Use open models for automation to save budget for frontier intelligence
omarsar0 · x · 2026-08-30
The author shares a strategy of shifting repetitive automation tasks (like tracking HN/arXiv trends) to local or open models. Leveraging in-context learning and self-tuned skills, this approach requires no model fine-tuning but significantly reduces token costs. The savings are redirected to closed-source frontier models for creative and research-heavy tasks. The author advocates for owning both the harness and the model to better leverage a mix of intelligences, predicting this hybrid approach will become best practice as AI ROI becomes a priority.
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