TL;DR: A context layer is the infrastructure between your raw information and the AI models that act on it: your knowledge compiled into a form models can actually use, served through scoped read tools, with writes only ever proposed for your approval, and every access leaving a record. Enterprises are building context layers for fleets of agents. UseMyContext is a context layer for one person, read by ChatGPT, Claude, Gemini and Perplexity over MCP.
Why everyone is suddenly saying "context layer"
On July 18, 2026, The New Stack published a piece titled "The bottleneck for AI agents isn't the model anymore. It's the context layer." Its author, Asaf Wiener, builds an enterprise execution isolation layer at Mate, and argues that model quality has stopped being the thing that separates working AI from broken AI: "The reasoning gap between major providers is narrow and narrowing. The infrastructure gap between teams that have built context plumbing and guardrails and teams that haven't is wide and widening."
Andrej Karpathy described the same shift earlier this year: "a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge." His answer was a personal wiki that an LLM helps compile: raw sources indexed, then distilled into summaries and backlinks a model can navigate.
What makes something a context layer
The word doing the work in both cases is compile. A pile of documents is not a context layer, and neither is a chat history. Teams that skip the compile step, the article notes, get "pattern-matched guesswork over a window full of noise." A working context layer, pairing the compiled context graph with the execution discipline around it, has four properties:
- Compiled. Raw sources go in; a curated, current picture comes out. The model reads the distillation, not the pile.
- Scoped reads. The model gets per-action permissions to read exactly what the task needs, nothing more.
- Proposed writes. The model never writes to the layer directly. It proposes; the layer, and its owner, decide.
- Owned and audited. The context has "an owner, an update cadence, and health checks", and every access leaves a record.
The personal context layer
Everything above describes company knowledge served to agent fleets. But the discipline is not inherently corporate, and the personal side is where most people actually feel the pain: four assistants, four half-guessed versions of you.
UseMyContext applies the same discipline to one person. Your profile and files are compiled into one picture, and the assistants you use read that same picture over MCP, scoped to the project you point them at. The one write tool, suggest_update, only ever proposes a change for your approval, every access leaves a record, and any assistant's access is revocable in one click. That is the personal context layer for AI.
FAQ
Do individuals need a context layer?
If you use more than one AI assistant, yes. Without one, each tool keeps its own inferred, partial picture of you; with one, they all read the same compiled context you own.
Is a context layer the same as AI memory?
No. Memory is inferred by one product and stored inside it; a context layer is compiled from what you deliberately wrote, owned by you, and readable by any connected tool.
Is UseMyContext a context layer?
Yes, a personal one. Your compiled profile and files, served over MCP with scoped reads, proposal-only writes, an audit trail, and one-click revoke.
How does UseMyContext compare to Supermemory, Zep, and Mem0?
Their core products are memory APIs and SDKs for teams building AI apps, though Supermemory and Mem0 also offer personal tools. The deeper difference is the model: they infer memory from usage; UseMyContext serves context you deliberately compiled, with a record of every access and one-click revoke.