Exciting Python features are on the way

In this tutorial, we explore CUP, Baidu's Common Useful Python library, as a practical utility toolkit for stronger Python workflows. We install it in a Colab-friendly environment and walk its subsystems step by step. We cover logging, decorators, nested configuration, caching, ID generation, thread pools, scheduling, and Linux resource monitoring. Along the way, we connect each module to real tasks like automation, concurrency, and reliability checks. The post CUP (Common Useful Python): Building Reliable Python Workflows with Baidu’s Utility Toolkit appeared first on MarkTechPost.

Check out this practical list of Python projects covering AI automation, machine learning, APIs, dashboards, data analysis, and portfolio-ready apps, with guides, demos, repositories, and datasets.

Deno Land has published Deno 2.9, an update of the company’s JavaScript/TypeScript/WebAssembly runtime that features deno desktop, a mechanism for building native desktop applications from the web stack developers already know. Introduced June 25, Deno 2.9 also improves startup time, memory use, and HTTP throughput, the company said. Deno installation instructions can be found at docs.deno.com. With Deno 2.9, users can point deno desktop at a script or a web framework project to produce a native and self-contained desktop application where the UI runs in a webview and the logic runs in Deno. Because deno desktop is built on the same machinery as deno compile, the output is a single, distributable binary with code and assets embedded, Deno Land said. Also in Deno 2.9, a hello-world program now cold-starts in about half the time it took in 2.8 (34ms down to 17ms), the company said. This improvement results from a combination of factors including lazy-loadingnode: globals out of the snaps

In this tutorial, we build a complete, self-contained OCRmyPDF pipeline in Python. We generate synthetic image-only PDFs so we can test OCR without external files, then convert them into searchable PDFs and PDF/A outputs. We extract sidecar text, validate results, measure word-recall, and compare file sizes. We also tune Tesseract, clean noisy scans, correct orientation, run OCR in memory, and batch-process whole folders. The post OCRmyPDF Tutorial: Convert Scanned Documents into Searchable PDF/A Files with Sidecar Text Extraction and Batch Processing appeared first on MarkTechPost.

Microsoft is continuing its push to bring generative AI (genAI) into Excel, with new Microsoft 365 Copilot skills designed to automate common processes and a “plan” mode to provide more control over Copilot’s outputs when handling financial data. Microsoft made Microsoft 365 Copilot generally available in Excel in late 2024 and since then has added several capabilities, including agentic tools, a Copilot function within Excel, and Python support for advanced data analysis.   On Thursday, Microsoft unveiled a skills feature that lets users define processes Copilot can perform in Excel — such as building a discounted cash flow, Microsoft suggested, preparing a variance analysis, or refreshing a monthly reporting model.   “Instead of starting from scratch each time, a skill guides Copilot through the steps, applying the right structure and formatting, and helping produce an output that is easier to review, reuse, and trust,” Brian Jones, vice president for Excel at Microsoft, said in a bl

SpatialClaw is NVIDIA Research’s latest AI framework that enables agents to write, execute, and refine their own reasoning through executable Python code rather than relying on predefined tool calls. The approach delivers significant gains in spatial intelligence across complex 3D and 4D tasks without requiring additional training.

In this tutorial, we build a fully offline Graphify pipeline that turns a multi-module Python application into a knowledge graph. We install Graphify, generate a connected sample app, and extract the graph locally using tree-sitter, with no API key or LLM backend. We load graph.json into NetworkX and analyze file types, relationship types, centrality scores, community detection, and shortest paths. We then create static and interactive visualizations to see how modules, classes, functions, and database objects connect. The post Using Graphify and NetworkX to Map Python Codebase Structure with God Nodes, Communities, and Architecture Visualizations appeared first on MarkTechPost.

In this tutorial, we build a multilingual ASR and speech translation pipeline with NVIDIA Canary-1B-v2. We load the model on a GPU-enabled runtime, prepare audio into 16 kHz mono, and run English ASR. We then translate speech into French, German, Spanish, and Italian, and extract word and segment timestamps. We export translated subtitles as an SRT file, test long-form transcription, run batch processing, and benchmark inference speed. The post How to Use NVIDIA Canary-1B-v2 for ASR, Translation, and Automatic SRT Subtitle Export in Python appeared first on MarkTechPost.