💡 The story introduces BrowserBee, a privacy-first AI web browser agent in Chrome's side panel that automates tasks using various LLMs (Anthropic, OpenAI, Gemini, Ollama), which directly aligns with the agents category covering autonomous agents and task automation.
💡 The story involves a license violation dispute between two AI-related projects (Ollama and llama.cpp), which falls under open source licensing issues in the legal category.
💡 The story covers Ollama's new engine for multimodal models; Ollama is a tool for local AI inference, which falls under the infra category (deployment and inference)
💡 The story presents Cogitator, an open-source Python toolkit for Chain-of-Thought (CoT) prompting that supports models like OpenAI and Ollama. This falls into the 'tools' category as it is an AI development tool for prompt engineering and working with CoT methods.
💡 The post from Ollama's official blog discusses world emulation using neural networks, which aligns with model capabilities or new architectures—key elements of the 'models' category.
💡 The story is a primer on MCP (a topic explicitly listed under the agents category) using AI tools like Ollama and LangChain, focusing on agent-related content.
💡 The story discusses setting up Ollama (a local LLM inference tool) on NixOS WSL with Nvidia support for continuous local inference, which aligns with the infra category covering deployment and local inference.
💡 The story asks about getting started with LLM-assisted programming, which directly falls under the 'coding' category focused on AI-assisted programming tasks using tools/models like ChatGPT and Ollama.
💡 The story focuses on AMD's Gaia open source project for local LLM inference on PCs, which falls under the infra category (deployment, local inference tools like llama.cpp/Ollama are examples here).
💡 The story introduces an open-source DocumentAI tool built with Ollama, which is a development tool for AI-powered document processing, aligning with the 'tools' category under Engineering.
💡 The story focuses on using Ollama (a local inference tool explicitly listed under the infra category) for structured extraction on documents/images, which aligns with the infra category's scope of deployment and local inference.
💡 The story discusses Vulkan support in llama.cpp and why Ollama does not have it yet; both tools are related to local AI model inference, which falls under the infra category.
💡 The story is about running LLMs locally, which aligns with the infra category's focus on deployment and local inference (e.g., llama.cpp, Ollama are examples in the infra description).
💡 The story focuses on Microsoft's Phi-3-Mini, a model release, which falls under the models category per the rule that model releases belong to models regardless of additional integration details with tools like Ollama.