This guide shows how OpenAI has embedded Codex into the ChatGPT mobile app, turning your phone into a remote control panel for a desktop‑running Codex instance, and walks through the free setup, Ollama installation, model switching, and connection workflow.
Ollama versions before 0.17.1 suffer a CVSS 9.1 heap out‑of‑bounds read vulnerability (CVE‑2026‑7482) that lets attackers upload malicious GGUF files, read server memory—including env vars and API keys—and exfiltrate data, affecting over 300,000 publicly exposed servers, so immediate upgrade and hardening are essential.
A critical CVE‑2026‑7482 flaw in Ollama’s model quantization pipeline, dubbed “Bleeding Llama,” allows unauthenticated attackers to craft GGUF files that read beyond buffer limits, potentially leaking prompts, API keys and other confidential data from over 300,000 internet‑exposed servers, with mitigation requiring an upgrade to version 0.17.1 and stricter network controls.
This guide shows how to bypass Claude Code's paid Anthropic API by installing a local proxy that forwards requests to free models such as DeepSeek, Ollama, or NVIDIA NIM, covering all required tools, configuration steps, and troubleshooting tips.
The article demonstrates how to construct a Spring Boot application that orchestrates multiple AI services using LangChain4j and Ollama, defining intent‑classification and tool‑based assistants, registering them as beans, and routing user requests through a controller to achieve multi‑step intent recognition and task dispatch in a simulated intelligent customer‑service workflow.
This guide walks through installing Ollama on Windows, downloading a Qwen2.5‑7B model, configuring Spring Boot with LangChain4j dependencies, setting up application.yml, defining AI service interfaces, adding conversation memory, creating REST and streaming controllers, and testing the end‑to‑end local LLM workflow.
This guide walks through setting up a local LLM with Ollama, configuring Spring Boot and LangChain4j, defining function‑calling tools for weather, order status, logistics and coupons, creating AI service beans, exposing REST controllers, and troubleshooting common integration issues.
This guide walks through why Retrieval‑Augmented Generation (RAG) is needed for large language models, explains the three‑step indexing and query workflow, details LangChain4j’s core components, and provides a complete SpringBoot example—including Maven setup, configuration, service code, and troubleshooting—to create a private document‑vector search system powered by Ollama.
This guide shows developers how to replace costly OpenAI APIs by configuring Codex to use Chinese large‑language models such as DeepSeek, GLM‑4.7, and Qwen, detailing three setup methods, benchmark results, cost savings of up to 90 %, and best‑practice tips for optimal performance.
The article compiles key information on DeepSeek V4, covering Ollama's one‑click launch, the model's FP4/FP8 mixed‑precision quantization, size reductions, high local deployment costs, recent benchmark rankings, and the accompanying stock price movements in both China and the US.
The author benchmarks Gemma 4 locally on a 24 GB M4 Pro MacBook Pro (llama.cpp) and on a Dell GB10 with an NVIDIA Blackwell GPU (Ollama), comparing token speed, tool‑call reliability, and task completion against cloud GPT‑5.4, showing the Mac runs faster per token but the Blackwell system achieves higher first‑pass success with fewer retries, and that the jump from Gemma 3 to Gemma 4 dramatically improves agentic coding viability.
This guide shows how to configure Claude Code to run on local or cloud models within five minutes, covering hardware requirements, recommended models, step‑by‑step installation for Ollama, llama.cpp, LM Studio, and cloud‑based options, plus performance and cost comparisons.
This guide walks through installing Ollama on Windows 10, downloading a Chinese‑friendly LLM such as Qwen2, configuring a CRM’s application‑dev.yml to point to the local Ollama service, restarting the backend, and handling optional CORS settings, highlighting zero‑cost, privacy, and stability benefits.
This step‑by‑step guide shows how to install Ollama on macOS, verify the installation, manage and run open‑source LLMs, create custom models, enable the OpenAI‑compatible API, integrate with Open WebUI, and troubleshoot performance issues across different Apple silicon chips.
This guide walks you through configuring IntelliJ IDEA to use AI coding assistants like Claude, Codex, OpenAI‑compatible APIs, and local models via Ollama, covering plugin installation, provider setup, API key entry, and usage tips with screenshots.
Edge AI runs AI models directly in browsers or devices, offering zero latency, zero API cost, and full privacy, and the article explains the three technical breakthroughs that make it possible, compares WebLLM, Transformers.js and Ollama, and provides a hybrid architecture with concrete engineering challenges and solutions that can cut total AI costs by 40‑55% for typical front‑end applications.
MiniMax‑M2.7, the newly open‑sourced 230‑billion‑parameter MoE model, offers self‑evolution, professional software engineering and agent capabilities, and can be deployed locally using Ollama, vLLM, SGLang or Docker with 4‑8 H200 GPUs, while the article details hardware needs, performance gains and tool‑calling/Thinking features.
This article walks through Google’s official three‑step tutorial for connecting the Gemma 4 language model to OpenClaw using Ollama, details hardware requirements, discusses performance and security considerations, and evaluates the model’s capabilities compared to larger LLMs.
This guide shows how to combine a locally deployed Ollama LLM with Python Requests to create an AI-driven automated API testing framework that generates test data, performs smart assertions, and produces markdown reports, dramatically reducing manual effort and improving test quality.
This guide shows how to leverage the 128 GB unified memory of a Mac Studio to run multiple open‑source LLMs simultaneously, using Ollama for installation and OpenClaw for automatic model routing based on task type, achieving zero‑API cost, full privacy, and optimal performance.
Ollama 0.19 replaces its inference backend with Apple’s MLX framework and adopts NVIDIA’s NVFP4 4‑bit quantization, delivering up to a 93% speed increase on M5 chips while keeping accuracy comparable to cloud‑based deployments, and adds three cache upgrades for smoother agent interactions.
The article explains how Apple’s updated 2025 Mac Studio, with its unified memory architecture and high bandwidth, determines the size of AI models it can run, compares M4 Max and M3 Ultra configurations, maps memory to model parameters, and recommends setups for various use cases.
The article reviews the four Gemma 4 model variants, analyzes their architecture and benchmark results versus Qwen3.5, and provides step‑by‑step instructions for local deployment using Ollama, llama.cpp, MLX and vLLM, while highlighting TurboQuant memory and weight compression techniques.
This guide shows how the high OpenAI Codex fees can be replaced by domestic large language models—DeepSeek, GLM‑4.7, Qwen3.5 and others—through three practical integration methods, providing step‑by‑step commands, configuration files, performance benchmarks and cost‑saving calculations for individual developers and teams.
This guide presents an end‑to‑end solution for creating a test‑focused AI agent that indexes project code and defect data, integrates a large language model via LangChain, generates compliant Pytest cases, parses Allure reports, and offers deployment tips for seamless PyCharm integration.
This article explains why Ollama has become popular for local LLM deployment, outlines its core features, and provides a detailed, step‑by‑step tutorial for integrating OpenClaw with Ollama—including model selection, configuration, troubleshooting common errors, and advanced tips for customization and multi‑model switching.
This guide shows step‑by‑step how to enable or disable the thinking mode of Qwen3.5 series large language models across Ollama, LM Studio (GGUF and MLX), llama.cpp, and vLLM/SGLang using command‑line flags, custom model YAML files, and API parameters.
This guide walks you through installing Ollama, downloading the qwen3:4b model, and using Python’s async client to perform streaming chat requests, then shows how to integrate the same model with LangChain, including setup, initialization, and both regular and streaming output examples.
This article walks through enabling Hyper‑V on Windows 11 Pro, configuring Docker Desktop with Chinese mirrors, adjusting storage, installing Ubuntu via WSL, cloning and setting up Dify, running Docker Compose, and linking Ollama's LLM so the AI agent runs entirely on a local machine.
With Ollama 0.17 you can start the powerful OpenClaw AI assistant using a single command, automatically install the software, choose cloud or local models, enable web‑search, connect to multiple messaging platforms, and keep all data private on your own machine.
The llmfit command‑line utility scans your CPU, RAM, GPU and VRAM, scores 157 models from over 30 providers, suggests the highest‑quality quantized version that fits, integrates with Ollama, and shows real‑world test results confirming its accuracy, though its model database is limited.
This guide walks you through installing Claude Code and Ollama, pulling and configuring various open‑source models, setting environment variables, and running Claude Code with both local and cloud‑hosted models, while covering context length, performance considerations, and tool‑calling examples.
The latest Ollama 0.16.1 release lets users switch models and tools instantly, use Claude Code, Codex, and OpenClaw without extra configuration, and enables Subagents and built‑in web search directly via simple commands.
The article evaluates GLM‑5, the claimed strongest open‑source large language model, comparing its benchmark scores to Claude Opus, Gemini and GPT, detailing its DeepSeek‑inspired architecture, quantized FP8 deployment requirements, and step‑by‑step usage of Ollama’s free cloud model with Agent, data‑analysis and document‑generation features.
GLM-OCR, a 0.9B‑parameter multimodal OCR model from Zhipu, achieves the highest score (94.62) on OmniDocBench V1.5, offers lightweight deployment via vLLM, Ollama, API and SDK, and outperforms larger rivals like DeepSeek‑OCR and PaddleOCR in speed and accuracy.
This step‑by‑step guide shows how to prepare an Ubuntu (or Mac) VM, install an agent tool, set up Ollama, install OpenClaw via npm, configure the daemon, launch the GLM‑4.7:cloud model, and connect OpenClaw to Telegram for AI‑agent interactions.
Programmer Xiao Meng walks through a complete Windows setup for AI‑powered customer service agents using RagFlow, covering prerequisites, Docker and Ollama installation, model download, container deployment, configuration of knowledge bases, and testing, based on five real‑world projects.
This tutorial explains the concepts, methods, and practical commands for fine‑tuning large language models using efficient techniques like LoRA and QLoRA, covering model selection, resource considerations, Docker deployment, dataset preparation, training configuration, evaluation metrics, model merging, and deployment with GGUF and Ollama.
The article introduces Ollama's new "ollama launch" command, which lets users configure and start programming tools like Claude Code, OpenCode, Codex, and Droid with a single command, and explains quick‑start steps, recommended local and cloud models, and an extended five‑hour cloud coding session.
GLM‑4.7‑Flash, a 30B‑parameter MoE LLM released as fully open‑source and free, delivers 30B‑class performance across six benchmarks, runs locally with a single Ollama command, and offers a faster cloud‑hosted version with modest token‑based pricing, though hardware costs still apply.
Ollama v0.14.0 now supports the Anthropic API, letting you run Claude Code locally with open‑source models like Qwen or Llama without an API key, network, or cost, and the article provides a step‑by‑step setup, SDK examples, and an objective assessment of the approach.
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 article provides a detailed, hands‑on tutorial for creating an AI agent, covering registration and API key setup for Alibaba Cloud, OpenAI, Dashscope and DeepSeek, installing and using Ollama for local model deployment, configuring CherryStudio, and implementing function‑calling and MCP techniques with full code examples.
This guide walks you through configuring OLLAMA for multi‑GPU load balancing, covering hardware checks, CUDA and Docker setup, native and containerized deployment methods, core parameter tuning, advanced sharding, dynamic monitoring, troubleshooting, production best practices, and a real‑world RTX 4090 case study.
This guide walks through the importance of Retrieval‑Augmented Generation, outlines the core Langchain4j and Ollama 3 components, and provides a complete Java example—including Maven setup, document ingestion, embedding creation, similarity search, prompt construction, and response generation—to demonstrate a functional RAG pipeline.
The article introduces the open‑source tool git‑rewrite‑commits, shows how to install Ollama, configure its cloud model, and run a single npx command that uses AI to rewrite messy Git commit messages and then force‑push the cleaned history.
This article walks you through creating a fully offline, extensible AI programming assistant in Go, using Ollama and DeepSeek‑R1, covering project layout, message formats, function calling, tool integration, a simple WebSocket UI, and future extension ideas.
This article explains why Retrieval‑Augmented Generation improves LLM accuracy, outlines the key Langchain4j and Ollama3 components, and provides a step‑by‑step Java example—including Maven setup, document ingestion, embedding, similarity search, prompt creation, and response generation—to demonstrate a functional RAG pipeline.
This guide walks you through setting up a locally deployed AI system using Ollama and FastGPT, covering model selection, Docker deployment, configuration, knowledge‑base creation, and testing so your team can query internal documents securely and efficiently.
The Explain Error Plugin for Jenkins now supports the Ollama local model, offering a community‑driven AI solution that simplifies configuration, enhances security, and speeds up build error analysis for both individual developers and enterprise teams.
This guide walks you through installing the Ollama local large‑language‑model runtime on Windows, deploying a Gemma2 model, then setting up ShellGPT on Linux to interact with the local LLM, covering configuration, basic commands, and advanced usage examples.
This guide walks you through installing Ollama, pulling the open‑source DeepSeek‑R1:1.5B model, wrapping it with a Go‑based Model Context Protocol (MCP) server, creating a client example, and enhancing the experience with Open‑WebUI while offering performance‑tuning tips.
This guide walks through deploying a local LLM on Kubernetes using Ollama for model serving and Open-WebUI for a web interface, covering namespace creation, storage setup, GPU support, service exposure, validation, and model download to ensure privacy, low latency, and high availability.
This guide walks you through a Bash script that automatically checks for Ollama, installs it if missing, lets you choose a DeepSeek‑r1 model size, starts the Ollama service, and runs the selected model locally, complete with usage examples and a token‑cost note.
This guide explains DeepSeek R1’s model variants, hardware requirements, local installation steps using Ollama, LM Studio or Docker, and how to add visual interfaces like Open‑WebUI and Dify for a complete on‑premise AI solution.
OpenAI’s new open‑source gpt‑oss models, available in 20B and 120B sizes, can be run locally via Ollama with features like agentic capabilities, configurable reasoning, fine‑tuning, and MXFP4 quantization, and the article provides step‑by‑step installation, usage, and integration instructions.
This article walks through the complete design and implementation of a locally deployed, enterprise‑level semantic search system using Ollama for embedding generation and Easysearch for vector retrieval, covering problem analysis, architecture decisions, pipeline configuration, bulk indexing, and hybrid query execution.
This article explains the key improvements of Qwen3‑Coder, walks through two local deployment methods (LM Studio and Ollama), showcases front‑end coding examples, compares performance and hardware requirements, and offers practical recommendations for developers seeking an on‑premise AI coding assistant.
The article explains how breaking complex LLM tasks into sequential steps—known as prompt chaining—improves answer accuracy, debuggability, flexibility, and enables sophisticated AI workflows such as report generation, chatbots, and content creation using tools like n8n and Ollama.
This article provides a comprehensive, hands‑on tutorial for installing Ollama, Docker, and the open‑source Coze Studio on a local machine, configuring various LLM services such as Qwen 3, DeepSeek‑V3, and OpenRouter, and running the platform via Docker Compose to create and test AI agents.
This guide walks you through a completely code‑free, step‑by‑step process to download Coco AI 0.7.0, configure the server and client, set up a local connector, link Ollama models, and verify both simple and deep‑thinking AI modes for document retrieval and intelligent Q&A.
Learn how to transform any PDF—including scanned documents—into well‑structured Markdown using a local LLM (Gemma 3 via Ollama), Python, PyMuPDF and Pillow, without cloud APIs or API keys, by converting pages to images, prompting the model, and saving the output.
This article explains how advanced quantization and model optimization enable running strong large language models on laptops or desktops with less than 8 GB of RAM or VRAM, outlines key technical concepts, recommends local inference tools, and lists ten compact LLMs with usage commands.
This guide walks you through configuring OLLAMA for multi‑GPU load balancing, covering hardware checks, CUDA setup, native and Docker deployment methods, detailed parameter tuning, advanced sharding strategies, troubleshooting, performance optimization, and production‑grade monitoring to maximize throughput and stability of large language models.
This guide shows how to create a fully local Retrieval‑Augmented Generation (RAG) system using n8n, Docker, Ollama and the free Qwen3 embedding model, enabling secure, up‑to‑date AI assistants that answer enterprise questions without exposing any proprietary data.
This article examines the Qwen3 Embedding series released by Alibaba's Qwen team, detailing its architecture, multilingual capabilities, benchmark superiority across MTEB and C‑MTEB tests, and provides practical deployment guidance via Ollama and API integration.
This step‑by‑step tutorial shows how to install Ollama, pull the DeepSeek‑R1 model, create a Spring Boot project with the Spring AI Ollama starter, code a ChatController, and test a local AI "Hello World" integration, illustrating AI‑enhanced backend development.
Version 0.9.0 of Ollama introduces a ‘thinking’ control that lets users view and manage the AI model’s reasoning process, with detailed CLI commands, REST API usage, model support list, scripting options, and advanced Modelfile configurations for models like DeepSeek R1 and Qwen 3.
This guide walks you through building a high‑performance local AI knowledge base using Ollama, Cherry Studio, and the Qwen‑3 model, covering RAG fundamentals, model selection, document preparation, system configuration, and step‑by‑step UI operations for non‑programmers.
Ollama 0.7 introduces a fully re‑engineered core that brings seamless multimodal model support, lists top visual models, showcases OCR and image analysis capabilities, explains technical breakthroughs, and provides a quick three‑step guide to deploy powerful local AI vision.
This guide walks you through cross‑platform environment variable configuration, Docker containerization, GPU resource strategies, concurrency tuning, and security hardening for Ollama, providing practical code snippets and best‑practice tables to unleash its full potential in development and production.
This tutorial walks developers through setting up a low‑cost, containerized AI service on Windows by installing Docker, deploying Redis and Ollama containers, pulling the DeepSeek‑R1 model, and integrating everything with Spring AI to enable continuous conversation support.
Qwen3 introduces a suite of open‑source models—from a 235B expert model to compact 0.6B versions—offering competitive performance against top proprietary models, multilingual support, flexible thinking modes, and low deployment requirements, with detailed usage instructions via Ollama and OpenRouter.
This guide compares DeepSeek’s local and online versions, outlines hardware and privacy advantages of offline deployment, and provides a detailed step‑by‑step tutorial—including Ollama installation, model selection, command execution, and UI plugin setup—to help users run DeepSeek on their own machines.
The VS Code v1.99 update introduces a Copilot Agent mode that deepens project‑level understanding and adds custom LLM integration—including OpenAI, Azure, Gemini, Anthropic, OpenRouter, and locally‑run Ollama—offering developers greater flexibility, cost control, privacy, and strategic advantages in the evolving AI‑IDE landscape.
This guide explains how to use the Ollama ‘show’ command to retrieve detailed specifications of locally stored large language models, covering architecture, parameters, context length, embedding size, quantization, capabilities, and licensing information for informed model selection.
This guide explains how to retrieve, analyze, and modify an Ollama Modelfile—using commands like `ollama show --modelfile`, dissecting key directives such as FROM, TEMPLATE, LICENSE, PARAMETER, SYSTEM, and ADAPTER—and walks through step‑by‑step creation of a custom model.
This article walks through the entire process of deploying the DeepSeek‑R1 large language model on a personal machine, covering hardware requirements, Ollama installation, model download, service startup, remote access configuration, and visual UI integration with Dify, complete with concrete commands and screenshots.
This guide walks you through deploying the open‑source QwQ‑32B model on Alibaba Cloud Function Compute using the Cloud Application Platform (CAP), covering architecture, required services, account setup, step‑by‑step deployment, cost considerations, model interaction via Open WebUI and Chatbox, scaling configuration, and resource cleanup.
The article explains why LLM responses can be inconsistent, introduces Pydantic as a way to define custom output schemas, and walks through concrete examples—both with OpenAI and Ollama models—showing how to build a LangChain pipeline that parses responses into structured data.
This tutorial walks you through installing Ollama on Windows, configuring and running a local large language model, then setting up ShellGPT on Linux to communicate with Ollama, including configuration files, command examples, and REPL usage, while omitting unrelated promotional content.
This article introduces Google’s open‑source Gemma 3 model, highlighting its multimodal capabilities, massive 128k token context window, multilingual support, and provides step‑by‑step instructions for installing Ollama, pulling the model, and running local tests with code examples.
This step‑by‑step guide shows how to create an AI Agent by configuring a local Ollama model, selecting an embedding model, building a knowledge base, uploading documents, and testing the agent's retrieval capabilities, providing a practical RAG workflow for developers.
This guide walks you through setting up a private AI knowledge base using Webman AI 5.4.0, deploying Redis‑Stack, installing the illuminate/redis component, adding Ollama with DeepSeek and other embedding models, configuring Redis, importing training data, running the training process, and configuring role prompts for accurate AI responses.
This tutorial explains how to set up Ollama for CPU or GPU environments, run the DeepSeek R1:7b large language model, and use the open‑source Dify platform to create and deploy a custom AI application, providing step‑by‑step commands and configuration details.
The QwQ-32B model, released by Alibaba Cloud, delivers DeepSeek‑R1‑level results with only 32 billion parameters, offers integrated agent capabilities, is open‑source under Apache 2.0, and can be quickly deployed locally via Ollama or integrated into Java applications using Spring AI.
As open‑source large language model tools like Ollama, OpenWebUI and ComfyUI gain popularity, numerous security flaws such as unauthenticated APIs, CVE‑exploits, model theft and remote code execution emerge, prompting the development of AI‑Infra‑Guard—a lightweight, cross‑platform scanner that identifies over 30 component vulnerabilities and offers both web UI and CLI modes for rapid risk assessment.
This guide walks through setting up a local personal knowledge base using Ollama, DeepSeek‑R1, and AnythingLLM, and demonstrates how to integrate the Continue AI code assistant into VSCode, covering installation, configuration, and usage tips for efficient, secure development.
This guide walks you through setting up a powerful local AI knowledge base using Ollama, DeepSeek‑R1, and AnythingLLM, and shows how to integrate the Continue extension into VSCode for seamless, secure, and efficient development workflows.
This tutorial explains how to deploy the DeepSeek‑r1:1.5b model on a cloud server using Ollama, create a retrieval‑augmented generation (RAG) vector database with the mxbai‑embed‑large embedding model, and build an interactive AI application that answers questions from uploaded PDFs.
This guide explains why DeepSeek is a compelling GPT‑4‑level alternative, provides hardware recommendations for various model sizes, and walks through a three‑step Windows deployment using Ollama, including installation, environment configuration, model download, performance tuning, and common troubleshooting tips.
This guide explains what DeepSeek R1 is, compares its full and distilled versions, details hardware requirements for Linux, Windows, and macOS, and provides step‑by‑step instructions for local deployment using Ollama, LM Studio, Docker, and visual interfaces like Open‑WebUI and Dify.
This guide explains how to install Ollama on a Windows machine, configure its environment, and use it to download and run the DeepSeek‑R1 1.5B large language model locally, enabling offline AI interactions without relying on remote servers.
This guide explains how to download, install, and configure Ollama on Windows and Linux, set up environment variables, select a DeepSeek model, and call the Ollama API from a SpringBoot application with example code snippets.
Learn how to create a locally hosted chatbot powered by DeepSeek R1 32b, using Ollama and Docker, integrate Dify for model management, and add web‑search capability through SEARXNG, covering environment setup, search logic, content extraction, testing, and optimization tips.
This guide explains how to locally deploy the DeepSeek large‑language model using Ollama on Windows, macOS, and Linux, configure model storage and CORS, build personal and enterprise RAG knowledge bases with AnythingLLM and Open WebUI, and integrate the model into a Spring AI application via Docker and Docker‑Compose.
This article provides a comprehensive tutorial on locally deploying the 671‑billion‑parameter DeepSeek R1 model using Ollama, covering model selection, hardware requirements, dynamic quantization, detailed installation steps, performance observations, and practical recommendations for consumer‑grade hardware.
This guide explains how to download, quantize, and run the full‑size 671‑billion‑parameter DeepSeek R1 model on local hardware using Ollama, covering model selection, hardware requirements, step‑by‑step deployment commands, optional web UI setup, performance observations, and practical recommendations.
This guide explains how to deploy large‑scale AI models such as Ollama and DeepSeek‑R1 on a Kubernetes 1.30 cluster, covering hardware requirements, PVC and deployment manifests, service exposure, image pulling, verification steps, API access, and monitoring with Prometheus and Grafana.
This guide walks you through preparing the environment, installing Docker, cloning the DeepSeek repository, running the model with Docker or Ollama for quick start, using the enterprise API, and deploying the same model on Alibaba Cloud's free Bailei service within minutes.
