JEDI — Just Enough Developed Intelligence

It’s about two weeks after the Context Is the New Code presentation at AIE London.

The competitive edge in AI-native development isn’t the model or the coding agent — it’s the organizational context you feed it.

If context is code, can we test it in a CI pipeline?

The Context Development Lifecycle described four stages: generate, evaluate, distribute, observe.

What if the agent could optimize its own instructions?

Every time AI removes one bottleneck, the system reveals the next one.

You’re managing the thing that writes code.

Cole Medin’s post on comprehension debt got me thinking.

Coding agents can write features and fix bugs.

I joined Olalekan Elesin (HRS Group), Mallika Rao (Netflix), and Martin Reynolds (Harness) for an InfoQ Live panel moderated by Renato Losio.

When you git clone a repository and open it with Claude Code, the first thing it does is read CLAUDE.md — a project instruction file that shapes how the AI behaves.

In this ThoughtWorks Technology podcast episode, Patrick Debois joins Nathan Harvey (DORA, Google Cloud) and host Ken Mugrage for an in-person conversation about what changes when AI agents enter the software development lifecycle.

When AI coding agents can navigate your filesystem, one wrong move and they’re “helpfully” editing files in your production branch while you’re working on a feature.

Electron apps support the Chrome DevTools Protocol.

It was right under my nose.

I’m increasingly writing my own tools with AI rather than reaching for existing open-source libraries.

A late night conversation with Claude that started with Steve Yegge’s Beads architecture and ended somewhere unexpected.

2025 was the year of making coding agents work — figuring out what they’re good at, wiring them into IDEs, running them async, mostly in solo dev environments.

Gave a talk at CTO Club Belgium on coding with AI.

I wonder if there are code speed reading courses — the way there are speed reading courses for text.

Spec-driven development is no magic bullet, but it’s useful nonetheless.

Senior devs often tell me they’re worried juniors won’t learn what good looks like.

In this episode of the Book Overflows podcast, Patrick joins hosts Carter Morgan and Nathan Tops to reflect on The DevOps Handbook, originally published in 2016.

Been pondering on what I call the “AI Coding Fabric” — a new infrastructure challenge emerging as AI agents move beyond traditional IDEs into sandbox environments.

Asked ChatGPT for more radical ideas to deal with code review volume.

Curated 8 talks from Devoxx 2025 related to spec-driven development for writing better code with AI.

A conversation with The Secure Disclosure covering the full arc from DevOps to AI-native development — and why the engineering discipline hasn’t changed, even as everything around it has.

Patrick presents a comprehensive framework for understanding how AI is transforming the developer workflow, drawing on his experience curating nearly 500 AI coding tools over the course of a year.

In this TechRox podcast episode recorded ahead of the TechRox Summit, Patrick Debois sits down with host Dimitri Bi to discuss the journey from DevOps to AI-native development.

In this episode of the Hangar DX podcast, Patrick joins host Anka Jan to discuss the parallels between the DevOps movement and the current AI transformation in software engineering.

Stan Girard’s lifecycle of modern software — there’s some truth to it: Day 1: Vibe code with Claude Week 1: Fix bugs for production Month 1: Senior engineer optimizes for scale Year 1: Rewrite in Rust The punchline: despite using AI extensively, most of t…

Fed my resume to AI and let it analyze me.

A framework of four patterns that describe how AI is fundamentally transforming the developer role beyond simple code completion.

Patrick presents the four patterns of AI-native development at the Future of Software event hosted by Eficode.

Patrick presents the four patterns of AI-native development at ServerlessConf, framing the rapid evolution of coding tools – from simple tab completions to multi-agent systems that autonomously work across entire codebases.

Igor Moochnick tested an autonomous distributed system built by an AI swarm.

Setting up Claude Code feels a bit like being the first DevOps on Mars.

The workflow is shifting: teams parallelize for speed and exploration.

Presented at PlatformCon 2025, this talk makes the case that any serious generative AI initiative within an organization deserves a dedicated platform team.

The four patterns of AI-native development, presented at TechRocks Paris.

Lessons from the AI Engineer’s World Fair on software engineering with AI: AI coding agents are ubiquitous — across every development environment Tool usage requires constant evolution — applying six-month-old approaches is already counterproductive Speci…

Not all bug fixes are equal: there are those LLMs can solve and then there are those developers don’t want to hear about.

In this presentation, Patrick Debois lays out a framework for understanding how generative AI is transforming software development beyond faster typing.

Presented at the AI Native Dev meetup in Stockholm, Patrick Debois traces the rapid evolution of AI coding tools — from simple autocomplete copilots through chat interfaces, codebase-aware editors, multi-file agents, and now headless CLI-based agent swarms

In this podcast conversation, Patrick discusses his journey from DevOps pioneer to generative AI practitioner, tracing how his interest in virtual production and automated media during the pandemic eventually led him into the world of large language models

I took the time to summarize GitHub Copilot’s upcoming capabilities — specifically prompt management, Agent mode, and MCP tools extension support.

Pattern #4 and final pattern in the Four Patterns of AI Native Development series.

Pattern #3 in the Four Patterns of AI Native Development series.

Pattern #2 in the Four Patterns of AI Native Development series.

In this episode of the AI Native Dev podcast hosted by Tessl, Patrick joins Simon Maple for an in-depth conversation about the emerging patterns of AI-native development.