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Blackholes – A macOS workspace for coding agents

Hacker News

Blackholes – A macOS workspace for coding agents

Hi HN, I'm building Blackholes, an open-source macOS app for organizing coding agents, Git repositories and terminals. A project can contain multiple repositories, and tasks can have their own branches and worktrees. For example, you can work on a feature across a frontend and backend repo, keeping both under the same task. It supports Claude, Codex, Gemini and OpenCode, with project and task notes, file editing and Git diffs available in the workspace. The app is built with Rust and React. Source code: https://github.com/andygeek/blackholes I'd appreciate feedback from people working with coding agents across multiple repositories, especially on what would make this fit into your existing workflow.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, macos · Missing: cursor, model, apple
99%99% 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, gemini · Missing: reddit linkedin, podcasting, created
73%73% 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: exist, existing · Missing: https docs, excited, just released
39%39% 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 · Missing: mobile apps, ios, personal
37%37% 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
19%19% 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.

Correct prediction on native model

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