I am James, focusing on AI Agent learning and growth. I continuously update two series: “AI Agent Mastery Path,” which systematically outlines core theories and practices of agents, and “Claude Code Design Philosophy,” which deeply analyzes the design thinking behind top AI tools. Helping you build a solid foundation in the AI era.
This article explains Hermes' three‑layer system prompt architecture—Stable, Context, and Volatile—detailing how ordered memory injection, snapshot freezing, SQLite caching, and ephemeral prompts dramatically improve LLM prefix‑cache hit rates while avoiding token waste and security risks.
This article explains how structuring AI image prompts in JSON boosts controllability, reusability, and iterative editing, and provides twelve detailed examples covering product shots, UI screens, posters, and illustrations, complete with field definitions, code snippets, a quick‑reference cheat sheet, and a debugging checklist.
This article dissects Hermes Agent’s four‑layer cross‑session retrieval system, covering persistent storage, dual‑table FTS5 indexing for CJK and English, a three‑path search strategy, intelligent truncation for LLM prompts, structured summarisation, and a holographic retrieval layer that blends FTS5, Jaccard similarity and HRR vector algebra.
The article details Hermes' bounded memory system, which uses character limits for persistent files, a three‑stage context compression pipeline, boundary alignment to protect tool calls, snapshot caching, triple redaction, and anti‑thrashing mechanisms, ensuring agents never overflow or lose critical information.
This article provides ready‑to‑copy AI prompt templates for four common visual scenarios—social‑media cover images, e‑commerce product main images, marketing posters, and logos—detailing dimensions, style guidelines, and specific element descriptions so you can generate appropriate graphics instantly.
The author consolidates a categorized index of AI programming resources—including official CodeBuddy documentation, open‑source agents, monitoring utilities, workflow tools, code‑review plugins, Skills ecosystem, Git worktree strategies, AI builder feeds, community standards, and a recent research paper—providing practical selection guidance for developers.
This article outlines six essential prompt‑engineering tricks—using quotation marks, limiting text length, specifying exact position, describing font style, adding a quality statement, and iterative fixes—plus multilingual mixing tips and common error‑recovery methods for reliable Chinese, English, and Japanese text generation with GPT Image 2.
This guide breaks down fifteen design and art styles, explaining their core concepts, essential keywords, and concrete prompt templates for AI image generation, while showing why vague style terms fail and how precise constraints produce the intended visual results.
The article explains Hermes' Background Review mechanism that triggers asynchronous self‑improvement after a dialogue ends, detailing trigger conditions, a forked sub‑agent architecture, prompt selection, cost‑saving cache inheritance, a four‑step skill‑update priority, result reporting, and common pitfalls.
This article breaks down three core prompt‑engineering techniques—Few‑Shot prompting for output format stability, Chain‑of‑Thought for multi‑step reasoning, and Self‑Consistency for answer robustness—showing when to use each, how to combine them in LangChain, and providing concrete code examples, performance data, and common pitfalls.
