Teams often end up rebuilding the same runtime code—connection handling, retries, logging—in hundreds of standalone Python scripts. SeaTunnel fixes this by moving those concerns into a shared runtime and connector framework, leaving developers to define just the source, transform, and sink for each data pipeline.

This snippet shows how to call NVIDIA’s integrate API to run the moonshotai/kimi-k2.6 chat model. It covers setting headers, payload fields (model, tokens, temperature) and handling both JSON and stream responses.

SpiderFoot is an open-source Python 3 framework for automating OSINT reconnaissance via a web UI or CLI. It includes over 200 modules, a YAML-driven correlation engine, data exports, TOR support and integrates with tools like Nmap, SHODAN and HaveIBeenPwned. For teams and large-scale scans, SpiderFoot HX adds cloud hosting, multi-user collaboration, REST APIs and change alerts.

PPT Master is an open-source Python skill that runs inside AI IDEs (Claude Code, VS Code Copilot, Cursor) to turn PDFs, DOCX, URLs or Markdown into fully editable PowerPoint decks with real shapes, text boxes, and charts—not images. It runs locally, keeps your data off third-party servers, and costs only your AI model usage.

This gist provides a single-file, dependency-free implementation of a GPT-style transformer, complete with a custom autograd engine, training loop using Adam, and inference routine. It trains on a list of names, demonstrating both the core algorithm and a brief benchmark discussion for a GPU-based microgpt.cu variant.

This article breaks down Andrej Karpathy’s zero-dependency, 243-line GPT implementation in plain Python. It explains how each part—tokenizer, autograd engine, embeddings, attention mechanism, residual connections, and MLP—mirrors a full-scale transformer on a tiny dataset of baby names.

This article introduces Pointblank, a Python library designed to streamline data validation. It emphasizes user-friendly features, automated validation suggestions, and customizable reports to enhance team communication about data quality issues.

This article explores an unconventional method for classifying text by leveraging compression algorithms. The author demonstrates how to concatenate labeled documents, compress them, and use the compressed sizes to predict labels for new texts. While the method shows promise, it is computationally expensive and generally underperforms compared to traditional classifiers.

This article explores how Python 3.14's zstd module enables efficient text classification through incremental compression. It outlines a method where text is classified based on the size of compressed output from different class-specific compressors, demonstrating improved speed and accuracy over traditional methods.

The removal of Python's Global Interpreter Lock (GIL) marks a significant shift in the language's ability to handle multithreading and concurrency. With the introduction of PEP 703, developers can now compile Python with or without the GIL, enabling true parallelism and reshaping how systems are designed, particularly in data science and AI. This change presents both opportunities and challenges, requiring developers to adapt to new concurrency patterns.