Diffusion Elites: surprisingly good, simple and embarrassingly parallelblog.christianperone.com·9 pts·perone·0
Notes on Gilbert Simondon's "On the Mode of Existence of Technical Objects"blog.christianperone.com·2 pts·perone·0
The geometry of data: the missing metric tensor and the Stein score [Part II]blog.christianperone.com·64 pts·perone·7
Memory-Mapped CPU Tensor Between Torch, NumPy, Jax and TensorFlowblog.christianperone.com·1 pts·perone·0
Large language model data pipelines and Common Crawl (WARC/WAT/WET) formatsblog.christianperone.com·2 pts·perone·0
Appreciating the complexity of large language models data pipelinesblog.christianperone.com·1 pts·perone·0
Appreciating the complexity of large language models data pipelinesblog.christianperone.com·2 pts·perone·0
Show HN: Feste, an open-source framework to optimize and parallelize NLP tasksfeste.readthedocs.io·2 pts·perone·0
Tutorial using LLVM to JIT PyTorch graphs to native code (x86/arm/RISC-V/WASM)blog.christianperone.com·2 pts·perone·0
Tutorial on using LLVM to JIT PyTorch graphs to native code (x86/arm/RISC-V)blog.christianperone.com·4 pts·perone·0
A new professional ethics: Karl Popper and Xenophanes’ epistemologyblog.christianperone.com·3 pts·perone·0
A sane introduction to maximum likelihood (MLE) and maximum a posteriori (MAP)blog.christianperone.com·1 pts·perone·0
Gandiva, Using LLVM and Arrow to JIT and Evaluate Pandas Expressionsblog.christianperone.com·1 pts·perone·0
Show HN: Gandiva, Using LLVM and Arrow to JIT and Evaluate Pandas Expressionsblog.christianperone.com·2 pts·perone·0
EuclideDB: Machine learning feature database tight coupled with PyTorcheuclidesdb.readthedocs.io·15 pts·perone·0