OpenAI introduced the open‑source openai‑cli, a native terminal tool that replaces cumbersome SDKs, offering Unix‑style structured output, image generation, speech transcription, project management, and seamless command‑line integration for AI workflows.
DS2API is an open‑source Go‑based service that converts DeepSeek’s web interface into OpenAI, Claude, and Gemini compatible APIs, offering multi‑API support, account pool management, long‑history handling, PoW verification, and a React admin UI, with simple Docker deployment.
The qwen2API project offers a FastAPI backend and React+Vite frontend that expose the Qwen web chat as OpenAI Chat Completions, Anthropic Messages, and Gemini GenerateContent interfaces, featuring tool calling, image generation, account pool management, multiple deployment options, and various execution engines.
This guide walks you through deploying Alibaba’s open‑source Qwen‑3 8B model on the SumW platform using vLLM, covering environment activation, server launch with OpenAI‑compatible parameters, SSH tunneling for remote access, and Python client calls, while highlighting key configuration tips and common pitfalls.
This article introduces LocalAI, an open‑source Go‑based self‑hosted LLM server that serves as a drop‑in OpenAI API replacement, outlines its key features, privacy and cost benefits, provides a Docker quick‑start guide, and explains its modular architecture for developers seeking private AI solutions.
This article explains the fundamentals of prompt engineering for large language models, covering LLM output configuration, length and sampling controls, various prompt types, chain‑of‑thought and tree‑of‑thought reasoning methods, and practical best‑practice guidelines for creating high‑quality prompts.
This tutorial explains the OpenAI‑style request and response schema, demonstrates low‑level API calls with the requests library, compares them to the high‑level openai package, and walks through building a streaming multi‑turn chatbot that interacts with a locally hosted large language model.
This article walks through two mainstream local deployment solutions—high‑performance VLLM for production Linux servers and lightweight Ollama for personal Windows machines—covering environment setup, model download, server launch, API testing, key configuration parameters, and the quantization technique that makes Ollama models compact.
This guide walks through the complete process of deploying a high‑throughput large language model inference service using vLLM, covering environment preparation, installation, configuration tuning, performance testing, real‑world case studies, monitoring, troubleshooting, and backup strategies for production‑grade deployments.
This article explains the origins of AI Agent Skills, walks through the standard tool‑use loop, provides step‑by‑step code for defining and invoking Skills with the OpenAI API, compares Skills with the emerging Model Context Protocol (MCP), and offers practical guidance for developers and architects building AI‑enabled systems.
This article explains the concept of Claude Skills, walks through a Go library that parses skill packages, demonstrates a CLI inspector, shows how to run skills with Deepseek‑v3 via an OpenAI‑compatible API, and outlines future security enhancements.
Recent CMU and Salesforce studies reveal that top‑tier AI agents like Gemini 2.5 Pro, Claude 3.7 Sonnet and GPT‑4o fail in 69‑70% of multi‑step tasks, but MiniMax‑M2’s Interleaved Thinking reduces failure dramatically, highlighting that execution mechanisms, not model size, are key to reliable AI agents.
This guide walks you through installing NVIDIA drivers and Docker, configuring the NVIDIA Container Toolkit, and deploying GPUStack in Docker to manage heterogeneous GPU resources, run large language, multimodal, diffusion, and embedding models, and scale from a single node to a multi‑node GPU cluster.
This article walks through installing and configuring vLLM for local large language model inference, compares it with Ollama and LM Studio, details environment setup, model download, testing scripts, and shows how to expose an OpenAI‑compatible API for production use.
This article walks through deploying the high‑performance vLLM LLM inference framework, covering GPU and CPU backend installation, environment setup, offline and online serving, API usage, and a performance comparison that highlights the ten‑fold speed advantage of GPU over CPU.
This guide walks you through installing NVIDIA drivers, Docker, and the NVIDIA Container Toolkit, then shows step‑by‑step how to run GPUStack in Docker, expand a GPU cluster, and serve large language, multimodal, diffusion, and embedding models with OpenAI‑compatible APIs.
This tutorial walks you through registering on OpenAI, installing required Python packages, setting up API keys, and writing a Jupyter Notebook that defines a chat function and an interactive loop to create a functional GPT‑3.5 chatbot.
This article reviews ten mainstream LLM deployment solutions—including WebLLM, LM Studio, Ollama, vLLM, LightLLM, OpenLLM, HuggingFace TGI, GPT4ALL, llama.cpp, and Triton Inference Server—detailing their technical characteristics, strengths, drawbacks, and example deployment workflows for both personal and enterprise environments.
This article explains how to configure key OpenAI chat completion parameters—such as temperature, top_p, streaming, response format, and tool selection—and walks through the structure of the API's JSON response, highlighting fields like id, model, choices, finish_reason, and usage for better control and cost estimation.
This article explains how to automate batch document processing with LLM APIs by mastering the messages parameter, defining system, user, and assistant roles, and iteratively polishing system prompts through scripts or OpenAI's GPTs editor and Playground interfaces.
This article explains how to construct effective SQL prompts by combining personal SQL expertise with large language model capabilities, introduces the open‑source sql‑translator tool for bidirectional SQL‑natural language conversion, and discusses practical productization scenarios.
This tutorial walks through creating a Spring Boot application that integrates the OpenAI ChatGPT API, covering prompt concepts, API endpoint details, required request parameters, project setup, necessary dependencies, DTO definitions, RestTemplate configuration, and a REST controller to generate AI‑driven responses.
Learn how to generate text embeddings with OpenAI's ChatGPT API, store them in Redis' vector database, and perform efficient similarity searches using PHP, covering installation, API calls, index creation, vector storage, KNN querying, and practical code examples.
This guide walks you through building a Spring Boot application that calls the OpenAI ChatGPT API, covering prompt concepts, API request details, required dependencies, configuration of RestTemplate with authentication, DTO definitions, controller implementation, and example use cases.
This article explains step-by-step how to develop an intelligent ChatGPT-powered chatbot with PHP, covering environment setup, installing the PHP ChatGPT library, writing code to send messages and receive responses via the OpenAI API, and testing the bot with example scripts.
This guide walks you through installing the CodeGPT extension in Visual Studio Code, configuring your OpenAI API key, and using its features—code generation, explanation, refactoring, and unit test creation—to boost productivity with AI-assisted programming.
This tutorial walks developers through setting up an OpenAI account, obtaining an API key, configuring the Python environment, and writing a complete Python script that uses the ChatGPT API to create an interactive chatbot, complete with code examples and execution tips.
This article explains practical principles and techniques for crafting clear, specific prompts for ChatGPT, demonstrates how to structure prompts with separators and output formats, and provides a step‑by‑step guide—including code examples—for installing the OpenAI Python library, obtaining an API key, and writing a reusable get_completion function to interact with the model.
ChatGPT’s web interface has been increasingly showing “Something went wrong” errors after idle periods, and this guide explains two remedies: using an OpenAI API key for more stable access, and installing the KeepChatGPT Chrome/Tampermonkey extension, which suppresses the error and adds several usability enhancements.
This guide walks you through preparing an OpenAI account, obtaining an API key, using the Completion endpoint with example JSON, calling the API from plain JavaScript/HTML, and integrating the same service into a Spring Boot application with Maven dependencies and Java code.
This guide walks you through creating a personal ChatGPT chatbot by first setting up an OpenAI account and API key, then demonstrating how to call the completions endpoint using plain JavaScript and HTML, followed by integrating the same API into a Spring Boot application with the official Java client library, including all necessary code snippets and configuration steps.
This guide walks you through registering an OpenAI account, obtaining an API key, understanding the completions endpoint parameters, and implementing a ChatGPT chatbot with both a plain JavaScript/HTML front‑end and a Spring Boot back‑end, including full code samples.
