TypeQuicker – The AI Typing Application

Hacker News

I built this app initially for myself to learn to type properly because most typing apps didn't offer exactly what I was looking for: interesting short-form text content, visual hand guides, detailed stats, etc. Typing random words on platforms like keybr and monkey type didn't appeal to me - I would get bored fairly quickly and this limited my practice time Hence, TypeQuicker was born. My speed went from averaging 30-40WPM to 80-120WPM using this app How it differs from other existing apps: it uses engaging content (trivia, quotes, jokes, book snippets) instead of random words. The AI analyzes your typing patterns and generates exercises targeting your specific weak character sequences. It offers very detailed stats after each text - measuring time between every keystroke Features include: - visual hand and keyboard guides for beginners - detailed analytics - daily typing tests with a global leader-board - custom racing rooms - 40+ themes (some community favourites like Gruvbox, Tokyo Night and Dracula) - multiple text topics to choose from - custom text: input whatever you want to type - drills mode: practice typing common character sequences - (Paid) AI personalization: see https://www.typequicker.com/pricing for more info on these features. Built with Go/Redis/Postgres backend and NextJS frontend. Still adding many more features - this is still very much an MVP so I would love feedback from the community. I'm hoping to make this a one-stop shop for everything typing related. Try it at: https://www.typequicker.com

Visit product View on Hacker NewsMay 26, 2025

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

87%87% 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: apps, visual, using · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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: platform · Missing: plus, intuitive, reviews

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

35%35% 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

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · 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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