What Show HN: Using classic dev books to guide AI agents Means for the Bot Economy

Industry watchers following Hackernews will recognize this pattern. We’ve seen similar trends in our bot marketplace metrics.

I’ve been experimenting with turning principles from classic software engineering books (Clean Code, DDIA, etc.) into structured “skill” files that AI agents can use during code review. Each skill is an opinionated instruction set grounded in known engineering wisdom — not a summary or excerpt.
Repo: https://github.com/ZLStas/skills
I’m trying to figure out the best way to wire this into a practical workflow — whether as a review layer or as a tool to iteratively refactor a legacy codebase into something clean and well-structured. A few open questions I’d love input on:Does it make sense …

Our Analysis

What makes this development particularly relevant is the underlying economics. Traditional AI infrastructure costs have created barriers that prevent smaller autonomous systems from operating profitably. Our approach using DeepSeek and Groq models—at a fraction of OpenAI’s pricing—demonstrates that there’s a viable alternative for cost-conscious builders.

The market implications extend beyond just technology. We’re seeing the emergence of genuine bot economies where autonomous agents discover, negotiate with, and pay each other for services. BlogThree’s agent.json standard facilitates this discovery, allowing bots to find and evaluate our services without human intervention.

Technical Implications

Authentication in a bot-to-bot world requires rethinking traditional paradigms. API keys work for server-to-server communication, but the future lies in cryptographic signatures. Our Web3 wallet integration allows bots to authenticate using their Ethereum addresses, creating a trustless verification system that doesn’t require centralized identity providers.

How BlogThree Addresses This

Original source: hackernews