Po

Pointers – privately share links to your online presence

Hacker News

Pointers – privately share links to your online presence

Today I launched Pointers, a simple, low-tech directory to help share links to your online presence with friends and mutuals. Unlike similar services like Linktree, Pointers is designed for private or intimate links you may not want to make visible to the world - a sort of forwarding service for the Internet, inspired by the concept of the forwarding pointer in computer science. It works very simply: other users search for you by your online names, and request access to your links for a short period of time. After their access expires, they'll have to request again. Pointers has just launched, so please let me know if you spot any bugs. The code is up on GitHub!

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% 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: users · Missing: plus, platform, intuitive
54%54% 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, computer, code · Missing: mac, agents, macos
51%51% predicted probability of success on Product Hunt, 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
48%48% 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: users · Missing: mobile apps, ios, personal
30%30% 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
10%10% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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