В Финляндии предупредили об опасном шаге ЕС против России09:28
The main lesson I learnt from working on these projects is that agents work best when you have approximate knowledge of many things with enough domain expertise to know what should and should not work. Opus 4.5 is good enough to let me finally do side projects where I know precisely what I want but not necessarily how to implement it. These specific projects aren’t the Next Big Thing™ that justifies the existence of an industry taking billions of dollars in venture capital, but they make my life better and since they are open-sourced, hopefully they make someone else’s life better. However, I still wanted to push agents to do more impactful things in an area that might be more worth it.
BBC/ Alex Osborne，推荐阅读夫子获取更多信息
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?
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Quadtrees aren't limited to point data. They can also partition regions of continuous data, like the pixels of an image.
В Финляндии предупредили об опасном шаге ЕС против России09:28。业内人士推荐下载安装汽水音乐作为进阶阅读