Why I built another GitHub star tracker

Hacker News

I often find myself wanting to quickly compare the adoption curves of different open-source projects, but I usually bounce when a tool asks me to generate and paste a GitHub personal access token just to view a chart. To make this completely frictionless, I built Startrail. It’s a simple visualizer that handles the API fetching under the hood, so you can just drop in some repos and get the data instantly. A few quick features: - Zero setup: No API keys, no logins, and no rate-limit warnings. - Side-by-side comparison: You can plot up to 12 repositories on the same chart. - Clean and accessible: Work smoothly on mobile for quick checks on the go. I originally built it for my own quick benchmarks, but I’m actively tinkering with it this weekend. I’d love to hear what the HN community thinks. What context would make this even more useful for you—forks, release version markers, or something else? Feel free to take it for a spin!

Visit product View on Hacker NewsMar 28, 2026

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: context, visual, open · Missing: mac, agents, macos

72%72% 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.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

45%45% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

37%37% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, visualize · Missing: mobile apps, ios, entrepreneurs

26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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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