January 2026 trending Go repos
The Go ecosystem continues to thrive with innovative projects spanning AI tooling, self-hosted applications, and developer infrastructure. This overview analyzes the top trending Go repositories on GitHub this month.
Technical guide to AI-generated content detection
The proliferation of AI-generated content has created a new challenge: distinguishing genuine human writing from “AI slop” - low-quality, mass-produced synthetic text.
Build AI search agents with Python and Ollama
Ollama’s Python library now includes native OLlama web search capabilities. With just a few lines of code, you can augment your local LLMs with real-time information from the web, reducing hallucinations and improving accuracy.
Build AI search agents with Go and Ollama
Ollama’s Web Search API lets you augment local LLMs with real-time web information. This guide shows you how to implement web search capabilities in Go, from simple API calls to full-featured search agents.
Speed-up FLUX.1-dev with GGUF quantization
FLUX.1-dev is a powerful text-to-image model that produces stunning results, but its 24GB+ memory requirement makes it challenging to run on many systems. GGUF quantization of FLUX.1-dev offers a solution, reducing memory usage by approximately 50% while maintaining excellent image quality.
AI model for augmenting images with text instructions
Black Forest Labs has released FLUX.1-Kontext-dev, an advanced image-to-image AI model that augments existing images using text instructions.
In july 2025, soon it should be available
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.
Whole set of MM* tools is on EOL...
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.
Awesome new AI model to produce image from text
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
Labelling and training needs some glueing
When I trained object detector AI some time ago - LabelImg was a very helpful tool, but the export from Label Studio to COCO format wasn’t accepted by MMDetection framework..
Let's test logical fallacy detection quality of different LLMs
Here I am comparing several LLM versions: Llama3 (Meta), Phi3 (Microsoft), Gemma (Google), Mistral Nemo(Mistral AI) and Qwen(Alibaba).
Quite some time ago I trained object detector AI
On one cold winter day in July … that is in Australia … I felt urgent need to train AI model for detecting uncapped concrete reinforcement bars…
