Posts

Evergreen Notes: Write Notes That Compound Over Time

Evergreen notes are atomic, standalone, and evolving — the opposite of notes you write once and forget. A practical guide for engineers who want technical knowledge that stays useful for years. Evergreen Notes: Write Notes That Compound Over Time

Digital Gardens: Grow Knowledge Instead of Just Publishing It

Digital gardens are publicly evolving knowledge spaces — not blogs, not wikis, but living networks of notes at different stages of development. A guide for engineers who want their thinking to compound publicly. Digital Gardens: Grow Knowledge Instead of Just Publishing It

LLM Hosting in 2026: Local, Self-Hosted and Cloud Infrastructure Compared

Complete guide to LLM hosting in 2026. Compare Ollama, llama.cpp, vLLM, TGI, Docker Model Runner, LocalAI and cloud providers. Learn cost, performance, and infrastructure trade-offs. LLM Hosting in 2026: Local, Self-Hosted and Cloud Infrastructure Compared

AI Systems: Self-Hosted Assistants, RAG, and Local Infrastructure

Build self-hosted AI systems with OpenClaw, Hermes, RAG, and local LLM infrastructure. Learn to orchestrate assistants with memory, retrieval, routing, and observability. AI Systems: Self-Hosted Assistants, RAG, and Local Infrastructure

Model Routing: Stop Using One Model for Everything

Routing tasks to the right model saves money and cuts latency. Capability-based, cost-aware, and latency-aware strategies with working Python code. Model Routing: Stop Using One Model for Everything

LLM Architecture: System Design for Production AI

System design decisions for production LLM systems: model routing, cost optimization, guardrails, multi-model orchestration, and prompt engineering. Practical patterns with working code. LLM Architecture: System Design for Production AI

Writing effective prompts for LLMs

Several points to pay attention to when writing prompts for LLMs - to make them effective Writing effective prompts for LLMs

LLM Guardrails in Practice: What Actually Works

Input validation, output filtering, and safety mechanisms that protect your LLM system without breaking it. Patterns with real Python examples and compliance notes. LLM Guardrails in Practice: What Actually Works