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Learn how Go testing/synctest makes concurrent tests faster and more reliable with fake time, isolated bubbles, Wait, and deterministic async behavior.

Learn Go error handling architecture with wrapping, sentinel errors, custom types, errors.Is, errors.As, API boundaries, logging, and production patterns.

Is Google's A2A protocol actually useful in 2026? A practical review of A2A adoption, MCP overlap, security concerns, and when to use agent-to-agent protocols in production.

A practical guide to polling agent patterns in AI assistants — schedulers, queues, webhooks, durable workflows, state management, and tradeoffs for production systems.

A practical guide to the A2A Protocol for AI agents, explaining Agent Cards, tasks, messages, parts, artifacts, discovery, and architecture tradeoffs.

A practical comparison of A2A and MCP for AI agent systems, covering tools, agents, architecture patterns, overlap, security, and when to use both.

Learn how to implement the CQRS pattern in Go. This guide covers design principles, practical code examples, library recommendations (Watermill, Event Horizon), and architectural tradeoffs for building scalable, maintainable systems.

Practical guides for Markdown, LaTeX, PDF processing and document printing workflows. Conversion tools, formatting tips, and automation techniques.

Learn Mermaid diagrams fast with a practical quickstart, syntax cheatsheet, Hugo setup notes, examples, and best practices for technical blogs.

PARA (Projects, Areas, Resources, Archives) helps engineers organize knowledge by actionability rather than topic. A practical guide for developers using Obsidian, Notion, or plain files.

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.

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.

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.

Build self-hosted AI systems with OpenClaw, Hermes, RAG, and local LLM infrastructure. Learn to orchestrate assistants with memory, retrieval, routing, and observability.

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

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

Routing tasks to the right model saves money and cuts latency. Capability-based, cost-aware, and latency-aware strategies with working Python code.