I have been experimenting more with agentic iterative optimization: using LLMs to actually speed up code by finding and testing lower-level optimizations, specifically by having it build a real-world representative benchmark, then tell the LLM to optimize that benchmark without a) cheating the benchmark and b) ensuring code quality by some metric does not regress, e.g. MSE for machine learning algorithms. This is extremely effective with GPT 5.5, and recently I found another prompt optimization (https://news.ycombinator.com/item?id=48413304) that surprisingly results in another 2x speed improvement on average.

So far, I have mostly feature-complete implementations of the following, which are faster than the state-of-the-art implementations, up to 20x faster in some cases while matching or beating them in quality:

- a new 2D data visualization library, along with more bespoke data viz implementations such as word clouds and Primitive.

- programmatic image generation

- image compression

- a new statistical machine learning library, along with more bespoke algorithms such as UMAP and HDBSCAN

- a novel modelless invisible image watermarking approach

- a novel machine learning approach which may be a crime against data science but the performance is really good

- local text embedding generation with MLX

- image-to-ASCII art conversion

- grep/jq replacement (faster than ripgrep)

I aim to open-source them over the next months but the main bottleneck is the inevitable barrage of "gtfo AI slop" comments even if I dot every i and check every t, in addition to the distribution of new software being extremely difficult nowadays due to the death of social media and "20x faster" raising red flags even if I have the data to justify it.