Rewriting Prime Agent in Rust
primeintellect.ai
primeintellect.ai
very nice outcome of this port
I don’t see many people talk about Prime Agent, I always wondered if it could just be a Pi extension cause it seems to be a subagent orchestration agent.
Very happy user. Loved it with deepseek-v4-flash, although I've moved on because newer models are so compelling.
I just get good results from it; I think it's the python requirement.
It has been very good with sub-agents, and also finding old context, for a long time. And had backgrounded agents that you can run from one instance without herdr. Herdr is kinda redundant (better UI than PA though).
I see nobody else talk about it. It's so little talk that when I mention it on X, the devs comment on my posts sometimes.
As far as the dev comments, it seems Prime Agent doesn't have the hype as other harnesses but doesn't mean its not worth trying.
Curious how this could be applied to greenfield coding rather than just making a copy in a new language or performance optimizing.
Naturally then there isn't source material for "we rewrote yet another slow scripting project into Go/Rust/Zig/C/C++/..." blog posts.
Now there is no excuses to not use Rust and it just shows in raw performance alone.
Eventually AIs will generate Assembly code directly anyway, no need for intermediate 3 GL languages.
Combine that with the fact that, unlike other code generators, LLM output can't as a rule be reliably reproduced from the original input in the future, sounds like a recipe for unpredictable long-term costs and regular regressions.
JIT compilers, GC workflows, PGO, and ML optimising compilers passes are also non deterministic, and yet work gets done.
If you are curious, there is already enough work out there into this direction.
For what its worth, Someone created an text editor application in pure assembly using AI (which is genuinely really cool!)[0]
To test this hypothesis, I tried to add scratchpad functionality to it and I was able to do so with an open source model almost autonomously by just having a single prompt which can have auto-saving, easy way to generate new files and its honestly really minimalist and that's the point while having multiple things as well and I personally really love it :-D[1]
I daily drive my scratchpad application and use it quite a lot
I was actually quite surprised by the fact that it actually worked and the fact that rhun itself can exist.
So it is possible that if AI agentic capabilities grow even further then assembly generation can be possible but I am unsure about preferred about some small use cases requiring pure minimalism.
But nonetheless, I think the way LLM's are trained, programming languages are good enough for them and they can express what they want to accomplish easily and without repeating themselves easily through it. I have thought about creating some more assembly applications but I just think that it would be a more token-intensive process, that's all.
Though pure speculation at this point but if some architecture optimizes for assembly (maybe speculative decoding extremely fine tuned to assembly?) or maybe some other architecture itself (world model?) who can express their ideas in better formats than tokens than perhaps its possible as code written in programming languages is much more token-efficient than assembly
[0]: https://rhun.app/
For Anthropic's reported benchmark numbers for Sonnet 5.5, when running terminal-bench 4.0, they had a comparison between $0.10 and $0.20 cache read for total task cost. That 50% cost reduction resulted in a 20.9-26.2% total cost reduction. Pretty specific though... single benchmark, model, harness, and provider. Cache reads make up ~42-52% of the total cost at the standard $0.20 price.