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Buboflash – spaced repetitions software with PDF incremental reading

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Buboflash – spaced repetitions software with PDF incremental reading

Greetings, Anki and Supermemo users, I created a new website for learning: https://buboflash.eu - it has incremental reading and flashcards repetitions. As a long term aficionado of spaced repetitions, I come in peace, to complement Anki and Supermemo :-) Buboflash is on the way of better and better integration with Anki now. Buboflash lets us upload a PDF, annotate it in terms of both text annotations and image occlusions (in incremental reading process), and then export it to Anki (or Supermemo, I figured out their XML "factors" later). Annotating works on regular websites as well, and in case of Wikipedia LaTeX, it creates actual editable LaTeX objects on Buboflash site. The other features are full versioning history that allows for non-conflicting edits by mutliple users and extensive search, like full text search on annotated PDF page ranges, flashcards derived from specific pages etc. I hope we can expand "global" database of flashcards related to particular books, subjects etc, rather than scattered notes - how many times you have seen a collection called "chemistry", that can be anything, from names of elements, to specialised notes from a lecture? I hope the way I implemented search and versioning will help towards the goal. With versioning, you can only see your edits and people you follow, so flashcards will not unexpectedly change under your feet, but you still benefit from updates from people you trust. Enjoy - and ask any questions you may have! Piotr PS. I am closer to importing Anki databases too, I am almost done with implementing Anki's templating system. https://buboflash.eu/static5/app/demo/2018.07.15/anki-from-b... https://buboflash.eu/static5/app/demo/2018.07.15/occlusion-i... https://buboflash.eu/static5/app/demo/2018.07.15/reading-fro... https://buboflash.eu/static5/app/demo/2018.07.15/whole-equat... https://buboflash.eu/static5/app/demo/2018.07.15/image-occlu...

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, latex · Missing: supports, reddit linkedin, podcasting
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.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, notes · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal
54%54% 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
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 · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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