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GoFigr – real-time, figure-centric version control for Jupyter and R

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GoFigr – real-time, figure-centric version control for Jupyter and R

Hi HN! After spending the better part of the last decade working in data science, I realized that figure management is (a) a real issue, (b) poorly supported by existing tools. Specifically: 1. Data Scientists generate a ton of figures, many of which invariably end up in Slack, emails, and PowerPoint. 2. Being able to trace a figure back to the notebook is a royal pain. Especially if the code has changed. Even if you use git, it's unrealistic to git commit for every figure, and searching for figures in git history is well, painful. Which is why I created GoFigr. GoFigr is a Jupyter plugin/R library which automatically adds QR codes to your figures and syncs them with the GoFigr service. We also store the notebook name and the full source code (IPython history in Python) as it existed when the figure was generated. Furthermore, we support text search within figures as well as reverse image search, so you can always find the exact revision and reproduce it with ease. Building this has been a ton of fun. The backend is mostly Python + Django + Postgres + Elastic Search. Frontend is React. I'm super excited to show it to the HN community!

Visit productView on Hacker NewsFeb 25, 2025
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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, 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, exist, existing · Missing: https docs, just released, lua
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: slack, email, code · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
53%53% 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
31%31% 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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