21–30 of 69 posts
Papers being voted high on Hacker News are usually uncorrelated with their actual importance. It's basically a lottery. There are regularly more interesting papers going semi viral on Twitter.
> δ-mem compresses past information into a fixed-size state matrix updated by delta-rule learning This doesn’t solve the capacity problem of memory. You can cram more into one context window, but then again you need to associate them with input queries. That’s very hard because slight variations in input create hugely different activations. So really, it doesn’t improve caching. This paper might do a thing or two app…
https://jdsemrau.substack.com/p/tokenmaxxing-and-optimizing-...
> δ-mem compresses past information into a fixed-size state matrix updated by delta-rule learning This doesn’t solve the capacity problem of memory. You can cram more into one context window, but then again you need to associate them with input queries. That’s very hard because slight variations in input create hugely different activations. So really, it doesn’t improve caching. This paper might do a thing or two app…
Earlier quoted context omitted.
I see why, but I don't feel this is the solution. Being able to search thru the endless LLM responses is not viable. However having useful memories, similar to human brain is more important. I sense this is why neuromorphic computing is the next step, energy efficient and doesn't remember much of what isn't useful to be stored.
Why not preserver the essential memories in text? Why neuromorphic?
> δ-mem compresses past information into a fixed-size state matrix updated by delta-rule learning This doesn’t solve the capacity problem of memory. You can cram more into one context window, but then again you need to associate them with input queries. That’s very hard because slight variations in input create hugely different activations. So really, it doesn’t improve caching. This paper might do a thing or two app…
Earlier quoted context omitted.
How would you conceptualize recall in this case? Is searching through the current version of your code and possibly git history not enough?
You would think git history should be the first thing an agent would look at, as they make so many mistakes before they get to the correct answer. They don't. I haven't measured, but documenting bug fixes and architecture seems to help, along with TDD patterns, including integration tests. I would probably add it to Claude.md to look for all of the above when tackling a new bug.
Hmm, this is a case where HN’s title mangling changed the meaning of the title. Lower case delta (δ) is used intentionally. I don’t think HN should automatically modify the casing of non-ascii chars.
> δ-mem compresses past information into a fixed-size state matrix updated by delta-rule learning This doesn’t solve the capacity problem of memory. You can cram more into one context window, but then again you need to associate them with input queries. That’s very hard because slight variations in input create hugely different activations. So really, it doesn’t improve caching. This paper might do a thing or two app…
> δ-mem compresses past information into a fixed-size state matrix updated by delta-rule learning This doesn’t solve the capacity problem of memory. You can cram more into one context window, but then again you need to associate them with input queries. That’s very hard because slight variations in input create hugely different activations. So really, it doesn’t improve caching. This paper might do a thing or two app…
I am currently working on deep context query which uses dynamically generated regex to pull only the relevant context blocks. By using lightweight RegEx pattern matching to detect semantic intent and filter structured context sections accordingly, you avoid the attention degradation that comes from stuffing semantically redundant information into the window https://jdsemrau.substack.com/p/tokenmaxxing-and-optimizing-…