Agents In Use
What’s new todayLast update
47 use cases added today

How to gate overnight coding loops

Ouroboros combines a clarification interview with a schema-stability stopping condition. Its author reports a seven-hour unattended hackathon run that produced a hardware-integrated system.

Source published news.ycombinator.comOuroboros
OUR ADAPTATION

Suggested prompt

Suggested prompt written by Agents In Use, not the source author. Replace bracketed inputs and review tool permissions first.

Interview me about [project] to clarify requirements before coding. Define an explicit ambiguity measure and schema-stability criterion, explain their limitations, then implement [scope] and report whether the agreed stopping conditions were met.

Evidence at a glance

Sourced account
Source poster / publisher
q00
Original account
Read the source ↗
Published
Source link checked
Reported result
The author reports a successful hardware-integrated build during a seven-hour unattended run, with approximately 100,000 generated lines including 70,000 lines of tests and mocks.
Human involvement
The workflow includes an interview phase; the post does not describe a subsequent human code review or hardware validation procedure.

A linked account supports attribution. It does not mean we independently reproduced its result.

Report an issue with this caseOpen correction email

Opens your email app with the case link included. Nothing is sent automatically.

How the agent did it

  1. Conduct a Socratic interview until calculated ambiguity falls below 0.2.
  2. Run the coding agent overnight using the experimental gates.
  3. Stop the execution loop when the system schema stabilizes across generations.

The author is building Ouroboros to address coding agents that repeatedly refactor code without making meaningful progress. They attribute many observed failures to ambiguous inputs and describe two experimental gates intended to make long-running loops more stable.

The first gate applies during a Socratic interview. That interview continues until calculated ambiguity falls below 0.2. The second changes the stopping condition for execution: instead of ending after a fixed number of iterations, the loop stops when the system's schema stabilizes across generations. The post does not explain how either measurement is calculated.

At a hackathon in Korea, the author used this approach for an unsupervised overnight run lasting seven hours. They report that the agent generated approximately 100,000 lines of code, including 70,000 lines of tests and mocks, and successfully built a hardware-integrated system. The post also says the project's Deep Interview pattern was merged into oh-my-claudecode version 4.6.0 as an official skill. It does not identify the agent used for the hackathon run.

Who it's for: Developers experimenting with unattended coding loops who want explicit input-clarification and stopping conditions.

Prompt
Suggested prompt (written by Agents In Use, not the author's)

Source: Hacker News · q00

Frequently asked questions

What are the two gates described in the post?

The interview continues until calculated ambiguity is below 0.2. The execution loop stops when the system's schema stabilizes across generations, rather than after a fixed iteration count.

Does the post explain how convergence is measured?

No. It describes schema stabilization as the stopping condition but does not provide its calculation, implementation or validation procedure.

More details

Remaining steps

  1. Produce the reported hardware-integrated system with code, tests and mocks.

About this account

The author reports letting an agent operate unsupervised for seven hours overnight using the experimental gating approach.

Platform: Hacker News. Attribution: https://news.ycombinator.com/item?id=47233847. Prompt: Suggested (written by Agents In Use, not the author's). Sourced by the Agents In Use pipeline.

Credit: q00 on Hacker News

Source published

Added to Agents In Use

Source checked

Tools and topics

Ouroboros

Agent loops · Requirements · Overnight coding

Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.