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?
Besides the Test PLA, the 386 has another PLA called the Entry PLA that maps opcodes to microcode entry points. One of its input bits is a "protected mode" flag. Many instructions have both a real-mode and a protected-mode entry point -- for instance, MOV ES, reg maps to address 009 (a single microcode line) in real mode, but to 580 (which initiates a full descriptor load with protection tests) in protected mode. The trick that makes V86 work is to define this flag as:
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"The stories and experiences Neil has been able to share with us are insane."
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to_be_deleted[classno] = j;