An API that returns random Alan Perlis epigrams

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Alan Perlis was a computer scientist known for being the first recipient of the Turing Award and for his contributions to the ALGOL and APL languages. Among his other work, he published a series of epigrams in 1982 about computers and programming that have since been widely quoted. I like these quotes for being humorous and sometimes hiding nuggets of wisdom, so I had the idea of making a website and API that puts them prominently on display. This project was also just an excuse for myself to try out some new tools like Fly.io and using AI code editing tools to build frontends with React (so far my opinion with the latter is that it's helpful, but it still requires a lot of hand holding).

Visit product View on Hacker NewsSep 16, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: computer, new, using · Missing: mac, agents, macos

91%91% 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

79%79% predicted probability of success on Indie Hackers, 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

57%57% 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 · Missing: mobile apps, ios, personal

40%40% 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

35%35% 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

14%14% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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