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License Detector – the fastest, most accurate license detection tool

Hacker News

License Detector – the fastest, most accurate license detection tool

Years ago when I worked for Shogun, I was handed a compliance task: gather up all the LICENSE files from our codebase, so we could make sure all our products were legally compliant. It was not as easy as it sounded -- I had to use multiple different command line tools, merge all the results from the disparate tools together, and then manually verify that the data was correct. I made a mental note at the time that I should build something that made this easier one day. Years later, I'm making good on that promise: introducing License Detector ( https://licensedetector.com ) -- a license detection tool that works across 21 ecosystems, basically every major language, and it's free in the web app for any open/public repo! I'm also releasing the CLI for free as well ( https://github.com/licensedetector/cli ), so you can run it on your own repo before installing anything. The tool is ~80x faster than licensee, and matches or beats askalono and scancode-toolkit on accuracy in head-to-head benchmarks (I've included the benchmarks as well in the repo if you wish to run them yourself). Hope people try it out and enjoy it, and would be glad to answer any questions! CLI: go install go.licensedetector.com/cmd/license-detector@latest GitHub App: https://github.com/apps/license-detector Benchmarks & how it works: https://licensedetector.com/compare#how-we-stack-up

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
66%66% 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: apps, code, open · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
52%52% 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: apps, para · Missing: mobile apps, ios, personal
40%40% 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
33%33% 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
17%17% 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.

Incorrect prediction on native model

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