The AI Native Dev - from Copilot today to AI Native Software Development tomorrow
The AI Native Developer, hosted by Guy Podjarny and Simon Maple, explores the future of software development through the lens of AI. The podcast covers how AI is transforming tools, practices, and team structures, targeting developers and development leaders. Weekly episodes provide insights into the latest AI tools and best practices, while bi-weekly deep dives feature experts and leaders in AI and software development. The show aims to help listeners leverage AI to build effective teams and groundbreaking software.
Episodes
Maria Gorinova: Feedback Loops That Fix Your Codebase
Agent feedback loops are how a software factory maintains itself: agents mining their own session logs, PR comments and production signals, then fixing what they find. Maria Gorinova, member of technical staff at Tessl, explains how to build them, and why vague intent turns a loop into a mess.What we cover:– What a software factory is: repeatable workflows, a shared execution environment and agent
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 o
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
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
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 lo
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
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:
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 w
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-bUsWhat we cover
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 authenti
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
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
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.W
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 "
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/pattern
Inside Anthropic: How Claude Tag Is Changing Agentic Work
Six people reacted to Boris's side-project Slack post. A year later, Claude Code is ubiquitous, and the company just launched its next evolution: Claude Tag, an AI teammate that lives in Slack.Lamis Mukta, Member of Technical Staff at Anthropic, joins Simon Maple to unpack how Claude Tag works, why Anthropic built it, and what it took internally to go from a scrappy side project to a company-
The Tessl Agent: Build Your Software Factory on Autopilot
What if the whole point of your AI agent was to eventually make itself redundant? Dru Knox, Head of Product at Tessl, introduces the Tessl agent — a new interface built not just for AI-assisted coding, but for building the software factory that keeps improving without constant human input.This is a conversation about loop engineering: how to set up automated feedback cycles so your agents get smar
Why Agents Are Forcing Enterprises to Finally Fix Their Dev Process
Enterprises are finally being forced to care about their software development lifecycle — not because anyone suddenly got disciplined, but because agents cost money and the waste is now visible. When it was humans, it was "Timmy's just lazy." Now it's a line item. Simon Maple sat down with Patrick Debois (the godfather of DevOps, now DevRel at Tessl), Tammuz Dubnov (co-founder
BONUS: DevCon London: Real Talk on AI ROI, Harnesses & Evals
From the expo floor of AI Native DevCon London, Simon Maple went straight to the developers — speakers, attendees, and sponsors — to ask what's actually working with AI in 2026. The verdict? Outcomes beat outputs every time, 4,000-hour workloads are collapsing to 20 minutes, and the real bottleneck isn't code.This is a conference floor walkthrough: honest, unscripted takes on harness eng
AI Security & the Agent-Ready Web: Experts Weigh In
What does it mean to build securely when agents can negotiate their own guardrails? And what happens to the web — CLIs, frameworks, even the browser itself — when the primary user is no longer human? At AI Native DevCon London, Simon Maple sat down with two panels of experts to find out. First: a security roundtable with Joseph Katsioloudes from GitHub, Liran Tal from Snyk, and John Groetzinger fr
Ryan Lopopolo: OpenAI's Framework for Shipping Code at 70 PRs/Week
Most engineering teams are still arguing about whether to use AI coding agents.Ryan Lopopolo's team at OpenAI shipped an entire product with no human-written code — and onboarding a new engineer made the team faster within two weeks.That outcome didn't come from better prompts. It came from what Ryan calls Harness Engineering: the systems, constraints, and feedback loops that sit around
Why Developers Hit a Wall at 4 AI Agents
Engineering teams are shipping twice as many pull requests with AI — but merge rates on AI-generated PRs have dropped from 80% to 60%. Nick Arcolano, Head of AI & Research at Jellyfish, sits on one of the most comprehensive datasets in the industry: 250,000 developers, 40 million data points, monthly benchmarks on real agentic coding adoption across enterprise companies. What he's seeing
Don't Secure the Code. Secure the Coder.
AI agents don't just write insecure code — they can escape their sandboxes, delete files, and do whatever it takes to complete a task. The security mental model that served us through the cloud era isn't enough anymore. Guy Podjarny, founder of Snyk and CEO of Tessl, made the case at London's AI Security Summit: it's time to stop securing the code and start securing the coder.R
The Hidden Security Risks of AI Coding Agents
Your AI coding agent has access to your secrets, pulls in content from the outside world, and can run shell commands. According to Joe Holdcroft, that combination makes you one prompt injection away from a very bad time. The tools haven't changed the fundamentals of security — they've just made every existing risk move faster, and introduced a few genuinely new ones. What we cover:Why th
"AI Doesn't Stand for Artificial Intelligence" — Venkat Subramaniam's Take Will Change How You Think About It
Is AI actually intelligent — or just very fast at guessing based on bad data? Venkat Subramaniam, 40-year programming veteran, educator, and co-founder of Arc of AI, joins the AI Native Dev Podcast to share a perspective that cuts through the hype: AI stands for Accelerated Inference — not Artificial Intelligence. And that reframe changes everything about how developers should use it. In this epis
The Creator of Spring Thinks You Can't Code Serious Software With AI
Rod Johnson — the creator of Spring Framework and founder of Embabel — joins Simon Maple on the AI Native Dev Podcast to share his unfiltered take on where enterprise AI is actually heading.In this episode, Rod breaks down why enterprises are making a huge mistake rewriting Java apps in Python, why vibe coding will destroy your codebase if left unchecked, and why this might be the last generation
What OpenAI, Stripe & ElevenLabs Devs Do Differently Now | AI Native Dev
How aligned are teams at Google DeepMind, OpenAI, Stripe, and ElevenLabs on what’s changing in software development?At AI Engineer London, with 100+ speakers and 1000+ engineers in the room, Simon Maple pulls together perspectives from across the ecosystem to understand where AI-native development is heading.• why traditional CI/CD “is dead”• the growing need for automated code review and guardrai
Logan Kilpatrick on Who Ships AGI, DeepMind and the Problem With More Software
"If you could have a system that could build anything with code, humans can't compete on the same level. That's narrow superintelligence, and we're close."In this episode of AI Native Dev, Simon Maple sits down with Logan Kilpatrick, who spent years at OpenAI working alongside Sam Altman before moving to Google DeepMind as Group Product Manager. They get into:There will be
Everything 100 Episodes Revealed About AI Native Dev
When did writing code stop being the job and start being the hobby?One hundred episodes in, Guy Podjarny and Simon Maple pull the clips, check the predictions, and trace the through line across conversations with guests from Datadog, ElevenLabs, GitHub, and more.They get into:The move from spec-driven to context-driven developmentWhy humans become the bottleneck in code reviewWhat changes when age
How DeepSeek leveraged Qwen and Llama to build its model in $5M
Meta’s Llama might not actually be open source AI, and the developers building on it have no idea.In this episode of AI Native Dev, Simon Maple sits down with Amanda Brock, CEO of OpenUK, to break down what open source actually means in the age of AI and why most of the industry is getting it wrong.They get into:what open washing is and why it is happening across major models right nowhow DeepSeek
Why Every Developer needs to know about WebMCP Now
An agent cannot read your website. And that needs to change.In this episode of AI Native Dev, Guy Podjarny sits down with Maximiliano Firtman, 30-year web developer and author of 14 books, to talk about what building for the web looks like when traffic comes from agents and humans both.They get into:why AI agents taking screenshots of your website is inefficient and expensivewhat Web MCP is and ho
Stop Maintaining Your Code. Start Replacing It
"The code that we have is a liability. The system is the asset we're building."Chad Fowler, VC at Blue Yard Capital and former CTO at Wunderlist, sits down with Guy Podjarny to discuss the Phoenix Architecture: software designed to be replaced rather than maintained.In this episode:• why was the code written by Chad never longer than a page• how he replaced 70% of a codebase in 3 mo
We Scanned 3,984 Skills — 1 in 7 Can Hack Your Machine
Most developers install skills without reading what's inside them. But that's exactly what attackers are counting on.Simon Maple sits down with Brian Vermeer from Snyk at DevNexus to get into the security risk hiding inside the skills and MCPs running on your local machine. They scanned over 4,000 skills and found that 1 in 7 had at least one critical security vulnerability.Here’s what y
The Greatest Time to Build a Startup (The AI-Native Advantage)
The best agentic developers throw away their agent's work without guilt, run three agents at once and only use one, and treat their AI like a junior developer they genuinely dislike. It sounds wrong. It works.Daniel Jones, Head of Product at re:cinq, has upskilled hundreds of developers across Northern Europe's largest enterprises. In this episode he joins Simon Maple to share the counte
Why Your Agent Needs Memory, Not Just Context
Not onboarding your agent is on you.Richmond Alake, Director of AI Developer Experience at Oracle, joins Simon Maple to make the case that most agent failures come down to one thing: memory. Not the model, not the infrastructure. Memory.On the docket:why skills are just SOPs your organisation already has written downthe job title that is replacing prompt engineersfile systems vs databases for agen
Cisco Principal Engineer's Fix for AI Code Security
Your AI coding agent learned from millions of lines of code, including insecure ones. That means by default, it can write vulnerable code too.So how do you fix that?John Groetzinger, Principal Engineer at Cisco, built CodeGuard, a security skills layer that teaches coding agents how to write and review code securely. He tested it against real scenarios.The result:84% success rate vs 47% baseline.
Why Context Beats Every Prompt You'll Ever Write
Most teams think agentic dev is about writing better prompts. It's not.Guy Podjarny and Simon Maple explain why managing context, not crafting prompts, is what separates teams that scale with agents from teams that don't. They walk through a practical framework for building, evaluating, and distributing the context your agents actually need.In this episode:• Why agents fail without struc
From IBM Acquisition to AI-Native Observability | Dash0 CEO
"Charts are good for users, not good for agents. Agents look at the underlying data and do deep analysis."Mirko Novakovic built Instana, sold it to IBM, and now he's building Dash0, rethinking observability for agents, not humans.In conversation with Guy Podjarny, he explains:• why OpenTelemetry turned out to be perfect for AI• how UX changes when agents are your primary users• why
The End of Fragmented Agent Context
One skill took coding success from 28% to 71%. Another made things worse.Guy Podjarny and Simon Maple tested 1000+ agent skills and reveal which ones actually work, which hurt performance, and why anecdotal evidence isn't enough anymore.Tessl Skills Registry is the first package manager for agent skills with built-in evaluations, versioning, and lifecycle management. Explore tested skills and
The Developer Skills That Will Actually Survive AI
“You have to prioritize between the thing you want to do and the thing that actually is driving the business, that’s what really big companies are fighting every single day.”As former CEO of GitHub and now a startup founder again, Thomas Dohmke brings a rare, inside-out perspective on innovation across both worlds.In conversation with Guy Podjarny, he explains:• why startups and incumbents fight i
How Too Much Information Destroys Agent Performance
Most AI agents fail because you're using them wrong. Here’s what actually works in production.In this episode, Simon Maple sits down with Itamar Friedman (CEO of Qodo) and Robert Brennan (CEO of OpenHands) at QCon AI. They pull back the curtain on why agents hallucinate, provide inconsistent answers, and ship low-quality code.On the docket:• Why a third of developer-reported AI output is inco
Intelligence ≠ Knowledge: Why Context Beats Bigger Models
In this special milestone episode, Simon Maple and Guy Podjarny celebrate 1 million views by looking back at the chaos of 2025 and forecasting the high-stakes reality of 2026.On the docket:• Why appearing on this show has become a leading indicator for getting acquired or raising billions (and whether Simon should start charging a 2% carry).• The end of prompt engineering and the rise of context a
What AI Engineering Looks Like at Meta, Coinbase, ServiceTitan and ThoughtWorks
What does it take to make AI work inside engineering teams?This high-stakes compilation episode with Ian Thomas (Meta), Wesley Reisz (ThoughtWorks), Sepehr Khosravi (Coinbase), and David Stein (ServiceTitan) goes inside the engineering rooms of the world's most sophisticated tech organisations to uncover how they're moving past AI hype into AI-native production.On the docket:• How Meta a
What Developers Need To Know About Agents Before 2026
2025 changed what it means to be a developer.And 2026 is about to change even more.This Tessl episode brings a year-end reflection on how agents reshaped software development and what developers need to unlearn next, featuring Reuven Cohen, Founder of the Agentic Foundation, Maor Shlomo, Founder of Base44, and Maksim Shaposhnikov, Technical Member at Tessl.On the docket:• Reuven Cohen on why most
Why Faster AI Development Often Increases Rework | Cian Clarke
Vibe coding is only good at creating a sense of progress for devs.In this episode of AI Native Dev, Cian Clarke, head of AI at Nearform, joins Simon Maple to talk about BMAD, their spec-driven approach that prioritizes clarity before code over prompt first development.They also get into:• speed upfront vs. maintainability over time• why senior engineers gain leverage as junior pathways narrow• the
Building an AI Agent in 100 Lines of Code | Yaniv Aknin
Before you add context, understand the context that’s already there.In this episode, Yaniv Aknin, founding engineer at Tessl, explains the built in instructions that precede every user prompt, and why acknowledging that hidden layer is critical.On the docket:• why tool design matters more than raw reasoning ability• how Codex does more with fewer tools • how subagents let Claude stay flexible unde
How New Libraries Saw a 50% Improvement | Maria Gorinova
We’re holding probabilistic systems to deterministic standards.In this episode of AI Native Dev, Simon Maple talks with Maria Gorinova, Member of Technical Staff at Tessl, about the mismatch between how developers expect software to behave, and how agents actually do.• how structured context improves abstraction use • why agent reliability can only be demonstrated through measurement • how Tessl’s
Agent Experience Is the New Developer Experience | Sean Roberts
Your codebase already has AI contributors. If they don’t understand it, that’s on you.In this segment from AI Native DevCon, Sean Roberts, VP of Applied AI at Netlify, explains why agent experience is, in fact, an extension of developer experience.He also shares:why hallucinations are what inference looks like when context is missing.why knowledge graphs matter for large codebases.using feedback l
What Holds Devs Back From Multi-Agent Thinking | Guy Podjarny
LLMs don’t get smarter when you dump everything into context, they get distracted. At AI-Native DevCon, Guy Podjarny unpacks the evolution of AI augmented development, and how devs can get the most from current tools.On the docket:• how to help agents close the capability-reliability gap• why 'context engineering is basically the same as specs.'• why statistical measurement is the only m
The Hidden Vulnerabilities Behind AI Code | René Brandel
If software can improve autonomously, why shouldn’t security?On this episode of AI Native Dev, René Brandel, founder and CEO of Casco, explores how upfront specs enable reliable agent generated software, and how that same discipline drives Casco’s autonomous, continuously improving security.On the docket:• how small teams with self-improving agents can outperform large security orgs.• why vibe cod
What Developers Can Build Next With AI
In this compilation, Simon Maple brings together Baruch Sadogursky (TuxCare), Liran Tal (Snyk), Alex Gavrilescu (Greentube), and Josh Long (Broadcom) to break down where AI-assisted development fails, and what teams must do to keep it reliable.On the docket:• why spec-compiled tests must come before letting AI generate code• the uncertainty around what “AI security engineering” actually means toda
Claude, TypingMind, AMP & MCP Servers: The Future Dev
How do you give an agent the same visibility a human developer has, without giving it full control?Alan Pope, Senior Developer Advocate at Tessl, explains how Model Context Protocols (MCPs) give AI agents structured access to dev environments, enabling tools like Claude Code and TypingMind to read, build, and execute safely under human oversight.On the docket:• how MCPs enable hybrid collaboration
AI Agents Beyond Context Limits | Maksim Shaposhnikov
Bots follow scripts. Assistants wait for your commands. Agents act autonomously. Maksim Shaposhnikov, AI Research Engineer at Tessl, joins Simon Maple to unpack the capabilities of AI coding agents, including how developers can test and trust the code they generate.On the docket:• how sub-agents operate independently, maintaining their own context windows to handle complex tasks without overloadin
Instant PR Feedback Without leaving GitHub | Merrill Lutsky on Graphite
As AI outpaces human review, latency compounds.On AI Native Dev, Graphite co-founder and CEO, Merrill Lutsky joins Guy Podjarny to explore how stack aware reviews remove friction and accelerate AI-native development.They also get into:• how Graphite’s architecture ensures traceability across AI generated commits• what engineering velocity means when code quality depends on alignment• why the next
AI-First Project Management for Developers | Alex Gavrilescu on Backlog.md
Even the smartest AI agent starts as a blank slate. Alexandru Gavrilescu, creator of Backlog.md, and Simon Maple explore how to give AI the right context and specifications so it can deliver like a human teammate, and sometimes faster.On the docket:• why humans still matter for review, but AI can accelerate work beyond traditional sprints• the rise of persistent agents that proactively manage task
Redefining Developer Workflows in the AI Era with MCP | Steve Manuel
The risk of letting AI do more than autocomplete? It can quickly spin out of control.On this episode of AI Native Dev, Steve Manuel, founder and CEO of Dylibso, unpacks MCP, the protocol that keeps AI extensions safe and predictable, and dives into mcp.run, his framework for tapping into shared MCP servers without losing control.With Simon Maple he shares:• why plugin-safe AI might be the most sig
How Slack AI Agents Accelerate Dev Productivity | Samuel Messing
AI only becomes effective in workflows when it speaks the language of the enterprise.In this episode of AI Native Dev, Simon Maple sits down with Samuel Messing, VP of Engineering for Search and AI at Slack, to explore how they are tackling one of AI’s toughest challenges: managing enterprise context at scale for 80% of the Fortune 100.On the docket:• why understanding context is critical for ente
Can Agentic Engineering Really Deliver Enterprise-Grade Code? | Reuven Cohen
What makes 2025 the “Year of the Agent”? In this episode of AI Native Dev, Simon Maple joined Reuven Cohen, founder of the Agentics Foundation and creator of Claude Flow, to explore how agentic engineering went from a fringe experiment to a movement reshaping how developers build software. On the docket: • what makes “agentic engineering” a new profession, not just a buzzword • how hive minds and
Revolutionising Spec-Driven Development with Tessl’s Framework & Registry
Devs spend more time fixing agent mistakes than shipping features. On this episode of AI Native Dev, Guy Podjarny, founder & CEO at Tessl, and Simon Maple, Head of Developer Relations, announce two new products, the Tessl Framework and the Tessl Spec Registry, built to make specs the source of truth, keeping agents aligned with intent and code dependable. On the docket:• Why agent development
The Man Behind World’s Largest Dev Con | Stephan & Devoxx
Stephan Janssen, the mind behind Devoxx, calls this moment the “renaissance of development” and explains why AI-native workflows are rewriting what’s possible. On the docket: • how vibe coding cut months of setup and build into days • why “spec to code” loops beat pure prompting every time • what juniors gain from a universal AI tutor • why agents are the big theme for Devoxx Belgium 2025 AI Nati
Is Your Team Ready for AI-Driven Modernization? | Birgitta Böckeler
What does it mean to be a developer when AI writes most of the code?On this episode of AI Native Dev, Birgitta Böckeler, Distinguished Engineer at ThoughtWorks, joins Simon Maple to unpack how AI is changing legacy migrations, and why the real value comes from pairing LLMs with proven software engineering practices.They also get into:• why “AI alone” won’t solve legacy modernization• how codemods
Can AI Really Build Enterprise-Grade Software? | Maor Shlomo & Base44
AI coding is great for demos, but can it handle production apps?On this episode of AI Native Dev, Maor Shlomo, founder of Base44, joins Guy Podjarny to share how he built one of the first “batteries-included” AI app builders and what it reveals about building real software in the age of LLMs.They dive into:• why less complexity helps LLMs perform• how Base44 ships apps with databases and integrati
Transforming Dev Practices with Kiro’s Spec-Driven Tools | Nikhil and Richard
Rapid prototyping is fun, but maintaining it? Not so much. In this episode, Simon Maple is joined by Nikhil Swaminathan, Head of Product and Richard Threlkeld, Principal Engineer at Kiro IDE (AWS), to discuss how spec-driven development moves the focus from writing code to understanding and communicating system behavior, building software that is easy to understand so that developers can maintain
Transforming Debugging and Root Cause Detection with Sentry | David Cramer
On this episode of AI Native Dev, David Cramer, co-founder of Sentry, joins Guy Podjarny to talk about what it really takes to ship faster with LLMs built into the workflow, and why over 4 million devs rely on Sentry to do it right.On the docket:• how Sentry’s moat is built on deeply interconnected app data• why David prefers Cursor over Claude• how a cynical mindset kills AI adoption before it be
Why Tracking AI Usage Drives Better Results | Justin Reock & DX
With AI adoption surging across orgs, Justin Reock, CTO at DX, joins Simon Maple to break down the difference between meaningful integration and just chasing trends.They also get into:• two levers of velocity: quality and maintainability.• why measuring real AI impact starts with knowing who’s using it and how• how AI supercharges developer productivity across the SDLCAI Native Dev, powered by Tes
What Next Gen AI Infra, Voice and Video Looks Like | Datadog, Eleven Labs and Synthesia
In this compilation, Olivier Pomel (Datadog), Mati Staniszewski (ElevenLabs), and Victor Riparbelli (Synthesia) join Guy Podjarny to unpack what it takes to build category defining products in the AI native era.On the docket:• why root cause detection felt like sci-fi a few years ago, and what’s changed• the early challenges ElevenLabs faced as an audio first AI company• how Synthesia got Mark Cub
What If Fixing Code Wasn’t Your Job Anymore? | Jonathan Schneider & Moderne
Jonathan Schneider, co-founder and CEO of Moderne, joins Simon Maple to share how real engineering teams are using Moderne’s rewrite engine to reduce technical debt at the source, and drive org-wide transformation.On the docket:the hard limit LLMs can’t scale pastwhy OpenRewrite uses a declarative frameworkthe real challenge: accessing the compiler’s truthAI Native Dev, powered by Tessl and our gl
Why AI Coding Agents Are Here To Stay | Patrick Debois
Patrick Debois, the mind behind “DevOps,” joins Simon Maple to unpack the system-level shifts AI is driving across software engineering, drawn from what he saw firsthand at the AI Engineer’s World Fair.They also get into:• how inconsistent codebases confuse AI• why running agents locally is becoming obsolete• inside OpenAI’s concept of “model specs”AI Native Dev, powered by Tessl and our global de
The Graph Layer Behind NASA’s Breakthroughs | Michael Hunger
Michael Hunger of Neo4j, joins Simon Maple to unpack how graph databases inject structure, intent, and traceability into modern AI systems.On the docket:why relationships in data encode intentthe black-box problem in vector based RAGwhy devs should build their own MCP serverAI Native Dev, powered by Tessl and our global dev community, is your go-to podcast for solutions in software development in
The Missing Gap In Workflows For AI Devs | Baruch Sadogursky
Baruch Sadogursky, Head of Developer Relations at TuxCare, joins Simon Maple to explore why automated integrity needs to be built in before we rely on AI outputs.On the docket:the difference between specs and testswhy PMs sidelined specsthe "intent-integrity gap" between human goals and LLM outputsthe non-determinism of LLMs as a feature, not a flawBaruch’s belief: devs are not going any
Why LLMs Keep Missing This One Thing | Jason Ganz
Can LLMs replace structured systems to scale enterprises?Jason Ganz, Senior Manager DX at dbt Labs, joins Simon Maple to unpack why, despite the rapid rise of AI systems, enterprises still rely on structured data for consistency and reliable decision making.They also discuss:the invisible edge cases LLMs can’t seedifference between software engineering and data engineering in AIthe mismatch betwee
CTO of $7B Snyk Talks AI Security, Risky Software & Enterprise Adoption
In this episode of AI Native Dev, Guy Podjarny and Danny Allan unpack how security has reduced just to a concern from a roadblock for devs.On the docket:why 80% of Snyk’s enterprise customers are actively using AI toolsnavigating security risks of today and tomorrowthe recurring flaw in every new stackwhy more code means more vulnerabilitiesAI Native Dev, powered by Tessl and our global dev commun
The $2B Nvidia Backed AI Video Platform
In this episode of AI Native Dev, Guy Podjarny is joined by Victor Riparbelli, Co-founder and CEO of Synthesia, who is leading the charge towards AI driven video that lets anyone become a video creation pro with no prior field experience.On the docket:• how Synthesia enables full video editability even after generation • Victor’s vision for fully coded videos that enable personalized, customizable
Why the Top 1% of Devs Love IntelliJ | Anton Arhipov
From early IntelliJ user to JetBrains advocate, Anton Arhipov continues his conversation with Simon Maple and Baptiste Fernandez on AI Native Dev, tracing his journey through tools, trends, and the changing face of developer experience.On the docket:• how IntelliJ responds to new tools in the market• understanding the pros and cons of tab-driven development for devs• why tools will need to get bet
How This Broadcom Dev Uses Spring to Power AI Workflows | Josh Long
What happens when the OG Spring Dev, Josh Long, teams up with Simon Maple on AI Native Dev?You get a front row seat to them building a Spring powered app that showcases the future of AI integration.While building the app, they discuss:why developers are adopting Spring AIhow Spring is becoming the go-to for AI engineeringwhy companies route users from humans to IVRsunderstanding model context prot
One CEO’s Plan to Rebuild How We Deploy AI | Ron Eforni & Flox
In this episode, our founder and CEO, Guy Podjarny speaks with Ron Efroni, CEO of Flox and President of the NixOS Foundation, about advancing AI from experimental phases to robust, production-ready environments. Flox optimizes reproducible environments, Nix provides the foundation, and Ron Efroni is uniting them to scale enterprise development.On the docket:the value proposition of using "env
Vibe Coding SimCity II: Injecting Chaos with Natural Disasters and AI Tools
Simon Maple and Joe Holdcroft swap their AI-generated apps and put Cursor and Windsurf to the test by adding natural disasters like earthquakes and lightning strikes. Discover how AI coding tools handle randomness, spreading effects, animation, and bugs—plus the devs’ thoughts on debugging AI-generated code.Subscribe for deep dives into modern dev workflows and AI-powered coding experiments.Watch
Vibe Coding SimCity: Prototyping Tiny Towns with AI Dev Tools
In this hands-on episode of AI Native Dev, Simon Maple and Joe Holdcroft go head-to-head in a “vibe coding relay” challenge—building a tiny town simulator from scratch using AI-powered dev tools BASE44 and Bolt.new. With only 10 minutes, vague prompts, and no prep, they put these rapid-prototyping tools to the test. Simon leans into SimCity nostalgia while Joe battles ghost-town bugs and infinite
Exploring LLM Observability with Traceloop's Gal Kleinman
In this episode, Simon Maple converses with Gal Kleinman, co-founder of Traceloop, to delve into the realm of LLM observability. Kleinman shares his expertise on constructing evaluation suites and overcoming the challenges associated with LLM applications. Learn about innovative solutions like Open LLMetry and gain insights into best practices for maintaining robust observability systems.Subscribe
From Builder to Orchestrator—Confronting the Software Engineer’s Identity Crisis
In this episode, host Simon Maple engages in a compelling discussion with Annie Vella, a skilled software engineer and manager, about the integration of AI into software development. Annie shares her insights on how AI tools are reshaping coding practices, the challenges engineers face in transitioning to management roles, and the skills required to thrive in this evolving landscape. This episode
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