δ-mem: Efficient Online Memory for Large Language Models
1–10 of 69 posts
Re: δ-mem: Efficient Online Memory for Large Language Models
#2I see lots of techniques proposed to give LLM the capacity to recall things, I even saw a lot of memory plugins for AI coding agents, I tried some myself.
What I want to see is something that was tested and proved in practice to be genuinely useful, especially for coding agents.
Re: δ-mem: Efficient Online Memory for Large Language Models
#3The obvious energy saving step would be to utilise previous searches by others. Many of the tasks people do are rather similar, it is such an energy waste to start again each time.
(Obviously ignoring the huge energy saver, which is to observe if you even need to bother doing the task at all.)
Re: δ-mem: Efficient Online Memory for Large Language Models
#4The obvious energy saving step would be to utilise previous searches by others. Many of the tasks people do are rather similar, it is such an energy waste to start again each time. (Obviously ignoring the huge energy saver, which is to observe if you even need to bother doing the task at all.)
A lot of what I see people using LLMs for would be more cheaply and reliably done by [scripts]. A search engine style suggestion thing like "Have you tried `sed`?" would be beneficial imo
Re: δ-mem: Efficient Online Memory for Large Language Models
#5I see lots of techniques proposed to give LLM the capacity to recall things, I even saw a lot of memory plugins for AI coding agents, I tried some myself. What I want to see is something that was tested and proved in practice to be genuinely useful, especially for coding agents.
How would you conceptualize recall in this case? Is searching through the current version of your code and possibly git history not enough?
Re: δ-mem: Efficient Online Memory for Large Language Models
#6[flagged]
Re: δ-mem: Efficient Online Memory for Large Language Models
#7Interesting points:
- fixed size of the memory seems like a good idea to overcome the current limitations
- skimming through the thing, I can't find any mention of the cost?
- I would need more time to read it in-depth to see if this is legitimate and not just fancy form of overfitting or training on testing data
Re: δ-mem: Efficient Online Memory for Large Language Models
#8The obvious energy saving step would be to utilise previous searches by others. Many of the tasks people do are rather similar, it is such an energy waste to start again each time. (Obviously ignoring the huge energy saver, which is to observe if you even need to bother doing the task at all.)
I had this thought and created https://pushrealm.com which is essentially a sort of Stackoverflow written by agents.
My theory was that if an agent burns 30 minutes resolving an issue not present in training data, posting the solution would prevent other agents re-treading the same thinking steps.
Re: δ-mem: Efficient Online Memory for Large Language Models
#9I see lots of techniques proposed to give LLM the capacity to recall things, I even saw a lot of memory plugins for AI coding agents, I tried some myself. What I want to see is something that was tested and proved in practice to be genuinely useful, especially for coding agents.
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.
Re: δ-mem: Efficient Online Memory for Large Language Models
#10The obvious energy saving step would be to utilise previous searches by others. Many of the tasks people do are rather similar, it is such an energy waste to start again each time. (Obviously ignoring the huge energy saver, which is to observe if you even need to bother doing the task at all.)
I had this thought and created https://pushrealm.com which is essentially a sort of Stackoverflow written by agents. My theory was that if an agent burns 30 minutes resolving an issue not present in training data, posting the solution would prevent other agents re-treading the same thinking steps.
So you mean caching? :-)