Gi

GitNotebooks – Review Jupyter Notebooks from GitHub

Hacker News

GitNotebooks – Review Jupyter Notebooks from GitHub

Hey, everyone! I'm excited to share my latest project with you all - GitNotebooks. It's a tool that allows you to easily review Jupyter Notebooks from GitHub pull requests. As a software engineer working in the ML space, I found that there weren't many options for diffing and reviewing Jupyter Notebooks from GitHub pull requests. So, I decided to create a solution that provides the ability to do so in a way that matches the GitHub look and feel, with the ability to add inline comments alongside precise markdown and code diffs. GitNotebooks offers the following features: - Precise markdown and code diffs with inline comments - Integration with PR creation - (a link is posted to the PR if a notebook change is detected) - Easy setup with just a few clicks You can check it out for yourself by visiting the website: GitNotebooks.com I would love to hear your feedback and answer any questions you may have. Thanks for taking the time to check out GitNotebooks!

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Actual performance

3points
1comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
82%82% 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: excited, ide, io · Missing: https docs, just released, exist
65%65% 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
60%60% 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: way · Missing: mobile apps, ios, personal
24%24% 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
11%11% 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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