My earliest prototype used ascii characters for snakes and fruit. This had a problem - since terminal characters are twice as tall as they are wide, vertical movement felt much faster than horizontal movement:
“As a lifelong Pokémon fan, it's been an incredible honor to help bring these Pokémon to life in LEGO brick form for the first time,” said Siddharth Muthyala, Design Director on LEGO Pokémon in a press release. “Pokémon has inspired generations of fans around the world, so our team approached the design of these sets with immense care and passion, ensuring every detail and pose truly celebrates what makes these characters so beloved."
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特点：在特定初始化下能实现自归一化，保持激活均值和方差稳定。，更多细节参见heLLoword翻译官方下载
我们可以去宽容人，却不可视他人宽容为理所当然。宽容他人，是放过别人；收敛自己，是成全自己，多了温和，多了尊重，多了友谊，多了互帮互利。时时自牧，你无需向任何人索取宽容，因为你早已让身边的人甘愿以温柔相待。
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?