Comment by agrippanux

3 days ago

Fable orchestrating DeepSeek v4 Flash to implement a plan is my new favorite thing.

It's so freaking fast, but you gotta tell Fable to watch Deepseek like a hawk or it'll go off the rails.

3 comments

agrippanux

Yes. It works very well for simple tasks. When I know the context grows over 200k, I implement with Kimi.

We run an agent company and we do a bunch of different things with agents. Where we used Gemini before Deepseek v4 Flash is taking the lead on price. It's like 5x cheaper than 3.6 and well 2.5x cheaper than 3.7 "introductory price". Comparable quality.

homebessguy 3 days ago

Interesting - how are you interacting and orchestrating this?

pimeys 3 days ago

Not the parent, but:

https://omp.sh/

You define roles for different agents like this:

  modelRoles: 
    task: fireworks/kimi-k3-fast:high
    plan: fireworks/kimi-k3-fast:max
    slow: fireworks/kimi-k3-fast:max
    smol: fireworks/deepseek-v4-flash-0731:low
    tiny: fireworks/gpt-oss-20b
    vision: fireworks/qwen3.7-plus:high
    designer: fireworks/qwen3.7-plus:high
    advisor: openai-codex/gpt-5.6-sol:high
    main_worker: fireworks/kimi-k3-fast:high
    fast_worker: fireworks/deepseek-v4-flash-0731:low
    vision_worker: fireworks/qwen3.7-plus:high
    research_worker: fireworks/glm-5.2:medium
    code_worker: fireworks/kimi-k2.7-code-fast:high
    review_worker: anthropic/claude-fable-5:high
    security_review_worker: fireworks/kimi-k3-fast:max
    minimal_worker: fireworks/gpt-oss-20b
    default: fireworks/kimi-k3-fast
  task: 
    agentModelOverrides: 
      task: "@main_worker"
      sonic: "@fast_worker"
      scout: "@fast_worker"
      designer: "@vision_worker"
      librarian: "@research_worker"
      reviewer: "@review_worker"
      security-reviewer: "@security_review_worker"

Then you first say /plan and use some big model like K3. Finally the harness shows you a markdown you approve, and in approval you switch to a smaller model and reset the context. The smaller model gets the full plan and starts working on it. When done, you say /review and it spawns N review agents and returns the change suggestions. And you iterate on that.