Watermelon – GPT-powered code contextualizer

Hacker News

Hey there HN! We're Esteban and Esteban and we are looking to get feedback for the new version of our GPT-powered, open-source code contextualizer. We're starting with a VS Code extension that indexes information from git (GitHub, GitLab, or Bitbucket integrations available), Slack and Jira to explain the context around a file or block of code. Finally, we summarize such aggregated context using the power of GPT. As devs we know that it's very annoying to look at a new codebase and start understanding all the nuances, particularly when the person who wrote the code already left the company. With this problem in mind, we decided to build this solution. You'll be able to get into "the ghost" of the person who left the company. Soon, we will also be building a GitHub Action that does the same thing as the VS Code extension but at the time of creating a PR: Index the most relevant information related to this new PR, and add it as a comment. This way we will provide context at one more moment, and also, we will be making the IDE extension better. Here's our open source repo if you also want to check it out: https://github.com/watermelontools/watermelon-extension Please give us your feedback! Thanks.

Visit product View on Hacker NewsMar 21, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, new, context · 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: open source, 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, soon · 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: way · Missing: mobile apps, ios, personal

39%39% 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.

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