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Ouijit, command terminals running coding agents

Hacker News

Ouijit, command terminals running coding agents

Hey folks, I started working on Ouijit around the start of the new year, and its since become the daily driver for myself and a handful of other engineers (that I know of). The initial reason I built it was over dissatisfaction with how most agent orchestrators abstract stuff behind chat UIs, require login, or only support their companies own model/harness. I didn’t want to keep cobbling together scripts for worktree isolation between tasks though, so the yak shaving began. A couple weeks in, I received a message from a friend sharing that they just finished an 8 hour session working in it and they really liked it, and that they’d even shared it with their coworkers unprompted. I’ve been a software engineer/designer for ~10 years and its hard to put into words how rewarding that felt. Some things I think are unique about Ouijit: - Sandboxing is a first-class consideration, Ouijit supports running tasks in per-project Lima VMs. Right now I'm looking into making sandboxing pluggable so you can bring-your-own agent environment local or in the cloud, with a few baked in defaults. - Integrated agents are automatically aware of a session-scoped CLI that enables them to orchestrate and drive tasks from any Ouijit terminal, with no additional setup. - Tasks have lifecycle hooks that can trigger prompts, scripts, or both when tasks start, resume, move to review, or move to done. - You can organize child terminals, markdown, and url previews to terminals. It's a little like Claude artifacts in that way. Supports macOS and linux; integrates with Claude Code, Codex, Pi, and OpenCode agent harnesses. Free and open source, no login, no telemetry. Website: https://ouijit.com Github : https://github.com/ouijit/ouijit

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Actual performance

4points
2comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: cursor, apple, agentic
97%97% 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 · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
56%56% 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
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive
35%35% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, reward · Missing: web3, crypto, cryptocurrency
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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