Original articles on artificial intelligence and machine learning, deep optimization. Less is more, life is simple! Shi Chunqi
The article outlines a five‑layer framework for AI scaling—application‑level Discovery Loop, open‑source model advances, compute‑power financialization, autonomous chip production, and off‑grid energy—showing how capital, open‑source ecosystems, and infrastructure are reshaping the path toward AGI.
Goldman Sachs’ AlphaAI platform shifts from broad AI‑theme exposure to selective stock picking by merging public and private market data, highlighting an emerging performance divide within the AI sector and describing the AI‑spiral closed‑loop that fuels this divergence.
Wang Hong, the 2026 Fields Medalist, traced a scholarly path through institutions from Peking University to MIT and collaborations with leading mathematicians, culminating in breakthroughs on the century‑old Kakeya problem by integrating insights from Tao, Guth, Stein, and others.
The article surveys the latest advances in large language model agents, covering multi‑agent collaboration, long‑horizon planning, self‑evolution, trust and safety, test‑time scaling techniques, new foundation and multimodal models, open‑source and closed‑source breakthroughs, world‑model integration, and emerging vertical applications.
The article analyzes Harness, a 2026 proposal that extends traditional agents with a seven‑layer architecture to fully emulate human experience, discusses rapid upgrades from prompts to skills, outlines development‑stack challenges, and presents six engineering principles for building reliable AI agents.
The article classifies today’s booming AI agents into three categories—foundation‑model RL agents, OpenClaw‑style autonomous agents, and ontology‑driven agents—detailing their architectures, key components, comparative strengths, and how they converge toward the envisioned L4/L5 AGI stages.
The article analyses DeepSeek V4’s architectural innovations—including Compressed Sparse Attention, Heavily Compressed Attention, a cross‑layer MoE design, and an Agent‑RL framework with Generative Reward Models and multi‑teacher distillation—while comparing its long‑context capabilities and efficiency to rival LLMs such as GLM, Kimi, Claude, GPT and Gemini.
The article dissects HermesAgent’s architecture, showing how it extends OpenClaw with self‑learning, reinforcement‑learning modules, and advanced prompt‑evolution techniques to mitigate token‑hole costs and achieve more deterministic results, while also detailing its TUI‑driven CLI and evaluation workflow.
This article dissects DeerFlow 2.0’s architecture, detailing its TypeScript‑React frontend, Python‑LangGraph backend, FastAPI interface, the deerflow‑harness core, agent and skill scheduling mechanisms, three collaboration modes, and how it compares to OpenClaw.
This comprehensive FAQ walks through 40 technical questions about OpenClaw, covering its innovations, architecture, multi‑agent collaboration, memory and context handling, security risks, token‑saving strategies, real‑world use cases, comparisons with other agents, and competitive landscape.
