Sharing the latest AI-native technologies and real-world implementations.
The article examines the 2025‑2026 surge of sub‑10B parameter models, outlines edge‑side AI agents, details multimodal interaction layers, and explains how Computer Use enables AI to operate computers, offering a roadmap for integrating these technologies across mobile, desktop, and IoT scenarios.
The article explains why most AI projects fail, introduces the concept of the Harness era where engineering and tooling outweigh pure algorithms, presents the RIDE methodology for enterprise AI adoption, and shows how AI‑native organizations transform roles, processes, and culture to achieve sustainable competitive advantage.
By 2026, teams find standard RAG insufficient and turn to knowledge engineering—using Ontology to structure domain concepts, GraphRAG to add graph‑based retrieval, Agentic RAG for proactive multi‑round searching, and Context Engineering to finely manage prompts—resulting in higher relevance, lower token cost, and richer AI answers.
The article explains why modern LLM‑based AI systems are opaque, defines AI observability as a four‑dimensional practice (Prompt, Tool Call, Trace, Token), and provides concrete architectures, code samples, best‑practice checklists, and real‑world case studies to turn black‑box AI into a transparent, monitorable service.
This article breaks down the complete cognition‑action system of modern AI agents into six inter‑connected layers—Perception, Planning, Tools, Memory, Execution, and Feedback—explaining their core problems, engineering designs, common pitfalls, and best‑practice metrics with concrete code examples and real‑world use cases.
The article breaks down the three core factors—tokens, electricity, and data—that together determine AI model cost, speed, capability, and industry competition, illustrating the trade‑offs with concrete numbers, examples, and future outlooks.
The 2026 Memento‑Skills system lets AI agents create and refine their own Skills, boosting General AI Assistants accuracy by 26.2% and Humanity's Last Exam accuracy by 116.2%, while outlining three generations of Skill evolution, self‑evolution frameworks, benchmark results, practical applications, and best‑practice guidelines for technical leaders.
The article explains how knowledge distillation lets a small LLM learn from a much larger teacher model, achieving near‑teacher performance while cutting inference cost, latency and memory, and provides a step‑by‑step guide, benchmark results, advanced on‑policy techniques, common pitfalls and best‑practice recommendations.
This article outlines a comprehensive methodology for evaluating large language models, covering classic benchmarks, human and multimodal assessments, common pitfalls such as data contamination and benchmark overfitting, and practical guidelines for building a scientific, multi‑layered AI evaluation framework.
The article explains how ontology—a 2,500‑year‑old philosophical concept—provides the structured knowledge backbone that large language models lack, detailing its definition, differences from databases and knowledge graphs, its role in reducing hallucinations, defining knowledge boundaries, enabling reasoning, and four practical AI application scenarios.
