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Monte Carlo modeling in Python with probabilit

The article introduces probabilit, a Python package designed for Monte Carlo modeling that simplifies uncertainty calculations by allowing users to model equations with probability distributions and sample from them. It demonstrates key features like Latin Hypercube Sampling for efficient sampling and inducing correlations between variables, highlighting its suitability for prototyping u...

https://tommyodland.com/articles/2025/monte-carlo-modeling-in-python-with-probabilit/
Python Pydantic Tutorial: Complete Data Validation Course (Used by FastAPI)

Learn how to use Pydantic to validate and structure data in Python using type hints, custom validators, and nested models. Pydantic simplifies data handling in web apps, pipelines, and AI tools by replacing messy manual validation with clean, reliable models.

https://www.youtube.com/c/Coreyms/videos
Async Django: a solution in search of a problem?

The article explains that Django added async support mainly to handle I/O-bound workloads more efficiently by allowing the server to process multiple requests concurrently without blocking. However, the async features add significant complexity and have seen limited adoption because most Django applications benefit more from offloading heavy tasks to background workers rather than rewrit...

https://www.loopwerk.io/articles/2025/async-django-why/
the bug that taught me more about PyTorch than years of using it

The blog post recounts how a silent PyTorch MPS backend bug caused training loss plateaus by failing to update model weights on non-contiguous tensors, leading the author through a deep debugging journey that revealed PyTorch internals and the complexity of device-specific GPU kernel implementations. This experience taught more about PyTorch's workings than years of usage, emphasizing th...

https://elanapearl.github.io/blog/2025/the-bug-that-taught-me-pytorch
Introducing FlashPack: Lightning-Fast Model Loading for PyTorch

The FlashPack package dramatically speeds up PyTorch model loading by flattening all weights into a single contiguous stream, memory-mapping the file, and overlapping disk, CPU, and GPU operations with CUDA streams. This approach yields 3-6× faster loading compared to traditional methods like loadstatedict(), reducing GPU idle time and improving overall performance, especially on syste...

https://blog.fal.ai/introducing-flashpack-lightning-fast-model-loading-for-pytorch