This article describes an experiment where two Gemma4 AI models, run via Ollama, were set up to converse with each other. One agent acted as a normal person and the other as a robot, both instructed to respond concisely in Vietnamese and end with a question.
The author replaced their AI agent's JSONL-based fact store, which suffered from near-duplicates and lacked relational understanding, with a graph database. This change addressed issues like multi-hop traversal and fact evolution in the agent's memory system, running efficiently within 85MB.
This post details the transformation of a Xiaomi 12 Pro into a 24/7 local AI server using LineageOS and Ollama to serve Gemma4. The setup includes OS optimizations, custom thermal management, and battery protection for continuous operation.
The content describes a war robots project where the Qwen3 model generates code to control the robots. The author reports a significant improvement in AI execution after transitioning from Ollama to llama.cpp.
The author built DocMind, a local multimodal RAG app for chatting with any PDF, image, or document, running entirely offline using Ollama and Xenova Transformers. The post details its full architecture, chunking strategy, vector similarity search, and code.
A beginner AI user is experiencing issues with their Gemma4 model, run via Ollama, which is responding in Japanese. They are seeking guidance on specific parameters or configurations to resolve the language problem.
An e-commerce team replaced $800/month GPT-4o API costs with a local Llama 4 setup using Ollama, reducing expenses to $40/month. This move also addressed data privacy concerns and improved performance for generating 80,000 product descriptions monthly.
This content provides a comprehensive guide to Ollama, explaining how it enables running Large Language Models (LLMs) locally, keeping data on your machine, working offline, and eliminating per-token costs. It details Ollama's functionalities, including model management and the ability to build private chatbots, coding assistants, and RAG systems.
This guide details how to run Large Language Models (LLMs) locally using Ollama, a free and private tool with an OpenAI-compatible API. It provides installation instructions for Linux, macOS, and Windows, along with commands to pull specific code-focused and general-purpose models.
This guide covers building terminal-based AI agents, a key tool in modern development workflows. It explores various solutions including existing CLI AI agents and optimizing performance by setting up API endpoints for local LLMs like Ollama.
Today's top stories feature the merger of speculative checkpointing in llama.cpp to accelerate local LLM inference and a new Ollama multimodal tool for local audio/video analysis. Additionally, a detailed comparison between MLX and GGUF is provided for optimizing Gemma 4 deployment on consumer hardware.
The author built a local, GPU-powered AI coding assistant using Ollama, as an alternative to cloud-based tools like Claude Code. This system aims to solve issues of cost, privacy, latency, and dependency, enabling complex agentic loops without fees or data leaving the machine.
This guide details the setup of local LLMs for data privacy and performance, recommending Ollama due to its easy installation, support for various models, and simple API interface. It covers hardware requirements, installation steps, and a comparison of frameworks.
This practical guide teaches developers how to build and optimize terminal-based AI agents, leveraging local LLMs for real-time code support. It details the setup of platforms like Aider and Ollama, and includes an example CLI agent with function calling capabilities.
This content outlines how to build a self-hosted Hermes Agent automation stack using open-source models like Llama 4 Maverick and Qwen 3 8B via Ollama. This setup eliminates API costs, requiring only a VPS, and offers practical workflow recipes for a complete DIY system.
This guide details how to deploy a production-ready Llama 2 inference server on DigitalOcean's App Platform for just $5/month. It offers a cost-effective alternative to AI APIs, eliminating rate limits and vendor lock-in.
Inspired by a YouTube Short, the author embarked on a deep dive to build a completely free local AI stack, connecting a local AI model to Claude Code without API keys or subscriptions. This post documents the learning process, including tools like Ollama, Gemma4, Docker, and Open WebUI, for running AI entirely on personal hardware.
Alibaba has released Qwen3.6-27B, a 27-billion parameter dense model that outperforms its previous MoE version on coding tasks. This content details how to run the model locally using Ollama, including commands for various quantizations and hardware requirements.
This content explains how to deploy Llama 3.2 with Ollama and Docker on a $5/month DigitalOcean Droplet, enabling zero-GPU inference for production RAG systems. It highlights significant cost savings compared to OpenAI APIs.
Reiseki is a local AI assistant leveraging Gemma 4 via Ollama to perform practical tasks like file management, document generation, and data analysis. It's designed for non-technical users, shipping as a Windows installer with a simple setup and persistent context across sessions.