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HexoRama – client-side crypto address decoder and debugger

Hacker News

HexoRama – client-side crypto address decoder and debugger

Crypto address validators usually answer one question: “valid or not?” HexoRama focuses on the next question: why. It’s a fully client-side web tool that decodes and visualizes the internal structure of cryptocurrency addresses. Currently supported: Bitcoin: P2PKH, P2SH, Bech32 (SegWit), Bech32m (Taproot); Ethereum / EVM: EOA addresses with EIP-55 checksum validation; USDT: ERC-20 (Ethereum), TRC-20 (Tron), Omni (Bitcoin); BNB Chain: BEP-20 (EVM-compatible). Instead of just validating, it breaks addresses into components like prefixes, payloads, checksums, witness versions, and contract metadata (for known token contracts). It also assigns a “format score” to indicate how modern and well-formed an address format is (e.g. Taproot vs legacy). Everything runs locally in the browser — no backend, no APIs, no address storage. I built this primarily as a debugging and learning tool. Feedback from people who’ve worked with address parsing, wallets, or crypto infra would be especially welcome. https://hexorama.com/about/

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Actual performance

1points
1comments
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
85%85% 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: ide, 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.
TrustMRRLess likely to generate early MRR · Strong signals: visualize · Missing: mobile apps, ios, personal
37%37% 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
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: visual, code, apis · Missing: mac, agents, macos
25%25% predicted probability of success on Product Hunt, 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 · Strong signals: crypto, cryptocurrency · Missing: web3, chat, make money
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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