Lecture 18: Monte Carlo Rendering (CMU 15-462/662) An introduction to Monte Carlo ray tracing
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- CHANGELOG.md — Changelog (Keep a Changelog format)，这一点在51吃瓜中也有详细论述
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?。下载安装汽水音乐对此有专业解读
around a year ago, we built a regex engine in F# that not only outperformed the ones in dotnet, but went above and beyond competing with every other industrial regex engine on a large set of industry-standard benchmarks. additionally, it supports the full set of boolean operators (union, intersection, complement) and even a form of context-aware lookarounds, which no other engine has while preserving O(n) search-time complexity. the paper was published at POPL 2025, and i figured it’s time to open source the engine and share the story behind it. consider it a much more casual and chatty version of the paper, with more focus on engineering aspects that went into it.，更多细节参见体育直播
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