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flashcasts: flashcards in a private podcast feed

Hacker News

flashcasts: flashcards in a private podcast feed

From the /about page: I just wanted to study biochem and spanish while digging holes in my garden. Computers recently learned how to talk like humans. Actually, they've been talking for a while[1], but it only recently became affordable[2]. [1] https://en.wikipedia.org/wiki/Speech_synthesis#Electronic_de... [2] https://openai.com/pricing And by some kind of nerd magic, the cost is low enough for me to share it freely on the internet! If you create an account, you can create mix-and-match flashcast decks and tweak all the knobs. Knowledge probably shouldn't be a class issue, so this site is committed to a frugly[3] pricing model. And yes, there's a lifetime license for all y'all with subscription fatigue. [3] https://taylor.town/frugly If you're curious about the Craigslist vibes, I went all-in on a cheap web[4] stack. I want this site to run on a 2010 potato phone clinging to one measly bar of cell service. Of course it's ugly, but at least it's somewhat accessible and not eating our sunshine[5]. [4] https://potato.cheap/ [5] https://en.wikipedia.org/wiki/Solarpunk To keep things extra cheap, I generated most of the cards with GPT. This is obviously not ideal, but I've only got ten fingers and a skull filled with glitter. If you can, please help me increase quality by contributing cards and reporting errors. Email me at hello@taylor.town and I'll get back to you eventually. Anyway, instead of learning biochem and spanish I went and built this stupid website. Now go forth and download knowledge[6] through your ears! [6] https://www.youtube.com/watch?v=jksPhQhJRoc ♡ taylor[7] [7] https://taylor.town

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3points
1comments
Did not reach leaderboard

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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: model, computer, email · Missing: mac, agents, macos
71%71% 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
68%68% 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: way · Missing: mobile apps, ios, personal
48%48% 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
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue
17%17% 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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