Building Time-Series Machine Learning Models with sktime in Python

Most LLM applications need a clear workflow, not an autonomous agent. Here's how to build one in plain Python. The post You Probably Don’t Need an Agent Framework appeared first on Towards Data Science.

We break down Google Cloud's new Open Knowledge Format (OKF), an open spec that formalizes the LLM-wiki pattern. We explain how a bundle works: a directory of markdown files with YAML frontmatter, where each concept needs only a type field. We cover the three design principles, the reference tools Google shipped, and how OKF differs from RAG. We include a working Python consumer and an interactive bundle explorer you can embed. The post Google Cloud Introduces Open Knowledge Format (OKF): A Vendor-Neutral Markdown Spec for Giving AI Agents Curated Context appeared first on MarkTechPost.

In this tutorial, we build a workflow that uses Docling Parse to analyze PDF documents at a detailed structural level. We prepare a stable Python environment, handle common Colab dependency issues, and generate a custom multi-page PDF with text, columns, table-like content, vector shapes, and an embedded image. We then extract words, characters, and lines with page-level coordinates, render visual overlays, and save results into structured JSON and CSV. We see how low-level parsing supports layout analysis, reading-order reconstruction, and retrieval-ready document preparation. The post How to Build a Parsing Pipeline with Docling Parse for Layout-Aware Document Intelligence appeared first on MarkTechPost.

PDFs are used everywhere, and these five Python scripts help you automate the most common PDF tasks.

Of all the reasons Python is a hit with developers, one of the biggest is its broad and ever-expanding selection of third-party packages. Convenient toolkits for everything from ingesting and formatting data to high-speed math and machine learning are just an import or pip install away. But what happens when those packages don’t play nice with each other? What do you do when different Python projects need competing or incompatible versions of the same add-ons? That’s where Python virtual environments come into play. What are Python virtual environments? A virtual environment is a way to have multiple, parallel instances of the Python interpreter, each with different sets of packages and different configurations. Each virtual environment contains a discrete copy of the Python interpreter, including copies of its support utilities (such as the package manager pip). The packages installed in each virtual environment are seen only in that virtual environment and no other. Even large, compl

Explore the best Python web development repositories for building APIs, full-stack web apps, dashboards, machine learning demos, internal tools, and interactive Python-based user interfaces.

In this tutorial, we implement a hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for CUDA-style kernels in Python. We prepare a Colab-friendly environment and check GPU, driver, CUDA, and cuTile availability before running kernels. We then build tiled vector addition, matrix addition, and matrix multiplication, keeping a PyTorch fallback so the notebook stays executable. We validate correctness against PyTorch and benchmark median runtimes at every stage. The post NVIDIA cuTile Python Tutorial: Building Tiled GPU Kernels for Vector Addition, Matrix Addition, and Matrix Multiplication in Colab appeared first on MarkTechPost.

Threat actors are continuing their onslaught against software supply chains, now with malware named after death itself. The newly-discovered Hades Campaign is a “highly sophisticated” supply chain compromise that targets Python developer environments and runs as soon as infected packages are imported. It uses the popular Bun toolkit to silently execute multi-layer payloads that can extract sensitive data, move laterally across compromised systems, exploit common security frameworks, and even hijack AI gatekeeper analyzer systems via adversarial prompt injection. Notably, the campaign exploited the popular C++ library ensmallen, as well as packages in the computational biology, bioinformatics, and genotype-phenotype analysis ecosystems. The most novel thing about this malware is its combination of advanced tactics, noted David Shipley of Beauceron Security. He noted that we’ve seen memory-focused malware, we’ve seen attacks that attempt to defuse large language model (LLM) powered analy