Resources
Read the work
The white paper behind Tapestry, what we have written about autonomous systems and the cost of supervision, and the code we have put in the open.
Documents
Long form
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Tapestry: A Trustworthy Autonomous System
The full explanation of Tapestry and its first persona, the Python Developer. Why supervision is the cost of agentic AI, how the operating model removes it, the five core elements and what each one carries, the authority boundary every request and release crosses, and the evidence standard the work is held to.
Read the white paper (PDF)
Articles
Newest first
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What the Human Was Actually Doing
Approval was never the whole job. A reviewer holds the requirement steady while the model drifts, reads a failed test and decides whether it matters, and chooses what happens next. Take the person out and that work moves into the system, where most of it turns out to be scheduling, lookup, and test execution, and belongs on a CPU rather than in a model.
Read the article -
Attention Is the Scarce Resource
OpenAI is testing a Codex mode that runs until it is put to sleep, and it ships as the most demanding setting in the reasoning-effort menu. Persistence bought with more inference does not reduce what agentic work costs, because the constraint was never tokens. It is the attention it takes to keep the work moving.
Read the article
Open source
Available now
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Engram
A lightweight, keyword-indexed retrieval layer that returns a held answer before the expensive call ever runs. Legible by design, it learns from use and stays small. Clone it and see for yourself.
View on GitHub