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Show HN: Continuous Claude – run Claude Code in a loop

(github.com)
170 points anandchowdhary | 1 comments | 15 Nov 25 16:27 UTC | HN request time: 0.198s | source

Continuous Claude is a CLI wrapper I made that runs Claude Code in an iterative loop with persistent context, automatically driving a PR-based workflow. Each iteration creates a branch, applies a focused code change, generates a commit, opens a PR via GitHub's CLI, waits for required checks and reviews, merges if green, and records state into a shared notes file.

This avoids the typical stateless one-shot pattern of current coding agents and enables multi-step changes without losing intermediate reasoning, test failures, or partial progress.

The tool is useful for tasks that require many small, serial modifications: increasing test coverage, large refactors, dependency upgrades guided by release notes, or framework migrations.

Blog post about this: https://anandchowdhary.com/blog/2025/running-claude-code-in-...

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janaagaard ◴[17 Nov 25 20:08 UTC] No.45957680[source]▶
Kudos on making Bash readable.

(https://github.com/AnandChowdhary/continuous-claude/blob/mai...)

replies(1): >>45958379 #
jdc0589 ◴[17 Nov 25 21:14 UTC] No.45958379[source]▶
im not saying OP did this, but I've actually had AI spit out some pretty stellar bash scripts, surprisingly
replies(3): >>45958439 #>>45961287 #>>45967647 #
1. rkozik1989 ◴[18 Nov 25 15:37 UTC] No.45967647[source]▶
I don't think its that surprising. Bash is old as dirt and scripts by definition are meant to be simple. Where AI struggles is when you add complexity like object-oriented design. That's when the effect of it trying to solve every problem in a way unique to it just takes shit off the rails. LLMs known design patterns exist but they don't know how to use them because that's not how deep learning approaches problem solving.