally) If you've ever wondered what mob programming looks like when applied to game development, AI-assisted coding, and competitive spice tolerance all at once, this episode delivers all three simultaneously — with physics.   References… James Herr's LinkedIn: [PASTE LINK] Woody Zuill's LinkedIn: [PASTE LINK] Godot Engine: https://godotengine.org/ Claude Code: https://www.anthropic.com/claude-code Mobster (mob timer): [PASTE LINK] Hot Sauces Featured… James: Hot Ones Apricot Sauce (#7), Hot Ones The Last Dab (#10) Woody: Taco Bell Hot Sauce, ~3 lbs pickled jalapeños (stuffed in a burrito) Chris: Fishwife Albacore in Spicy Olive Oil, Oni Yuzu Lemon Hot Sauce (Japan), Marie Sharp's Carrot & Habanero (2-habanero, 4-habanero Blazing Hot, and 5-habanero BEWARE) on a PB&J Thanks to G-SLiK (https://soundcloud.com/g-slik) for the intro and outro music. Chris Lucian and Austin Chadwick discuss all things #agile and product development from a #MobProgramming perspective. Chris Lucian is the Director of Software Development at Hunter Industries and a founder of mob programming. https://www.chrislucian.com/p/chris-lucian-biography.html Austin Chadwick is a Mob Programmer at Hunter Industries and is a passionate agilist and craftsman with experience in several roles (e.g. coach, developer, tester, scrum master, business analyst). https://www.linkedin.com/in/austin-chadwick-3a58151a4/ We would love your feedback and ideas for future episodes! Please add comments to the video or reach out to us on Twitter ( https://twitter.com/mob__mentality & https://twitter.com/ChristophLucian ). All statements and opinions expressed by Chris and Austin are solely their own and do not represent the views of any company. Chris and Austin are just sharing and not recommending ( https://justsharing.dev/ ).

In this episode of Zero to CEO, I speak with Technology and Operations Expert Issac Hicks about why most AI projects fail — and how entrepreneurs can be part of the small percentage that actually generate real returns. Issac breaks down the gap between AI marketing hype and operational reality, explains why nearly 95% of AI initiatives never make money, and shows what successful AI implementations really look like inside growing businesses. If you're tired of gimmicks and want to understand how AI can truly drive revenue, efficiency, and scale, this episode is a must-listen.

Join Tyler Wells, Co-founder and CTO of BrainGrid, for a forward-looking discussion on how artificial intelligence is rewriting the rules of product development. Boasting over 25 years of distributed systems engineering—including a foundational tenure at Skype building Facebook's first video-calling engine and 7+ years directing Video and global SRE at Twilio—Tyler has built infra where structural failure was not an option. In this episode, we explore why the traditional constraints of software engineering—headcount, timelines, and budgets—are dissolving, leaving a brand-new bottleneck at the front of the innovation cycle: human imagination.

Today our hosts talk about their favorite off-label uses for pedals: an Expression Ramper as an expression splitter, a trereo overdrive pedal as a lofi tape machine, a pedal with perfectly spaced knobs as a phone holder, etc. They take calls and some comments from the Discord, all focused around pedal usage that goes beyond the marketing and expectations of the user interface into unexpected sonic corners. Buy some Old Blood: https://oldbloodnoise.com/ Join the conversation in Discord: https://discord.com/invite/PhpA5MbN5u Follow us all on the socials: @carolinegco, @danfromdsf, @andyothling, @oldbloodnoise Subscribe to OBNE on Twitch: https://www.twitch.tv/oldbloodnoise Leave us a voicemail at 505-633-4647!

Mazen and Robin welcome Gant Laborde of Infinite Red to discuss AI Triforce, a practical framework for AI-powered software teams. They explore agentic coding, governance, testing, code quality, and emerging engineering roles that help teams move faster with AI while maintaining scalability, reliability, and long-term maintainability.   Show Notes AI Triforce announcement   Connect With Us! Gant Laborde: @gantlaborde Robin Heinze: @robinheinze Mazen Chami: @mazenchami React Native Radio: @ReactNativeRdio   This episode is brought to you by Infinite Red! Infinite Red is a premier mobile app consultancy, especially focused on Expo and React Native, located fully remote in the US. We're a team of 30 with highly experienced mobile app developers and have been doing this for over a decade. We are also one of the first development teams to adopt agentic coding in a way that keeps high quality standards and aren't afraid to do things the old school way if we need to. If you're looking for mobile app or React Native or Expo expertise for your next project, hit us up at infinite.red/radio.

In this episode of Elixir Wizards, hosts Charles Suggs and Emma Whamond sit down with Marek Šuppa, creator of the Missing GitHub Status page, a project that reconstructs GitHub's historical uptime data and reveals discrepancies between official status reporting and the platform's actual reliability. Marek tells us about his dev journey from open source contributor at DuckDuckGo to machine learning engineer at Cisco-acquired Slido. Then, we discuss GitHub's evolution from a hosted Git service into a critical developer tool. We cover reliability, transparency, AI-driven platform growth, developer workflows, and the challenges of balancing convenience with resilience. Along the way, we cover alternative platforms, self-hosted solutions, and whether recent outages are changing how developers think about ownership, dependency, and the future of software collaboration. Topics Discussed in this Episode: Why did Mr. Shu create the Missing GitHub Status Page? GitHub's reported uptime versus developer experiences How open source contributions shaped Marek's career The evolution of GitHub from tool to critical infrastructure Centralization risks in modern software development Git's distributed roots and today's platform-centric workflows Developer reactions to GitHub outages Transparency and accountability in status reporting AI's impact on developer platforms and infrastructure demands Microsoft's stewardship of GitHub Forgejo, Codeberg, and alternative Git hosting platforms Self-hosted Git solutions and tradeoffs Network effects and platform lock-in The social side of software collaboration Building resilience into developer workflows What GitHub outages teach us about infrastructure dependency Links Mentioned: The Missing GitHub Status Page https://mrshu.github.io/github-statuses/ Slido https://www.slido.com/ https://duckduckgo.com/ The official GitHub Status Page https://www.githubstatus.com/ Statuspage.iohttps://www.atlassian.com/software/statuspage Zig Leaves GitHub https://ziglang.org/news/migrating-from-github-to-codeberg/ Ghostty Leaves GitHub https://mitchellh.com/writing/ghostty-leaving-github GitLab https://about.gitlab.com/ Codeberg https://codeberg.org/ https://git.kernel.org/ Forgejo Lightweight Self-Hosting https://forgejo.org/ Former GitHub CEO Thomas Dohmke launches Entire https://entire.io/news/former-github-ceo-thomas-dohmke-raises-60-million-seed-round Update on Spain and LALIGA blocks of the internet https://vercel.com/blog/update-on-spain-and-laliga-blocks-of-the-internet

In this episode, we look at why some developers now refuse to work without AI tools and how that shift is changing expectations in software teams. We also break down the concerns around AI-generated code quality, productivity, and whether businesses should embrace or limit these tools. Our AI Hustle Skool Community: https://www.skool.com/aihustleGet the top 80+ AI Models for $8.99 at AI Box: ⁠⁠https://aibox.aiGet the AI Chat Daily Newsletter: https://www.aichatdaily.com/newsletter See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

GoConf, Sept 11 & Moscow, RussiaCFPProposalsAccepted: Formal GODEBUG removal policyNew: Allow explicit conversion from function to 1-method interfaceBlog: The 10 Go Error Handling Commandments by Preslav RachevLearn Logging & Observability in Go @ boot.dev, use code CUPOGO to save 25%Video: Practical Go Development with AI Agents by Miki TebekaBlog series: Understanding the Go Runtime by Jesús Espino ★ Support this podcast on Patreon ★

Kelly talks with Philip Guo, creator of Python Tutor, about how the tool helps students trace code and understand programming basics. They also discuss the challenges AI-generated code creates in the classroom and possible ways to support student learning. *Wins of the Week * Philip: Hiring a second undergraduate student for Python Tutor, including one focused on user experience research with K-12 teachers Kelly: Finishing a year of in-person teacher trainings and reflecting on how far the teachers have come *AI, Coding, and Classroom Understanding * Much of the conversation focuses on how AI-generated code affects student learning. Kelly describes using AI code with eighth graders and how difficult it can be for them to understand functions, parameters, returns, and other fundamentals when the code is generated all at once. Philip suggests that tools like Python Tutor may be useful for helping students trace code and understand what is happening behind the scenes. Python Tutor and Possible AI Features Philip explains that Python Tutor currently visualizes execution and has an AI chat feature that can answer questions about code and errors. They discuss possible future features, including simplified AI-generated examples, alternative execution views that show only the lines actually run, and more guided inline help tied to specific code or variables. Oral Explanations and Assessment Kelly describes using a Socratic-style code review with students, where they discuss code aloud in groups. They also talk about using spoken explanations or short oral assessments to check whether students can really explain what code is doing, rather than just copying or prompting AI-generated answers. Broader Research and “Beyond the Desk” Philip briefly discusses a new research direction with a PhD student focused on AI support for work beyond the desk, including physical and embodied tasks in science labs and fieldwork. He says this differs from desk-based AI work and involves activities that are harder for current AI systems to support. **Chapters **0:25 Python Tutor and AI Learning 1:55 Hiring Help for Python Tutor 4:07 Classroom Wins and AI Reflections 6:11 Teaching Code Through Python Tutor 9:03 AI Code and Student Confusion 14:11 Simplifying Execution Traces 17:19 Functions Are the Hard Part 20:25 Keeping Fundamentals in AI Era 24:25 Socratic Seminars for Code 26:27 Voice-Based Code Thinking 29:27 Learning Beyond Lockdown 36:10 Prompting as a New Skill 36:25 Hardware Troubles and NeoPixels 40:15 Beyond the Code Editor 45:01 New Research on Embodied AI 49:12 PyCon and Community Plans 50:42 Teacher Call to ActionSpecial Guest: Philip Guo.

Today our hosts welcome Philippe Herndon of Caroline Guitar Company. He talks about their latest pedal, a self-proclaimed normie pedal called the Aaron Graves Overdrive. We talk about specific design choices, the story of its namesake, and the importance of versatility even in a fairly fundamental pedal. Plus, we get the scoop on why they use pictures instead of labels, and Philippe has a surprise for us in lieu of the traditional object talk. It's basically a two parter, so fire it up and get listening! Check out the Aaron Graves Overdrive and other pedals from Caroline: https://carolineguitar.com/ Buy some Old Blood: https://oldbloodnoise.com/ Join the conversation in Discord: https://discord.com/invite/PhpA5MbN5u Follow us all on the socials: @carolinegco, @danfromdsf, @andyothling, @oldbloodnoise Subscribe to OBNE on Twitch: https://www.twitch.tv/oldbloodnoise Leave us a voicemail at 505-633-4647!

Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect) of RocketRide join the MLOps Community to walk through AIDE — the AI Integrated Development Environment. RocketRide is an open-source AI pipeline platform that lets developers build, debug, and run production-grade agentic AI workflows directly from their IDE, with support for 13+ LLM providers, 8+ vector databases, and full multi-agent orchestration.AI Is Fast. AI Projects Are Slow. Let's Fix That. // MLOps Podcast #378 with JRocketRide's Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect)A huge shout-out to  ⁨RocketRide⁩  for this collaboration!

In this episode of Elixir Wizards, hosts Charles Suggs and Emma Whamond sit down with Saša Jurić, Elixir mentor and author of Elixir in Action, to discuss software craftsmanship in the age of AI. As AI coding tools become increasingly capable, Saša argues that the real challenge isn't generating code, it's maintaining quality, clarity, and shared understanding within a codebase. We explore the difference between correct code and good code, and why code is more than a set of instructions for a machine to execute. Code is also documentation, communication, and a long-term investment that future developers must be able to understand and maintain. Saša shares his concerns about the growing "theater of pull requests," where teams go through the motions of code review without creating meaningful opportunities for learning, feedback, or knowledge sharing. The hosts and Saša talk about practical ways to work effectively with AI, including taking smaller steps, carefully reviewing AI-generated code, and using AI as a collaborative tool rather than an autonomous developer. Throughout the discussion, Saša challenges the industry's obsession with speed and makes the case that the principles of good software development (incremental progress, clear communication, and human judgment) remain important in the age of AI. Key Topics Discussed The difference between correct code and good code Code as communication, documentation, and shared understanding The "theater of pull requests" and ineffective review practices How AI is changing software development workflows Using AI as a collaborator rather than a replacement Why smaller, incremental changes lead to better outcomes Human oversight in AI-assisted development Balancing development speed with maintainability Pull request size and review effectiveness Commit history as a tool for storytelling and context The risks of accumulating technical debt faster with AI Testing and validating AI-generated code Refactoring AI-generated solutions for clarity Applying agile principles to AI-assisted workflows The role of experience and judgment in software design Why software craftsmanship still matters in the age of AI Links mentioned Code Complete by Steve McConnell https://khmerbamboo.wordpress.com/wp-content/uploads/2014/09/code-complete-2nd-edition-v413hav.pdf Harness AI for DevOps, Testing, and AppSec https://www.harness.io/ Claude Code https://claude.com/product/claude-code Claude Code GitHub https://github.com/anthropics/claude-code Pull Request for Oban https://github.com/oban-bg/oban/pull/331 SMPP https://en.wikipedia.org/wiki/Short_Message_Peer-to-Peer OpenAI Codex https://chatgpt.com/codex/ Opus AI https://opus.ai/ Tidewave https://tidewave.ai/ Credo Static Code Analysis https://github.com/rrrene/credo https://smartlogic.io/podcast/elixir-wizards/s11-e09-static-code-analyzer-elixir-credo-ruby-rubocop/ Link to Sasa's X post https://x.com/sasajuric/status/2029522378196238503 Saša Jurić “Tell Me A Story” at Goatmire https://www.youtube.com/watch?v=GOrKfCs-mr0 https://meks.quest/blogs/the-theatre-of-pull-requests-and-code-review Looks Good to Me: Constructive Code Reviews by Adrienne Braganza https://www.manning.com/books/looks-good-to-me Towards Maintainable Elixir: Testing https://medium.com/very-big-things/towards-maintainable-elixir-testing-b32ac0604b99 TDD, Where Did It All Go Wrong (Ian Cooper) https://youtu.be/EZ05e7EMOLMSpecial Guest: Saša Jurić.

AI is everywhere. A lot of companies are not only providing AI to their developers, they are forcing their developers to use AI constantly. This puts your career in extreme danger. So, how can you protect your career? How can you ensure that AI doesn't ruin your career? These are the questions we will answer in today's episode of DevQuestions.Website: https://www.iamtimcorey.com/  Ask Your Question: https://suggestions.iamtimcorey.com/ Sign Up to Get More Great Developer Content in Your Inbox: https://signup.iamtimcorey.com/

Brent Peterson and Paul Byrne discuss the near future of AI, particularly its implications for software development and coding. Paul shares insights from his new book 'Adapt or Die', focusing on the different types of AI learning, the importance of human oversight in AI applications, and the challenges faced in integrating AI into development processes. They explore the democratization of coding through AI tools, the economic implications for software agencies, and the future trajectory of AI technology.TakeawaysAI is currently limited to type one learning, which is reactive.Type two learning in AI requires reflective thinking and goal-seeking capabilities.Human oversight is crucial in AI applications to handle exceptions and ensure quality.AI tools can significantly speed up development processes but cannot replace human developers.The democratization of coding allows non-technical individuals to engage in software development.AI's limitations can lead to wasted resources if not properly understood.The economic model for software development may shift towards fixed pricing due to AI efficiencies.AI can handle tedious tasks, freeing up developers for more complex work.The future of AI may involve running models on local machines for better control and privacy.Continuous adaptation to AI advancements is necessary for developers and agencies.Chapters00:00 Introduction to AI and E-commerce02:46 Understanding AI Learning Types05:27 AI in Development: Tools and Use Cases07:58 The Role of Humans in AI Workflows10:59 Challenges and Limitations of AI13:50 Future of Software Development with AI16:17 The Democratization of Coding19:07 Economic Implications of AI in Development21:51 Closing Thoughts and Book Promotion

In this episode of Teaching Python, Kelly Schuster-Paredes and Julian Sequeira are joined by engineer and maker Todd Kurt to discuss what happens when code leaves the screen and starts interacting with the physical world. The conversation centers on CircuitPython, MicroPython, and physical computing, with a focus on how these tools are used in classrooms and maker projects. Todd explains his background in engineering, web development, and open source hardware, including his work on LED devices and his recent focus on CircuitPython. He describes the differences between CircuitPython and MicroPython, emphasizing that CircuitPython is designed to feel closer to desktop Python and to support teaching, while MicroPython makes more efficiency-focused tradeoffs. The discussion also covers the practical challenges of hardware-based learning. Todd and the hosts talk about bootloaders, UF2 files, board compatibility, library management, and common mistakes such as using the wrong cable, the wrong board file, or wiring power and ground incorrectly. They note that these issues can make hardware feel frustrating, especially for beginners and teachers preparing classroom kits. Kelly and Julian share their classroom experiences, including using preloaded boards, NeoPixels, sensors, and simple student-designed projects. They discuss how hardware can support troubleshooting skills, file-system awareness, and persistence, and why students often engage more when they are building something tangible, such as a sensor-based wearable or a small robot. The episode also includes Todd's stories about early embedded work, including a costly lab mistake, and his involvement in hardware that contributed to space missions. He closes by describing a compact synthesizer project built around a Raspberry Pi Pico and by noting that he shares work through his website and online accounts.Special Guest: Tod Kurt.

Today our hosts welcome themselves! Andy talks about his recent show at a planetarium: the gear involved, the style of preparation, and the experience of performing with previous guest Matt Kidd. Then they take some calls with questions about a variety of things, and their answers range from "we've never heard of it" to "sorry, that was a lot of information and I don't know if I answered your question." It's another hour of unabashed whatever this is! Buy some Old Blood: https://oldbloodnoise.com/ Join the conversation in Discord: https://discord.com/invite/PhpA5MbN5u Follow us all on the socials: @danfromdsf, @andyothling, @oldbloodnoise Subscribe to OBNE on Twitch: https://www.twitch.tv/oldbloodnoise Leave us a voicemail at 505-633-4647!

In this episode, Rob Moffat, author of Risk-First Software Development and chief technical architect at the FinTech Open Source Software Foundation (FINOS), speaks with host Brijesh Ammanath about how all of software development is actually risk management. Rob introduces the concept of 'risk-first software development,' which sits in the context of existing methodologies like scrum and kanban. Showcasing multiple real-world project patterns to illustrate how things can go wrong when risk is ignored, he makes the case for why risk should be the primary lens behind every development decision, from architecture to prioritization. Through various examples, he shows how every developer action can be viewed as a risk trade-off and why making that explicit can lead to better outcomes. The conversation takes a deep dive into the risk-first framework and how teams can apply it in their existing processes.

Feeling overwhelmed by all the new AI coding tools for React Native development? In this episode, Robin and Mazen talk through how they're actually using tools like Claude, Cursor, and Expo Agent to build React Native apps faster. They share real-world AI coding workflows, lessons learned from building AI-assisted mobile apps, and why React Native still matters in an AI-driven development world.   Show Notes Robin's meme about Redux on X (featuring Mazen) Mobile PR Reviewer on GitHub Matt Pocock's AI Skills for Real Engineers   Ask us anything!  We'll be recording a very special AMA episode of RNR in the future! Ask us anything by replying on our X, Bluesky, or LinkedIn posts.   Connect With Us! Robin Heinze: @robinheinze Mazen Chami: @mazenchami React Native Radio: @ReactNativeRdio   This episode is brought to you by Infinite Red! Infinite Red is a premier mobile app consultancy, especially focused on Expo and React Native, located fully remote in the US. We're a team of 30 with highly experienced mobile app developers and have been doing this for over a decade. We are also one of the first development teams to adopt agentic coding in a way that keeps high quality standards and aren't afraid to do things the old school way if we need to. If you're looking for mobile app or React Native or Expo expertise for your next project, hit us up at infinite.red/radio.

In this episode of Book Overflow, Carter and Nathan discuss The Mythical Man-Month by Fred Brooks!Join the Book Overflow Discord here! https://discord.gg/ZwS2fqW7ZZ -- Want to talk with Carter or Nathan? Book a coaching session! ------------------------------------------------------------Carterhttps://www.joinleland.com/coach/carter-m-1Nathanhttps://www.joinleland.com/coach/nathan-t-2-- Books Mentioned in this Episode --Note: As an Amazon Associate, we earn from qualifying purchases.----------------------------------------------------------The Mythical Man-Month by Fred Brooks--Spotify: https://open.spotify.com/show/5kj6DLCEWR5nHShlSYJI5LApple Podcasts: https://podcasts.apple.com/us/podcast/book-overflow/id1745257325X: https://x.com/bookoverflowpodCarter on X: https://x.com/cartermorganNathan's Functionally Imperative: www.functionallyimperative.com----------------Book Overflow is a podcast for software engineers, by software engineers dedicated to improving our craft by reading the best technical books in the world. Join Carter Morgan and Nathan Toups as they read and discuss a new technical book each week!The full book schedule and links to every major podcast player can be found at https://www.bookoverflow.io

Today our hosts welcome Phillip Carter of the 40 Watt Podcast. It takes them 45 minutes to get to his object because they're all just so good at conversing! They talk about blues, jazz, early influences, discovery of tube amps, the importance of the right speaker, and of course ask the big questions like: are guitar solos good? Check out all things 40 Watt: https://40wattpodcast.com/ Buy some Old Blood: https://oldbloodnoise.com/ Join the conversation in Discord: https://discord.com/invite/PhpA5MbN5u Follow us all on the socials: @40wattpodcast, @danfromdsf, @andyothling, @oldbloodnoise Subscribe to OBNE on Twitch: https://www.twitch.tv/oldbloodnoise Leave us a voicemail at 505-633-4647!

In Season 15 episode 3, Charles Suggs sits down with Greg Medland, aka “The Elixir Fixer,” to talk about the current state of hiring and the software jobs market in 2026.   Greg shares what he's seeing from both sides of the hiring process as an Elixir-focused recruiter, from shifting company expectations to the growing importance of specialization, communication skills, and real-world product thinking. We discuss how the market has changed since the 2021–2022 hiring boom, why things feel more uncertain today, and how developers are adapting to a slower, more competitive landscape.   The conversation also explores how AI is affecting hiring workflows, résumé quality, technical interviews, and even the rise of fraudulent candidates. Greg explains why human relationships and reputation still matter more than ever, especially in smaller ecosystems like Elixir where community connections carry real weight.   Along the way, we talk about what junior developers are up against, why senior engineers with domain expertise continue to stand out, and what developers can do to position themselves more effectively in today's market. Greg shares practical advice for building a sustainable career, developing a clear professional identity, and navigating a rapidly changing industry.   Topics discussed in this episode: The current state of the Elixir job market Hiring trends and market shifts since 2021–2022 How AI is changing hiring and recruiting workflows Fraudulent candidates and AI-generated résumés Domain expertise vs. generalist engineering skills Product thinking and customer-focused development What companies are looking for in 2026 Junior developer challenges in the current market Why senior specialists remain in demand Networking and relationship-building in tech Open source contributions and visibility in the Elixir community Standing out in a crowded hiring environment Résumé quality and application strategies The role of personal branding for developers Remote work trends and geographic hiring patterns Technical interview expectations and evaluation changes Startup vs. enterprise hiring differences Human connection in an increasingly automated industry Career resilience and long-term positioning Building a sustainable software engineering career   Links mentioned: Socially Responsible Recruitment https://sr2rec.com/en/ Greg's LinkedIn https://www.linkedin.com/in/elixirfixer/ Greg's email address: greg@sr2rec.com

SummaryIn this episode, Tyler Wells, co-founder and CTO of BrainGrid.ai, shares his extensive experience in entrepreneurship, AI development, and the evolving role of software engineers in the age of AI. Discover insights on building AI-driven platforms, managing projects, and the future of software creation.TakeawaysEntrepreneurship journey and lessons learnedBuilding AI platforms and managing projectsThe role of human ingenuity versus AI automationChapters00:00 Introduction to Tyler Wells and His Journey02:55 The Entrepreneurial Journey: Lessons from Failures05:41 Transitioning from Employee to Entrepreneur10:15 The Impact of AI on Business and Employment11:14 Building with AI: The Evolution of BrainGrid14:14 The Role of Engineers in an AI-Driven World24:05 The Future of Software Engineering and AI Pilots27:07 Challenges in Software Development with AI32:14 Transforming Ideas into Actionable Plans40:47 Navigating the Challenges of Non-Technical Users47:18 Prototyping vs. Production: Understanding the Difference53:06 The BrainGrid Framework: Build, Verify, TrustConnect with Tyler: https://www.braingrid.ai/https://www.linkedin.com/in/tylerswells/Credits:Hosted by Ryan RoghaarProduced by Ryan RoghaarTheme music: "Perfect Day" by OPM  The Eggs Podcast Spotify playlist:bit.ly/eggstunesThe Plugs:The Show: eggsthepodcast.com@eggsthepodcast on X and InstagramMike "DJ Ontic": Shows and info: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠djontic.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@djontic on twitterRyan Roghaar:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠rogha.ar⁠

On episode 10 of High Leverage, Joe Ruscio sits down with Steve Krouse to discuss the rapidly evolving relationship between AI and programming. Steve shares lessons from building Val Town at the center of the AI tooling wave, why he believes better abstractions will define the future of software, and how engineers can avoid becoming passive operators in an increasingly agent-driven world.

Is AI actually helping us escape the build trap, or just helping us build the wrong things faster?In this episode of Arguing Agile, hosts Brian Orlando and Om Patel discuss the new AI4Agile Practitioners Report 2026 from Scrum.org. The data reveals a startling trend: while 83% of practitioners have access to AI tools, the primary fear isn't job replacement, it's that AI is becoming a "supercharged way into the feature factory."Listen or watch as we examine the report's key findings, including the gap between AI access (83%) and actual competence (only 15% received formal training). We discuss why the reported productivity gains (73.7%) might be masking the erosion of agile values like reflection and collaboration. Citing Melissa Perri's "Escaping the Build Trap," we explore how organizations are using AI to accelerate output without redesigning workflows to improve outcomes.Key topics include:Why "speed of delivery" was never the real bottleneckWhat practitioners really fearThe lack of workflow redesign in the AI eraFive actionable questions to test if your team is escaping or acceleratingTune in to learn how to ensure your AI adoption drives value, not just volume.#Agile #ProductManagement #AIEscaping the Build Trap by Melissa Perri, The Goal by Eliyahu M. Goldratt, AI4Agile Practitioners Report 2026 by Scrum.org, Crossing the Chasm by Geoffrey MooreLINKSYouTube: https://www.youtube.com/@arguingagileSpotify: https://open.spotify.com/show/362QvYORmtZRKAeTAE57v3Apple: https://podcasts.apple.com/us/podcast/agile-podcast/id1568557596INTRO MUSICToronto Is My BeatBy Whitewolf (Source: https://ccmixter.org/files/whitewolf225/60181)CC BY 4.0 DEED (https://creativecommons.org/licenses/by/4.0/deed.en)

Daniel Williams stops by to chat with Mazen and Robin! We dig into Storybook 10's leaner bundle, the 10.3 MCP server unlocking AI agent workflows, what Chromatic is cooking up for visual regression testing in React Native, and his work on Repack.   Show Notes Storybook announcement on X How to Cleanly Swap Between React Native Storybook 10 and Your App Storybook website Agent Device: iOS & Android Automation for AI Agents React Native Porto Meetup X   Connect With Us! Daniel Williams: @Danny_H_W Robin Heinze: @robinheinze Mazen Chami: @mazenchami React Native Radio: @ReactNativeRdio   This episode is brought to you by Infinite Red! Infinite Red is an expert React Native consultancy located in the USA. With over a decade of React Native experience and deep roots in the React Native community (hosts of Chain React and the React Native Newsletter, core React Native contributors, creators of Ignite and Reactotron, and much, much more), Infinite Red is the best choice for helping you build and deploy your next React Native app.

Steve finally fixed phillycocoa.org, and the journey from broken CircleCI pipelines and hijacked S3 buckets to a blazing-fast Cloudflare Pages site took one Side Project Saturday and an embarrassing number of Codex tokens. Then The Trio turns to the AI hype machine, and they're tired: tired of opaque token costs, tired of reviewing generated code that complicates everything it touches, and tired of an industry that mistakes syntax speed for software engineering. Fred Brooks called it in 1986, and The Trio is calling it now.## Chapters00:00 Introductions01:47 The Journey of Updating the Website06:38 Challenges with CircleCI and S3 Buckets09:23 Exploring Cloudflare Pages11:14 Navigating Cloudflare's User Interface14:22 Setting Up Automatic Deployments17:35 Managing DNS and SSL with Cloudflare23:07 LLM Development Fatigue26:15 Navigating Concerns and Costs in AI Usage29:11 LLMs are No Silver Bullet31:57 The Exhaustion of Code Review and Architectural Decisions36:25 Token Management and Cost Awareness in AI Tools40:07 The Economics of AI and Software Development42:45 The Hype vs. Reality of AI Tools46:34 Future Prospects of LLMs and Universal UI50:16 The Future of Edge Computing with LLMs53:08 The Evolution of Software Development and AI Integration54:17 AI in Sci-Fi: Myths vs. Reality57:54 The Challenges of Local Models and Hardware Limitations01:03:21 Outro & Upcoming Event01:09:21 Tag## Show Notes- Steve spent Side Project Saturday migrating phillycocoa.org from a broken CircleCI/S3 setup to Cloudflare Pages, burning his entire weekly Codex token budget in about three hours.- Cloudflare Pages handles Hugo builds automatically and manages SSL and CDN without manual config, all on a free tier that's plenty for the site.- Cloudflare's UI hides the Pages "Get Started" link below giant worker buttons, which Kotaro calls "the weirdest dark pattern."- Steve argues that syntax generation was never the real bottleneck in software engineering, citing Fred Brooks' 1986 essay "No Silver Bullet."- Aaron is worn out from reviewing AI-generated code and still having to make every architectural decision himself.- LLM costs are nearly impossible to forecast: a single prompt can burn a significant chunk of your plan, depending on model, tool calls, and context.- The Trio sees firms rushing to adopt LLM tooling before the ROI math makes sense, driven by hype rather than evidence.- ThePrimeagen's recent take on the shifting AI economy lines up with what Steve sees at work: token-based billing is starting to expose the real cost.- The Trio agrees local models running on personal hardware are the interesting long-term play, but RAM shortages make even basic setups expensive.- Kotaro closes with a dad joke: he thought his LLM skills landed him his current job, but it turns out...## Links**PhillyCocoa.org Update**Website: https://phillycocoa.org**Articles & Essays**"Let's talk about LLMs" by James Bennett: https://www.b-list.org/weblog/2026/apr/09/llms/"No Silver Bullet" by Fred Brooks: https://www.cs.unc.edu/techreports/86-020.pdf**Videos**"The AI economy is about to change" by ThePrimeagen: https://youtu.be/_Q-e_nczWqM**One More Thing**"Beyond the Simulator: Perspectives on Modern App Development": https://luma.com/i00ll61z**PhillyCocoa:** https://phillycocoa.orgIntro music: "When I Hit the Floor", © 2021 Lorne Behrman. Used with permission of the artist.

GopherCon Agenda is live!  Aug 3-6 @ SeattleGo 1.26.3 and 1.25.10 released with 11 security fixesGo + LLM projectsgosymdb: A Go symbol and call-graph database backed by SQLite.cli-bridge: If you want agents to actually use your CLI, this is the missing piece. ★ Support this podcast on Patreon ★

In Season 15 episode 2, Elixir Wizards Sundi Myint and Charles Suggs chat with Micah Cooper to talk about distributed systems, data replication, and what it actually looks like to build these ideas in Elixir.   Micah shares his journey from Ruby to Elixir and walks us through Visor, a library he's building based on the Viewstamps replication algorithm. Inspired by systems like TigerBeetle, Visor explores how you can replicate state across nodes using GenServers, giving you fault tolerance and recovery without relying entirely on traditional database patterns.   We talk about the difference between distributed systems and data replication, where things tend to get misunderstood, and what changes when you start thinking about state this way. The conversation also touches on event sourcing, tradeoffs in system design, and how Elixir's distributed model makes some of these concepts more approachable than you might expect.   Along the way, we talk about building for curiosity, experimenting with new ideas, and how projects like this push the ecosystem forward.   Topics discussed in this episode: Building Visor and working with the Viewstamps replication model Replicating GenServer state across nodes Distributed systems vs. data replication Lessons from TigerBeetle and financial system design Event sourcing challenges and tradeoffs Rethinking database-first architectures Snapshotting, recovery, and fault tolerance The role of Elixir's distributed model Experimentation, learning, and building for curiosity   Links mentioned: Micah's GitHub https://github.com/mrmicahcooper Micah's GitLab https://gitlab.com/mrmicahcooper The Visor repository: https://gitlab.com/mrmicahcooper/visor Visor Hex Package https://hex.pm/packages/visor Ruby on Rails https://rubyonrails.org/ Phoenix LiveView Framework https://www.phoenixframework.org/ Zig Programming Language https://ziglang.org/ TigerBeetle https://tigerbeetle.com/ TigerBeetle internal docs https://github.com/tigerbeetle/tigerbeetle/tree/main/docs/internals The BEAM https://www.erlang-solutions.com/blog/the-beam-erlangs-virtual-machine/ GenServer https://hexdocs.pm/elixir/GenServer.html Apache Kafka https://github.com/apache/kafka RabbitMQ https://www.rabbitmq.com/ Redpanda https://www.redpanda.com/ SQL https://www.ibm.com/think/topics/structured-query-language Kubernetes https://kubernetes.io/ YAML https://yaml.org/ Nomad Workload Orchestrator https://developer.hashicorp.com/nomad Flutter https://flutter.dev/ Commanded https://hexdocs.pm/commanded/Commanded.html Go Programming Language https://go.dev/ Clojure Programming Language https://clojure.org/ Nebulex https://hexdocs.pm/nebulex/Nebulex.html Mnesia https://www.erlang.org/doc/apps/mnesia/mnesia.html Cachex https://hexdocs.pm/cachex/Cachex.html libgraph https://hexdocs.pm/libgraph/Graph.html Horde https://hexdocs.pm/horde/Horde.Registry.html NocFree split keyboard https://www.nocfree.com/ Micah's LinkedIn https://www.linkedin.com/in/micah-cooper-4a737560/ 

Enrique Ibarra, CIO and Head of Business Transformation at GNP, Mexico's largest insurance company, walks through an enterprise-scale pilot of autonomous software development involving roughly 1,000 internal and external developers. The episode examines how agentic AI changes developers' roles from creators to editors and orchestrators.In CXOTalk episode 918, Ibarra explains why AI co-pilots alone were insufficient to modernize a 20-year-old mainframe system, how GNP evaluated the Blitzy autonomous development platform across four real-world use cases, and how developer roles are shifting from creators to editors and orchestrators. The episode covers legacy modernization, enterprise AI adoption, change management, measurable results, and the two-year roadmap to retool the full engineering organization.YOU'LL DISCOVER✅ The CIO's phased human-in-the-loop playbook: target high-effort, low-risk friction points first (documentation, test suites, version upgrades)✅ Measured outcomes: 5 to 10X engineering velocity, near-100% autonomous completion on language upgrades, roughly 80% on frontend modernization✅ Why GNP's 20-year-old mainframe system forced a modernization decision tied to cost and the coming COBOL talent shortage✅ How the pilot was structured across four use cases: Java 8 to Java 21 migration, Angular frontend upgrade, new feature build, and security vulnerability remediation✅ Why autonomous platforms differ from co-pilots, and when to use each (Blitzy for heavy lifting, IDE-based co-pilots for the final 20%)✅ How to encode technical, security, and architectural guidelines as prompt inputs rather than post-hoc review✅ The change management approach that converted skeptical developers into active users within weeks✅ Strategic payoff: shipping new insurance products in weeks rather than months, and shifting IT from maintaining the business to dictating market paceTIMESTAMPS0:00 Introduction and headline results0:39 Why GNP needed to modernize a 20-year-old mainframe system1:15 From coding co-pilots to an autonomous platform2:36 Designing the four-use-case pilot4:26 Autonomous platforms versus vibe coding5:49 What autonomous development means in practice7:24 Encoding security and governance as prompt inputs8:24 Results: velocity, autonomy rates, and the final 20%10:16 How developer roles and daily work change11:19 Managing developer skepticism and change resistance12:25 Advice for CIOs: the phased human-in-the-loop playbook13:34 Strategic business benefits and first-to-market product launches14:58 Rolling out across seven teams and a two-year horizon16:34 Final advice for engineering leaders getting started

Autonomous software development creates a dilemma for leaders in regulated industries: adopt AI coding at scale or fall behind on product velocity without compromising auditability and code quality. In CXOTalk episode 917, Kris Tokarzewski, Group Chief Technology Information Officer at Vitality, describes how a 14,000-employee multinational insurer is rebuilding its software development life cycle around AI. This episode examines the impact of agentic AI on software development in the enterprise.Recorded at Blitzy's headquarters, the conversation examines deterministic code generation, Blitzy's infinite code context, context engineering, test-driven development, and the shifting bottlenecks that surface as throughput accelerates.YOU'LL DISCOVER✅ Why regulated industries require deterministic, auditable code rather than the probabilistic output most AI coding systems generate✅ How Blitzy's infinite code context (ingestion of codebases, engineering standards, and business rules) creates high-quality software aligned with compliance requirements✅ How Vitality reverse-engineers legacy systems with autonomous AI, achieving a measured 5x acceleration over manual methods✅ Why optimizing end-to-end SDLC throughput matters more than local efficiency at any single stage✅ How code review of 50,000 to 100,000-line pull requests becomes the next limiting factor, and how AI reviewers close the gap✅ How test-driven development pairs with autonomous code generation to raise quality and compliance pass rates✅ How the roles of requirements engineers, software engineers, and product teams converge inside an AI-native SDLC✅ How to instrument AI spend against velocity, quality, end-to-end throughput, and customer value rather than isolated gainsTIMESTAMPS0:00 Deterministic code vs. probabilistic AI output0:14 Meet Kris Tokarzewski, Group CTIO of Vitality0:32 Why Vitality is modernizing legacy insurance systems1:30 Event-driven architecture as agentic AI's natural partner3:00 Building an AI-native software development life cycle with Blitzy4:28 Throughput optimization versus local efficiency6:02 Reverse engineering legacy systems and deterministic code generation9:05 Infinite code context: ingesting codebases, standards, and rules10:00 Test-driven development with autonomous code generation10:49 Results: 5x faster legacy reverse engineering13:17 Product, engineering, and DevOps convergence15:04 Roles level up: requirements engineers and software engineers16:18 Reviewing 50,000 to 100,000-line pull requests17:56 Instrumenting AI spend against business outcomes19:16 Executive sponsorship for autonomous development20:16 Advice for CIOs and CTOs adopting AI-driven development

kin-openapi v0.136.0 released with support for OpenAPI 3.1.0git 2.54 releasedgo-gitAccepted proposal: modernize for atomic, embedlit, errorsastype, plusbuild, stringscut, stditeratorsATL Builder Night, May 11Interview with Tomasz Tomczykcrit.md / on GitHubBlogXSuperpowerscontext-mode ★ Support this podcast on Patreon ★

SUMMARY: Exploring how to fully embrace AI-driven, agent-based software development, resulting in dramatically increased productivity and faster feature delivery. It highlights a broader shift in engineering—from writing code to orchestrating AI agents.GUEST: Sam Ramji, CEO/Co-founder at SailplaneSHOW: 1023SHOW TRANSCRIPT: The Reasoning Show #1023 TranscriptSHOW VIDEO: https://youtu.be/q50s0oL37pQSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!SHOW NOTES:Halt and Retool (presentation) OpenAI Harness EngineeringAnthropic Harness Engineering1. The “Halt and Retool” MomentA single-day build and deployment of a production feature triggered a company-wide realizationPaused all development to reassess how AI fundamentally changes engineering workflowsCreating “shock moments” (like stopping work) is key to driving mindset shifts2. From Coding to Agent OrchestrationDevelopers are shifting from writing code → managing AI agentsWork resembles “multi-boxing” or conducting an orchestra of parallel agentsSuccess depends on coordinating tasks, not executing them directly3. The Rise of Harness EngineeringDefined as everything between raw AI prompts and production-ready outputFocus: eliminating friction across the software development lifecycle Key practices:Logging agent errors and friction pointsContinuously refining workflows and toolingLetting AI reflect on and improve its own mistakes4. Spec-Driven Development Becomes CriticalPoor specifications lead to exponential inefficienciesTeams now spend significantly more time on design and specs than coding5. Measuring the Impact~3x increase in code velocityNear-zero “bit rot” Faster feature delivery—sometimes within 24 hours6. Token Maxing & Developer FitnessHigher token usage often signals better workflows and deeper integration with AIPerformance becomes about system design, not efficiency constraints7. New Tools & InterfacesIncreased use of voice interfaces over typingTerminal-first workflows replacing traditional IDE-centric approachesAI-accessible knowledge bases becoming standard8. The Future of Software EngineeringWithin ~6 months: developers may stop writing codeWithin ~12 months: developers may stop reading codeFocus shifts to:Intent, design, and orchestration. Domain expertise and problem modelingFEEDBACK?Email: show @ reasoning dot showBluesky: @reasoningshow.bsky.socialTwitter/X: @ReasoningShowInstagram: @reasoningshowTikTok: @reasoningshow