422 related articles

Tencent Cloud open-sources TencentDB Agent Memory — a fully local AI Agent memory system with a 4-tier progressive pipeline, zero external API dependencies, and 8,100+ GitHub Stars. Ideal for finance, healthcare, and privacy-sensitive use cases.

A developer deeply tests Grok 4.5 High Fast in Cursor, finding it rivals Claude Opus in quality but runs 5x faster with cleaner, filler-free output. Full hands-on review and analysis.

A complete AI Agent learning roadmap covering BDI theory, core components (Perception/Planning/Execution), AutoGen multi-agent frameworks, and DeepSeek RAG projects for beginners.

OpenAI's new Sites tool lets anyone describe an idea and instantly generate a live, shareable website or lightweight app — no coding skills needed.

GLM-5.2 spotted in testing, Anthropic launches Claude Fable 5, Moore Threads open-sources MusaCoder for domestic GPUs, and Google releases Gemini real-time translation.

FDE (Forward Deployed Engineer) is the hottest emerging role in the AI deployment wave, combining a technical CTO, full-stack AI engineer, and business consultant. Learn the two FDE tracks, core skills, and how to transition into one.

This week in AI: OpenAI launches GPT-5.6 in three tiers (Sol/Terra/Luna) hitting 91.9% on coding benchmarks; DeepSeek and PKU open-source DSpark for 85% faster inference; Prime Intellect trains trillion-param models on just 28 H200s; Anthropic Claude enters Slack.

A deep dive into Anthropic's Agent Skills architecture — clarifying its difference from MCP, and how modular Skill packages enable scalable, maintainable enterprise agent development.

Google confirms the Made by Google hardware event in NYC, unveiling next-gen Pixel phones with new Tensor chips and deep Gemini AI integration. On-device AI gets a major upgrade.

Frontend hiring now treats AI capabilities as a core assessment, covering RAG knowledge bases, AI Agent development, and LangChain.js engineering. Learn how LangChain.js + Nuxt.js helps frontend developers build memory- and retrieval-capable AI full-stack apps.

A hands-on guide to building an enterprise-grade AI Agent workflow orchestration app with Electron Forge and LangGraph, covering local LLM deployment (Qwen3-0.6B), node-based visual canvas design, and full Function Calling integration.

An in-depth look at why TypeScript is the top choice for AI Agent development: covering Zod structured output validation, LangGraph's graph state machine design, and a full learning path for front-end devs transitioning to full-stack AI.

Want to learn Python from scratch but don't know where to begin? This article breaks down three stages—basic syntax, advanced mastery, and hands-on practice—with real projects in crawling, automation, and data analysis to help you build programming thinking.

A deep dive into Google's latest AI monthly updates: Gemini multimodal upgrades, AI Agent breakthroughs, product ecosystem integration, and developer toolchain improvements.

Unsloth v0.1.45-beta adds Gemma 4 MTP support, AMD ROCm & NVIDIA Blackwell fixes, a new Hub download manager, and a compact RAG system for local LLM fine-tuning.
Google Drops Two New Models: 4-Second …
Google launches Imagen 3 Nano (Flash) for 4-second text-to-image generation and Veo 3 Flash for conversational video editing — now available via Gemini API and Google AI Studio.

A developer found GPT-5.5 couldn't fix a mind map vertical centering bug, but GLM-5.2 solved it quickly. This article analyzes the capability differences and the value of multi-model collaboration in AI-assisted programming.

The rise of Zhipu's GLM 5.2 is accelerating the democratization of LLM capabilities. This article analyzes the commoditization of foundation models, the logic behind margin collapse, and the opportunities and challenges facing application-layer and foundation model firms.

A beginner's guide to the LangChain open-source framework: explaining how to use the init_chat_model unified interface, tips for disabling DeepSeek's thinking mode, and core essentials of Agent development.

A deep dive into the four-layer engineering design of AI Agents: planning, memory, tool use, API cost optimization, MCP protocol integration, and Skill encapsulation.