AI Models: Capability Analysis, Architectures, and Family Guides
The model itself, analyzed: families, architectures, tradeoffs.
The model itself, analyzed: families, architectures, tradeoffs.
What comes after LLMs? The post-transformer era.
AI hype follows a rhythm: roughly every three years, the architecture everyone is betting on gets replaced by something newer. The next shift is already forming in research labs.
LLM speed test on RTX 4080 with 16GB VRAM
Running large language models locally gives you privacy, offline capability, and zero API costs. This benchmark reveals exactly what one can expect from 14 popular LLMs on Ollama on an RTX 4080.
October 2026 trending Python repos
The Python ecosystem this month is dominated by Claude Skills and AI agent tooling. This overview analyzes the top trending Python repositories on GitHub.
Choose the right Python package manager
This comprehensive guide provides background and a detailed comparison of Anaconda, Miniconda, and Mamba - three powerful tools that have become essential for Python developers and data scientists working with complex dependencies and scientific computing environments.
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.
Unify text, images, and audio in shared embedding spaces
Cross-modal embeddings represent a breakthrough in artificial intelligence, enabling understanding and reasoning across different data types within a unified representation space.
Deploy enterprise AI on budget hardware with open models
The democratization of AI is here. With open-source LLMs like Llama, Mistral, and Qwen now rivaling proprietary models, teams can build powerful AI infrastructure using consumer hardware - slashing costs while maintaining complete control over data privacy and deployment.
Master Linux environment setup for data science work
Linux has become the de facto operating system for data science professionals, offering unmatched flexibility, performance, and a rich ecosystem of tools.
Nice framework for ETS/MLOPS with Python
Apache Airflow is an open-source platform designed to programmatically author, schedule, and monitor workflows, - entirely in Python code, offering a flexible and powerful alternative to traditional, manual, or UI-based workflow tools.
Thinking of installing second gpu for LLMs?
How PCIe Lanes Affect LLM Performance? Depending on the task. For training and multi-gpu inferrence - perdormance drop is significant.
AI requires a lot of power...
In the midst of the modern world’s turmoil here I’m comparing tech specs of different cards suitable for AI tasks (Deep Learning, Object Detection and LLMs). They are all incredibly expensive though.
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.
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…