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Argus-seal – Forensic-ready log integrity using Merkle Trees

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Argus-seal – Forensic-ready log integrity using Merkle Trees

I built Argus-seal because standard log hashing (like SHA256 on a flat file) makes it impossible to pinpoint which specific entry was tampered with in a massive dataset. It uses a Merkle Tree structure to "seal" log batches. The core value-add is the "Strict Mode" verification: it doesn't just return a boolean, but traverses the tree to identify the exact index of the compromised log entry in O(N *log N) time. Key Technical Highlights: - Deterministic Canonicalization: Ensures that JSON/Dict entries produce identical hashes regardless of key ordering. - Granular Diagnostics: Pinpoint specific tampered leaves without re-hashing the entire historical log. - Lightweight & FOSS: Zero-dependency Python implementation designed for easy integration.I’m looking for feedback on the tree traversal logic and whether this approach satisfies real-world compliance (SOC2/HIPAA) requirements. I'd love to hear your thoughts or "roasts" on the implementation! GitHub: https://github.com/gamer-null/argus-seal

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
43%43% 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
36%36% 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
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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