I'm interested in their Planner -> Implementer -> Reviewer -> Verifier process they used for this transition to Rust. I've see similar but curious how they actually implemented this.
Curious how this could be applied to greenfield coding rather than just making a copy in a new language or performance optimizing.
So they used Prime Agent and GLM 5.3 to swarm and rewrite the code in Rust. This also shows Prime Agent doing what it preaches by rebuilding itself. Since Prime Agent is Pi under the hood, will they push a Rust rewrite to Pi? Pi extensions use Typescript so I wonder if they will work.
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.
Kinda wish people break down token usage into input (cache hit), input (cache miss) and output when talking about it. Giving a total 200B tokens number doesn’t help gauge costs.
Rewriting in Rust helps memory efficiency, but for most agent stacks, the bottleneck remains model inference latency and external tool calls, not the orchestration runtime.
I'm interested in their Planner -> Implementer -> Reviewer -> Verifier process they used for this transition to Rust. I've see similar but curious how they actually implemented this.
Curious how this could be applied to greenfield coding rather than just making a copy in a new language or performance optimizing.
If anyone wants a 100% parity Rust port of v1.0 DeepSeek Harness, I have one here: https://github.com/trevorprater/SeekDeep-Harness
So they used Prime Agent and GLM 5.3 to swarm and rewrite the code in Rust. This also shows Prime Agent doing what it preaches by rebuilding itself. Since Prime Agent is Pi under the hood, will they push a Rust rewrite to Pi? Pi extensions use Typescript so I wonder if they will work.
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.
Kinda wish people break down token usage into input (cache hit), input (cache miss) and output when talking about it. Giving a total 200B tokens number doesn’t help gauge costs.
Rewriting in Rust helps memory efficiency, but for most agent stacks, the bottleneck remains model inference latency and external tool calls, not the orchestration runtime.
Writing comments with an LLM helps posting efficiency, but for most HN accounts, the bottleneck remains having something worth saying, not the typing.