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PFP: A Probabilistic Functional Programming Library for Haskell (2006)

A Probabilistic Functional Programming Library for Haskell Version: June 2006 Distributions can represent events, such as the roll of a die or the flip of a coin. For example, or example, the outcome of a die roll can be expressed as follows. die :: Dist Int die = uniform [1..6] die > die 1 16.7% 2 16.7% 3 16.7% 4 16.7% 5 16.7% 6 16.7% uniform uniform succOrId x = uniform [x, x+1] choose succOrId x = choose 0.5 x (x+1) droll = die >>= succOrId droll = do d <- die succOrId d To use t

AGI is an engineering problem, not a model training problem

Published: Aug 13, 2025 | at 11:00 AM We’ve reached an inflection point in AI development. The scaling laws that once promised ever-more-capable models are showing diminishing returns. GPT-5, Claude, and Gemini represent remarkable achievements, but they’re hitting asymptotes that brute-force scaling can’t solve. The path to artificial general intelligence isn’t through training ever-larger language models—it’s through building engineered systems that combine models, memory, context, and determ