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Litterbox – Somewhat Isolated Development Environments

Hacker News

Litterbox – Somewhat Isolated Development Environments

Have you ever worried about supply chain attacks (or even rogue AI agents) possibly compromising your entire development system? To minimise damage in such a scenario, I've built https://litterbox.work/ ( https://github.com/Gerharddc/litterbox ) as a way to have somewhat isolated Linux development environments without sacrificing too much convenience. It's still in the very early stages with plenty of rough edges so any contributions or suggestions would be greatly appreciated!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: 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
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: agents, agent · Missing: mac, macos, cursor
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
25%25% predicted probability of success on Indie Hackers, 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
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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