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The /goal Command: A Complete Guide to Autonomous Agents on Codex, Hermes & Claude Code
In the span of six weeks in early 2026, three major AI coding platforms — OpenAI Codex, Nous Research Hermes, and Anthropic Claude Code — each shipped a /goal command. This wasn't coincidence. It was the industry converging on a shared interface for the next generation of autonomous agents. This guide covers what /goal is, how each platform implements it differently, and how to hand off work across platforms effectively.
5 Skills That Turned Me From Junior Into an AI-Proof Senior
Prompt Engineering Overview for
This overview introduces prompt engineering within the Claude API documentation, outlining essential prerequisites like clear success criteria and evaluation methods. It clarifies that prompt engineer
Claude 101
The Founder’s Playbook
This playbook outlines how AI is revolutionizing startup creation in 2026, enabling lean teams and non-technical founders to build, launch, and scale rapidly. It redefines the traditional startup life
AI & Design Digest · 2026-05-21
May 21, 2026: Google I/O 2026 launches Gemini 3.5 Flash and goes all-in on AI agents, Figma ships its AI Design Agent in beta, and OpenAI's GPT-5.5 Instant resets the accuracy benchmark while launching personal finance tools and an ad platform.
02 RAG Technology and Applications
This note delves deeply into RAG technology. Starting from core principles, advantages, and application development models, it provides a detailed introduction to embedding model selection and a practical case study on building a local knowledge base retrieval system using DeepSeek and Faiss. Additionally, it covers advanced optimization strategies such as query rewriting and online query search, aiming to improve the accuracy and timeliness of large-scale model question-answering systems.
03 RAG Multimodal Data Processing
This document provides a detailed overview of RAG multimodal data processing, including Gemini’s multimodal capabilities and API usage. Using the Disney RAG Assistant case study, it delves into Multimodal-Embedding, Faiss index construction, the unified multimodal vector space, and the query processing workflow. Finally, it compares and analyzes various knowledge slicing strategies and their applicable scenarios.
04 RAG Advanced Techniques and Optimization
This material explores advanced techniques and optimizations for RAG systems, structured across four main dimensions: Knowledge Base Processing, Efficient Retrieval, GraphRAG, and Agentic RAG. It cove
05 Hands-on Project: Enterprise Knowledge Base
This material explores a winning RAG system for an enterprise knowledge base challenge, focusing on processing complex annual reports for Q&A. It details the complete RAG pipeline from custom PDF pars