MCP-enabled file storage for AI agents, auth via Ethereum wallet

Hacker News

I built a live demo of an MCP (Model Context Protocol) server backed by FTP storage. It lets AI agents manage files — read, write, search, edit, upload — through a structured tool interface, while the same files stay accessible via standard FTP over TLS. Authentication is wallet-based (Ethereum signatures — no passwords, no email). Try it: https://service.c33b.org Provision: https://signin.c33b.org Stack: ProFTPD + custom MCP layer, runs on Linux. any feedback apreciated - if you can use it, we'll find a way to give it to you, if it's not of use please tell me - so i can stop wasting my time and continue drinking

Visit product View on Hacker NewsFeb 25, 2026

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

90%90% 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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, 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

43%43% 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: interface · Missing: plus, platform, intuitive

26%26% 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

19%19% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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