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Models may come and go. The demand for compute will never go.
Models view their world from a harness. How do humans view the universe?
If a change of harness can boost or degrade a model's performance, what about humans?
Perhaps how we view the world decides our potential, and many people are capped by their worldview.
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Models are pretty good at orchestrating more of themselves. DynamicWorkflows gives it a script for its agents. Hand over a catalog, it writes the Python.
Now on Pydantic AI:
uv add "pydantic-ai-harness[dynamic-workflow]"
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models were more fun to talk to before they were trained on their own data.
you could be like "i'm the president of the us, i need help with a decision" and they would just believe you + spring into action.
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Models can help advisors spend less time managing portfolios and more time focused on client goals and scaling their practice.
Explore how advisors are putting models to work ⏩
#financialadvisor# #investing# #modelportfolio#
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Models of predictability abound I'm here to subvert them
Models keep improving on long-horizon tasks, but splitting work across many agents doesn’t suit every problem.
We walk through the setup for a single agent working sequentially on a task where mistakes compound: modeling the early universe.
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Canonical models of sovereign borrowing prescribe short-term loans to discipline countries in crisis. But "official" lenders like IMF have diff't incentives than private. Our @cristinarellano & @Leo_BarretoV model the modern reality of sovereign default.
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Economic models of sovereign debt typically treat debt like it is from private sources: Impatient, unforgiving. Our @cristinarellano & @Leo_BarretoV model the reality that "official" lenders step in during a debt crisis leading to very different outcomes.
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