Fs-dir-cache – easy and correct CLI file system based caching

Hacker News

I couldn't find any standalone CLI tool like that (there were some libraries for some languages), so I wrote it. It's in Rust, and it was written to speed up larger Rust project CI, on a self-hosted github action runner (where the target directories can be persisted across builds). If it's unclear from the README: it will take care of all the details (allocate, lock, unlock, garbage collect) of handling directories ("values" in the cache), by deriving each directory name ("key") from some files/strings (e.g. Cargo.lock or package.lock). In my use-case: we give up perfect isolation of CI jobs (starting from scratch every time), and instead store and re-use build artifacts. Huge performance gains (most CI runs are now tiny incremental builds) for some extra storage and a small concession.

Visit product View on Hacker NewsSep 8, 2023

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

47%47% 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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · 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.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% 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.

Correct prediction on native model

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