@omarsar0: Just had a great discussion on dynamic workflows. Rough notes: - applies to a very small set of use cases - think of it…

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Summary

Discussion of dynamic workflows for test-time compute, including their limited use cases, benefits for research experiments, and the need for better benchmarks. Mentions models like Mythos and Opus 4.8 for agent orchestration.

Just had a great discussion on dynamic workflows. Rough notes: - applies to a very small set of use cases - think of it as a new paradigm of (test-time compute) TTC - strong for hill-climbing research experiments - careful planning leads to better results - you can often get better results by just increasing the reasoning level - /goal + /loop is a subset of dynamic workflows - verifiers/judges are crucial to get good results - combine/fuse different coding agents for even better results - great for when you need different perspectives from agents (llm council) - frontier models are not equipped for optimally generating harnesses on the fly - newer models like Mythos are probably better trained to do more optimal agent orchestration - benchmarks on TTC are lacking, but we need them to measure how effective dynamic workflows are - meta prompt dynamic workflows are a lot of fun; even opus 4.8 might surprise you - dynamic workflows can be packaged as skills for further optimization of them Longer post coming soon.
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Cached at: 06/27/26, 05:53 AM

Just had a great discussion on dynamic workflows.

Rough notes:

  • applies to a very small set of use cases
  • think of it as a new paradigm of (test-time compute) TTC
  • strong for hill-climbing research experiments
  • careful planning leads to better results
  • you can often get better results by just increasing the reasoning level
  • /goal + /loop is a subset of dynamic workflows
  • verifiers/judges are crucial to get good results
  • combine/fuse different coding agents for even better results
  • great for when you need different perspectives from agents (llm council)
  • frontier models are not equipped for optimally generating harnesses on the fly
  • newer models like Mythos are probably better trained to do more optimal agent orchestration
  • benchmarks on TTC are lacking, but we need them to measure how effective dynamic workflows are
  • meta prompt dynamic workflows are a lot of fun; even opus 4.8 might surprise you
  • dynamic workflows can be packaged as skills for further optimization of them

Longer post coming soon.

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