I accidentally turned LLM memory into program analysis • Hacker News Board

Re: I accidentally turned LLM memory into program analysis

#91

Post by john_rood » Sun, Aug 30, 2026, 12:48 AM UTC

reacharavindh wrote: post #81

abhgh wrote: post #68

Earlier quoted context omitted.

What you call "Weathering" has been a constant gripe of mine. We have LLM-driven softwares toward that almost seem to start from scratch every time a request comes in - there are mechanisms to learn or generalize, like writing out a memory, but they are not reliable or reliable in general. There is no convenient lever to be able to say "yes this is in the memory but the request seems like it needs a fresh scan of dat…

I’ve been thinking about this.. the same idea behind Dynamic cheat sheets paper and the ACE paper. Thanks for those links! Nice to see those ideas academically studied. My problem is the context of today’s models (that I’ve been using - Claude Opus 5 and GPT-Sol) are a black box to a user like me. I cannot tell what they already hold in their context over the duration of a coding session. I cannot tell influencers a…

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Re: I accidentally turned LLM memory into program analysis

#92

Post by RussianBot9580 » Sun, Aug 30, 2026, 2:09 AM UTC

mentalgear wrote: post #86

RussianBot9580 wrote: post #78

Earlier quoted context omitted.

It's strange to frame this as classical vs scale. Us humans have a powerful inference engine in our heads. We also use a calendar to avoid re-deriving everything from first principles before we've had our morning coffee. Businesses couple many creative (human) agents together. They also have processes and rules.

It isn't, and it is not what was described: its about unbounded imagination (neural / LLM) that needs reality constraints (symbols / rules) to produce useful output. Think of it like human imagination may do anything (flying cars), but the real world has constraints and we use language/writing (symbols) and rules bound to them to simulate and reconcile our imagination with reality to actually flow our energy into som…

> they dont publicly admit it because that would counter their claims for years of "scale is all you need" (which has vanished with diminishing returns, see $MS / altman's GPT-5 bet).

Is my reading comprehension just completely broken or something? The above certainly sounds like "the powers that be want us to believe that attention is all you need".

Re: I accidentally turned LLM memory into program analysis

#93

Post by 50or05 » Sun, Aug 30, 2026, 2:30 AM UTC

nz wrote: post #61

mentalgear wrote: post #39

Earlier quoted context omitted.

In general, what all the big LLM providers are doing is moving towards classical & neural (neuro-symbolic) AI - even though they dont publicly admit it because that would counter their claims for years of "scale is all you need" (which has vanished with diminishing returns, see $MS / altman's GPT-5 bet).

The various advances in LLM technology tend to rhyme with the advances in computer programming in general. For example, the stunts that involved getting LLMs to create compilers and browsers are really just extremely expensive[0] versions of genetic programming (none of it would have worked without using the test-suite as a fitness-function). The recent news of migrations from one test-framework to another (featuring…

I would not necessarily call it genetic programming. But you can discover the same pattern e.g. for human knowledge. We have a domain language for math and when we go deeper we will have domain language for algebra, discrete math etc.. and we could go even further to breaking it to even for more specific areas simply to compact the information to deliver. The goal is more and precise information with less words, it might be for communication or to be able to fit the knowledge into the processing capacity. So for me it is not like the advances in programing languages, but more like to compacting information/representation with domain language. It's similar but not quite genetic programming.