Agents produce everything and remember nothing | Building Relinkia — the context layer you own | Author @ManningBooks, @packtpub, Eyrolles
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Count the AI tools you used this month. ChatGPT, Claude, Copilot, Gemini, Perplexity.
Now: how many of them know what you were thinking last Tuesday?
Zero. Every single one starts from a blank page.
Sort your notes app by last modified and scroll to the bottom. Most of what's down there was worth writing the day you wrote it.
Nothing in the app ever brought those notes back next to something new, and a note that never meets another one stops mattering.
Ask ChatGPT about a book you read last spring and you get the internet's average opinion of it.
Your margin notes and the objection you wrote next to chapter 3 never reach the answer. No model was trained on them, and they're the part that was yours.
You don't have a reading problem. You have a forgetting problem.
Most "learning" is just well-organized forgetting: highlight, save, archive, never touch again.
The bottleneck isn't input. It's what happens to ideas after you close the book.
Unpopular observation from running agent pipelines: the hard problem isn't giving agents access to knowledge. It's deciding what they don't get.
Everyone's building bigger context windows. Almost nobody's building the permission model that should sit in front of them.
Unpopular observation from running agent pipelines: the hard problem isn't giving agents access to knowledge. It's deciding what they don't get.
Everyone's building bigger context windows. Almost nobody's building the permission model that should sit in front of them.
Everyone is suddenly building the same thing: an Obsidian vault + Claude + MCP, with the agent reading and linking your notes.
Karpathy gave the pattern a name back in April: the LLM Wiki.
It's the best thing to happen to personal knowledge in years. And it has a blind spot almost nobody mentions.
Second gap: time.
Your 2023 position and last month's quiet reversal sit in the vault as equally true. An agent retrieving by similarity will hand you the stale one: confidently, fluently, wrong. A wiki that "gets smarter every day" should at least know which of your ideas are still yours.
So the LLM Wiki proves the demand: everyone wants a context layer they own.
Portable: solved. Structured and temporal: still wide open. That's the actual frontier.
Longer argument: relinkia.substack.com/p/your…
Your brain has about 86 billion neurons and holds far more concepts than that. So does an LLM, with its dimensions.
That's not a coincidence. It's geometry: concepts live as directions in a space, and a space can hold exponentially more directions than axes. It's called superposition, and it explains both why these models work and why they're so hard to read.
I made a video walking through it, no math degree required.
Unpopular observation from running agent pipelines: the hard problem isn't giving agents access to knowledge. It's deciding what they don't get.
Everyone's building bigger context windows. Almost nobody's building the permission model that should sit in front of them.
And flat memory ("user prefers TypeScript") misses what thinking is. You held X in 2023, nuanced it in 2024, contradicted it last month.
Similarity retrieval will serve your 2023 position as today's: confidently, fluently, wrong.
A context layer worth owning needs three properties: structured, temporal, portable. Most attempts have at most one.
Full argument: relinkia.substack.com/p/your…
