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Containerized remote attestation with TPM-style hash chaining

Hacker News

Containerized remote attestation with TPM-style hash chaining

I’ve been exploring how remote attestation works and wanted to understand it more deeply, so I built a simple prover–verifier system in Python. It uses TPM-style PCR hash extension, nonces for freshness, and Docker to simulate real-world isolation. The verifier has a web UI where you can upload files to define trusted state, and the prover measures those files and submits a signed quote. It’s not production-grade, but I’d love feedback if you’re into systems security or want to learn a bit about how attestation works under the hood.

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, 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 · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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