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

"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 months and cut costs by 75%
• shipping AI code no human ever reviewed, and how to make it safe
• the shadow specs your agents are making without you
• why your system should work with the worst LLM, not just the best

If you're still thinking about your codebase the old way, this one will change that.

Connect with us here: 
Chad Fowler: https://www.linkedin.com/in/fowlerchad/
Blue Yard Capital: https://www.linkedin.com/company/blueyard-capital/
Guy Podjarny: https://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: [email protected]

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 you need to know:
Why prompting your agent to write secure code doesn't make it secure
How a trusted skill can update silently and start offloading your credentials
What prompt injection actually looks like inside a skill file
Why vibe coding makes the attack surface bigger, not smaller
How the Snyk agent scan catches what you'd never spot manually

Every skill on the Tessl registry now has a Snyk security scan attached. Check before you install.

Connect with us here:
Simon Maple: https://www.linkedin.com/in/simonmaple/
Brian Vermeer: https://www.linkedin.com/in/brianvermeer/
Snyk: https://www.linkedin.com/company/snyk/
Tessl: https://www.linkedin.com/company/tesslio/
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: [email protected]

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 counterintuitive habits, hard data, and practical frameworks behind high-performing agentic development teams.

On the docket:
Why bad engineering practices get worse, not better, with AI agents
The exact conditions that make your agent hallucinate every time
Why your AGENTS.md is quietly working against you
How to manage context before it kills your productivity
What enterprise AI rollout actually looks like at scale
Why the worst managers get the most out of agentic coding

If your team is adding AI and wondering why things aren't getting faster, this episode is for you.

Connect with us here:
Daniel Jones: https://www.linkedin.com/in/danieljoneseb/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: [email protected]

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 down
the job title that is replacing prompt engineers
file systems vs databases for agent memory (and why one gets you hacked)
the memory trick that makes agents feel actually intelligent
why the agent loop and training loop are about to become one

The developers who figure this out first are going to be very hard to compete with.

Connect with us here: 
Richmond Alake: https://www.linkedin.com/in/richmondalake/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: [email protected]

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. Nearly 2× improvement.

In this episode we get into:
how CodeGuard works
why Cisco open sourced it
the surprisingly simple method that gets agents to fix their own mistakes

Try CodeGuard: cisco/software-security on the Tessl registry.

Connect with us here:
John Groetzinger: https://www.linkedin.com/in/john-groetzinger/
Cisco: https://www.linkedin.com/company/cisco/
Simon Maple: https://www.linkedin.com/in/simonmaple/
Tessl: https://www.linkedin.com/company/tesslio/
Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh

Ask us questions: [email protected]