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Room Service – understand what's filling your Mac

Hacker News

Room Service – understand what's filling your Mac

Built this after repeatedly running into “Disk is full” on macOS with no clear explanation. Started as a cleanup tool for dev environments (Xcode, Docker, node_modules), but the harder problem turned out to be understanding what’s actually taking space. macOS groups a lot of this into “System Data”, which isn’t very actionable. Recently, local AI tools made this worse. Model files and caches (Ollama, Hugging Face, Whisper, etc.) grow silently and are spread across different locations. The goal is to make disk usage more transparent before deciding what to remove. Would appreciate feedback, especially from people running heavier dev setups.

Visit product View on Hacker NewsMar 25, 2026

Actual performance

3points

1comments

Did not reach leaderboard

Launch Intel predictions

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

91%91% 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: started · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, 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

33%33% 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

27%27% 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

20%20% 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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