50 related articles

Spring AI 1.0 is here — Java developers can now build AI apps without switching to Python. This guide covers LLM integration, RAG, intelligent customer service, and Agent patterns for enterprise deployment.

Vibe Coding is the new AI-era programming paradigm. Describe what you want in plain language; let AI generate the code. Learn the 3-stage path: mindset, quality, and real projects.

DeepSeek's paper 'Thinking with Visual Primitives' was online for just 4 hours before being pulled. It uses bounding boxes and points as reasoning primitives, letting models 'point at' images to outperform GPT, Gemini, and Claude on maze navigation and counting.

Learn how Claude Code and its Skill mechanism can automatically convert a requirements document into production-ready test cases in under 10 minutes with a 3-stage pipeline.

Tongyi Qianwen Qwen-Image-3.0 image generation model gets a comprehensive upgrade: supporting 4,500-token ultra-long instructions, pixel-level detail rendering, 12-language knowledge understanding, and ancient painting restoration. This article analyzes its three core capabilities.

DeepSeek open-sources DeepSpec, a full speculative decoding training and evaluation toolkit featuring three draft model algorithms (Ego3, DeepFlash, DeepSpark), 12 checkpoints, MIT license, and 60–85% real-world speedup.

Should you implement ML algorithms from scratch or just use sklearn? This guide breaks down the optimal learning path for ML engineers by career stage and company type.

With Claude Code and a Skills framework, go from requirements doc to test cases in under 10 minutes. A 3-stage automated pipeline: breakdown, extraction, and generation.

One vague prompt triggers GPT to write a storyboard, drive Blender MCP to build a 3D layout, and hand it to Seedance 2.0 for anime stylization. Here's how the pipeline works.

GPT-Live hands-on: Voice chat now powered by GPT-5.5 Thinking, full-duplex architecture, real-time search, visual cards & tool calling. Full review inside.

OpenAI launches ChatGPT Voice powered by GPT Live One, featuring full-duplex real-time conversation, multi-task reasoning, and live translation. A deep dive into its capabilities and what it means for the future of voice AI.

GPT Live full-duplex voice mode tested: instant English correction, real-time interpreting, and business rehearsal. Will AI replace simultaneous interpreters?

A viral experiment video "pushing" ChatGPT's Live Voice mode reveals the real capabilities and design boundaries of AI real-time voice interaction — and the risks of emotional AI.

A complete AI Agent development learning roadmap covering three stages: Fundamentals (environment setup, tool use, memory), Advanced (multi-agent systems, RAG, ReAct), and Practical Projects (enterprise chatbots, automation tools).
Paper Reproduction as an Entry Point i…
How can applied math students efficiently enter Scientific Machine Learning (SciML)? This guide covers the value and pitfalls of paper reproduction, with a layered path from numerical PDEs to research.

OpenAI unveils GPT Live One voice model with full-duplex conversation—AI listens and responds while you speak. Real-time reasoning, web search, multitasking, and bidirectional translation redefine voice AI.

In one week, OpenAI, xAI, Google, and Microsoft all cut AI prices, driving near-frontier inference costs sharply lower. Meanwhile, Microsoft Copilot's paid conversion across 450M seats is under 4.5%, exposing the monetization challenge of general AI assistants.

A systematic AI Agent development learning path covering fundamentals, prompt engineering, tool calling, multi-agent collaboration, and hands-on practice with LangChain, CrewAI, and Dify.

OpenAI unveils the GPT-Live voice model family, with full-duplex interaction enabling AI to listen and speak simultaneously and delegate complex reasoning to GPT-5.5. GPQA benchmark jumps from 45% to 80%.

An in-depth hands-on test of GPT's real-time voice feature, covering Cantonese and Sichuanese dialect recognition, emotional tone switching, complex role-play, and cross-voice contextual memory—objectively presenting the true level and remaining gaps of AI voice interaction.