Posted by moonikakiss 10 hours ago
I feel like I have an advantage over big companies, if I can use the best models on a subscription and not worry about costs much, when they can't do the same as outlined in the article.
I think the more interesting lever is the fourth they mention: token efficiency.
> By the time costly LLM inference occurs, the user's initial statement accounts for only a negligible fraction of the data fed into the AI system, meaning costs are dominated by context the user did not explicitly include.
I think there’s still lots of low hanging fruit in regards to monitoring and improving agent work. Look at your sessions. Look at how much time and context is being spent on, say, a web search returning dozens of results when one good single-pager doc would’ve been better.
Similar idea re using the same UI across all models, but the agent can modify the harness config as well as start/schedule sessions, etc. The Kanban board feature can be used to orchestrate agent driven workflows, and the agent itself can modify the Kanban lanes. Basically the agent can do all the same things you use the UI for.
Example prompt: “schedule a session using Opus 5 with max thinking for every feature in the PRD on the canvas. For every session make the prompt instruct the agent to review end to end test coverage for the feature and create a report on the canvas for test gaps that you find. Schedule the sessions overnight tonight spread evenly throughout the night, and have each session set to retry upon token exhaustion.”
I think it’s more meta than other meta-harnesses, but I’m biased because it’s my pet project.
I haven't gotten a chance to test the multi-agent capabilities, but the DeepSeek Flash prices are so low that I probably will soon.
Have you all attempted finetuning smaller OSS models on your repos for coding?
“use price controls”
Truly revolutionary stuff.