Py

Python.cards – Learn Python with spaced repetition

Hacker News

Python.cards – Learn Python with spaced repetition

Hi HN! During the last year, I've been building python.cards: a site to learn Python using spaced repetition. My goal is to make the most of spaced repetition by making it extremely simple to use and by providing high quality flash cards. The site has been live for a month, with a few daily users who had joined the waitlist. The feedback has been quite positive, with most of the users using the site every day. Currently, we have a free deck (A Tour of the Stdlib) and a paid one (Pathlib in depth, for $9.99). I have other decks in the works, covering topics such as f-strings, collections, nomenclature, built-ins, exceptions and more. Your feedback is very welcome for me to prioritize which decks to build first. In my way to launch, I've open sourced simple-spaced-repetition, a Python module that implements the classic Anki algorithm ( https://github.com/vlopezferrando/simple-spaced-repetition/ ), and I released some videos live-coding the site and building the decks ( https://www.youtube.com/channel/UCyWUj9r0soytotuuh2JnPrw ).

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: user, using, coding · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: video, month, users · Missing: mobile apps, ios, personal
51%51% 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
48%48% 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
18%18% 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.

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