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Limmat – Local Immediate Automated Testing

Hacker News

Limmat – Local Immediate Automated Testing

Every job I've worked in has had CI that is in some way unsatisfactory for me personally. But, I tend to work on projects with lots of configurations and tests that I want to run. So, I made Limmat as a way to get want I want out of the CI as a developer (which is often a bit different from what is needed from the CI for project health, and always different from what the CI infrastructure is capable of delivering). It started as an exercise to get used to Rust, but it turned into a pretty valuable part of my workflow. Basically it adds a new point on the spectrum of <things that run tests>. It's more lightweight and easier to configure than CI, but it supports a more rigrous workflow than stuff like Bacon that just optimises for fast feedback. [1] https://github.com/Canop/bacon

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
78%78% 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: supports, started · Missing: reddit linkedin, podcasting, created
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
51%51% 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
24%24% 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
18%18% 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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