51–60 of 107 posts
> We upstreamed it to omp For those not sufficiently cracked... what is omp? Is there a way I can have this command or workflow in Cursor / Claude Code? I tried going to the homepage ( https://stencil.so/ ). It didn't really help.
Wait this is new? Large context models figure out the architecture of the request. Miss sized models plan out each requirement, and smaller models implement a tightly detailed task? Isn't this the standard approach?
It is, but the article is suggesting something different. The central idea is to use /prewalk to load slightly edited context from the more expensive model into the one, so that the cheaper model doesn't burn tokens reading all the files again.
> We upstreamed it to omp For those not sufficiently cracked... what is omp? Is there a way I can have this command or workflow in Cursor / Claude Code? I tried going to the homepage ( https://stencil.so/ ). It didn't really help.
https://omp.sh/ - It's maintained by the author of the article.
Earlier quoted context omitted.
Yes, this is a core part of my workflow; use the smartest model (e.g. Fable) to generate a large hierarchical implementation plan, then clear the context and have a lesser model (e.g. Sonnet) track and execute the plan [0], often in parallel. It all depends on how much work you're doing; if it's a simple task, there's virtually no need for plans at all; just have the smart agent do it. But if the work is going to tak…
This is exactly what the post argues against though, as it leads to higher overall cost.
Lately I'm trying a variation on this: Have the main agent make a phased implementation plan, then for each phase, have it start an implementation subagent with a focused prompt, then review that agent's work in the main session. The theory being that the main session still contains all the research, but it can review just the diff rather than have the entire implementation session in context as well. The post doesn'…
I’m using Claude code dynamic workflow like this. I tell Fable to use a workflow. He is the planner, orchestrator and reviewer. Opus agents are implementers. Works unbelievably well.
Per the OP's technique, I'll use Fable for intensive planning moments, then switch back to opus when things are going well.
This is really smart, like the author said, old idea but cleverly applied. In case anyone wants a summary: don’t one shot, don’t use plan mode and hand off the plan to cheap executors, ask the frontier model to explore, create a todo list, and then start when it feels confident; stop it after first code edit, then prefill the context to cheap executor to continue.
The build plan should be better than the context that generated it since it strips out wrong turns and other noise. I think?
This is really smart, like the author said, old idea but cleverly applied. In case anyone wants a summary: don’t one shot, don’t use plan mode and hand off the plan to cheap executors, ask the frontier model to explore, create a todo list, and then start when it feels confident; stop it after first code edit, then prefill the context to cheap executor to continue.
It's hard to argue with the numbers, but starting with a (mostly true!) “research is the most expensive part” premise, this strikes me as an odd direction to go to optimize costs: 1. As others pointed out, we feed all the same research turns to a smaller model, so we pay the uncached price for all of them. 2. During research, the model typically reads more code than is relevant, to figure out what is relevant and wha…