Posts under the category AI & Machine Learning

The 2026 AI battlefield is filled with smoke and gunpowder as Google, OpenAI, and Anthropic have all unveiled their nuclear weapons. Facing Gemini 3 Pro's multimodal capabilities, GPT-5.2's extreme logic, and Claude Opus 4.5's long-text advantages, how should domestic developers make their choice? This article provides a practical comparison of large models and reveals a perfect purchasing solution to possess all three computing powers simultaneously through n1n API."I have a $100 budget—should I charge GPT or buy Claude?"This is probably th...

SummaryOn February 6, 2026, Anthropic officially released Claude Opus 4.6, just three months after its predecessor. As a technical blogger long focused on AI programming tools, I immediately conducted comprehensive testing on this new model.Core Highlights:83% improvement in new problem-solving capabilities (ARC-AGI 2 benchmark)Supports 1 million token ultra-long context window76% accuracy in long-text retrievalIndustry-leading performance in enterprise knowledge workMaintains competitive pricing strategyThis article takes you deep into Opus...

This is a complete comparison guide to China's 7 major Coding Plans (Volcengine/Alibaba/Tencent/Baidu/Kimi/Zhipu/MiniMax), listing each platform's models, pricing, quotas, and characteristics. This essentially represents the full landscape of the domestic AI Coding ecosystem.Coding Plans are subscription packages for AI programming models offered by cloud providers.Core Characteristics:Fixed monthly feeProvides large AI request quotasCan be used in tools like Cursor, Claude Code, Cline, OpenClaw, etc.Essentially: Coding Plan = AI programming...

In today's rapidly evolving artificial intelligence landscape, developers and professionals face a paradoxical challenge: the problem isn't a lack of tools, but rather an overwhelming abundance of options. On one side stand established international giants like ChatGPT and Claude, while on the other, domestic Chinese contenders such as DeepSeek, Kimi, and Tongyi Qianwen are rising with impressive momentum. The question on everyone's mind: do foreign models truly offer superior capabilities, or do homegrown solutions better understand local c...

In the previous article, we completed the full logic of Vision Transformer: cutting images into patches as tokens and feeding them into Transformer Encoder for global modeling.However, we also mentioned an unavoidable pain point of ViT:Without sufficiently large data scales, ViT is often difficult to train well.From a paradigm perspective, this is because ViT is essentially a "weak prior, strong data-driven" modeling approach.Expanding on this further, regarding the question:Why does ViT require large amounts of data to perform well, while C...

Anyone working on AI Agent implementation has likely encountered this dilemma:You're using a flagship model, have revised your prompts hundreds of times, and tuned your RAG system countless times. Yet when deployed in real-world scenarios, the task success rate simply won't improve—the agent sometimes performs brilliantly, other times goes completely off-track.The problem doesn't lie with the model itself, but with the operating system running outside the model—the Harness.What Is Harness Engineering?The term "Harness" originally refers to r...