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TX

TXTD – Instant Markdown Page and Links Sharing (Go, HTMX and SQLite)

Hacker News

TXTD – Instant Markdown Page and Links Sharing (Go, HTMX and SQLite)

Introducing TXTD - a minimalist platform for creating and sharing Markdown-based pages and notes with custom URLs. Try it out: https://txtd.cc Example: https://txtd.cc/example Clean and distraction free interface. Ideal for developers, students, researchers or anyone needing to create and share formatted text content. We'd love to hear your thoughts and feedback. Tech: Golang, HTMX & SQLite

Visit productView on Hacker NewsSep 20, 2024
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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: notes · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
26%26% 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
16%16% 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
6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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