Fedv.link – a way to share Mastodon links that can be interacted with

Hacker News

One of the great, defining characteristics of the Fediverse is that it is on numerous, autonomous servers that all work together. You can follow other users on other servers — but sharing links to profiles and posts can be tedious. You can view the content on the origin server, but you need to be on the server hosting your account to boost, favorite, or follow. Much like how the mailto: link opens your preferred email application to let you email anyone, fedv.link links are designed to easily open ActivityPub posts and profiles in the end user's preferred server.

Visit product View on Hacker NewsJul 13, 2024

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, activity · Missing: mac, agents, macos

71%71% 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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

59%59% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive

37%37% 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

13%13% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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