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Data Encoding for Human Input

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Data Encoding for Human Input

A combination of Reed-Solomon forward-error-correction codes, and a binary-to-text encoding that allows common human-input errors to be detected and possibly corrected. This results in a highly resilient code which is very likely to work. Use for recovery codes, coupons, user account IDs, document tracking, or anywhere else you really want the user to be successful even if they've written it down incorrectly or made a mistake in typing. It's 4 bits per character, so the same density as hex, plus RS codes.

Visit productView on Hacker NewsSep 29, 2025
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Actual performance

1points
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Launch Intel predictions

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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.
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
59%59% 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, coding, code · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
43%43% 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
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
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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