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perone

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  1. Where the wild Discovery Loops are (blog.christianperone.com)
    1 points 0
  2. Gemma3n architecture: a short guide [slides] (drive.google.com)
    2 points 0
  3. Diffusion Elites: surprisingly good, simple and embarrassingly parallel (blog.christianperone.com)
    9 points 0
  4. Show HN: VectorVFS, your filesystem as a vector database (vectorvfs.readthedocs.io)
    279 points 149
  5. Notes on Gilbert Simondon's "On the Mode of Existence of Technical Objects" (blog.christianperone.com)
    2 points 0
  6. 1 points 0
  7. The geometry of data: the missing metric tensor and the Stein score [Part II] (blog.christianperone.com)
    64 points 8
  8. Memory-Mapped CPU Tensor Between Torch, NumPy, Jax and TensorFlow (blog.christianperone.com)
    1 points 0
  9. Large language model data pipelines and Common Crawl (WARC/WAT/WET) formats (blog.christianperone.com)
    2 points 0
  10. Appreciating the complexity of LLMs data pipelines (blog.christianperone.com)
    1 points 0
  11. PyTorch 2 Internals (slideshare.net)
    4 points 0
  12. PyTorch 2 Internals [slides] (drive.google.com)
    2 points 1
  13. Appreciating the complexity of large language models data pipelines (blog.christianperone.com)
    1 points 0
  14. Appreciating the complexity of large language models data pipelines (blog.christianperone.com)
    2 points 0
  15. Show HN: Feste, an open-source framework to optimize and parallelize NLP tasks (feste.readthedocs.io)
    2 points 0
  16. Tutorial using LLVM to JIT PyTorch graphs to native code (x86/arm/RISC-V/WASM) (blog.christianperone.com)
    2 points 0
  17. 1 points 0
  18. Tutorial on using LLVM to JIT PyTorch graphs to native code (x86/arm/RISC-V) (blog.christianperone.com)
    4 points 0
  19. Arduino WAN, Helium network and cryptographic co-processor (blog.christianperone.com)
    1 points 0
  20. Show HN: Episuite, open-source framework for epidemiology in Python (perone.github.io)
    12 points 0
  21. Introduction to gradient-based optimization in Deep Learning [slides] (drive.google.com)
    1 points 0
  22. Slides: Gradient-based optimization in Deep Learning (drive.google.com)
    3 points 0
  23. A new professional ethics: Karl Popper and Xenophanes’ epistemology (blog.christianperone.com)
    3 points 0
  24. A sane introduction to maximum likelihood (MLE) and maximum a posteriori (MAP) (blog.christianperone.com)
    1 points 0
  25. Gandiva, Using LLVM and Arrow to JIT and Evaluate Pandas Expressions (blog.christianperone.com)
    1 points 0