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Lectito, a speed reader for Windows Phone

Hacker News

Lectito, a speed reader for Windows Phone

I am a big speed reading fan, especially the idea of being able to use rapid serial visualization presentation (RSVP) on a phone. The idea behind RSVP is that it takes a considerable amount of effort and time to move your eyes' focus from word to word when you are reading, especially on a phone where the font might be tiny. Because of this you can increase immensely your reading speed if instead of having to move your focus from word to word, you focus on a point and the word changes at a certain cadence. Typically, with some practice you can increase that cadence to numbers such as 500 words per minute comfortably, which is a lot more than what the average person can read and works brilliantly if you read light stuff (I wouldn't use it for hard technical stuff, because you need more time to process the information for it to make sense). I looked for speed reading apps for WP8 and found a few, but they required you to either copy&paste the text you want to speed read, required the text to be in OneDrive or that you'd copy the url of the page you wanted to speed read. So I did Lectito (it means reading in Latin): http://www.windowsphone.com/en-us/store/app/lectito/c4f00daf-d815-4868-8e65-a55ae40708e2 Lectito can be "launched" from Internet Explorer using the sharing option and it also supports pictures (it will display them and their "alt" text), which was another feature lacking in all the apps I've tried. I'm still adding features and making stuff better in general. I'd really appreciate feedback in terms of features/problems that the app might still have. All of the code that is not UI related is PCL, so it isn't too hard to port it to other platforms. I've chosen Windows Phone because that's the phone I have now (Lumia 920 and I really like it, especially the camera), and because I thought it would be the mobile platform where the market is less saturated. Thanks! Rui

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
92%92% 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, tiny · Missing: mac, agents, macos
72%72% 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
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.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
36%36% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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