AI

Immich is a revolutionary open-source, self-hosted photo and video management solution that gives you complete control over your memories. With features rivaling Google Photos - including AI-powered facial recognition, smart search, and automatic mobile backup - all while keeping your data private and secure on your own server.

I dug up some interesting performance tests of GPT-OSS 120b running on Ollama across three different platforms: NVIDIA DGX Spark, Mac Studio, and RTX 4080. The GPT-OSS 120b model from the Ollama library weighs in at 65GB, which means it doesn’t fit into the 16GB VRAM of an RTX 4080 (or the newer RTX 5080).

The Model Context Protocol (MCP) is revolutionizing how AI assistants interact with external data sources and tools. In this guide, we’ll explore how to build MCP servers in Python, with examples focused on web search and scraping capabilities.

Docker Model Runner (DMR) is Docker’s official solution for running AI models locally, introduced in April 2025. This cheatsheet provides a quick reference for all essential commands, configurations, and best practices.

Running large language models (LLMs) locally has become increasingly popular for privacy, cost control, and offline capabilities. The landscape shifted significantly in April 2025 when Docker introduced Docker Model Runner (DMR), its official solution for AI model deployment.

The future of AI isn’t just about smarter models - it’s about smarter silicon. Specialized hardware for LLM inference is driving a revolution similar to Bitcoin mining’s shift to ASICs.

NVIDIA DGX Spark is real, on sale Oct 15, 2025, and targeted at CUDA developers needing local LLM work with an integrated NVIDIA AI stack. US MSRP $3,999; UK/DE/JP retail is higher due to VAT and channel. AUD/KRW public sticker prices are not yet widely posted.

This guide provides a comprehensive overview of available Go SDKs for Ollama and compares their feature sets.

Here is a comparison between Qwen3:30b and GPT-OSS:20b focusing on instruction following and performance parameters, specs and speed.

Ollama’s GPT-OSS models have recurring issues handling structured output, especially when used with frameworks like LangChain, OpenAI SDK, vllm, and others.

Large Language Models (LLMs) are powerful, but in production we rarely want free-form paragraphs. Instead, we want predictable data: attributes, facts, or structured objects you can feed into an app. That’s LLM Structured Output.

Here I am comparing how much VRAM new version of Ollama allocating for the model vs previous Ollama version. The new version is worse.

Ollama has quickly become one of the most popular tools for running LLMs locally. Its simple CLI, and streamlined model management have made it a go-to option for developers who want to work with AI models outside the cloud.

Locally hosted Ollama allows to run large language models on your own machine, but using it via command-line isn’t user-friendly. Here are several open-source projects provide ChatGPT-style interfaces that connect to a local Ollama.

The Pragmatic Engineer letter published a couple days ago survey statistics of the popularity of programming languages, IDEs, AI tools and other data for mid-2025.

Nvidia is about to release NVIDIA DGX Spark - little AI supercomputer on blackwell architecture with 128+GB unified RAM and 1 PFLOPS AI performance. Nice device to run LLMs.