I built an active community of trans people online

Hacker News

A year ago I surveyed the internet and noticed there was only one popular space for trans and gender-non-conforming people to meet; Lex. Lex is not well liked by its users. Its software feels heavy and it is full of cash grabs and anti-patterns. It was recently acquired and is sure to only become more hostile to its users as it turns towards profit generation. With this in mind I built t4t, an alternative specially designed for not only queer people, but specifically trans people. It is an extremely lightweight service. I built it with my most ideal stack: Flutter, Svelte, Supabase, Posthog. It has grown in the last year to about 4,000 monthly active users. I think it could grow way beyond that this year.

Visit product View on Hacker NewsJan 23, 2025

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

90%90% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month, monthly, users · Missing: mobile apps, ios, personal

61%61% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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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