AI

AI Agents the Unix Way – built with bash, curl, and jq

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AI Agents the Unix Way – built with bash, curl, and jq

While working on an educational exercise tinkering with local models and trying my hand at setting up agents, I went down a rabbit hole: to see how far I could build a custom agent loop using exclusively command-line building blocks and stripping out dependencies wherever possible. It turns out you can get pretty far with pipes, text streams, append only logs, and standard command-line components - concepts pretty well aligned with classic Unix philosophy. The agent is a wrapper composed of a handful of smaller programs, which should allow for flexibly injecting various tool to inspect, filter, redirect, and audit different stages of the agent loop. This project as it stands is a proof-of-concept, but packs enough punch with tool calling support - which in theory should make it indefinitely extensible. With that said, it does appear that the sophistication of tool calling is largely limited by the underlying model, and so far I’ve only experimented with lightweight local models (e.g. llama3.2:1b) that have modest success for tools. Nevertheless, I wanted to share this here and am curious to see if others find it interesting enough to build upon or extend!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
87%87% 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.
Hacker NewsStrong engagement from HN community · Strong signals: llama, pipe, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: exclusive · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, education · Missing: mobile apps, ios, personal
40%40% 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
21%21% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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