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/

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

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/

How to Build Advanced AI Agents – Course for Beginners (LiveKit, Exa, LangChain)

The video teaches beginners how to build advanced AI agents, such as voice sales agents, research assistants, and multi-agent workflows, using LiveKit, Exa, LangChain, and Cerebras. It provides step-by-step guidance, hands-on code, and free API credits to help developers quickly create real-world AI applications.

https://www.youtube.com/watch?v=B0TJC4lmzEM

Python Singleton Pattern: Smarter Than You Think?

This video analyzes the strengths and weaknesses of the singleton pattern in Python, explaining why global state is risky but controlled instantiation can be valuable in certain cases. It recommends module-level singletons and thread safety measures, while cautioning against tight coupling and testing pitfalls with traditional singleton implementations.

https://www.youtube.com/watch?v=p_UQ7tzUFLo

LLMs from Scratch – Practical Engineering from Base Model to PPO RLHF

This video provides a hands-on guide to building a large language model entirely from scratch in PyTorch, covering every step from core transformer design to advanced alignment with RLHF. By the end, viewers gain practical experience in implementing, training, scaling, and aligning their own custom LLMs.

https://www.youtube.com/watch?v=p3sij8QzONQ

Unlocking Performance in Python's Free-Threaded Future: GC Optimizations

A description of the performance optimizations made to the free-threaded garbage collector for Python 3.14.

https://labs.quansight.org/blog/free-threaded-gc-3-14

Air

The new web framework that breathes fresh air into Python web development. Built with FastAPI, Starlette, and Pydantic.

https://github.com/feldroy/air

onyx-dot-app / onyx

Open Source AI Platform - AI Chat with advanced features that works with every LLM

https://github.com/onyx-dot-app/onyx

Helium

Private, fast, and honest web browser.

https://github.com/imputnet/helium

memvid

Video-based AI memory library. Store millions of text chunks in MP4 files with lightning-fast semantic search. No database needed.

https://github.com/Olow304/memvid

DuckDB vs Polars. Wait. DuckDB and Polars.

The article emphasizes that DuckDB and Polars are not direct competitors but complementary tools in the Modern Data Stack, with each excelling in different contexts: DuckDB is best for SQL-heavy analytics and embedding as a query engine, while Polars suits end-to-end ETL pipelines and DataFrame-centric workflows. The choice depends on your problem context, team comfort, and use case rath...

https://www.confessionsofadataguy.com/duckdb-vs-polars-wait-duckdb-and-polars/

search_evals

Batteries-included eval framework for search APIs.

https://github.com/perplexityai/search_evals

Simplifying Resource Management in mssql-python through Context Manager

The article introduces context manager support in the mssql-python driver, allowing Python applications to manage SQL Server and Azure SQL resources more safely and efficiently using Python's "with" statement. This feature automates opening and closing of connections and cursors, as well as commit and rollback of transactions, reducing boilerplate code, preventing resource leaks, and ens...

https://devblogs.microsoft.com/python/simplifying-resource-management-in-mssql-python-through-context-manager/

bytedance / Dolphin

The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.

https://github.com/bytedance/Dolphin