If you program with any LLM, you know the challenge. The context window has a limit, and it’s not as high as you think. Sure, Opus 4.7 says 1M — but good luck stuffing 20 compacts’ worth of memory into the window you’re actively prompting against. The usable space shrinks fast, and your “memory” is lossy at best. I spent 1.5 years developing a math model to solve hallucinations using an HDC primitive we call a glyph. What is it? A multi-dimensional mathematical structure that stores meaning directly in the math. So what? HDC got sidelined during the deep learning wave — bipolar vectors looked crude next to learned embeddings. But that comparison missed the point: HDC isn’t trying to learn meaning, it’s trying to bind it deterministically. That was chapter one. The book’s still being written. What’s clear now: the LLM can’t slide the window effectively enough to cram history alongside current context at scale. The obvious answer is a bigger window. It won’t work. How do I know? Math. The compute required exceeds the largest window we can currently fathom by 10x. Ask Claude to write a proper “editor plot-hole finder using only the LLM, or even with RAG embeddings” — it gets mind-numbingly complex. And 70k words fits in the window easily. The reality is we need memory on demand, constantly — and the inverse is also true. We can’t compact at the end of a session and call it a day; that’s exactly the lossy garbage that makes us want to throw a keyboard at the screen. So what’s the answer? We need a substrate that operates without an LLM, in sub-milliseconds, purpose-built for stored memory. Welcome HDC to the chat. You might be thinking: why not just RAG? Let that roast for a second. When a human recalls a thought, it’s not as effortless as people think. Sure, there are patterns the body learns and stores — walking, talking, typing. Do you think about walking, or do you just do it? You might think about balance, but you don’t think about walking… unless you’re on a tightrope, or a white line after a late night when the police nabbed you. 😁 The point: memory is orthogonal to remembering. Memory is storage. Remembering is recall. The LLM has weights it learned during training, but surfacing the right encoded concept given the current context is an entirely different problem. This is why bigger windows and bigger RAG stores don’t fix the feeling that the model “forgot.” They optimize storage. They don’t solve recall. A glyph does — because the meaning is bound into the math itself, content-addressable, deterministic, sub-millisecond. You don’t search for the memory. You cue it, and it surfaces. RAG retrieves text. HDC retrieves meaning. That’s the difference, and that’s why .yo runs on glyphs. yousup.dev and our patent pending glyphh ai adaL hdc inspired memory store drops soon. Run our Claude code agent loop with your subscription or just stand up our local mcp server. It might just be the best experience you have ever had with Claude. submitted by /u/Successful-Seesaw525
Originally posted by u/Successful-Seesaw525 on r/ClaudeCode