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Context complexity: what is the Big-O of an agent API?
TLDR; small, local models are a good instrument for measuring what a harness costs an agent. an API is only as good as its outputs’ specificity, parsimony, and truthfulness. obvious code repair is cheaper done in your tools than elicited from the model th…
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A first look at token efficiency
A while ago I saw the article Which languages are most token efficient.
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Pandas feels clunky coming from R. What about Haskell?
Some years ago I came across an issue in the Frames repo that mentioned a blog post titled “Why pandas feels clunky when coming from R.”
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Grow and mow: interpretable models with boosting, symbolic regression and e-graphs
This post is the convergence of two ideas that have been floating in my head for about a year.
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Type-level programming is still programming
I was showing a friend the typed dataframe API.
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What Category Theory Teaches Us About DataFrames
Every dataframe library ships with hundreds of operations. pandas alone has over 200 methods on a DataFrame.
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Learning better decision tree splits - LLMs as Heuristics for Program Synthesis
A lot of tabular modeling gets easier the moment you stumble onto the right derived quantity.
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Installing docker on a Chromebook
I couldn’t find any instructions online so I thought I’d post them here for anyone who goes through a similar struggle. $ sudo apt-get update $ sudo apt-get install ca-certificates curl gnupg $ sudo rm -f /etc/apt/keyrings/docker.gpg # Remove any broken s…
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An introduction to program synthesis (Part II) - Automatically generating features for machine learning
Introduction This post kicks off the second part of a hands-on series about program synthesis.
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Progress towards Kaggle-style workflows in Haskell
There’s been a lot of work in the Haskell ecosystem that has made it easier to write interactive Kaggle-like scripts.