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Terminal-Based Flashcards in Bash

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Terminal-Based Flashcards in Bash

Mostly a proof of concept based on fla.sh[1]. Proposed changes are : ## minimalist interface I wanted the quiz to show up in the middle of an otherwise empty screen, kind of like with what is done in the so called "distraction free" editors. ## only two results per quiz For various reasons I don't like the typical 5 possible notations (hard, difficult, normal, mild and easy or something like that). So only two possibilities : either you know the answer, or you didn't. So only two keys to press : enter or space. ## A different spaced-repetition algorithm. The most ambitious parts. I want to experiment using softmax and a somewhat complicated (but easily customizable) scoring system that would be entirely based on the activity log. Very much a work in progress, so far. 1. https://github.com/tallguyjenks/fla.sh

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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: activity, using · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
49%49% 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: interface · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% 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.

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