InfoWorld AI·
If Python developers have one consistent gripe about their beloved language, it tends to be this: Why is it so hard to take a Python program and deploy it as a standalone artifact, the way C, C++, Rust, Go, and even Java can be deployed? Are we stuck with requiring everyone to install the Python runtime first before they can use a Python program? And why are all the workarounds for this problem so clunky? One of the features that makes Python so appealing — its dynamism — is also the reason Python apps are so difficult to bundle and deploy. Not impossible, but challenging. Bundled Python apps end up being big packages, never less than a dozen megabytes or more. Plus, the tools for creating those bundles aren’t the friendliest or most convenient. So what is it about Python’s dynamism that’s reponsible for this? The pleasures and perils of Python’s dynamism When we talk about Python as a “dynamic” language, that means more than the fact that Python apps are executed with an interpreter.
Read full articleBeat the 8GB VRAM limit. Learn how to run three different LLMs on a single 8GB GPU using C++ layer multiplexing and admission control. The post 3 Agents. 3 LLMs. 1 Aging GPU: Engineering Parallel Inference on Bare Metal appeared first on Towards Data Science.
Microsoft has been rethinking its commitment to Windows for a while now, with Insider builds of the operating system showing a swing away from web-based user experiences and back to native code. That commitment got a boost at Build 2026 with a bundle of announcements that focused on tools and features that help developers take advantage of the platform. The most obvious is support for the standard core Unix utilities, in the shape of a Microsoft-maintained fork of the popular Rust-based uutils coreutils package, Coreutils for Windows. Coreutils for Windows installs as a single binary, making it easier to update and manage. And it is one of those tools that does exactly what it says on the tin, providing a Windows implementation of the commands you’re using in Linux virtual machines or in Windows Subsystem for Linux (WSL). Building on Windows Terminal with Coreutils Much of the Windows developer experience has moved back to the command line via Windows’ rearchitected terminal, underscor
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.
OpenAI has launched a program with cybersecurity firm Trail of Bits to use AI to find and fix vulnerabilities in widely used open-source software, as enterprises face growing risks from flaws buried deep in their software supply chains. The initiative, called Patch the Planet, uses AI-assisted vulnerability research alongside human review to help turn security findings into tested fixes that can be disclosed through existing project channels. Initial participants include Python, Go, cURL, Sigstore, NATS Server, aiohttp, freenginx, pyca/cryptography, and python.org. These projects support software development, networking, cryptography, and supply chain infrastructure used across a wide range of enterprise applications and services. OpenAI said each engagement will begin with consultation with maintainers to identify where security support is most needed. Researchers will then investigate potential vulnerabilities, validate meaningful issues, develop or refine patches, support testing, a
Most AI agent tutorials start with an API.
In this tutorial, we build a Prefab application that creates interactive dashboards entirely in Python. We design an operations dashboard with reactive state, charts, tables, filters, forms, tabs, and metrics. We generate synthetic pipeline monitoring data and connect it to live UI controls. We then export the app as static HTML and preview it directly inside Google Colab. The post How to Design Python-First Interactive Dashboards with Prefab Reactive UI Components and Static HTML Export appeared first on MarkTechPost.
