Bluesky has no built-in analytics so I created them

Hacker News

Hey HN, my name is Danilo Fiumi and I'm building small startups, pretty much weekly. While creating a startup nowadays can be very simple, creating an audience turns out to be extremely complex. Sometimes the problem isn’t even the quality of your content. But for sure the lack of consistency weighs a lot. So, few weeks ago I was messing around on Bluesky and I was really frustrated that it didn't have built-in analytics to track my growth. I had to equip myself. So, I built something simple, designed to scratch my own itch and keep myself consistent along the way. I hope it can help some of you as well! Would love to hear your feedback.

Visit product View on Hacker NewsFeb 28, 2025

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

84%84% 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.

Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue

20%20% 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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