Insights
Notes on shipping AI and software
Longer pieces from the Soludev team: crawlable on this site, not locked inside a LinkedIn feed.
LLM + Action = Agent
An agent is an LLM that can call tools. Then a reasoning loop, YAML to instantiate agents, and a UI so the client can create them.
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mcp-raganything is not a RAG product
mcp-raganything wraps RAG-Anything as two MCP tools, index_corpus and ask_corpus. A socket for Claude, Codex, or Cursor. Not tenancy, rights, or production RAG.
Read →Humaniseur v4: bilingual AI prose cleanup
Humaniseur v4 white paper: 53 EN + 12 FR patterns, 0-100 AI density scoring, voices, French track, CLI validation. Open source.
Read →No More Slop: dual-score AI cleanup
No More Slop white paper: 22 regex patterns, structural scoring, cost breakdown, style calibration, Rocky escalation. Open source.
Read →Rocky white paper: agentic eng MCP
Rocky white paper: devkit gateway, handbook agents vs skills, architecture profiles, senior workflow, measured token savings.
Read →robots.txt decides, llms.txt suggests
llms.txt is not robots.txt for AI. One file answers crawl permission. The other is a reading list. Start with robots and a clean public graph.
Read →Rocky: free pre-PR MCP quality gate
Rocky is a free pre-PR quality gate for AI coding tools. Same model, about 65% fewer tokens. Works with Cursor, Claude Code, VS Code.
Read →Rocky MCP value report: tokens and cost
Same feature, same model: about 255k to 89k tokens (~65%), discovery down 89%. Side-by-side without vs with Rocky MCP.
Read →Agentic coding: token and cost breakdown
One gateway vs many MCP tools, graphify before grep, pre-PR gate vs rework loops. Rocky keeps handbook overhead around 4-6k tokens.
Read →Why Rocky splits agents and skills
Agents are the short front door. Skills are deep reference loaded only when needed. Keeps Rocky sessions around 4-6k handbook tokens.
Read →Is RAG right for a messy implant catalog?
Stock sizes in tables. Preference notes and FAQs in chunks. Keep inventory and prose out of one pipe. We standardise on Xberg.
Read →Rocky profiles: detect before impose
Rocky detects the repo before imposing structure: Next.js App Router, hexagonal, layered React SPA, or node-api-only.
Read →Rocky senior workflow: discover to PR
End-to-end pipeline Rocky reinforces: discover, match conventions, implement small diffs, verify locally, pre-PR gate until ready, then open the PR.
Read →I rebuilt an agent for the fourth time
Agent architectures converged: model, tools, MCP, sub-agents, observability. I wrapped it in YAML, then bigger players shipped better tools.
Read →Agents and RAG are no longer the hard part
After shipping agents for large accounts and startups, the pain was not implementation. It was cost, permissions, and observability.
Read →The ChatGPT that said yes. The API that said no.
A jewelry try-on looked perfect in ChatGPT. Same model on the API failed. We shipped a three-model pipeline that never rewrites the customer.
Read →The harness war is already over
LangChain, Claude Managed Agents, Agno AgentOS: same architecture. The harness is a commodity. The fight moved to infra and privacy.
Read →MCP is dead! Long live MCP
Perplexity walked away from MCP and obituaries started. Token waste is real, but a contract still helps when LLMs talk to custom APIs.
Read →If AI wrecks your codebase, fix the frame
Blame the hammer, or fix the frame. AI amplifies what already exists. Rigorous standards accelerate. Missing standards accelerate debt.
Read →The RAG paradox: buy or build carefully
Standardise RAG and long-term memory instead of rewriting from scratch. Cognee looks ready for industrialisation: vectors, graph, MCP.
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