The Merge (by CodeRabbit)

CodeRabbit
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The Merge by CodeRabbit is a podcast that brings you deep conversations with legendary developers who've shaped the tools we use every day. We explore how artificial intelligence is transforming software development while celebrating the creators and tools that built our foundation. Each episode features intimate discussions about building developer tools, maintaining open source projects, and navigating the evolution of technology.

  1. 17h ago

    What should you learn? | Kunal Kushwaha

    Which skills will matter most for software engineers as AI takes on more of the coding? Kunal Kushwaha joins The Merge to discuss how developers can stay relevant, what beginners should learn, and where experienced engineers can bring the most value. Kunal shares how contributing to Kubernetes helped him build his career, how he got into developer education, and what he’s learned from running hackathons through WeMakeDevs. We discuss why communication, technical fundamentals, and understanding what users need deserve as much attention as the latest coding tools. We also debate how much engineers should trust AI-generated code. When agents can write and review a pull request, what should the human still check? And who takes responsibility when something breaks? In this episode:• Building experience through open source before landing your first job• Using AI to learn without losing your understanding of the code• Developing product judgment and stronger problem-solving skills• What successful hackathon teams do differently• Reviewing AI-generated code and understanding the risks of a change• Why Kunal recommends learning to run LLMs locally• Practical advice for junior and experienced software engineers Kunal Kushwaha is the founder of WeMakeDevs, a CNCF Ambassador, and a GitHub Star. Explore WeMakeDevs and its hackathons:https://www.wemakedevs.org/ Learn more about CodeRabbit:https://www.coderabbit.ai/ What are you spending more time learning as AI changes your workflow? Share your experience in the comments, and subscribe to The Merge for more conversations with people building software. #SoftwareEngineering #KunalKushwaha #TheMerge

    What should you learn? | Kunal Kushwaha

  2. Oct 1

    Jev Explained: Intelligence infused 'If-Statements' for Your Code | Allie form TypeSafe AI

    What is Jev AI, and how do you actually use it? TypeSafe's Allie Laabs explains how Jev works as a "smart if statement" for your code, how to find Jev-shaped problems in your app, and why coding agents still get it wrong. Recorded at CodeRabbit HQ in San Francisco during the first Jev hackathon, run by TypeSafe and the AI Collective, just 11 days after launch. Most apps use LLMs the long way: turn program state into a prompt, get text back, then parse that text into a value the code can branch on. Jev skips the text. You send a state (a ticket, a transaction, a tool call) plus typed questions, and you get back a choice, a score, or a yes/no probability. There's nothing to parse, and it can't answer with an option you didn't allow. In this episode of The Merge:→ What Jev AI is and how it's different from an LLM→ Jev use cases: classification, routing, and "gut feeling" logic you couldn't write before→ How to spot where Jev belongs in your code, even if you vibe code→ Speculative prompting: why you should ask every question in one call→ The one-sentence fix Allie gives coding agents when they write Jev code→ Jev vs LLMs: when it's the wrong tool, and when it's the right one→ Generative UI, real-time use cases, and what builders made at the hackathon CHAPTERS0:00 Meet Allie Laabs, DevRel at TypeSafe0:18 Inside the Jev launch2:34 TypeSafe's biggest challenge right now6:16 What is Jev AI? Skipping the text10:10 Jev use cases: smart if statements13:48 Jev use cases that surprised the team17:05 Generative UI and more accessible software20:02 Jev vs LLMs: right tool, wrong tool22:17 How to use Jev: practical tips26:53 Speculative prompting and coding agents30:08 What builders are making at the Jev hackathon LINKS🧠 Jev docs: https://docs.typesafe.ai/introduction⚡ Speculative fan-out pattern: https://docs.typesafe.ai/patterns/fan-out🐰 Try CodeRabbit for AI code reviews: https://coderabbit.ai📚 CodeRabbit path instructions: https://docs.coderabbit.ai/configuration/path-instructions ABOUT THE MERGEThe Merge is CodeRabbit's interview series with the people building the tools developers use, from AI models to coding agents. #JevAI #TypeSafe #AIcoding

    Jev Explained: Intelligence infused 'If-Statements' for Your Code | Allie form TypeSafe AI

  3. Sep 25

    Your Tests Pass. Can Your AI Agent Finish the Job? | Francesco Bonacci, Cua

    AI agents can click, type, and operate software—but how do you know they actually finished the job? Cua co-founder and CEO Francesco Bonacci joins The Merge to discuss computer-use agents, agent evaluation, and the human judgment behind reliable automation. Hendrik Krack and Francesco explore what it takes to give AI agents computers they can work in—and how to check the results. From testing computer controls to evaluating real tasks in KiCad, the conversation examines the gap between an action succeeding and an assignment being complete. Francesco also shares his experiments with bot-driven development through Slack, including a bot CTO and chief of staff, and explains why he stays close to his coding agents to steer their work. In this episode:• Computer-use agents and the infrastructure behind them• Why working tools don’t guarantee successful tasks• Agent evaluation, Cua-Bench, and circuit-design tasks in KiCad• Catching regressions while shipping quickly• Isolated environments and protecting sensitive data• Delegating to bots while retaining human judgment• Why visibility matters: how repeated attempts led to five identical customer emails What task would you trust an AI agent to handle today—and what would you still check yourself? Tell us in the comments. Explore Cua:https://cua.ai/ Cua on GitHub:https://github.com/trycua/cua Learn about computer use:https://cua.ai/docs/concepts/what-is-computer-use Explore CodeRabbit:https://www.coderabbit.ai/ The Merge is CodeRabbit’s podcast about the people building the future of software. Subscribe for more conversations with founders, engineers, and open-source builders. #AIAgents #ComputerUse #TheMerge

    Your Tests Pass. Can Your AI Agent Finish the Job? | Francesco Bonacci, Cua

  4. Aug 31

    The Last Software Engineer: What AI Can’t Replace | Kent C. Dodds

    AI coding is changing software engineering—but when agents can implement the code, what remains uniquely valuable? Kent C. Dodds joins Hendrik Krack on The Merge to discuss open source, coding agents, product engineering, React, Remix, and why the “last software engineer” will be the person who understands what to build. Kent has spent his career teaching web development and working in open source. In this conversation, he explains how AI is making implementation cheaper, why developers need stronger product judgment, and how his agent-to-PR workflow uses CI and CodeRabbit for independent review while he evaluates system design, migrations, and downstream effects. In this episode: • Kent’s path through PayPal, open source, React, Remix, and Epic Web• Why coding agents change the developer’s role• The “last software engineer” thought experiment• Product engineering versus ticket taking• Choosing the right problem before writing code• What junior developers should learn in the AI era• Reviewing systems, not only diffs• Kent’s AI coding workflow with pull requests, CI, and CodeRabbit• Durable skills: judgment, communication, ownership, and feedback loops Learn more about Kent C. Dodds: https://kentcdodds.com/Epic Product Engineer: https://www.epicproduct.engineer/Epic AI: https://www.epicai.pro/Epic Web: https://www.epicweb.dev/Try CodeRabbit: https://www.coderabbit.ai/ What do you think the last valuable software engineer will still do? Share your answer in the comments, and subscribe for more conversations with the builders shaping open source and AI-powered software development. #AICoding #SoftwareEngineering #OpenSource

    The Last Software Engineer: What AI Can’t Replace | Kent C. Dodds

  5. Aug 27

    All the alpha is in the remaining 20%

    How does a hackathon project become infrastructure for more than 2,000 open-source repositories and reach 1.4 million monthly npm downloads? In this episode of The Merge, Hendrik Krack sits down with Simon Farshid, founder of Assistant UI, to explore what it really takes to build production-ready AI chat for AI agents. Simon explains why the visible chat window is only the surface. Behind it are streaming responses, state management, message editing, attachments, voice, interruptions, agent backends, and the level of polish users now expect from every AI product. The conversation also examines how AI coding agents are changing software engineering. When implementation becomes easier, writing clear specifications, understanding the product, reviewing edge cases, and having good taste become the real competitive advantages. As Simon puts it: “All the alpha is in the remaining 20%.” In this episode: • Why state management is the hardest part of AI chat  • How Assistant UI grew from a hackathon project  • The future of AI agents and generative interfaces  • Why product taste is becoming an essential engineering skill  • TypeScript vs. Python for building AI agents  • How coding agents are creating more full-stack engineers  • Simon’s automated CodeRabbit review and repair loop  • How Assistant UI approaches open source and monetization  • Why great engineers will lean forward instead of letting AI do everything   CHAPTERS 00:00 — The 80% trap and the rise of product taste  01:19 — Meet Simon Farshid  01:56 — What is Assistant UI?  02:44 — From hackathon project to startup  03:32 — 2,000 repositories and 1.4M monthly downloads  05:28 — Why building AI chat is harder than it looks  07:23 — Designing a modular open-source product  08:43 — Why taste is the new engineering bottleneck  10:29 — TypeScript vs. Python for AI agents  13:24 — How coding agents are changing engineering teams  16:25 — Building a business around open source  20:38 — The future of AI chat and generative interfaces  23:17 — “They said chat was dead”  27:38 — Generative dashboards and new UI primitives  29:28 — The future role of software developers  30:36 — Running coding agents in a CodeRabbit review loop  34:10 — Lean forward: where the remaining 20% lives  35:57 — Rapid-fire questions   Explore Assistant UI:https://www.assistant-ui.com/ Assistant UI on GitHub:https://github.com/assistant-ui/assistant-ui Try CodeRabbit:https://www.coderabbit.ai/ Subscribe for more conversations with the engineers and founders building the future of software development. #AIAgents #AIChat #AICoding

    All the alpha is in the remaining 20%

  6. Aug 20

    We still don't understand LLMs today.

    How do LLMs actually work—and why do they remain black boxes?In this episode of Merge, Shriyash “Yash” Upadhyay, co-founder of Martian, joins CodeRabbit to explore LLM interpretability, AI research, code review benchmarks, and the search for the “steam engine of AI.” We discuss:- Why we still don’t fully understand how LLMs work- How Martian is researching machine intelligence- Why code review is a crucial test of AI code generation- How precision and recall shape AI code-review performance- Why static AI benchmarks eventually become unreliable- How real-world developer behavior can improve evaluations- What more reliable and interpretable AI could unlock- The tools and programming languages Yash uses in his own work- How aspiring researchers can get started in machine learning Today’s language models can generate code, solve complex problems, and power increasingly autonomous systems. But without understanding why they succeed, when they will fail, and how their internal mechanisms produce their outputs, building AI systems we can truly trust remains difficult. Could interpretability provide the scientific foundation for the next generation of AI? Learn more about Martian: https://withmartian.com/ Learn more about CodeRabbit: https://coderabbit.ai/ Subscribe for more conversations about AI, software engineering, code review, and the future of developer tools. #LLM #AIInterpretability #ArtificialIntelligence

    We still don't understand LLMs today.

  7. Jul 16

    The Last Skill Software Engineers Need to Survive the AI Era

    Is coding dead? As autonomous AI agents get better at generating code, the actual implementation of software is becoming incredibly cheap. But what is the last skill that keeps us as humans valuable? In this episode, we sit down with legendary software educator Kent C. Dodds to discuss the critical transition from traditional software development to Product Engineering. If you are wondering how to protect and grow your software engineering career in the age of agentic AI, Kent shares a masterclass on how to build user empathy, develop "product sense," and master the ultimate skill: knowing what to build, not just how to build it. If you are a web developer, software engineer, or tech leader trying to navigate the future of your career, this is the most important conversation you will listen to this year. What You'll Learn in This Episode:The Rise of the Product Engineer: Why traditional coding is getting automated, and why "product sense" is the ultimate survival skill.The "Problem Tree" Framework: How to stop falling in love with your code and start identifying the right problems to solve.Empathy Tactics for Developers: Practical, low-cost habits (like watching users live and "having lunch with support") to instantly build better product instincts. The Blurring Line Between PM & Engineer: How roles are shifting as technology trade-offs and user insights merge.How to Career-Proof Your Future: Actionable advice for mid-to-late career developers preparing for the next wave of tech disruption. Timestamps:0:00 - Is Coding Dead? The Reality of AI Agents2:15 - What is an "Epic Product Engineer"?4:50 - Why Users Give You Solutions, Not Problems7:30 - The "Problem Tree" Framework Explained10:15 - Software Engineer vs. Product Manager: The New Blended Role13:40 - How to Develop True User Empathy (Stop Over-Engineering!)16:50 - Why Customer Support is a Goldmine for Developers19:20 - Career Advice: How to Prepare for the AI Wave Resources & Links Mentioned:Learn more about Epic Product Engineer: https://www.epicproduct.engineerFollow Kent C. Dodds on Twitter/X: https://x.com/kentcdoddsTry CodeRabbit for free: https://coderabbit.link/themerge  Subscribe for more episodes on the future of software engineering, AI coding tools, and product design!#SoftwareEngineering #ProductEngineering #AI #Coding #WebDevelopment #ProductSense #SoftwareDeveloper #TechCareers

    The Last Skill Software Engineers Need to Survive the AI Era

  8. Jun 18

    How Founders Actually Engineer Product-Market-Fit

    In this episode of The Merge, we sit down with Michael Grinich, founder and CEO of WorkOS. Checkout Coderabbit: https://coderabbit.link/themerge Michael shares one of the best explanations of Product-Market Fit we’ve heard: it’s not the finish line — it’s more like getting the Mario Star. Once you have it, you become untouchable enough to survive your own biggest mistakes… but only if you keep innovating. We also dive into:- How to actually engineer PMF instead of just hoping for it- Crossing the enterprise chasm that kills most startups- Why product engineers are outperforming traditional teams- The new authentication and permissions challenges for AI agents- His optimistic (and very practical) view on the future of software development If you're building a startup or scaling a product in the AI era, this conversation is full of actionable insights. Connect with Michael:X/Twitter: https://x.com/grinichWorkOS: https://workos.com Timestamps:00:00 - Intro02:45 - The Mario Star theory of Product-Market Fit08:30 - How to engineer PMF instead of chasing it15:10 - Crossing the enterprise chasm22:40 - Product engineers vs traditional teams31:00 - Building for AI agents & the new auth problem42:15 - Michael’s take on the future of software development

    How Founders Actually Engineer Product-Market-Fit
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The Merge by CodeRabbit is a podcast that brings you deep conversations with legendary developers who've shaped the tools we use every day. We explore how artificial intelligence is transforming software development while celebrating the creators and tools that built our foundation. Each episode features intimate discussions about building developer tools, maintaining open source projects, and navigating the evolution of technology.

Information

  • Creator
    CodeRabbit
  • Years Active
    2024 - 2026
  • Episodes
    21
  • Rating
    Clean
  • Copyright
    © 2026 CodeRabbit
  • Show Website
    The Merge (by CodeRabbit)

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