Comparison between an unsorted and a luminance sorted candidate set, using Knoll’s algorithm on an 8-colour irregular palette. Left to right: unsorted, sorted.
Moment of introspection aside, I’m not sure what the future holds for agents and generative AI. My use of agents has proven to have significant utility (for myself at the least) and I have more-than-enough high-impact projects in the pipeline to occupy me for a few months. Although certainly I will use LLMs more for coding apps which benefit from this optimization, that doesn’t imply I will use LLMs more elsewhere: I still don’t use LLMs for writing — in fact I have intentionally made my writing voice more sardonic to specifically fend off AI accusations.
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As a data scientist, I’ve been frustrated that there haven’t been any impactful new Python data science tools released in the past few years other than polars. Unsurprisingly, research into AI and LLMs has subsumed traditional DS research, where developments such as text embeddings have had extremely valuable gains for typical data science natural language processing tasks. The traditional machine learning algorithms are still valuable, but no one has invented Gradient Boosted Decision Trees 2: Electric Boogaloo. Additionally, as a data scientist in San Francisco I am legally required to use a MacBook, but there haven’t been data science utilities that actually use the GPU in an Apple Silicon MacBook as they don’t support its Metal API; data science tooling is exclusively in CUDA for NVIDIA GPUs. What if agents could now port these algorithms to a) run on Rust with Python bindings for its speed benefits and b) run on GPUs without complex dependencies?
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