Learn - A guided way into the context-as-code landscape
Instead of one long explanation, use the paths below to choose whether you want concepts, market context, or implementation-oriented reference material.
Foundations
Foundations
Start here if you want the conceptual model before the market map.
Context engineering
What context engineering is, why the term is taking off, and how we practice it: structured context, traceability, and tools.
Open topic →AI context terminology
Skills, MCP, agents, and context: what they mean, who uses which term, and how we fit in.
Open topic →What are packs?
Installable assets: the process, knowledge, and context you can use with or without AI tools.
Open topic →Landscape
Landscape
Use these to understand the players, releases, and positions shaping the space.
Ecosystem & roadmap
Latest releases and what's coming from key players - plugins, skills, MCP, and tooling. Updated from public feeds.
Open topic →Movers and shakers
Big players in the AI context space: position, aims, offerings, and how they're moving to own their corner.
Open topic →AI Thinkers
The researchers, builders, and thinkers whose frameworks are shaping AI — their core bets, key positions, and where they genuinely disagree.
Open topic →AI Stack
The layered architecture behind modern AI systems - infrastructure, orchestration, memory, execution, and governance - explained tool-independently, with key debates and reference stacks.
Open topic →Reference
Reference
Use the engineering and documentation material when you need implementation-oriented depth.