How Well Do New Python Type Checkers Conform? A Deep Dive into Ty, Pyrefly, and Zuban
The Python type checking landscape in 2025 includes three new Rust-based tools: Astral's ty, Meta's pyrefly, and Zuban. Ty emphasizes gradual adoption with fewer false positives, pyrefly focuses on aggressive inference to catch more issues early, and Zuban aims for seamless mypy compatibility; while conformance tests reveal differences, all show promise for real-world Python development.
https://sinon.github.io/future-python-type-checkers/
The Python type checking landscape in 2025 includes three new Rust-based tools: Astral's ty, Meta's pyrefly, and Zuban. Ty emphasizes gradual adoption with fewer false positives, pyrefly focuses on aggressive inference to catch more issues early, and Zuban aims for seamless mypy compatibility; while conformance tests reveal differences, all show promise for real-world Python development.
https://sinon.github.io/future-python-type-checkers/
Rob's Blog | Python • Rust • Ramblings?
How Well Do New Python Type Checkers Conform? A Deep Dive into Ty, Pyrefly, and Zuban — Rob's Blog | Python • Rust • Ramblings?
A comparison of three new Rust-based Python type checkers through the lens of typing spec conformance: Astral's ty, Meta's pyrefly, and David Halter's zuban
Cloud-Native Pipelines for Scientific Data Processing with Prefect and Dask
This article explains how to build scalable, cloud-native scientific data processing pipelines using Prefect for workflow orchestration and Dask for parallel computation. It covers cloud-optimized formats (like Zarr), integration with tools like xarray and echopype, and demonstrates end-to-end ETL pipelines that load, process, and store multidimensional data directly in the cloud.
https://oceanstream.io/cloud-native-data-processing-pipelines-with-prefect-and-dask/
This article explains how to build scalable, cloud-native scientific data processing pipelines using Prefect for workflow orchestration and Dask for parallel computation. It covers cloud-optimized formats (like Zarr), integration with tools like xarray and echopype, and demonstrates end-to-end ETL pipelines that load, process, and store multidimensional data directly in the cloud.
https://oceanstream.io/cloud-native-data-processing-pipelines-with-prefect-and-dask/
OceanStream
Cloud‑Native Pipelines for Scientific Data Processing with Prefect and Dask
An extended tutorial on the open-source libraries that we use to build the OceanStream cloud‑native data processing stack used to ingest data from sonar instruments and other marine sensors.
LLM-Deflate: Extracting LLMs Into Datasets
LLM-Deflate is a technique for systematically extracting structured datasets from trained large language models by probing their internal knowledge with hierarchical topic exploration and prompt engineering. This reverse-compression process enables model analysis, knowledge transfer, training data augmentation, and debugging, potentially making knowledge extraction a standard tool as inf...
https://www.scalarlm.com/blog/llm-deflate-extracting-llms-into-datasets
LLM-Deflate is a technique for systematically extracting structured datasets from trained large language models by probing their internal knowledge with hierarchical topic exploration and prompt engineering. This reverse-compression process enables model analysis, knowledge transfer, training data augmentation, and debugging, potentially making knowledge extraction a standard tool as inf...
https://www.scalarlm.com/blog/llm-deflate-extracting-llms-into-datasets
ScalarLM
LLM-Deflate: Extracting LLMs Into Datasets
Large Language Models compress massive amounts of training data into their parameters. This compression is lossy but highly effective—billions of parameters can encode the essential patterns from terabytes of text. However, what’s less obvious is that this…
The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data
The Kaggle Grandmasters Playbook presents seven proven techniques for tabular data modeling, emphasizing fast experimentation and careful validation powered by GPU acceleration to handle large-scale data effectively. Key strategies include advanced exploratory data analysis, building diverse baselines, extensive feature engineering, ensembling with hill climbing and stacking, pseudo-labe...
https://developer.nvidia.com/blog/the-kaggle-grandmasters-playbook-7-battle-tested-modeling-techniques-for-tabular-data/
The Kaggle Grandmasters Playbook presents seven proven techniques for tabular data modeling, emphasizing fast experimentation and careful validation powered by GPU acceleration to handle large-scale data effectively. Key strategies include advanced exploratory data analysis, building diverse baselines, extensive feature engineering, ensembling with hill climbing and stacking, pseudo-labe...
https://developer.nvidia.com/blog/the-kaggle-grandmasters-playbook-7-battle-tested-modeling-techniques-for-tabular-data/
NVIDIA Technical Blog
The Kaggle Grandmasters Playbook: 7 Battle-Tested Modeling Techniques for Tabular Data
Over hundreds of Kaggle competitions, we’ve refined a playbook that consistently lands us near the top of the leaderboard—no matter if we’re working with millions of rows, missing values…