39 related articles

A tailored ML guide for control theory learners covering reinforcement learning, data-driven control, Learning-based MPC, and a three-stage roadmap with practical advice.

Explore RRT co-inventor James Kuffner's career from Cloud Robotics and Google Robotics to Symbotic CTO, driving robots from labs to Walmart warehouse-scale deployment.

Deep dive into Google DeepMind's Gemini Robotics 2: its whole-body intelligence, dexterous manipulation, adaptive reasoning, and how multi-robot collaboration is advancing embodied AI from lab to reality.

Deep dive into Google DeepMind's Gemini Robotics 2: its three core capabilities of whole-body intelligence, dexterous manipulation, and adaptive reasoning, plus how multi-robot collaboration is pushing embodied AI from labs into the physical world.

Deep dive into Google DeepMind's Gemini Robotics 2: how whole-body intelligence unifies perception, reasoning, and motor control, and the challenges from lab demos to commercial deployment.

Deep dive into Google DeepMind's Gemini Robotics 2: how whole-body intelligence unifies perception, reasoning, and motor control, and the challenges of bringing embodied AI from lab to commercial deployment.

Exploring how AI drives large-scale MMO development, from scalable content generation to dynamic NPC interaction, analyzing technical pathways, challenges, and industry implications.

A detailed guide to LangChain Guardrails covering layered ecosystem architecture, middleware implementation, deterministic and model-driven protection for building production-grade secure AI Agents.

D-Flash solves the autoregressive drafter latency bottleneck in speculative decoding via fast diffusion parallel drafting and target feature KV injection. 16 tokens in just 6ms, up to 3.5x speedup on HumanEval, beating EAGLE3 and MTP.

D-Flash uses fast diffusion parallel drafting and target feature KV injection to solve the autoregressive Drafter latency bottleneck in speculative decoding. 16 tokens in just 6ms, up to 3.5x speedup on HumanEval, surpassing EAGLE3 and MTP.

Deep dive into LangChain v1.3: compare LangChain, LangGraph, and DeepAgent paradigms, explore RAG pipelines, multi-agent systems, and local LLM deployment for enterprise AI apps.

Microsoft Power Platform's Dataverse plugin for coding agents supports GitHub Copilot, Claude Code, and more — enabling natural language data modeling, queries, security config, and docs generation.
Painterly: Turning Photos into Oil Pai…
Painterly transforms photos into digital paintings using NPR stroke-rendering algorithms — no Stable Diffusion or generative AI required. Explore its tech, privacy benefits, and copyright advantages.

ShunCode is a VS Code-based AI code editor that connects ChatGPT to a local Agent execution system via Bridge Mode, enabling full-loop code reading, editing, and testing with Diff approval and MCP support.

A deep dive into OpenAI Plugins: how the system works, its technical architecture, and why it matters. Learn how ChatGPT uses plugins to access real-time data, take real-world actions, and lay the groundwork for AI Agents and Tool Use.

NASA's JPL open-sourced the F´ (F Prime) flight software framework: C++-based, component-driven, and validated in real space missions. Ideal for CubeSats, drones, and embedded systems, it has over 11,000 GitHub Stars.

A German engineer built a fully automated chess YouTube channel with an AI Agent, combining LLMs and chess engines to auto-generate explainer videos nightly, reaching 500K views. Here's the tech architecture, tool design, and real costs.

A Power Platform MVP demonstrates how to use MCP to securely expose Power Apps business data to M365 Copilot. Covers declarative agent creation, custom tool development, and VS Code setup.

Meta launches Muse Spark 1.1, an AI coding assistant targeting enterprise agentic workloads, automated bug fixing, and large-scale code migration to compete with GitHub Copilot, Cursor, and Claude Code.

A deep dive into Agent Skills: from basic prompts to fully encapsulated AI capability units. Five levels of human-AI interaction evolution, with clear distinctions between Skills, MCP, and Workflow.