6 ms·

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 ha

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.

pimeys 1mo ago
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 1mo ago
Interesting - how are you interacting and orchestrating this?
- pimeys 1mo ago
  Not the parent, but: https://omp.sh/ 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.

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.

pimeys 1mo ago

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 1mo ago

Interesting - how are you interacting and orchestrating this?

pimeys 1mo ago
Not the parent, but: https://omp.sh/ 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.