Building AI powered tools to augment human creativity and problem solving in San Francisco. atelier.dev Previously @GitHub Copilot, @Google, 🇨🇦
narphorium.com
Shawn Simister
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Now, with vibe coding we don't even do that. The agent is often the only one with a mental model of the code, and it throws it away after each session.
I feel like over the years, we've sort of given up documenting our mental models of code and just accepted that everyone who reads the code builds their own model from scratch.
Every time an agent explores your code base it builds up its own mental model of how the code works, and then throws that model away when the session ends. Code walkthroughs turn that model it into a step-by-step guide to bring you up to speed on any part of the system.
finally a dedicated device for the 20X per day you need to open MS authenticator 😄
In 1985 Peter Naur argued that a program is more than just its source code. "Programming As Theory Building" explained how we build theories of the code which help us debug and refactor it but those theories rely on knowledge from outside of the code.
pages.cs.wisc.edu/~remzi/Naur....
Atelier walkthroughs can use the Chrome MCP in Claude Code to build UI walkthroughs with live screenshots of the app. Grounding the walkthrough in execution exposes gaps in the implementation that you wouldn’t catch with code review.
You can verify any task on the atelier.dev kanban board and it will automatically build a walkthrough of the acceptance criteria grounded in deep links back into the code that it wrote.
Rather than reading diffs line-by-line, you step through the agent's account of what it did and diff it against your own mental model. This is exactly the sort of high-level decision point which surfaces misunderstandings and gaps in the design.
narphorium.com/blog/decisio...
What are users thinking during their interactions with LLMs?
Introducing ThoughtTrace — the first dataset capturing what users think during real-world human-AI conversations.
These thoughts improve user behavior prediction and model alignment, opening a new paradigm of user-centric LLM research.
Most engineers stopped reading docs ages ago. But now, agents are reading them.
What if you had a way to measure freshness on every PR? Is it as easy as implementing three signals, plus a Claude Code layer? Let's find out together!
