This tutorial shows how to equip an AI agent with long‑term memory using Retrieval‑Augmented Generation (RAG), covering the concepts of vector embeddings, FAISS indexing, building and querying a knowledge base, and providing complete Python code examples.
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.
Most users only ask Claude simple questions or ask it to rewrite text, but this guide provides 20 powerful prompt patterns that let Claude act as a research analyst, devil's‑advocate, writing coach, salary‑negotiation partner, travel planner, and more, dramatically expanding its utility.
The author walks through a hands‑on test of the Skywork cloud AI Agent, showing how it can ingest exported Excel data, generate a data‑analysis report, automatically create a PPT, produce narrated video and images, all via a single input without any local deployment.
This article showcases six unique open‑source repositories that extend Claude Code with capabilities such as persistent subconscious memory, phone call integration, prompt improvement, interactive canvas panels, real‑time peer messaging, and a comprehensive job‑search assistant, detailing their key features, usage scenarios, and setup requirements.
This article curates ten open‑source AI projects on Gitee that Java developers can use to learn RAG pipelines, AI agents, knowledge‑base construction, Text‑to‑SQL, workflow orchestration, and multi‑model integration, offering concrete use cases, learning goals, and guidance on selecting a learning path.
This article explains how to turn simple API calls into robust, noise‑filtering tools—Search, Visit, Scholar, and Python—by adding domain blacklists, relevance scoring, query‑driven extraction, safety sandboxes, and a unified registry, ultimately boosting the success rate of LLM‑driven research agents.
The article walks through using Step Plan’s unified API to integrate Claude Code for automated code review and to build a voice‑agent pipeline that transcribes meeting recordings, generates structured summaries, and produces audio briefs, while discussing setup, costs, model selection, practical demos, and observed limitations.
This article reviews five standout GitHub‑trending AI Agent and developer‑tool projects—ruflo, AstrBot, context‑mode, AionUi, and TradingAgents—detailing the problems each solves, why they attracted rapid star growth, and which engineers would benefit from adopting them.
This tutorial explains the planning-with-files Claude Code skill, its three‑file architecture, installation steps, activation methods, real‑world use cases such as a 2025 front‑end framework research demo, and advanced tips for recovery, collaboration, and version control, showing how external file persistence transforms AI conversations into durable, multi‑step workflows.
MiniMax M3 claims to be the first open‑source LLM that simultaneously delivers top‑tier coding/agentic ability, a 1‑million‑token context window, and native multimodal understanding, and our benchmarks on coding suites, long‑context efficiency, and multimodal tasks confirm it exceeds expectations.
The open‑source Pi Agent Harness, starring over 57 K GitHub stars, provides a terminal‑based AI coding assistant, a unified Node.js LLM API covering 20+ providers, and an extensible plug‑in system for skills, themes, and custom agents.
Anthropic unveiled Claude Opus 4 and Claude Sonnet 4, the strongest coding models to date, detailed benchmark results, new memory and tool‑use capabilities, the Claude Code IDE extensions, and four fresh API functions that together expand AI agent development.
This article shows how structured JSON prompts unlock three high‑difficulty scenarios—precise infographics, style‑fusion creative images, and stable multi‑round iteration—by walking through twelve concrete examples and four key fields that make AI models follow instructions reliably.
This article walks through why LangChain is the leading framework for AI agents, compares it with low‑level implementations, and provides step‑by‑step code examples for installation, prompt templates, LCEL pipelines, memory modules, RAG, custom tools, and a complete customer‑service agent, concluding with a concise feature comparison.
NVIDIA’s Nemotron 3 Nano Omni 30B‑A3B‑Reasoning model, an open‑source multimodal LLM with 30 B parameters, 256K context and video‑audio‑image‑text capabilities, outperforms comparable models by up to 9.2× in video throughput, runs on consumer GPUs via 4‑bit GGUF quantization, but currently supports only English input.
