AI

AI-agent MPC crypto wallet protects you from malicious transactions

Hacker News

AI-agent MPC crypto wallet protects you from malicious transactions

HI HackerNews, I'm Jacob. Me and my team have spent the last 8 years turning academic MPC cryptography papers into production systems that secure digital assets. I/we've come from the largest players in the industry such as Bitfinex, Tether, Binance, FTX, Monzo, Saxo Bank, Deltec bank and more. We've implemented cutting edge security, consulted with companies that have been attacked, and identified/remediated attacks. Now we're applying this tech to solve a critical problem: making crypto safe and accessible for everyone through AI. Our latest wallet ( https://campaign.iofinnet.com/ai-chatbot ) for retail uses that same Multi-Party Computation to create a cryptographic approval system where both you and your AI agent must approve transactions. This means: - Your AI can abstract away blockchain complexity (swaps, bridges, staking) - You maintain final approval authority - The Guardian agent automatically scans and simulates transactions to prevent malicious activity The architecture allows flexible key-sharing configurations: - Give AI a share, keep final approval yourself - Keep all shares, use AI for guidance only - Give AI the full threshold for autonomous operation - Run multiple AI agents with different roles, and allocate shares to each of them with a custom threshold. (Example: Complince agent, security agent, and treasury agent all need to approve) We're building a marketplace for specialized AI agents (tax, yield optimization, compliance) and opening it to developers to create secure AI agents that must abide by cryptographic rules rather than just promises. I'd love feedback from the HN community on our approach. As developers building autonomous AI agents, what features might be useful to you? Lastly, if you'd like to try out our live enterprise MPC solution, then you can try it for free at iofinnet.com

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: agents, agent, new · Missing: mac, macos, cursor
64%64% predicted probability of success on Product Hunt, 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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, crypto · Missing: web3, cryptocurrency, make money
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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