Shawn Simister

@narphorium.com

Building AI powered tools to augment human creativity and problem solving in San Francisco. atelier.dev Previously @GitHub Copilot, @Google, 🇨🇦 narphorium.com

With Codex/Claude the bottleneck in software development is now expressing your intent. Writing the code is no longer the rate limiting step. This completely upends software development. Take issue trackers. The key problem they were solving was allocation of scarce coding resources to features.

This week's #PaperILike is "People construct simplified mental representations to plan" (Ho et al., Nature 2022). People generate ("construe") simplified task representations on-the-fly. (Robots should too!) Also: awesome first paragraph. PDF: arxiv.org/abs/2105.06948

People construct simplified mental representations to plan

One of the most striking features of human cognition is the capacity to plan. Two aspects of human planning stand out: its efficiency and flexibility. Efficiency is especially impressive because plans...

arxiv.org

"the phoenix must be reborn with its empty spaces intact — intent includes the intent to remain open, and “immortality of intent” is only half-achieved by a system that can’t carry a dodge through the fire." wtf you talking about fable? 😂

What's new isn't the loop. It's that the controller is now expressive enough to handle unstructured goals: natural language specs, ambiguous tasks, open-ended iteration. That's the actual innovation. Not the feedback loop itself.

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.

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!

How Fresh Are Your Docs? Score Documentation Freshness in CI

Learn how to score documentation freshness in CI pipelines to keep your docs accurate and up to date. Automate quality checks and catch outdated content before it reaches users.

go.dosu.dev

I sketched this out a few years ago. The HTML vs Markdown debate is conflating substrate with information density. The real question is what kind of feedback an artifact actually invites. Hi-fi invites parameter critique. Lo-fi invites paradigm critique.

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For the past few years, humans have been doing “prompt engineering” to coax the best performance out of different LLMs. In this work, we explored what happens if we train an AI to do that job instead. Link to our #ICLR2026 paper: arxiv.org/abs/2512.04388 Thread:

Sakana AI@sakanaai.bsky.social · 4mo ago

Introducing our new work: “Learning to Orchestrate Agents in Natural Language with the Conductor” accepted at #ICLR2026 arxiv.org/abs/2512.04388 What if we trained an AI not to solve problems directly, but to act as a manager that delegates tasks to a diverse team of other AIs? Thread:

This is how I use AI to augment my design process. Instead of having the agent show me a giant diff of changes, Atelier links each edit back to the original thread of feedback so I can review them in context

The CLI is great for a lot of things but reviewing Claude Code plans is not one of them. So much scrolling back and forward just to figure out what the agent changed

Revising a document with an agent should feel like collaborating with someone sitting next to you reading the same page. You highlight what needs to change, the agent suggests revisions, and you decide. That's what I'm building in Atelier