A tiny (480 LOC) AI coding assistant for your shell

Hacker News

Hi HN. This is a script I have been using for some time for coding tasks, which I have polished a bit and published. The motivation behind this script was to have an agent with persistent session which I can use from the shell like a regular command. It _can_ also be scripted, although I don't use it in such a way. The script uses a minimal prompt, which just tells it to use standard unix tools to do everything. I was surprised at how well it worked, when using a good model. Qwen3-coder is on openrouter, and it works well with the minimal instructions. I also tested qwen2.5-coder with ollama and that works well too.

Visit product View on Hacker NewsJul 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, tiny · Missing: mac, agents, macos

94%94% 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

63%63% 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: llama, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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.

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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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