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Memorific, SRS for Software Developers

Hacker News

Memorific, SRS for Software Developers

Howdy Hacker News, Over a year ago I was inspired after reading Derek Sivers post on using Spaced Repetition to improve programming skills so I set out to do likewise. I couldn't find anything that enabled my criteria, so I built it, http://www.Memorific.com is: 1) the usual flashcard stuff with all sorts of question types 2) mobile web experience so I could use it anywhere but offline 3) an ability to easily collaborate on decks, not the usual import/export, but "build together" 4) code highlighting, github-flavored markdown, etc It's been in beta for many months and we've got the core of our MVP locked in. Hopefully y'all will find it useful too. Cheers, Zack

Visit productView on Hacker NewsFeb 10, 2014
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, code · Missing: mac, agents, macos
87%87% 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
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released
63%63% 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: month · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · 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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