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Fluffy – dead simple file authenticity with domain-linked signatures

Hacker News

Fluffy – dead simple file authenticity with domain-linked signatures

yooo wsg HN ppl. was working on an application on behalf of a government org and they asked me to send in a letter with the org's letterhead as proof we are who we say we are. i thought that was dumb especially in the year of our lord 2025. so i prototyped this in like 4 hours. it's called fluffy: you generate a keypair, put the public key on your domain's DNS TXT record, and you can sign files with your private key + enter in a unique code. (Like pidgon.com>hr/23) now when the receiver wants to verify the file, they upload it, enter the code and fluffy verifies if its legit. core is dead simple, it's literally ed25519 and three functions, there's no create account thing for now and it's all free bc i store almost nothing (except the signature mapping, which expires in 30 days, and some basic org verification) that's about it. would love any suggestions and feedback (esp on if you think its too simple lol). open to feedback here or if you want to chat directly, hmu at mihir[at]pidgon[dot].com!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% 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 HuntUnlikely to reach the leaderboard · Strong signals: code, open · Missing: mac, agents, macos
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% 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
38%38% 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
34%34% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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