HN

HN Karma Tracker Using GitHub Actions

Hacker News

HN Karma Tracker Using GitHub Actions

Hi HN! I built a simple tool that automatically tracks and visualizes your HN karma over time using GitHub Actions. How it works: - Uses GitHub Actions to fetch karma data daily - Stores historical data in JSON format - Generates visualization with trend analysis using seaborn - Self-hosted: Fork the repo and add your HN username as a secret Technical details: - Written in Python - Automated with GitHub Actions workflow - Data persistence through Git commits - Visualization includes moving averages and trend lines Quick start: 1. Fork the repo 2. Add HN_USER_ID secret 3. Enable GitHub Actions 4. Get daily karma updates and visualizations Repository: https://github.com/nkkko/hn-karma-tracker I built this to track my karma growth over time and learn more about GitHub Actions automation. Would love feedback on making it more useful!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
65%65% 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.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, visual, using · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: visualize · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
19%19% 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.

Correct prediction on native model

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