TiniText – Small tools for transcription, summaries and drafts

Hacker News

Hi HN, This is my first time sharing something like this here. I built TiniText to solve a problem I kept running into myself: I needed simple tools for transcription, summaries, and drafts, but most options felt heavier than necessary for everyday use. Instead of building a large platform, I focused on small, single-purpose tools with predictable behavior and minimal setup. The app supports: - Audio transcription (single and multi-speaker) - Text summarization with adjustable detail - Simple draft generation (blogs / meeting notes) - A few practical text utilities It’s production-ready but still early, and I’d really appreciate feedback on: - where the UX feels unnecessary - what’s missing for real-world use - whether “small focused tools” is still a compelling direction Happy to answer questions.

Visit product View on Hacker NewsJan 28, 2026

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Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

77%77% 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 · Strong signals: single, notes · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, 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

32%32% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · 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 · Strong signals: audio · 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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