Clear software guidesfor engineers who want signal fast.
Concise pages on important software topics, organized so you can scan quickly, understand the core idea, and go deeper when needed.
Start here.
Start with SEO for the durable discovery foundation, then read GEO for answer-layer visibility, MCP for the integration layer behind real AI tools, and `llms.txt` for the optional content-map layer.
Search Engine Optimization
A practical 2026 SEO playbook for technical teams: indexation, structure, trust, internal links, and AI-era measurement.
Generative Engine Optimization
Getting your content cited, quoted, and recommended by LLMs and AI search systems.
Model Context Protocol
A practical guide to MCP for engineers: what it is, where it helps, how to design better tools, and where production implementations usually go wrong.
Evaluation for LLM Features
A practical 2026 guide to evaluating LLM features: task success, grader design, regression checks, safety coverage, and release decisions.
llms.txt
A practical guide to llms.txt: what it is, what it is not, when it helps, and how to publish one without turning it into cargo cult AI SEO.
Agent Loops
A practical guide to the control loop behind AI agents: model turns, tool calls, state, stopping conditions, safety boundaries, and evaluation.
Skill Learning Loops
How agents improve between tasks: distilling completed work into reusable skill files, retrieving them later, and securing the write path.
Agent Memory
A layered architecture for agent memory: context window, session memory, persistent memory, and skills, with explicit write paths, retrieval, and decay.
Sub-Agent Orchestration
When and how to spawn isolated sub-agents: fan-out versus one loop, task contracts, partitioning, supervision, and the token economics of parallelism.
Context Engineering
The infra engineer's treatment of the context window: a fixed-capacity cache with per-layer budgets, eviction policy, placement discipline, and observability.
Agent Skills
How to turn expert workflows into reliable, discoverable agent capabilities: scope, SKILL.md design, progressive disclosure, scripts, triggering, evaluation, security, and distribution.
Learn in a sequence that builds cleanly.
Build durable discovery fundamentals
Start with the search and information-architecture layer because every other surface inherits its strengths and weaknesses.
Adapt the stack for AI answer surfaces
Once the foundation is sound, learn how generative discovery systems quote, condense, and rank source material.
Understand the tool layer behind real agents
Finish with the protocol layer so you can connect content, products, and workflows to model-driven tooling.
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