We

We have just released our first Debloating tool for Containers

Hacker News

We have just released our first Debloating tool for Containers

We have been working on tools for safe debloating of software for a few years now at the university. BLAFS is one of our first tools to release for file debloating, a bloat-aware filesystem for container debloating. It detects the files used by the container, and the debloats the container removing the unused files. The debloated containers are fully functional and can run the same workload as the original containers, but with a much smaller size and faster deployment. Check the paper for more details: https://arxiv.org/abs/2305.04641

Visit product View on Hacker NewsFeb 20, 2025

Actual performance

5points

4comments

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: just released, filesystem, io · Missing: https docs, excited, exist

69%69% 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.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

40%40% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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