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Raven – Encrypted messages hidden inside ordinary text

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Raven – Encrypted messages hidden inside ordinary text

Solo dev. built in my spare time. Raven uses a combination of steganography and cryptography to create a metadata resistant messenger. It encrypts any piece of text on your device and stores the ciphertext on Solana. It works pairwise with keys, users decrypt locally on their device- no accounts. I do run the relayer and a retention sweeper (not claiming peer to peer). One more thing, every write is signed by the same pool wallet, so anyone can see volume/timing. Check the white paper below and there’s been no audit. Several variations include: public, private, and stealth (for fun, i’ve hidden a public raven in this post) White Paper: https://oara.ai/raven-whitepaper The crypto and encoding core is open source. Apps are not open yet. Open source: https://github.com/OAraLabs/raven-core iOS: https://apps.apple.com/us/app/raven-hidden-messages/id677947... Chrome: https://chromewebstore.google.com/detail/raven-%E2%80%94-hid... For fun public raven read key: https://sendraven.ink/k/Ot2YzGEoYYaMN1Uc

Visit productView on Hacker NewsSep 9, 2026
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6points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
72%72% 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 HuntOn track for Day 1 leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, google · Missing: mobile apps, personal, entrepreneurs
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
37%37% 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
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, 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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