The AI Native Dev - from Copilot today to AI Native Software Development tomorrow

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Welcome to The AI Native Developer, hosted by Guy Podjarny and Simon Maple. Join us as we explore and help shape the future of software development through the lens of AI. In this new paradigm of AI Native Software Development, we delve into how AI is transforming the way we build software, from tools and practices to the very structure of development teams.Our target audience includes developers and development leaders eager to stay ahead of the curve. If you're passionate about the future of software development and curious about how to leverage AI to build effective teams and groundbreaking software, this podcast is for you.Each week, we bring you insights into the latest AI tools and best practices, keeping you up-to-date with the cutting-edge advancements in the industry. Additionally, every two weeks, we present deep dives with experts and leaders in the AI and software development space, offering a glimpse into the future of AI development.Tune in to discover how AI will revolutionize your workflows, roles, and organizations. Get inspired by the latest tools and best practices, and prepare to be part of the next generation of software development.

  1. 22h ago ·  Video

    Legora CTO: Why AI Agents Need More Than a Chat Window

    AI coding agents now write almost all of Legora's code, with engineers running up to ten in parallel. Yet the CTO behind that shift thinks measuring adoption by tokens burned is a mistake. Jacob Lauritzen, CTO of Legora, joins Simon Maple to unpack what actually scales when agents do the typing, and where human judgment still has to stay in charge. What we cover: – How Legora's engineers orchestrate many AI coding agents at once, from shared local dev resources to cloud agent setups – An agentic loop that reproduces, fixes and tests bugs straight from Slack – Why token maxing is a bad way to drive AI adoption, and what to measure instead – Agent evaluation: regression testing the harness as models and prompts change every week – Decision models, the verifier's rule, and where autonomy ends and human judgment begins – Why AI agents need more than a chat interface Chapters: 00:00:00 - Introduction 00:01:47 - Legora's growth from $100M to $200M ARR 00:03:44 - Parallel coding agents and bug-fix loops 00:07:15 - Senior engineers vs AI-first engineers 00:10:10 - Why token maxing is the new lines of code 00:14:13 - What AI can and can't verify in legal work 00:17:30 - Decision models vs autoregressive LLMs 00:24:22 - Weekly model benchmarks and harness evals 00:26:58 - Why AI agents need more than a chat interface 00:35:03 - The verifier's rule and litigation strategy Build your software factory, one workflow at a time, with Tessl: https://tessl.co/lpg 🔔 Subscribe for weekly episodes on AI-native development Is token count telling you anything real about how your team uses AI coding agents? Tell us in the comments.

    Legora CTO: Why AI Agents Need More Than a Chat Window

  2. Sep 29 ·  Video

    Liz Fong-Jones: 2x the PRs, 1.5x the Incidents

    Honeycomb went from 30 to 70 merged pull requests a day in three months. The catch: automated code review, not code generation, became the real bottleneck of software automation. Liz Fong-Jones, Technical Fellow at Honeycomb, explains why incidents still rose 1.5x, how their internal bot Autobot now reviews every PR, and why AI amplifies whatever org you already have. What we cover: – How automated code review lets humans focus on design, not trivial bugs – Using a decision model like Jev to decide which PRs are safe to auto-merge – What makes a codebase ready for AI coding agents – When to trust AI agents with production incidents, and when they're just throwing darts – Why "Claude did it" isn't an excuse, and what ownership means with AI – How open source maintainers can handle a flood of AI slop pull requests Chapters: 00:00:00 - Introduction 00:06:41 - Why AI amplifies dysfunctional engineering orgs 00:10:45 - What makes a codebase AI ready 00:14:01 - Honeycomb's Autobot and automated code review 00:19:06 - Using Jev to decide which PRs are safe to merge 00:26:51 - Trusting AI agents during production incidents 00:30:40 - Least privilege and guardrails for coding agents 00:33:25 - If your name's on it, you own it 00:38:55 - AI slop pull requests and open source 00:45:56 - Will observability engineering survive as a role? Build your software factory, one workflow at a time, with Tessl: https://tessl.co/nlr 🔔 Subscribe for weekly episodes on AI-native development Is your team's review capacity keeping up with your AI coding agents? Tell us in the comments.

    Liz Fong-Jones: 2x the PRs, 1.5x the Incidents

  3. Sep 22 ·  Video

    Dexter Horthy: Why We Stopped Trusting AI to Write the Plan

    HumanLayer CEO Dexter Horthy on why his team's bet on spec driven development nearly wrecked their own codebase, and what changed his mind about reviewing AI-written code. Dexter walks through the "YOLO pull request" experiment that ran for months before the codebase became unusable, the "dumb zone" that limits how much context you can hand a model, and why he now believes there will always be alpha in reviewing something. What we cover: – How HumanLayer's "read the plan, skip the code" experiment quietly broke their own codebase – Why context windows have a "dumb zone," and what that means for context engineering – Sean Grove's idea that specs, not code, are becoming the durable artifact – Building overnight agents that review and fix code before a human ever sees it – Why treating software development like a factory changes how bugs compound – What Dexter thinks his advice on reviewing AI-written code will look like in 12 months Chapters: 00:00:00 - Introduction 00:02:15 - Meet Dexter Horthy, CEO of HumanLayer 00:06:03 - The "dumb zone": why more context makes models dumber 00:06:40 - Sean Grove's "the spec is the new code" 00:09:22 - The YOLO pull-request experiment that broke their codebase 00:11:09 - Letting the model own the architecture 00:17:03 - Planning as expected-pain management 00:28:15 - Slop Code Bench and the maintainability oracle problem 00:37:59 - Why software factories aren't like car factories 00:42:49 - "There will always be alpha in reviewing something" Build your software factory, one workflow at a time, with Tessl: https://tessl.co/4ep 🔔 Subscribe for weekly episodes on AI-native development Where do you land on the determinism-to-adaptability slider — plan tightly, or let the agent run? Tell us in the comments.

    Dexter Horthy: Why We Stopped Trusting AI to Write the Plan

  4. Sep 16 ·  Video

    AWS's Marc Brooker: Specs, Not Code, Are the Hard Part

    Spec-driven development is reshaping what software engineers actually do all day, and Marc Brooker, VP and Distinguished Engineer at AWS, has read 3,000 to 4,000 postmortems that convinced him the code was never the hard part. In this episode of The AI Native Dev, Marc explains why testing and specification are now the real engineering work, why metastable failures keep taking down systems that look perfectly healthy, and why he still won't let AI write a single word of his blog. What we cover: – Why spec-driven development is turning testing into the hardest part of software – What metastable failures are, and why they keep taking healthy-looking systems down – How agentic coding tools can learn from postmortems and build their own memory – Why classic authorization breaks down once you're writing agentic policy – Why Marc won't let AI write his blog, but is fully comfortable with AI-generated code – What it takes to bring junior engineers up to speed in an AI-native industry Chapters: 00:00:00 - Introduction 00:02:17 - Marc Brooker: 18 years at AWS building agentic dev tools 00:04:36 - Inside Strands, AWS's open source agent SDK 00:06:44 - Fifteen years on call, and what agents still can't debug 00:11:23 - Metastable failures and the humility of 4,000 postmortems 00:14:32 - Teaching agents to learn from postmortems and build their own memory 00:20:14 - Why agentic policy needs more than classic authorization 00:32:14 - The agentic software development hypothesis: spec-driven development, oracles, and testing 00:39:56 - Why Marc won't let AI write his blog (but will let it write code) 00:46:37 - Advice for leveling up junior engineers in the AI era Build your software factory, one workflow at a time, with Tessl: https://tessl.co/[SLUG] 🔔 Subscribe for weekly episodes on AI-native development Do you trust an agent to run its own on-call rotation yet? Tell us in the comments.

    AWS's Marc Brooker: Specs, Not Code, Are the Hard Part

  5. Sep 8 ·  Video

    You Don't Need Juniors to Code. Hire Them Anyway.

    Coding agent reliability isn't a model problem, it's a gates problem. Ran Aroussi, creator of yfinance and founder of Automaze, argues that an "agentic workflow" is a contradiction in terms, and that the only thing standing between you and full autonomy is deciding where a human still signs off. He also thinks you stopped needing junior developers for their coding skills about a year ago, and that hiring them anyway is the only way the industry gets its next generation of architects. What we cover: – Why "agentic workflow" is a contradiction, and when agentic coding is the wrong tool for the job – Where a human still has to sign off, and what coding agent reliability actually depends on – What a software factory changed about delivery times, pricing and the client backlog – Why you no longer need juniors for their coding skills, and why you should hire them anyway – Teachable knowledge vs earned knowledge, and the experience agents cannot compress – The law firm model: why dev agencies may end up looking more like Kirkland & Ellis than a SaaS startup Chapters: 00:00:00 - Introduction 00:03:05 - yfinance, open source and 30 million downloads a month 00:06:03 - Automaze and the MUXI agent application server 00:10:42 - Where to draw the line on agent autonomy 00:15:12 - Software factories and fully autonomous merges 00:22:25 - The architect archetype and the four-phase ladder 00:28:11 - Teachable knowledge vs earned knowledge 00:30:14 - What agents did to delivery times and pricing 00:37:03 - Why "agentic workflow" is an oxymoron 00:41:08 - Anthropic, OpenClaw and the end of flat subscriptions Build your software factory, one workflow at a time, with Tessl: https://tessl.co/vut 🔔 Subscribe for weekly episodes on AI-native development Where's your gate? Tell us in the comments where you still refuse to let a coding agent merge without a human looking.

    You Don't Need Juniors to Code. Hire Them Anyway.

  6. Sep 2 ·  Video

    850 PRs a Week: How Tessl Runs a Software Factory

    The software factory that put up 850 pull requests in a week, with 85 to 90% of them handled by agents end to end. Tessl's Head of Product Dru Knox on how it actually got built, why it started with skills rather than automation, and the two things nobody expects a factory to change. Guy Podjarny digs into the skills-to-loops-to-factory path, and what breaks when you skip a step. What we cover: – What a software factory actually is, and how it differs from a pile of disconnected automations – Loop engineering: turning a skill into something that gets better every time it runs – Why context engineering has to come before automation, and what stalls when it doesn't – The two gains nobody forecasts from extra agent capacity: quality and fungibility – Verifiers and evals, or how you enforce a standard that agents are free to ignore – Context-driven code review, and why a general-purpose reviewer plateaus Chapters: 00:00:00 - Introduction 00:03:23 - Kikimora: Tessl's own dark factory 00:04:45 - Skills, loops and factories, defined 00:09:09 - 850 PRs a week, and the road to it 00:11:27 - The surprises: quality and fungibility 00:13:16 - Why a factory has to be context centric 00:16:40 - Context-driven code review and the wall teams hit 00:22:51 - Skills inventory across your whole code estate 00:28:38 - Verifiers, evals and enforcing what you wrote down 00:37:30 - Loops, automations and owning your software factory Build your software factory, one workflow at a time, with Tessl: https://tessl.co/coi 🔔 Subscribe for weekly episodes on AI-native development If you've hit the plateau Dru describes, tell us where it stopped compounding for you.

    850 PRs a Week: How Tessl Runs a Software Factory

  7. Aug 25 ·  Video

    Lada Kesseler: I Trust AI Tests Less Than AI Code

    Lada Kesseler's approach to loop engineering is brutally simple: give the agent one step, make it write the result to a file, then read it back and go again. She calls it a centrifuge. Spin fast enough and the stupid comes out. The Logic20/20 principal engineer has spent the last year and a half building the ground rules, skills and refinement loops that fight the defaults coding agents ship with, starting with an instruction to stop trying to please her. Along the way she explains why she trusts AI-written tests less than AI-written code, why the description field in an agent skill was never meant for humans, and why most teams chasing a software factory are about to drown in garbage. What we cover: – Why agent ground rules have to give the model a mission to disagree with you – The refinement loop that gets to quality when the first attempt never does – Why an agent harness only works if it does one thing at a time – Sketch prototypes: replacing your code with a markdown file and an agent – Why AI-generated tests are more dangerous than AI-generated code – Software factories, and what happens when you try to switch into one overnight *AI DevCon is coming back to New York this November* Get 15% off your ticket with code POD15 https://tessl.io/devcon#tickets Chapters: 00:00:00 - Introduction 00:03:38 - Using AI for everything, not just code 00:06:15 - Ground rules: fixing the defaults agents ship with 00:10:02 - Agent skills and why the description isn't for humans 00:13:09 - Loop engineering: how you actually get to quality 00:16:03 - The biggest misconception about coding agents 00:20:43 - Sketch prototypes and the limits of AI architecture 00:27:48 - Reverse direction: you are the decider 00:31:58 - Why she trusts AI tests less than AI code 00:35:00 - Software factories, or drowning in garbage Build your software factory, one workflow at a time, with Tessl: https://tessl.co/k2t 🔔 Subscribe for weekly episodes on AI-native development Where are you on the loop engineering curve, still hoping for a good first try or already spinning? Tell us in the comments.

    Lada Kesseler: I Trust AI Tests Less Than AI Code

  8. Aug 18 ·  Video

    Every Repo Is a Software Factory Now | Don Syme, GitHub

    Turning every repo into a software factory sounds like marketing until you see the machinery underneath. Don Syme, Principal Researcher at GitHub, breaks down continuous AI and GitHub Agentic Workflows, now in public preview. The short version: the agents are the easy part, the quality gates and guardrails are the work. Watch Don's AI DevCon Talk here: https://youtu.be/kbvqRWY-bUs What we cover: – What continuous AI is, and why it sits beside CI/CD rather than inside it – Why bounding the context is what stops automated agents going off the rails – Whether the repo is really the right unit for a software factory, and where monorepos break it – One workflow or an agent zoo? Cost control, scheduling and model exams – Quality gates, human review, and equipping the reviewer instead of drowning them Chapters: 00:00:00 - Introduction 00:03:52 - What is continuous AI? 00:07:33 - From single player to multiplayer automation 00:09:42 - Bounding the context so agents don't go off the rails 00:12:57 - Software factories, loops and harnesses 00:18:44 - Inside GitHub Agentic Workflows 00:25:19 - Why the repo became the unit of production 00:37:11 - What belongs in the repo, and what doesn't 00:45:07 - One workflow or an agent zoo? Cost control in the factory 00:52:01 - Quality gates, human review and equipping the reviewer 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development If you're building a software factory of your own, tell us in the comments where your bottleneck actually sits.

    Every Repo Is a Software Factory Now | Don Syme, GitHub
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Welcome to The AI Native Developer, hosted by Guy Podjarny and Simon Maple. Join us as we explore and help shape the future of software development through the lens of AI. In this new paradigm of AI Native Software Development, we delve into how AI is transforming the way we build software, from tools and practices to the very structure of development teams.Our target audience includes developers and development leaders eager to stay ahead of the curve. If you're passionate about the future of software development and curious about how to leverage AI to build effective teams and groundbreaking software, this podcast is for you.Each week, we bring you insights into the latest AI tools and best practices, keeping you up-to-date with the cutting-edge advancements in the industry. Additionally, every two weeks, we present deep dives with experts and leaders in the AI and software development space, offering a glimpse into the future of AI development.Tune in to discover how AI will revolutionize your workflows, roles, and organizations. Get inspired by the latest tools and best practices, and prepare to be part of the next generation of software development.

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