EK-1 – A local-first, sovereign AI agent built in Go and Rust

Hacker News

I’ve spent the last few months thinking about why personal AI feels like a leash rather than a tool. Current "agents" are just thin wrappers over centralized LLMs that harvest your data to sell it back to you. You still have to do the work and make decisions whenever a tool proposes a solution. More pressure on you. I’m building Ego-Kernel (EK-1). It’s an attempt to build a "Unitary Intelligence" - a single, local-first agent that manages your digital life, financial trades on Solana, and social reputation without your data ever leaving your enclave. The Tech Stack: The Brain (Go): A high-concurrency orchestrator running locally. It handles "Titan Handshakes" (P2P agent negotiations) and indexes your local life-data. The Court (Rust/Anchor): An on-chain Reputation Ledger on Solana that makes "Social Honor" unforgeable and programmable. The Shield (BUSL 1.1): I’m using the Business Source License to prevent the "Big Tech Vampirism" we saw in '24-'25. It’s free for individuals, but enterprises pay their share. Why this is different: Most AI projects are trying to be the next big chatbot. I’m trying to build a protocol for cognitive independence. The system uses a "Value-Weighting Matrix" that you tune locally. If your stress (biometrics) is high or your bank balance is low, your agent's aggression threshold shifts automatically. The Manifesto explains the "Soul Drift" problem and how we're solving it through graduated autonomy. GitHub: https://github.com/EgoKernel/The-Protocol Manifesto: https://github.com/EgoKernel/The-Protocol/blob/main/Manifest... Would love to hear your thoughts on the local-first approach and the staking-for-reputation model.

Visit product View on Hacker NewsFeb 26, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

80%80% 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, model · Missing: mac, macos, cursor

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% 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

36%36% 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 · Missing: plus, platform, intuitive

34%34% 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

16%16% 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

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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