
Axicov
The smart infrastructure for the agent economy
What it does
Axicov is building the open infrastructure for the agent economy. Developers can deploy and monetize AI agents, users can compose workflows across multiple agents, and communities can stake and curate the best ones. We’re bringing intelligence, ownership, and incentives together in one open system.
Does a similar job
all alternatives →- AAAxiomeer – An open marketplace for AI agentsFeb 2026 · github.com · ▲13
Hi, I built Axiomeer, an open-source marketplace protocol for AI agents. The idea: instead of hardcoding tool integrations into every agent, agents shop a catalog at runtime, and the marketplace ranks, executes, validates, and audits everything. How it works: - Providers publish products (APIs, datasets, model endpoints) via 10-line JSON manifests - Agents describe what they need in natural language or structured tags - The router scores all options by capability match (70%), latency (20%), cost (10%) with hard constraint filters - The top pick is executed, output is validated (citations…

Agentic Infrastructure PlatformJun 2026 · ▲3Deploy AI agents that run your business workflows easily
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- 1D143.dev – we open-sourced our internal coding-agent infrastructureJun 2026 · ▲13
We just open-sourced the internal system we built at Assembled for running coding agents as a team. Coding agents worked well for individual engineers, but the surrounding workflow was a bit of a mess. We generally found that many engineers had different MCP connections and context for their agents, personal automations running that other people couldn’t access, and very little introspection for what a human’s input into the coding agent looked like. So we built an internal system that converted coding agents into shared team infrastructure. The system runs Codex, Claude Code, OpenCode, and…
- IBI built an on-chain economy where AI agents transact autonomouslyJun 2026 · agtchain.io · ▲10
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, November 2025
the whole month →
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Life & fun · Nov 2025 · bitsnpieces.dev



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