The article walks through using WorkBuddy to generate professional PPTs in three formats—traditional PPT, HTML, and image—detailing material preparation, prompt crafting, style selection, and editing tips, and advises when to choose each format based on compatibility, visual impact, and efficiency.
The article breaks down agent engineering into five concentric layers—Prompt, Context, Harness, Loop, and Graph—explaining how each governs task definition, current facts, execution environment, iterative exploration, and stable cross‑task relationships, illustrated with a payment‑callback fix case and AutoResearch insights.
The article presents a comprehensive analysis of AI developer experience (DevEx), detailing why traditional DevEx falls short for LLM‑driven applications, describing a seven‑component platform that spans Playground, Trace replay, Prompt diff, RAG debugging, Eval dashboards, SDK/CLI, and scaffolding templates, and offering concrete architecture, code, metric baselines, framework comparisons, and best‑practice recommendations for enterprise adoption.
This guide walks through the /grill-me and /grilling skills, explains their hierarchical design, demonstrates a complete export‑feature scenario, extracts five hard rules to prevent scope drift, shows how a shared‑understanding gate safeguards decisions, and advises when to use or avoid grill‑me in modern AI‑assisted development workflows.
This article provides a complete, step‑by‑step reference of the Codex command‑line interface, detailing each command’s purpose, usage examples, and typical scenarios such as interactive sessions, one‑off prompts, login management, configuration, updates, environment checks, code review, testing, automatic fixes, and Git commit generation.
This article explains how Harness Skills let you package repeatable instructions into reusable directories, distinguishes Reference and Action Skills, shows dynamic shell‑command injection, details front‑matter controls, and provides concrete PR‑summary and pre‑commit examples for Claude agents.
The article demystifies common AI terminology—LLM, Token, Context, Prompt, Tool, MCP, Agent, and Agent Skill—by explaining each concept, how they interrelate, and why understanding this chain clarifies the operation of modern AI products.
This article introduces LangChain, an open‑source framework for building LLM applications, covering its modular design, supported models, core components such as Prompt templates, Chains, Memory, Agents, and practical Python code examples for each feature.
This article provides a detailed walkthrough of Codex's command-line interface, covering each command's purpose, usage examples, applicable scenarios, and output, helping developers leverage the AI coding assistant effectively.
The article explains that habit change failures stem from a design flaw, not personal weakness, and introduces BJ Fogg’s B=MAP model—Motivation, Ability, Prompt—showing how the weakest element blocks behavior and offering a three‑step method to redesign habits.
Jensen Huang’s vision repurposes the military OODA loop—Observe, Orient, Decide, Act—into an AI‑driven agent architecture where LLMs, prompts, tools, and memory form a fast‑cycling loop that lets software continuously monitor, reason, decide, and act without static code.
The article explains how Prompt, Model Context Protocol (MCP), and Skill serve distinct but complementary roles in AI interaction—Prompt provides instant, conversational instructions, MCP connects the model to external tools and data, and Skill offers pre‑packaged expert capabilities, together forming a layered control hierarchy.
This article dissects the most frequent LangChain Memory pitfalls—missing placeholders, wrong memory type, shared instances, and multi‑process issues—provides correct code patterns, compares the five built‑in memory classes, introduces the new RunnableWithMessageHistory approach, and offers a production‑ready checklist.
This article demystifies dozens of AI buzzwords—from AI and LLM to Prompt, Token, Agent, and emerging concepts like Multimodal and Retrieval‑Augmented Generation—by providing both formal definitions and everyday analogies, complete with concrete examples that make each term easy to grasp.
This guide walks through two practical approaches—Skill and Prompt—to configure OpenClaw for automated RSS aggregation, AI‑driven filtering, summarization, and channel delivery, comparing their setup steps, code examples, advantages, limitations, and when to choose each method.
This tutorial walks you through installing Claude Code on macOS, Linux, and Windows, configuring its environment, creating and managing Skills—including their directory layout, SKILL.md structure, progressive‑disclosure loading, and a real‑world pre‑sales proposal Skill—then running the Skill via the CLI with sample output.
This article introduces the open‑source Go AI Agent framework Blades, showing how to quickly set up a chat with a large model, enable streaming responses, and implement multi‑turn conversations using prompts, memory, and custom middleware, with complete code examples.
This article walks through Spring AI’s three core classes—Message, Prompt, and ChatModel—explaining their roles, showing concrete code examples for constructing messages, building prompts, and invoking a large language model via a REST controller, and provides a complete demo repository.
This article provides a concise, easy-to-understand introduction to large language models, the transformer architecture, prompt engineering, temperature settings, function calling, the Model Context Protocol (MCP), agent communication (A2A), and future AI programming trends, using simple analogies and illustrative examples.
This guide demonstrates building a Model Context Protocol (MCP) server in Python to enable large language models to query Grafana dashboards, retrieve folder lists, and return real‑time visualizations, covering installation, server definition, tool creation, and integration with Cherry Studio.
Huolala created a visual, drag‑and‑drop LLM application platform that streamlines AI integration, reduces development costs, and enables rapid deployment of agents across marketing, invitation, advertising, and modeling scenarios, boosting efficiency by over 98% while cutting integration time from hours to minutes.
This guide walks you through installing Fish Shell on macOS, switching from Bash or Zsh, exploring its standout features such as syntax highlighting, autosuggestions, tab completion, web‑based configuration, and shows how to customize prompts and greetings with practical code examples.
This guide explains how to view and modify the Bash prompt using the PS1 variable, add time stamps, apply ANSI color codes, and make the changes permanent by editing .bashrc or global configuration files, with step‑by‑step commands and examples.
This guide explains how to customize the Bash prompt by editing the PS1/PS2 variables in .bashrc, using special escape sequences for user, host, directory, date, and colors, and creating multi‑line prompts with clear examples and explanations.
Learn how to personalize the Bash command‑line prompt by setting the PS1 variable, using special escape sequences for user, host, directory, date, and colors, and creating multi‑line prompts, all with clear examples and safe configuration steps.
