ml • search • abstraction • cog sci
Texas
Joined December 2021
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I guess context engineering is the new feature engineering.
It's still kind of wild how powerful end-to-end learning and learned representations can be.
loop(org --> do)
One thing I'm grappling with is how the model of implementation you describe affects the planning/organizing on the other side of the loop of iteration.
Most work abstracts to an organize-then-do loop. What I'm finding is the ease of generating implementation and local tools is driving me to sometimes think through doing which sort of blends the two parts of organize-then-do.
Remember how Dr. Isbell,
in his Machine Learning class,
said he didn't care about where you got the code for your projects?
That comes to mind a lot. It's especially interesting now.
It's ok to let some of the details fade away. It's less ok to let go of the intuition learned from the details.
BM25 and decision models
are all you need?
Fun thread
Ok folks, informal poll: use the little browser app at the link below to say how you think the sets of physical systems, living beings, and minds fit together in a Venn diagram:
three-circles.space.fast/
One of the best things about AI code gen is not just that I can make local tools quickly, but that they can also be tailored and made to look nice. It's such an improvement to my daily work.
Behind the phrase vibe code is a masked problem. There is a continuum of good to bad engineering and adding AI to bad engineering will mire a team in problems and slow everything down.
This is a nice analysis, as expected, from Hamel and Antaripa. Good illustration of more traditional analysis in the newer domains of LLM evals and decision models.
For critique I would say it's pushing the boundaries of decision models by using a very broad prompt and single decision vs breaking the trace eval into several smaller decisions.
Really appreciate the ongoing work from @HamelHusain and @doesdatmaksense. They are so helpful for advancing this eval field.
hamel.dev/blog/posts/decisio…
