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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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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. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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-wide habit. What we cover: – What Claude Tag actually is, and how it differs from Claude Code and Cowork – Why trust in AI agents is really a function of model capability, not just comfort – The internal "dogfooding" culture that shaped Claude Code and Claude Tag – How Anthropic secures multiplayer AI with agent identities and channel-level permissioning – Where Claude and Claude Tag show up outside of engineering at Anthropic – Dreaming: how Anthropic's managed agents continually improve their own memory Watch Lamis' talk from AI Native DevCon London 2026 here: https://youtu.be/tTcxVv8HHNw Chapters: 00:00:00 - Introduction 00:01:46 - Meet Lamis Mukta from Anthropic 00:02:56 - What is Claude Tag? 00:10:07 - From single-player to multiplayer agentic coding 00:15:50 - Trust, capability, and the METR chart 00:21:02 - How the industry is really using Claude Code 00:26:33 - The Claude Code origin story 00:33:47 - Agent identities and permissioning at scale 00:40:23 - Claude beyond engineering at Anthropic 00:48:16 - Dreaming and practical tips for rolling out Claude Tag 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development What's the one thing your team would delegate to an AI teammate first? Let us know in the comments. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 smarter, your code review gets tighter, and your team ships more without adding more toil. What we cover: – What the Tessl agent is and how it fits into the broader Tessl platform – Loop engineering: why building automated feedback loops is the right place to start – How the agent sets up and continuously improves agentic code review – Why optimising your AI agent costs is usually the wrong lever to pull – The case for open, modular software factories — and the risk of vendor lock-in – UX expectations in the AI era: why outcome-oriented interfaces are now the baseline 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development What's your approach to building feedback loops into your agent workflows? Drop a comment — we'd love to hear how teams are thinking about this. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 and CEO of Autonomy AI), and Daniel Jones (Head of Product at re:cinq) at AI Native DevCon London for a wide-ranging panel on AI enablement — who owns it, what's breaking, and what the organisations getting it right are actually doing differently. What we cover: – Who should own agentic coding adoption inside an enterprise, and why platform teams are already filling the vacuum – The "Timmy's lazy" problem: why agent cost visibility is forcing process discipline that humans never got – Why PR-based workflows are an anti-pattern inside enterprises once you're moving at agent speed – The PUMP framework (Plan, merge, polish): how one team is shipping features with developers, PMs, and designers all opening PRs – Rethinking what a "test" is in an agentic world — and why feedback loops matter more than first-pass correctness – The biggest mistake enterprises are making right now: piecemeal adoption with no mandate and no shared tooling 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development What's your team's approach to AI enablement — central mandate or letting individuals find their own way? Drop it in the comments. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 engineering, evals, AI adoption mindsets, and the change management challenge that nobody talks about enough. What we cover: – Why measuring token usage, code commits, and outputs will lead your team astray – How NearForm cut a 4,000-hour AML backlog to 20 minutes using agents – Harness engineering and evals as the developer skills that matter most in 2026 – Why change management — not tooling — is the missing ingredient for real AI ROI – How AutonomyAI is letting PMs and designers ship directly to production Chapters: 00:00:00 - Welcome to AI Native DevCon London 00:01:03 - Chris Baty: Outcomes Over Outputs 00:05:15 - Martin: How Tooling Changed Everything 00:07:21 - Ryan: Harnesses, Evals and Skills 00:08:50 - Manny Saka: Plan Before You Prompt 00:11:58 - Cian O Maidin, NearForm: Real AI ROI 00:15:21 - Snyk: Building Trust at Scale 00:17:01 - AutonomyAI: Shipping Without Engineering 00:21:35 - Tessl Agent: Harness Engineering 00:22:41 - Closing Thoughts 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development Whether you're cautious or YOLOing it with AI — drop where you land in the comments. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 from Cisco. Then: a web AI conversation with Dana Lawson from Netlify, Maximiliano Firtman from codemia, and James Moss from Tessl. What we cover: – Why 83% of enterprises plan to deploy AI but only 29% feel ready to do so securely – Prompt injection as a risk you have to accept — and how least privilege and sandboxing are your real defences – The "agent experience" concept: why systems built for human eyes fail at machine scale – Whether fundamentals like HTTP, semantic HTML, and accessibility still matter when agents do the heavy lifting – How WebMCP lets websites expose tools directly to agents — and why blocking them is like trying to turn off the sun Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 the agent — the context it sees, the tools it can call, the tests and linters that close the loop, and the asynchronous CI jobs that catch slop before it compounds. We sat down with Ryan at AI Native DevCon London 2026, and he got into the specifics: how his team went from 3.5 PRs per engineer per week to 70, why he inverts spec-driven development (build the code first, distill the spec second), and what he means when he says it's "borderline negligent" not to use a billion tokens a day. It's one of the most grounded, production-focused conversations we've had on The AI Native Dev. The trailer is live now — and the full episode drops this week. #HarnessEngineering #AINativeDev #SoftwareEngineering Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 in that data is both more promising and more complicated than the headlines suggest. What we cover: Why experienced engineers hit a hard ceiling at 4 concurrent agents, and what it would take to break through it The 80/20 vs 60/40 merge rate gap between human and AI-generated pull requests — and what's actually causing it How AI adoption reached 71% weekly active usage across 250K developers, and what "depth of use" really means Why 2026 is the year the CFO gets involved — and how engineering leaders should prepare to show their receipts The biggest misconception engineering leads have about what it takes to get to true AI-native development Why companies have jet engines but are still building cars, and what the real architectural changes look like Links: 🌐 Tessl: https://tessl.io 🔔 Subscribe for weekly episodes on AI-native development If you're an engineering leader trying to make sense of the gap between the AI hype and what's actually showing up in production, drop your take in the comments. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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. Recorded live at the AI Security Summit in London, this episode features conversations with Brian Vermeer (Snyk), Sam Stepanyan (OWASP London), and a full recording of Guy's keynote on why agentic development demands a fundamentally different approach to security. What we cover: Why shadow AI is the new shadow IT — and why CISOs can't secure what they can't see Skills as a new supply chain attack surface (malicious, vulnerable, and negligent skills) Why more context is not always better — and what the data says about focused skill design The OWASP Top Ten for Agentic AI and what it means for teams building today Why security must become agentic to keep up with the attackers who already are The Context Development Lifecycle (CDLC) and how leading orgs are using it Links: 🌐 Tessl: https://tessl.io Subscribe for weekly episodes on AI-native development What's the biggest security risk your team isn't talking about when it comes to agentic development? Drop it in the comments. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 the "lethal trifecta" of agent capabilities creates a novel threat surface How text and markdown files have become a new class of vulnerability Slop squatting: the attack vector created by agents hallucinating package names The context supply chain — and why it mirrors the early days of npm security What a "CBOM" (context bill of materials) might look like and why we may need one How to think about agent trust using the contractor mental model Chapters: 00:00 Introduction 01:40 The Lethal Trifecta: why agents are inherently risky 03:23 Same hygiene, higher stakes 04:08 Text as a vulnerability: markdown as a security risk 06:08 Do AI tools make you more or less secure? 08:09 Snyk + Tessl: scanning skills in the registry 10:10 The context supply chain problem 14:28 The CBOM: do we need a context bill of materials? 17:35 Secrets, credentials, and principle of least privilege 22:25 Balancing security with developer velocity 36:54 One piece of advice for CTOs going all-in on AI Links: 🌐 Tessl: https://tessl.io Subscribe for weekly episodes on AI-native development If you're thinking about AI governance in your team, drop a comment — how are you handling context supply chain today? Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 episode, Venkat unpacks why the speed of AI generation has outpaced our ability to review it, why you can delegate work to AI but never your reputation , and tells the story of a $2 million feature that got cancelled with one simple question: "Why are we building this?" 🔑 Topics covered: — Why "Accelerated Inference" is a more honest name for AI — The code quality crisis AI has inherited from decades of bad human code — Accountability, lawsuits, and professional responsibility in the AI era — What makes a senior developer — and why AI widens that gap — The one question that should precede every feature decision — Why vibe coding is the most overrated AI trend right now 🎙️ Guest: Venkat Subramaniam — author, educator, and co-creator of Arc of AI 🎙️ Host: Simon Maple 📅 Recorded live at Arc of AI, Austin 🔔 Subscribe for weekly conversations with practitioners (not vendors) building with AI. 🎧 Listen on Spotify / Apple Podcasts — search "AI Native Dev" 🌐 Community & events: tessl.io/community Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 of frameworks that developers ever choose for themselves. Rod also pulls back the curtain on Embabel — the new JVM-native agentic framework he's building — including how it borrows its planning algorithm from NPC AI in video games, why he's skeptical of MCP despite its hype, and the AI failure pattern he keeps seeing in large enterprises. Whether you're a Java developer navigating the AI wave or a tech lead trying to figure out where to actually invest, this is essential listening. Topics covered: 0:00 Intro — Rod Johnson, Spring creator, returns to building 5:16 PhD in 19th century Parisian piano music (yes, really) 6:15 What made Rod come back and build Embabel 7:36 "The universe would end before Python could execute inference" 13:08 The #1 enterprise AI failure pattern Rod keeps seeing 16:23 "You cannot vibe code serious software" 19:37 Why Embabel uses a video game NPC planning algorithm 27:47 Why Rod is an MCP skeptic 53:54 "This is the last wave of frameworks chosen by people" Links: 🔗 Embabel: https://embabel.com 🎟 AI Native Dev London (June 1–2): https://ainativedev.io — use code POD30 for 30% off. Subscribe for weekly conversations with the engineers and founders building the future of AI-native development. Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 guardrails • the move from more context is better to right context at the right time • the difference between general-purpose models vs specialized domain models To catch conversations like these in person, register for AI DevCon in London on 1st and 2nd June 2026 . Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
"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 100x more developers in the world because of AI AGI will be a product, not a model The way you used AI tools three months ago is already wrong What's actually changing inside the Gemini team and why it matters for developers building with it today The developers who win won't be the ones who mastered today's tools. They'll be the ones who never stopped learning the new ones. Building AI-native? AI DevCon is coming to London, 1st and 2nd June 2026: https://tessl.io/devcon/ Connect with us here: Simon Maple: https://www.linkedin.com/in/simonmaple/ Logan Kilpatrick: https://www.linkedin.com/in/logankilpatrick/ Google DeepMind: https://www.linkedin.com/company/googledeepmind/ Tessl: https://www.linkedin.com/company/tesslio/?originalSubdomain=uk Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 development Why humans become the bottleneck in code review What changes when agents run the SDLC end-to-end Adoption across orgs vs depth of actual usage Thanks to every guest and every listener who made this possible. On to the next hundred. Want to have these conversations in person? AI DevCon is coming to London on 1st and 2nd June, 2026. Book your tickets here - https://tessl.io/devcon/ Connect with us here: Guy Podjarny- https://www.linkedin.com/in/guypo/ Simon Maple- https://www.linkedin.com/in/simonmaple/ Tessl- https://www.linkedin.com/company/tesslio/ AI Native Dev- https://www.linkedin.com/showcase/ai-native-dev/ Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 now how DeepSeek built a frontier model for $5 million instead of $100 million why Chinese developers are already on Kimi and the west is sleeping on it what the west needs to learn from China's open source strategy before it is too late Eight companies already control the world's digital infrastructure. We cannot let that happen again with AI. Connect with us here: Simon Maple: https://www.linkedin.com/in/simonmaple/ Amanda Brock: https://www.linkedin.com/in/amandabrocktech/ OpenUK: https://www.linkedin.com/company/openuktechnology/?originalSubdomain=uk Tessl: https://www.linkedin.com/company/tesslio/ Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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 expensive what Web MCP is and how it gives agents a direct API into your website how a 200MB Apple model running offline is opening up a whole new category of web apps why every vibe coded app is a web app and what that means for the future of the web Your next visitor might not be human. Are you ready for that? Connect with us here: Maximiliano Firtman: https://www.linkedin.com/in/firtman/?locale=es Guy Podjarny: http://uk.linkedin.com/in/guypo Tessl: https://www.linkedin.com/company/tesslio/ Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions: podcast@tessl.io
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Observed September 14, 2026. Cached outside the daily freshness window; the positions keep the date they were taken on.
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