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Crocodile - Better code review for GitHub

Hacker News

Crocodile - Better code review for GitHub

Hi HN! I've been working on a code review app for GitHub called Crocodile for about a year. I used to work at Microsoft where we used a tool called CodeFlow for reviewing code and I missed it after I left. I know many other ex-Microsoft engineers feel the same. Here are some of the distinguishing features of Crocodile that are inspired by CodeFlow: * Comments float above the code instead of being inline. Long discussions that are displayed inline make it really hard to review the code. * Comment on any text selection in the file, even a single character. * Comments don't get lost when code changes. I hate it when comments become "outdated" because I rebase or the line is edited. I also implemented lots of features that I wish CodeFlow had which you can read more about on the blog. [1] For those curious about the tech stack: it's mostly written in Go with Alpine.js, HTMX, and Tailwind CSS for the frontend. For storage I use PostgreSQL, S3 compatible object storage, and Redis for caching. I use Pulumi for infrastructure provisioning and Kubernetes deployments. Everything is hosted on DigitalOcean. Feedback is welcome! [1] https://www.crocodile.dev/blog/why-crocodile

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, code · Missing: mac, agents, macos
88%88% 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: code review, io · Missing: https docs, excited, just released
75%75% 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 · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
25%25% 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
15%15% 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.

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