strict.writer.write(chunk1); // ok (not awaited)
Еще в начале января зафрахтовать танкер такого класса можно было менее чем за 29 тысяч долларов в сутки. Нынешние цены в последний раз наблюдались в 2020 году, когда ценовая война России и ОПЕК+ из-за пандемии COVID-19 привела к накоплению непроданной нефти на судах и их дефициту для новых поставок.
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Раскрыты подробности похищения ребенка в Смоленске09:27
There’s a whole ecosystem of secrets management tools — Doppler, Infisical, HashiCorp Vault, SOPS, dotenvx. They’re all good, and if you’re running a team of 50+ engineers you should probably be evaluating them.
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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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