Mike

Architect, Software Engineer and Data guy

[Mike @teaser380]

If you want agents that survive prod, stop optimizing prompts and start engineering the control plane.

Full ArchonHQ breakdown:
https://t.co/53Uy4w9IRz

[Mike @teaser380]

Your AI agent working in dev means almost nothing.

Prod is where hidden state, missing retries, flaky tools, and silent failures crawl out of the walls.

The demo is not the system.

[Mike @teaser380]

Production agent framework:

1. Bound the tools
2. Validate every output
3. Make steps retry-safe
4. Log decisions + inputs
5. Add human stops for high-risk actions
6. Test ugly edge cases before users do

[Mike @teaser380]

I wrote the full Forge walkthrough: build your own MCP server from scratch, understand the protocol, then scaffold one with a tiny CLI.

https://t.co/r6FuehA7Ip

[Mike @teaser380]

Most MCP tutorials start with a framework. That hides the useful part.

An MCP server is just JSON-RPC: read a message, dispatch a method, return structured content. If you understand the wire, every framework becomes optional.

[Mike @teaser380]

Why it matters: MCP servers are becoming the integration layer for agents. IDEs, desktop assistants, internal workflows — all need typed tools they can call safely.

Protocol fluency beats template fluency.

[Mike @teaser380]

I wrote the Bastion walkthrough for building a private RAG knowledge base: architecture, tradeoffs, and the local-first pattern that keeps the boundary enforced by code.

Read it here: https://t.co/YqngymLdaT

[Mike @teaser380]

Every query to a cloud RAG system leaks more than a prompt. It sends your docs, questions, retrieved chunks, and intent outside your perimeter. If your knowledge base handles private data, “just use managed RAG” is a governance bug.

[Mike @teaser380]

The stack:

1) sentence-transformers for local embeddings
2) markdown-aware chunking with overlap
3) SQLite for docs, vectors, queries, retrieved chunks
4) local LLM for synthesis
5) audit logs your compliance team can actually inspect

[Mike @teaser380]

I wrote the complete Bastion guide to running your own LLM on a laptop: hardware checks, model choices, inference engines, and deployment patterns.

Read it here: https://t.co/IAGHV1ZpTW

[Mike @teaser380]

Your prompts are leaving your machine.

Every document, half-formed idea, and weird private workflow you send to a cloud LLM becomes someone else’s infrastructure problem. Local LLMs fix that.

[Mike @teaser380]

Framework:
1. Audit your hardware
2. Start with llama.cpp or Ollama
3. Use quantized models
4. Serve through an OpenAI-compatible endpoint
5. Benchmark latency before you build workflows

[Mike @teaser380]

I broke down the full Caliber framework for pricing your first digital product or service without vibes, panic, or Twitter cosplay.

Read it here: https://t.co/x09URjyDmH

[Mike @teaser380]

Most first offers fail in the price field.

Not because the product is bad. Because the founder guesses, panics, discounts, and accidentally builds themselves a burnout machine.

[Mike @teaser380]

The Offer Equation:
Value = Pain × Urgency × Trust × Specificity

If one lever is weak, price resistance explodes. If all four are strong, the offer stops sounding like a commodity.