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Flashpaper – Self-destructing secret sharing with no database

Hacker News

Flashpaper – Self-destructing secret sharing with no database

Hi everyone! This is my first HN and I’m very new to the scene. My name is Min from Bangkok. At first, I just want to create a dead man's switch for personal use and for fun. then, I think about information that self destruct like a spy movie. after that, I try to come up with the better version of Privnote or Bitwarden with self-destruct and some kind of censoring or blocking download ability. Somehow, end up with this product. :O Flashpaper is for sending any information that would be burned after reading (with counting down timer like Mission Impossible movie ! or after 24 hrs max if not opened) Encryption happens in browser and because the key stays after # in the link; server never sees the key (Zero-knowledge for web use) and— because I’m a newbie. I don’t want to connect to database because I don’t have the money and I want things light and simple. So, that’s why Flashpaper keeps things in RAM-only, no database. For AI Agent side, Flashpaper provides a REST API and an MCP server so agents can create secret links easily in dead-drop style that can be claimed only once. The second claim would get a 404 which means someone already took it. However, for the agent API flow, the server sees the plaintext for a moment before encrypting, so this flow is not zero-knowledge like the web flow. Overall, I think it work quite well for web use, but for agent API use, I am not sure this is enough security. All the limitations are listed in SECURITY.md. Some feedback would be appreciated. I make it open source with MIT license, with honorware policy for Enterprise use, like self-hosted docker. Here is my repo https://github.com/mmmpym/flashpaper and you can try it here https://flashpaper.app Again ! Please feel free to tell me what I missed. Min

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, mcp · Missing: mac, macos, cursor
91%91% 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
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released
57%57% 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: personal · Missing: mobile apps, ios, entrepreneurs
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
44%44% 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
22%22% 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.

Correct prediction on native model

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