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HoneypotScan – Detect crypto scam tokens

Hacker News

HoneypotScan – Detect crypto scam tokens

I built a tool to detect honeypot tokens (ERC20 contracts that let you buy but block selling). It uses 13 regex patterns to scan for tx.origin abuse and other sell-blocking techniques. *How it works:* - Fetches verified source code from Etherscan - Runs pattern matching for common honeypot techniques - Returns results in ~2 seconds - Threshold: 2+ patterns = 95% confidence honeypot *Architecture:* - Cloudflare Workers for edge computing - KV for caching (95% hit rate, 24hr TTL) - 6 Etherscan API keys with rotation - Supports Ethereum, Polygon, Arbitrum *Key insight:* Smart contracts are immutable, so aggressive caching works perfectly. This lets the free tier handle high traffic ($0/month). *Limitations:* - Only detects sell-blocking honeypots - Doesn't catch obfuscated code, proxy patterns, or time bombs - Not a full security audit Live: https://honeypotscan.pages.dev Source: https://github.com/Teycir/honeypotscan Open to feedback on detection patterns or evasion techniques I might be missing.

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
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.
Product HuntOn track for Day 1 leaderboard · Strong signals: code, open · Missing: mac, agents, macos
68%68% 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
54%54% 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: month · Missing: mobile apps, ios, personal
42%42% 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
30%30% 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
21%21% 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, smart · Missing: web3, chat, cryptocurrency
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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