Original articles on artificial intelligence and machine learning, deep optimization. Less is more, life is simple! Shi Chunqi
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.
The article analyzes Palantir's ontology-driven knowledge graph dominance, its shift from graph to vector databases, the three‑layer partnership with Anthropic and AWS, the Digital Twin scaling law, and the technical challenges of data heterogeneity, scaling uncertainty, annotation scarcity, and real‑time computation in modern warfare information mining.
The article outlines how AI‑native coding in 2026—driven by models like Claude Optus 4.6, GPT Codex 5.2 and Gemini 3.1—relies on refined task prompting, rigorous testing, multi‑model perspectives, and specialized coding‑agent skills such as code review, debugging, and data analysis.
The article surveys emerging vector databases, explains how various vector‑search algorithms such as FLAT, IVF, HNSW, DiskANN and ScaNN differ in accuracy, speed, memory use and build time, and provides practical guidance for choosing the right index based on data size, latency and resource constraints.
The article analyzes how large‑model development has moved from reasoning to the agentic stage, compares open‑source and closed‑source capabilities, details Reasoning RL versus Agentic RL designs, and proposes skill‑centric data and verification mechanisms to close the performance gap.
The article explains how randomly masking a large portion of gradient updates during large‑model training—sometimes up to 99%—can accelerate convergence and even surpass traditional optimizers like Adam, supported by recent Google research and empirical observations.
