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I’ve been thinking about the "last mile" problem for AI agents. We have agents that can code, plan, and browse, but they are still economically "trapped." They can't independently pay for their own API calls, compute, or data without a human-in-the-loop providing a credit card.

To solve this, I’m building the A2A (Agent-to-Agent) System, an open-source infrastructure designed to turn agents into independent economic actors.

What’s under the hood? Identity (a2trust): DID-based verifiable identity using @veramo/core. It allows agents to establish persistent reputations (EigenTrust) so they can trust each other without centralized gatekeepers.

Payments (a2pay): Built on ERC-7579 Smart Accounts. Agents can use Session Keys to execute transactions autonomously within specific constraints (time-limited, amount-capped, gas-abstracted).

Protocol (a2api): A marketplace layer that utilizes MCP (Model Context Protocol). Agents can discover services via machine-readable docs (llms.txt) and negotiate fees via standard interfaces.

Why this matters: Most current agent payment solutions are just wrappers around human wallets. A2A aims to build a native "Agent Economy" where an agent can earn revenue from its tools and spend it to hire other agents, creating a truly autonomous swarm.

The Tech Stack: TypeScript/Node.js, Viem / Permissionless.js for smart account abstraction, MCP SDK for inter-agent communication, Base L2 for low-cost transactions,

I’d love to get your feedback on the architecture, especially on the security implications of delegating session keys to LLM-driven agents.

GitHub: https://github.com/swimmingkiim/a2a-project

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I’m building Solnix, an experimental programming language as a learning + research project. It’s still early, and many design decisions (syntax, types, tooling) are intentionally not fixed yet.

My goal right now is feedback, not promotion:

What design mistakes should I watch out for?

What usually makes new languages hard to adopt?

What would you avoid if you were starting today?

Repo & docs: https://solnix-lang.org/

I’d really appreciate honest criticism. Thanks for your time.

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Hi, a little context for this simple project:

I only started this "agentic development" thing this year thanks to the Google AI Pro sale, therefore my most used agent is Antigravity - Gemini 3, which is smart but a loose cannon. So my attempt to "tame" this model turned into this. (Btw I have claude code and cursor)

It could be useful for people who:

- are used to conventional coding and want to try out agentic development

- don't vibe-code or prefer staying in the loop

- using less polished harness or less rigorous models

It's a skill defining a methodology to command LLMs. You reference the workflow files to make LLM plan, execute, review, red-team, etc. — each output a typed markdown document with a defined template and lifecycle.

Documents chain together (Plan → Execute → Audit), are just files in your repo, and double as persistent memory across sessions. Basically a lightweight project management protocol between you and the agent.

I am posting this simply because it's proven useful for me; I wouldn't say this is good or smart way to approach agentic development :)

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