Meta’s long-awaited Pyrefly linter is out in a 1.0 version, and the forthcoming Python 3.15 has a super-efficient sampling profiler. Plus we have a comprehensive rundown of Python’s indispensable virtual environments — and a warning about a novel breed of malware that exploits Python’s package ecosystem.
Top picks for Python readers on InfoWorld
How to use virtual environments in Python
Isolate and protect your Python projects from each other, and empower them to do more, with virtual environments and their native-to-Python tooling.
Pyrefly 1.0: A fast, forward-looking Python linter
The first full release of Meta’s long-awaited linting and type checking tool for Python delivers speed and offers advanced features for type-checking PyTorch and Django projects.
Hands-on with the new sampling profiler in Python 3.15
Among Python 3.15’s best new features is a sampling profiler, for instrumenting your code and finding its bottlenecks with a minimum of performance impact or fuss. See up-close
We implement an end-to-end workflow for Salesforce CodeGen, loaded from Hugging Face. We move past basic inference by adding function extraction, syntax checking, static safety checks, and unit-test validation. We rerank best-of-N candidates, compose multi-turn program synthesis, and experiment with prompt styles. We finish by visualizing a mini benchmark and exporting the generated artifacts as reusable files.
The post Salesforce CodeGen Tutorial: Generate, Validate, and Rerank Python Functions With Unit Tests and Safety Checks appeared first on MarkTechPost.
Rising TIPS yields could shift investment from speculative assets to government securities, challenging crypto's appeal as an inflation hedge.
The post US government sells $24B in 5-year TIPS auction with nearly 2% yield appeared first on Crypto Briefing.
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.
In this article, we’ll build time-series machine learning models in Python using sktime and explore its core data structures for forecasting workflows.
