AI

Black Forest Labs has released FLUX.1-Kontext-dev, an advanced image-to-image AI model that augments existing images using text instructions.

Token optimization is the critical skill separating cost-effective LLM applications from budget-draining experiments.

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.

Here’s a side-by-side support comparison of structured output (getting reliable JSON back) across popular LLM providers, plus minimal Python examples

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 is an up-to-date GitHub Copilot cheat sheet, covering essential shortcuts, commands, usage tips, and context features for Visual Studio Code and Copilot Chat

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.

Here we have a description of The Model Context Protocol (MCP), short notes on how to implement an MCP server in Go, including message structure, protocol specifications.

Since standard Ollama doesn’t have a direct rerank API, you’ll need to implement reranking using Qwen3 Reranker in GO by generating embeddings for query-document pairs and scoring them.

This little Reranking Go code example is calling Ollama to generate embeddings for the query and for eache candidate document, then sorting descending by cosine similarity.

The Qwen3 Embedding and Reranker models are the latest releases in the Qwen family, specifically designed for advanced text embedding, retrieval, and reranking tasks.

Vibe coding is an AI-driven programming approach where developers describe desired functionality in natural language, allowing AI tools to generate code automatically.

I’ve used MMDetection (mmengine, mdet, mmcv) quite a bit, And now looks like it’s out of the game. It’s a pity. I liked it’s model zoo.

Recently Black Forest Labs published a set of text-to-image AI models. These models are told have much higher output quality. Let’s try them out

Testing how Perplexica performs with various LLMs running on local Ollama: Llama3, Llama3.1, Hermes 3, Mistral Nemo, Mistral Large, Gemma 2, Qwen2, Phi 3 and Command-r of various quants and selecting The best LLM for Perplexica