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- Hacker News
- This was a compelling writeup to me. I read through the spec and found it easy to understand and make sense of.
I wonder how much my system needs something like this. Between the invisible system memory of my random chats with Gippity, my Matt Pocock skills saving terminology and plans, and whatever else Cursor and Codex do, I don't think I feel a need for more agent memory. I do like how it's exposed and searchable, and not invisible. But I honestly just send my questions/tasks away to my magic agent and eventually it gets it right anyway; do I need more discrete memory my team has to maintain? (That's an earnest question, not disregard for this)
by ltsSmitty - I think this is the wrong approach because everything is external to the model. You end up creating an ad-hoc externalized model scaffolded out of coarser systems, RAGs, files, and so on.
This leads to, if useful at all, to this process of ad-hoc recall inference which has to happen in time. By itself this is not a problem.
The problem is that the model has to be constantly injected in context with the newest version of the "memory state" at each turn or relevant turn.
The newest coherent memory state is also a problem. More or less 50 years of not failures, but not success either. This may be even deeper problem than the externalization problem.
I think we are just in the very beginning and we are slapping database stuff to the transformer hoping it will work, but these deep neural-net architectures categorically show that they are not databases.
There will be a synergistic middle ground, but its shape is still not clear.
by _bobm - > A memoryfield page looks like this:
So basically recfiles[1], and an entire suite of tools replaced by fopen and sed. Anyone who doesn't know of recfiles is doomed to reimplement it(poorly).--- title: Carbon Fibre Woks created: '2026-03-01T09:00:00Z' updated: '2026-08-22T14:30:00Z' uuid: 6aa615f0-486f-48a7-a210-ba4f5ff18c8b summary: Thermal properties of carbon fibre cookware --- Carbon fibre woks conduct heat evenly, but...by kelseyfrog - "Irrelevant material is simply never surfaced by the semantic search." thats quite optimistic. there's lots of "memory" or past chats with agents that should be suppressed and forgotten because they were looking in the wrong place or were eventually proven wrong. yet semantically they'd look very relevant to a future search. thats why you shouldn't search both textbooks and scifi when trying to solve an examination.
- That's a whole lot of text to say "it's markdown".
- I'm starting to think that 'memory' may be the wrong analogy for what we want.
I do think that having a set of token that are highly personalized to your project and to way you work is beneficial. I also think that the idea that this set of token will be constructed in the background without any work from the user is really appealing. So it's understandable that the 'memory' analogy became so popular.
But in my experience having a really good AGENTS.md file almost always produce better results than enabling memory.
Maybe we should start to think about how we 'train'/'onboard' agents into our projects, in a similar way that we do for new co-workers. Imagine if we could send the agent to our repo and ask it to learn our patterns and in the end we could quiz the agent to gauge how much it actually understood the project. Once he 'understands' the project we can start to use it to help with development.
In a very small scale (example, individual new features) I will sometimes ask the agent to explain me how things work (even though I already know how it works) so I can 'prime' the agent context with good data before starting any real work. But I'm not sure if this approach could be reliably scaled to work with any repo for any kind of work.
by nzach - Was ready to write something snarky because this is essentially RAG, but I think the author is getting at some subtle details which are seemingly important.
- memory systems are a specific type of knowledge base where you generate all the documents. You might as well generate them to be less than your embedding token limit to obviate the need for chunking.
- embedding models are getting better and are no longer just semantic averaging.
- small models are getting dirt cheap, making parallel reads cost manageable
What they describe is sort of the simplest architecture that takes advantage of these observations. I believe them when they say it works well.
I do suspect though that things like keyword lookup will completely fail if every memory is just a vector. Hence why something like Typesense hybrid search can still be useful.
by morelandjs - Does anyone else not use memory?
I find once there is one poisoned line of text it negatively affects everything else downstream. Instead, I use a temp/ folder with documents and use different files for different agents and models. Then I have to constantly prune and delete the files. Any information that can be extrapolated is just noise which negatively affects the agent. If you have a definition of a database structure and it has been implemented, that information should not be contained in any text document -- it is noise, will drift, and be impossible to debug why the agent keeps producing undesired behavior.
I have a ~/Projects folder. For example, I use Playwright with Chrome DevTools Protocol in order to do performance testing and leak detection. There is a script that handles this. My prompt is "Search ~/Projects for perf testing with CDP and Playwright and implement here". Point being, if I need anything I point to a resource or ask to search a resource and it will find it quick and, most importantly, tends to improve it every iteration.
If I was in an institution, I would have a repository and would rather just point the resource and say use that than have memory of it locally.
by dataviz1000