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. قبل يومين ·  فيديو

    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

  2. ١٨ أغسطس ·  فيديو

    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

  3. ١١ أغسطس ·  فيديو

    The Background Check You Can't Run on an AI Agent

    The thing that makes an agent useful is the exact thing that makes it dangerous. Keycard co-founder Ian Livingstone breaks down why non-determinism is both the feature and the bug, and why identity, not model quality, is what really gates how much autonomy you can hand an agent. If you have ever clicked "always allow" without reading it, this one is about you. What we cover: – Why authentication was enough in the cloud era and stops being enough with agents – The background check you can't run on an agent, and what has to replace it – Mission identity: who is acting, on whose behalf, and for what purpose – Cross App Access, Agent Auth and the protocols trying to fix OAuth – Consent fatigue, LLM as a judge, and where hard boundaries still belong – Why MCP ships with an auth story and CLI tools don't Chapters: 00:00:00 - Introduction 00:03:43 - Why every wave of computing rewrites identity 00:04:50 - The feature and the bug are the same thing 00:10:23 - Why shared secrets break for agents 00:13:36 - The background check you can't run on an agent 00:19:42 - Chargebacks, delegation and proving intent 00:22:16 - Mission identity: a new layer 00:28:07 - Cross App Access, Agent Auth and emerging protocols 00:33:02 - MCP vs CLI, and how Keycard works 00:43:22 - Identity three years from now 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development Where do you draw the hard line for your own agents? Tell us in the comments.

    The Background Check You Can't Run on an AI Agent

  4. ٤ أغسطس ·  فيديو

    Datadog Deleted All Its AI Context. It Worked.

    Datadog's Language Foundations team deleted an entire folder of AI context files that had been carefully written and maintained for over a year, expecting a performance hit. Instead, their evals got better. Simon Boudrias, who runs Language Foundations at Datadog, walks Guy through what that taught his team about context rot, and the full journey of scaling AI coding agents to 4,000 engineers. What we cover: – How Datadog scaled Cursor and Claude Code to 4,000 engineers in under a year – Why Datadog deleted all of its AI context files, and what happened to eval scores – Building an eval-driven code review system that replays old PRs to catch real incidents – Where open-weight models like GLM 5.2 stand against the frontier – Rethinking hiring and career ladders now that AI can run a real codebase interview Chapters: 00:00:00 - Introduction 00:03:19 - Simon's role and Datadog's 4,000-engineer org 00:04:31 - The Cursor rollout that took off overnight 00:07:53 - How Claude Code entered the picture 00:10:51 - Building dedicated Signals and Flows teams 00:11:58 - Why Datadog bet early on evals 00:33:44 - Deleting all their AI context and getting better evals 00:47:51 - Where open-weight models stand today 00:51:45 - Rethinking hiring and career ladders for AI 00:59:24 - The real prize: better decisions, not just productivity 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development What's the oldest file in your AGENTS.md or CLAUDE.md that you're afraid to delete? Tell us in the comments.

    Datadog Deleted All Its AI Context. It Worked.

  5. ٢٨ يوليو ·  فيديو

    Inside the Dark Factory: AI That Ships Code Solo

    At Tessl, 95% of the code shipped by their internal "Dark Factory" has never been looked at by a human, and the team still ships hundreds of pull requests a week, including through entire weekends. Rob Willoughby, who leads AI engineering at Tessl, joins Simon to open up the hood on how it actually works: the orchestrator, the verification layers, and the failures that forced the team to rebuild trust from scratch. What we cover: – How Tessl routes 65-70% of its own pull requests through an autonomous "Dark Factory" – Why context in the repo matters more to output quality than which model you use – How natural language "verifiers" turn code review taste into fast, cheap checks agents can pass or fail – The queue bug that took dozens of pull requests to fix, and the from-scratch Elixir rewrite that stress-tested the whole system – How to start building your own software factory, one verification layer at a time Chapters: 00:00:00 - Introduction 00:01:44 - Rob Willoughby joins: Tessl's PR numbers 00:04:01 - Live demo: kicking off two pull requests 00:14:03 - Building the Dark Factory: orchestrator vs. context 00:19:01 - Code review layers: Code Rabbit, Tessl Change Verify, and verifiers 00:29:58 - Earning trust: accountability in an autonomous system 00:33:20 - What broke: the queue bug and the Elixir rewrite experiment 00:39:56 - Onboarding new engineers into the factory 00:42:58 - Advice for teams starting their own software factory 00:52:03 - Back to the demo, and the road to 100% adoption 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development What would your own verification layer catch, and where would it break? Let us know in the comments.

    Inside the Dark Factory: AI That Ships Code Solo

  6. ٢٣ يوليو ·  فيديو إضافي

    BONUS: Snyk Found Malware Inside AI Agent Skills

    One Snyk developer's AI skill quietly handed their coding agent production credentials, and the security team found out the hard way. Krzysztof Huszcza, who leads AI security incubation at Snyk, joins this special Tessl and Snyk live stream to unpack the ToxicSkills research that uncovered 76 malicious agent skills in the wild, and what it actually takes to run coding agents safely at scale. What we cover: – How Snyk's security team found 76 malicious skills hiding inside a popular open agent skill repository – Why skills have become the go-to way developers hand context to their coding agents – The internal incident at Snyk where a developer's skill exposed production credentials to an agent – How the Tessl and Snyk integration scans every skill and MCP server for risk before you install it – What's coming next with Snyk's new Evo product for governing coding agents at scale Chapters: 00:00:00 - Introduction 00:01:33 - Chris's role: AI security incubation at Snyk 00:02:16 - Snyk's roots as a developer-first security company 00:03:58 - New security challenges from AI coding agents 00:06:26 - Skills: the new way to give agents context 00:07:35 - Inside Snyk's ToxicSkills research: malware and prompt injection 00:11:35 - How developers can vet skills before installing them 00:14:38 - A real incident: exposed production credentials at Snyk 00:17:45 - Building a secure-by-default agent stack 00:23:35 - What's next: Snyk's new coding agent security product 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development What's the riskiest skill you've installed without checking it first? Let us know in the comments.

    BONUS: Snyk Found Malware Inside AI Agent Skills

  7. ٢١ يوليو ·  فيديو

    From Living Room Hack to 30 AI Agents at Cyera

    An engineer builds an AI agent to manage his own life, decides an unrestricted "does everything" agent is too dangerous to trust, and ends up creating the internal agent platform that now runs 30 agents across his entire company.  Ori Shoshan, tech lead at Cyera, walks through the guardrails, citation system, and knowledge graph that turned "let the agent do anything" into "let the agent do exactly what it's supposed to, and nothing else." What we cover: – Why one engineer built his own AI agent in his living room, and how it grew into Cyera's internal agent platform – Whitelisting tools instead of blacklisting them, plus the "escape hatch" that keeps agents honest – Backing every claim with a citation and using a second model to catch hallucinations before they reach a human – Trading RAG for a knowledge graph the agent can walk like a wiki – Turning "use this platform" into "build your own agent" to drive adoption across an entire engineering org – Running agents on confidential data that can investigate everything but can only ever say what's been cleared Chapters: 00:00:00 - Introduction 00:02:04 - Meet Ori Shoshan and what Cyera does 00:06:50 - The living-room spark: why Ori built his own agent 00:11:09 - Whitelisting tools instead of blacklisting them 00:12:55 - Why every claim needs a citation 00:15:35 - Reducing hallucinations with clean-context verification 00:21:04 - Taking Borg to Slack: the first agents at work 00:25:33 - Why naming your agent drives adoption 00:39:53 - Knowledge graphs over RAG 00:52:00 - The AI Captains: scaling adoption with carrots, not sticks 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development Have you built guardrails like this into your own agents? Let us know what's worked (or blown up) for you in the comments.

    From Living Room Hack to 30 AI Agents at Cyera

  8. ١٤ يوليو ·  فيديو

    Patrick Debois Maps the Patterns of AI-Native Dev

    After DevOps, Patrick Debois has a new map. The godfather of DevOps returns to walk through his AI patterns research: how he tracks where AI-native development is heading by mining social signals instead of surveys, why the stack is settling into patterns worth learning, and why your AI budget problem is really an optimization problem. What we cover: – Inside the AI patterns site at Tessl.io/patterns and how it's auto-generated – The four layers of AI enablement: agent, team, platform, and organization – Solo, shared, and multiplayer modes: the compounding effect of shared context – Managing agent costs: budgets as a forcing function for optimization – Scaling adoption: find the success story, don't fight the skeptics – Hiring system thinkers instead of elegant coders Chapters: 00:00:00 - Introduction 00:02:28 - Why AI grips Patrick Debois 00:04:33 - Agent, team, platform and org enablement 00:06:36 - Is AI-native development maturing? 00:11:19 - Inside the AI patterns site 00:18:14 - Quality, security and the IDE as review interface 00:22:45 - Changing roles and harness engineering 00:26:32 - Hiring in the AI era 00:30:31 - Scaling adoption across the org 00:42:03 - Cost management and ROI 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development Where is your organization on the maturity curve? Drop your take in the comments.

    Patrick Debois Maps the Patterns of AI-Native Dev
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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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