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Quickify – EBooks/Web/RSS Android Reader Boosted by Speed-Reading Modes

Hacker News

Quickify – EBooks/Web/RSS Android Reader Boosted by Speed-Reading Modes

Hey, HN. I am excited to present to you what my pet project has turned into after three years of development. One day, I decided to create the most convenient application for reading all my favorite articles, as well as RSS feeds from Hacker News and other sources. The usual reading approach was not enough for me, so I loaded this application with five different reading techniques that help to achieve maximum productivity and reading speed. 1. Text-to-speech is the most popular reading method among Quickify users. I'm using it when I'm busy or my eyes need a rest. This method is available in many other applications, but trust me, I've optimized it to be the best among them all. 2. Speed-Reading, also known as RSVP or Spritz. This method is based on the fact that the speed of reading is limited by the speed of eye movement. By displaying one word at a time, you can read almost twice as fast with almost no training. 3. The Cursor reading method, with its moving cursor that highlights text at a preferred speed, is designed for enhanced focus and faster reading. It guides through the text, reducing distractions and the likelihood of skipping or re-reading lines. 4. The Bionic Reading method emphasizes the initial parts of words to speed up reading and comprehension. It helps the brain quickly process text with less cognitive effort, which is great for managing ADHD. 5. The traditional method of reading is still represented in the application, with all the usual tools such as highlights, notes, word translation, etc.

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Actual performance

7points
4comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, user, new · Missing: mac, agents, macos
79%79% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, adhd, hacker news · Missing: https docs, just released, exist
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
19%19% 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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