Recursively detects and decodes multiple formats in nested data

Hacker News

I built an auto-decoder library that can: 1. Automatically detect and decode various formats (URL, Base64, JWT, QueryString, etc.) 2. Recursively parse nested encodings (e.g. URL with Base64 parameters containing JSON) 3. Handle ambiguous inputs by providing multiple decode possibilities with confidence scores 4. Preserve the decode path for nested structures For example, it can parse: - URLs containing Base64-encoded JSON parameters - Query strings with JWT tokens that contain nested Base64 values - JSON objects with multiple encoded fields

Visit product View on Hacker NewsDec 28, 2024

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Did not reach leaderboard

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Indie HackersIH features products with proven revenue · Strong signals: para · Missing: supports, reddit linkedin, podcasting

50%50% 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.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: coding, code · Missing: mac, agents, macos

37%37% 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: ide, io · Missing: https docs, excited, just released

31%31% 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 · Missing: plus, platform, intuitive

29%29% 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

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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