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Fume (YC W24) – bot to test all of your pull requests

Hacker News

Fume (YC W24) – bot to test all of your pull requests

Hi HN! We are building an AI agent that can use a computer for software tasks. You can have it review and manually test all your pull requests. Here is a demo video of how it works: https://www.loom.com/share/71135bb2ef784ff593940fbe6fb42c4c?... We realized the current technology is not yet capable of automating non-trivial software tickets reliably but is pretty good at manual testing code changes for bugs. On the other hand, we knew tools like CodeRabbit got interest from developers yet could only point out obvious bugs or style nits as they are limited by ‘reading’ the code. Fume can use its own computer. If you’ve made UI changes, it can spin up a browser and click through edge cases. If you’ve made more functional changes, it can write and run test scripts to ensure there are no bugs. We are language/framework agnostic. The setup is pretty easy. You just install a Github app and set up Fume’s computer (once) like you would set up your local environment. We’re thinking of adding integrations like PostHog to give Fume more context around using your complex app. We’d love to hear your feature requests too! We have a 30-day free trial (no CC): https://fumedev.com - please do tell us what you think!

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, computer, new · Missing: mac, agents, macos
95%95% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
59%59% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
40%40% 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
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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