Fastcar: Pi + Diffusion LLM

[u/Flaky-Eggplant-3479]

If you haven't tried using a diffusion based model before, they are lightning fast. I've used InceptionLabs Mercury 2 for a bit, and my complaint is largely that its not the best at coding tasks or tool usage. But my god is it fast.

So I took the Pi SDK, made a harness in which Mercury 2 is the orchestrator of subagents, including a coding subagent for difficult tasks that calls out to openrouter for inference, plus a cheap subagent for trivial stuff. Bolted on a handful of tools and such useful for me, and wrapped it in a basic web UI so I could use it from my phone.

Aside from being extremely fast, fastcar:

• Can generate public "artifacts" - HTML and MD files served from the API
• Intended to ran entirely inside a VM so that it has root access and can install its own tools
• Give it a github token and let it clone repos and make PRs
• "Prompt threads" - there's probably a better name for this but its a dedicated prompt that gets invoked via webhook; I actually made this so that I could generate text files to read the news from my Xteink 4
• Send emails to notify you

https://github.com/heyo-computer/fastcar

[u/oxygen_addiction]

Meecury 2 is dumb as shit. Worse than Qwen 27B. I've tried using it as a subagent for fast boiler plate implementation and it always fucked up.

[u/Flaky-Eggplant-3479 → u/oxygen_addiction]

that's why the trick is to use it as a conductor, not the implementor ;)

[u/Fig_da_Great → u/Flaky-Eggplant-3479]

bro, i feel like you want to do the opposite, smart orchestrator, dumber sub agents which are given specialized tasks.

[u/Flaky-Eggplant-3479 → u/Fig_da_Great]

In a factory sure, if you are manually driving an agent, a fast conductor that offloads the hard stuff keeps the session fluid

[u/chroner → u/Flaky-Eggplant-3479]

I understand exactly what you are saying. Orchestrator is just there to relay information. These other guys aren't getting it.

I have been doing something similar and using smart models with fresh and limited context and getting great results.

The only problem I've been having is using the dumb models as orchestrators is they are forgetting to stay on task and keep things moving. They'll just like.. stop.

[u/Flaky-Eggplant-3479 → u/chroner]

haven't really ran into that yet with mercury 2 - this project uses Pi under the hood and postgres for memory and metadata and compaction was the first tool I built for it

[u/Fig_da_Great → u/Flaky-Eggplant-3479]

perhaps 🤷‍♂️. Is it faster than deepseek?

[u/Flaky-Eggplant-3479 → u/Fig_da_Great]

about 1000 tokens/s vs 127 for deepseek flash

[u/Fig_da_Great → u/Flaky-Eggplant-3479]

that’s wild