Nonstop, two files that keep Claude Code working while you sleep

Hacker News

I use Claude Code for big tasks overnight maybe twice a week. About 70% of the time I come back to nothing. It stopped in the first few minutes because it had a question. Or hit a permission error on step 2 of 47 and just sat there. All night. nonstop is my fix. Two files, no dependencies. Before you walk away, /nonstop makes Claude do a pre-flight. It thinks through the whole task, asks every question it might have, and you approve or deny anything destructive (rm -rf, force pushes, posting PRs, calling paid APIs). All the "would block me at 3am" stuff gets resolved while you're still at the keyboard. Then a stop hook keeps it from quitting. If it gets stuck: try to solve it, try to work around it, or mark it blocked and move on to the next thing. No spinning. There are other tools for this (Ralph is a ~2k line bash orchestrator that wraps the CLI, taskmaster uses completion tokens). nonstop is deliberately minimal. Skill file + shell script. Felt like the right tradeoff for something I want to trust overnight. Repo: https://github.com/andylizf/nonstop

Visit product View on Hacker NewsMar 25, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, tasks, code · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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