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Cloud file conversion security model (S3-only, 24h retention)

Hacker News

Cloud file conversion security model (S3-only, 24h retention)

Hi HN — I’m building Docpose.cloud (online file conversion + API). File processing is a trust problem, so I wrote a security + processing architecture page that explains exactly how files move through the system. Highlights: S3-only pipeline: workers read from object storage and write results back No permanent local disk persistence on workers Source files deleted immediately after processing Converted files expire after 24 hours by default Operational logs kept up to 30 days (metadata only, no file contents) I’m posting this because security questions come up every time someone evaluates file-processing infrastructure. Feedback welcome — especially on what you’d want clarified for a vendor security review.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, plain · Missing: mac, agents, macos
71%71% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, pipe, io · Missing: https docs, excited, just released
40%40% 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
33%33% 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
32%32% 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
14%14% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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