“The goal was to evaluate whether large language models can determine causal and logical consistency between a proposed character backstory and an entire novel (~100k words), rather than relying on local plausibility.”

“The article highlights discussions on X (formerly Twitter) about which small LLM is best for Japanese and how to disable 'thinking mode'.”

“"This article provides a valuable benchmark of SLMs for the Japanese language, a key consideration for developers building Japanese language applications or deploying LLMs locally."”

“まずは「動くところまで」”

“"OpenAI不要！ローカルLLM（Ollama）で完全無料運用"”

“I built this as a personal open-source project to explore how EU AI Act requirements can be translated into concrete, inspectable technical checks.”

“”

“"The original project was brilliant but lacked usability and flexibility imho."”

“"You just open it and go. No Docker, no Python venv, no dependencies."”

“LLMのアシストなしでのプログラミングはちょっと考えられなくなりましたね。”

“The core idea is to queue LLM requests, either locally or over the internet, leveraging a GPU for processing.”

“Open WebUIを使っていると、チャット送信後に「関連質問」が自動表示されたり、チャットタイトルが自動生成されたりしますよね。”

“The initial conclusion was that Llama 3.2 Vision (11B) was impractical on a 16GB Mac mini due to swapping. The article then pivots to testing lighter text-based models (2B-3B) before proceeding with image analysis.”

“The author, a former network engineer, is new to Mac and IT, and is building the environment for app development.”

“The article mentions concerns about API costs and data privacy as the motivation for using Ollama.”

“Automatically scrapes documentation websites and converts them into organized, categorized reference files with extracted code examples.”

“I built this because I had hundreds of Claude conversations buried in JSON exports that I could never search through again.”

“I feel like with every update they are seriously straying away from the main purpose of their application; to provide a secure inference platform for LOCAL AI models.”

“Hello, I'm Hiyoko. When I became interested in local LLMs (Large Language Models) and started researching them, the first name that came up was the one introduced in the previous article, "Easily Run the Latest LLM! Let's Use Ollama."”

“Cactus enables deploying on phones. Deploying directly on phones facilitates building AI apps and agents capable of phone use without breaking privacy, supports real-time inference with no latency...”

“The main advantage is the browser extension form factor which makes it more convenient for day to day use, especially for less technical users.”

“Ollama violating llama.cpp license for over a year”

“Llama.cpp supports Vulkan.”

“Onit is ChatGPT Desktop, but with local mode and support for other model providers (Anthropic, GoogleAI, etc). It's also like Cursor Chat, but everywhere on your computer - not just in your IDE!”

“Ollama 0.4 is released with support for Meta's Llama 3.2 Vision models locally”

“Ollama now supports tool calling with popular models in local LLM”

“How to implement a local Retrieval-Augmented Generation pipeline with Ollama language models and a self-hosted Weaviate vector database via Docker in Python.”

“Ollama v0.1.33 now supports Llama 3, Phi 3, and Qwen 110B.”

“The user integrated local LLM support to Apple Notes through Ollama.”

“An end-to-end generative feedback loop demo using Weaviate, Ollama, Mistral and Snowflake’s Snowpark Container Services!”

“Ollama allows users to run LLMs on Linux with GPU acceleration.”

“While not exactly the same as running linux containers, running LLMs shares quite a few of the same challenges.”