Entire blog post reeks of LLM.
If there was an adequately reliable way of detecting LLM text i'd build a browser extension that would just filter it out altogether.
Hey, author here.
I wrote the blog post and of course got assistance by AI to rephrase sections, gather data and do comparison, and make graphics - also because I'm no native english speaker and wanted to improve the writing.
That would be great. Or, for the physical world, combining some kind of smart glasses with the new Pangram image detection which seems to work well on rephotographed data. Like the _They Live_ glasses.
Thought the same thing and was surprised how high this was ranking. I wonder if we are starting to look past the LLM stank and into the core of the idea (which is solid in this case)? If HN, a highly discerning crowd, is able to look past it then many other audiences are probably okay with it too
On the general premise, I agree. But I don't think that moment is today, not yet anyway. I don't think we understand the right way to approach context and memory yet or how long they should last for, never mind standardizing on an abstraction prematurely that ends up being like SOAP or SGML.
I agree this is very much needed but I doubt the other ai companies would a-comply with this as they need users and mainly paid users otherwise they'll lose themselves.
What I think is that with 20$ subs of 2-3 ai can give you more usage compared to their 100$ subs if you do it that way.
I don't know will these companies work together to form a standardized memory system. I highly doubt that.
What about .. reference identity.. currently memories and recall are referencing local things, that are specific to the environment they were made. and in ways that are as context eficient as possible like @message100Line10-50 or sessionsabc@100-300 is ./yourfiles/thisfile.mb ..
but is were going to make the memorys portable.. we need to make the things they are pointing to portable also..
The need for a unified format or spec is greatly reduced in the case of Agent memory because by definition it is read by agents, and agents are already instructed / programmed to read memory files of different formats. Even if such instructions did not exist, agents are extremely good at traversing files to get the necessary context it needs.
This is true and it probably is actually fine, but in a way you don't want it to be wasting its reasoning abilities and context window on figuring out which text files to read.
It would be like trying to accomplish something as a human but all your reference material is always in a different format, it would be maddening.
> Agent memory because by definition it is read by agents, and agents are already instructed / programmed to read memory files of different formats.
Only it's not read by agents, and largely ignored.
From Anthropic's recent self-congratulatory article:
---start quote ---
Injected memories use up valuable tokens, and an incorrect note can be carried into every session. Loading a note also doesn’t guarantee that Claude acts on it: our documentation on Claude Code warns that longer instruction files reduce adherence
...
It loads only the first 200 lines or 25 KB of MEMORY.md
I think It needs to be as diverse as people .. If agents are to become our constant "muse" like companions, I want the wiring of the brain of mine to be as wired as possible.. Why not juggling chain saws in orbit.. https://www.youtube.com/watch?v=JcJSW7Rprio&t=17s
at this point i'm pretty convinced agent memory is strictly harmful. it remembers the wrong things, and then turns them into an inane focus for each task. i disable it universally on every harness. if i want memory, i specifically instruct the agent to record findings into a file.
Same. Everybody builds one, and then eventually realizes it makes the model stupid and confused. The right solution is pretty much always developer documentation in the repo, in roughly the same shape you'd make developer docs for humans that you want to be able to get up to speed about your codebase really quickly.
This has the benefit of being something you can easily read over every now and then to make sure the model hasn't baked in some random comment you made about a specific circumstance a month ago and made a weird religion out of it.
Also introduces massive confirmation bias. Majorly gimps the model. I remember I was exploring hexagonal architecture on chatgpt and then future conversations with open ended architecture questions just gave me those same patterns.
“Oh damn look its recommending something that looks like hexagonal architecture. I guess hexagonal is a really good - hey wait a second …”
I strongly agree. This is becoming a general principle along the lines of “code comments that describe what the code does are bad.”
Memory just ensures there is a fairly high level of drift and inaccuracy in your context. Unless YOU are manually curating it.
Its similar to wiki systems where your agent produces tons of documentation and decision records, etc. Its a bunch of assertions about your codebase that you aren’t vetting and which is at most 90% correct. Thats good enough for a feature or bug fix that you can validate with tests but for the love of god throw the details away. You don’t want to codify that. It’s polluting the context and gimping the model.
Not trying to be disingenuous but what's wrong with just a normal filesystem?
The best version of memory I've experienced is when I give my agent a memory directory and just let it do what it wants in there, with minimal rules on my end.
This is true but is it proven with reasonable confidence that indexing and vectorizing the code provides better results. I would assume it would if another agent is responsible for it and your coding agent only talks to that agent. Even there I am not 100% sure. Probably not worth the effort though.
The sections, points, ideas are by me :)
but is were going to make the memorys portable.. we need to make the things they are pointing to portable also..
It would be like trying to accomplish something as a human but all your reference material is always in a different format, it would be maddening.
Only it's not read by agents, and largely ignored.
From Anthropic's recent self-congratulatory article:
---start quote ---
Injected memories use up valuable tokens, and an incorrect note can be carried into every session. Loading a note also doesn’t guarantee that Claude acts on it: our documentation on Claude Code warns that longer instruction files reduce adherence
...
It loads only the first 200 lines or 25 KB of MEMORY.md
https://www.anthropic.com/research/components-of-agent-memor...
--- end quote ---
This has the benefit of being something you can easily read over every now and then to make sure the model hasn't baked in some random comment you made about a specific circumstance a month ago and made a weird religion out of it.
“Oh damn look its recommending something that looks like hexagonal architecture. I guess hexagonal is a really good - hey wait a second …”
Memory just ensures there is a fairly high level of drift and inaccuracy in your context. Unless YOU are manually curating it.
Its similar to wiki systems where your agent produces tons of documentation and decision records, etc. Its a bunch of assertions about your codebase that you aren’t vetting and which is at most 90% correct. Thats good enough for a feature or bug fix that you can validate with tests but for the love of god throw the details away. You don’t want to codify that. It’s polluting the context and gimping the model.
The best version of memory I've experienced is when I give my agent a memory directory and just let it do what it wants in there, with minimal rules on my end.
Index it all. Embed it all. Give your agents the ability to query it all. Eval different configurations to figure out what works best for you.