Python Programming Tutorials & Engineering Articles

Adding AI models to local applications requires a portable model format, a reliable runtime, and acceleration that works across target systems.

Automatic speech recognition must handle how people actually speak, not only the languages and styles that dominate pretraining data.

Generative recommender (GR) systems are emerging as a powerful new approach for large-scale personalization. Instead of treating recommendation as a set of…

An agent can finish a task and still take an inefficient path. A failed search can trigger another search. A truncated file read can lead to a command fetching…

ClickHouse Workload for Microsoft Fabric brings sub-second analytics to OneLake data through a native Fabric workload, with dedicated ClickHouse Cloud compute for agents, dashboards, and applications.

The chDB Durable Layer keeps agent memory fast to query locally while making its analytical state recoverable across laptops, CI jobs, and short-lived sandboxes.

As language models grow, scaling dense architectures becomes increasingly expensive. In a dense transformer, every token passes through every layer…

Google Cloud API Gateway now acts as a native remote Model Context Protocol (MCP) server, eliminating the need to build and maintain custom middleware to expose REST APIs to AI agents. By simply adding specific annotations (like x-google-api-management.mcp) to existing OpenAPI 3.x specifications, developers can instantly convert standard REST operations into discoverable, agent-ready tools. The gateway automatically transcodes incoming MCP JSON-RPC requests into REST calls, ensuring that your existing authentication, quotas, and logging policies apply seamlessly to agent traffic without requiring new infrastructure.

The Google Antigravity SDK now empowers developers to execute offline, agentic workflows locally using models like Gemma 4 26B A4B via LiteRT. This update facilitates powerful hybrid orchestration architectures, allowing a cloud model to act as a lightweight planner while local models securely handle token-intensive tasks—like code auditing and patching—directly on-device. Furthermore, the SDK provides drop-in support for OpenAI-compatible inference servers like Ollama and vLLM, enabling the seamless creation of privacy-first, autonomous local utilities.

When you ship an AI agent, the key question is whether it can execute a chain of work across dozens of sequential tool calls against a live environment…

Weather-sensitive industries increasingly have access to observations that offer an earlier, more local view of changing conditions.

We’re open-sourcing Rebalancer, the assignment-problem solver that has been used to solve resource allocation problems throughout Meta for over nine years. Rebalancer separates several relate…

You’re deploying a model on a system. It starts up, prompts are getting responses. Now the hard question: Is this fast? Your instincts might lead you to send…

The official ClickHouse provider for Apache Airflow simplifies data workflows with standard SQL operators, bulk inserts, and shared setup across self-managed Airflow and Astronomer.

The ClickHouse adapter for dbt v2 is in public beta, powered by dbt’s Rust engine. ClickHouse also joins the dbt platform in private beta, supporting open-source ClickHouse and ClickHouse Cloud.

Google has partnered with Speakeasy to open-source their OpenAPI code generation suite under the AGPLv3 license, a strategic move prompted by the sudden shutdown of Google's previous proprietary SDK provider. The newly open-sourced suite equips developers with deterministic, multi-language SDK generators that natively support strict typing and SSE streaming, alongside tools for compiling agent-native CLIs and documentation MCP servers. Engineering teams can now safely integrate this robust tooling directly into their CI pipelines to automatically generate reliable client libraries for their own APIs, all while retaining complete licensing control over the output code.

cuTile Rust () is a tile-based system for safe, idiomatic GPU kernel authoring in the Rust programming language. Extending the Rust ownership model to tile…

This blog post explores how to transition AI agents from static, build-time security controls to dynamic runtime governance using the Gemini Enterprise Agent Platform. It highlights three primary managed defenses: Model Armor for screening edge prompts, Semantic Governance Policies for evaluating tool intent against business rules, and Agent Anomaly Detection for catching multi-turn exploits. By shifting these capabilities to the platform level, security administrators can dynamically enforce policies and neutralize complex attacks without needing to modify or redeploy the agent's underlying code.

Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE…

Explore ClickHouse AI Functions for classification, generation, translation, embeddings, semantic search, and cost controls—all directly from SQL.

Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently.

Every NVIDIA CUDA Toolkit release adds functionality and performance improvements that help developers get more from NVIDIA GPUs and the broader NVIDIA software…

While end-to-end benchmarks like SWE-bench provide broad performance scores for AI agents, they are often expensive, slow, and lack the root-cause diagnostics needed to explain exactly where an agent's logic broke down. To solve this, developers should adopt behavioral evaluations—fast, local, unit-style tests that assert on discrete intermediate actions, such as verifying specific tool calls or file modifications rather than final string equality. By building these inexpensive micro-checks alongside macro benchmarks, engineering teams can confidently iterate on system prompts and upgrade models without the risk of regressions.

Google has officially released version 1.0 of the Agent Development Kit (ADK) for Kotlin, achieving full feature parity with the Python and Java ADK cores to enable idiomatic, multi-agent AI development. Built on Kotlin Multiplatform (KMP), the framework leverages Kotlin Symbol Processing (KSP) for zero-reflection, type-safe function calling, alongside advanced orchestration capabilities like human-in-the-loop workflows and context compaction. Additionally, the release introduces a robust suite of Android-first extensions, allowing mobile developers to integrate local models via LiteRT-LM, cloud reasoning through Firebase AI, session persistence using Room, and semantic memory powered by AppSearch.

In September 2026, NVIDIA announced it is leaning into native GPU programming in Rust. CUDA C++ and CUDA Python are mature, enterprise-grade toolchains…

Open AI models are evolving faster than ever, but bringing them into native applications can still require model-specific conversion, preprocessing…

Astroparticle physics sits at the exciting intersection of astrophysics and particle physics and stu...

Google Cloud has natively integrated TPU support into the vLLM serving engine, allowing developers to elastically scale high-demand embedding pipelines using Google Kubernetes Engine (GKE). To handle massive 15K+ token contexts for models like Qwen3-Embedding-8B, the engineering team implemented TPU-specific optimizations such as hardware-safe tensor alignment, JAX/XLA compilation pre-warming, and a hybrid StepPool architecture for chunked prefill management. These enhancements achieve near-perfect numerical parity with reference GPU baselines, and developers can immediately leverage the open-sourced setup recipes on the AI-Hypercomputer GitHub to build their own high-throughput semantic retrieval applications.

For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain…

How we adapted our application storage platform, Habitat, in Python to manage unprecedented growth.By Jon Lee, Chaomin Yu, and Ben Ries, Members of Technical Staff

We’ve published the anonymization code for Project Lighthouse, and two new papers that describe the algorithms and data quality checks.

AI factories are interconnected systems where fleet economics depend on how efficiently the entire stack converts power and capital into completed agent tasks.

Moving live voice agents from demo to production requires rigorous, automated testing to handle the unpredictability of real multi-turn conversations. ADK now provides native live evaluation, allowing developers to test graph-based agent workflows against LLM-driven simulated users that generate actual audio via Gemini TTS. By defining evaluation scenarios and natural-language rubrics, you can automatically score audio responses and tool executions, inspect the resulting transcripts in ADK Web, or run the CLI directly in your CI/CD pipeline.

Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and…

NVIDIA Holoscan is a platform for building real-time AI applications at the edge, from medical imaging to robotics. HoloHub is its companion repository: a…

Modern vision-language models (VLMs) can support tasks such as visual question answering, captioning, and image-text reasoning. In practice, however…

Atomistic simulation requires three things: knowledge of the science, compute-efficient implementation of simulations, and accessible interfaces to the…

Uniform Manifold Approximation and Projection (UMAP) is a dimensionality reduction technique widely used for visualization and feature extraction.

Building autonomous AI agents that mutate production state requires moving beyond soft system prompts to a robust zero-trust architecture. To secure Google Agent Development Kit (ADK) workflows against prompt injections and malicious execution, developers must implement hardware-backed cryptographic signatures for database writes, kernel-level sandboxing with gVisor for dynamic code, and deterministic semantic gateways for I/O validation. By enforcing these hard security boundaries at the infrastructure level, you can safely deploy multi-tool AI agents without risking unauthorized data manipulation or server compromise.

Video is a core data path across NVIDIA Jetson applications, from robotics and intelligent video analytics to industrial automation, healthcare…

As AI coding assistants shift the developer's primary role from writing boilerplate to reviewing and maintaining systems, language choice becomes critical for long-term architectural integrity. Go directly addresses this new paradigm by utilizing its strict compiler, integrated toolchain, and uncompromising readability to provide deterministic guardrails that help AI models self-correct and generate highly standardized code. By enforcing ecosystem-wide consistency and strict backward compatibility, the Go platform empowers engineering teams to efficiently verify, optimize, and maintain high-velocity, AI-generated output in production environments.

This release brings speedups for GROUP BY ... ORDER BY ... LIMIT, three JOIN improvements, four vector search improvements, position-aware phrase search, EXPLAIN ANALYZE, unified URL access, and more!