The tools used to measure LLM output reinforce the illusion. scc‘s COCOMO model estimates the rewrite at $21.4 million in development cost. The same model values print("hello world") at $19.
As the number of parts increases, queries invariably will slow as a result of the need to evaluate more indices and read more files. Users may also experience slow startup times in cases where the part count is high. The creation of too many parts thus results in more internal merges and "pressure" to keep the number of parts low and query performance high. While merges are concurrent, in cases of misuse or misconfiguration, the number of parts can exceed internal configurable limits (parts_to_throw_insert, max_parts_in_total). While these limits can be adjusted, at the expense of query performance, the need to do so will more often point to issues with your usage patterns. As well as causing query performance to degrade, high part counts can also place greater pressure on ClickHouse Keeper in replicated configurations.，更多细节参见新收录的资料
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?，详情可参考新收录的资料
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