From production RAG systems processing thousands of daily queries to fine-tuned LLMs, autonomous AI agents, and neural network architectures — these articles cover artificial intelligence implementation from a CTO's perspective. I've built AI-powered applications used by hundreds of thousands of people daily at Extremoo and CasinoAlpha. Here I share the architecture decisions, cost optimization strategies, and hard lessons learned from deploying AI at enterprise scale. Topics include large language models (LLMs), retrieval-augmented generation (RAG), MLOps, prompt engineering, convolutional neural networks, AI ethics, and the tools every LLM engineer needs in their toolkit.
Three months into production, a RAG system was hemorrhaging money. $50,000 in monthly API costs, hallucination rates hovering at 15%, and user complaints flooding support channels. The prototype had worked beautifully in testing. In production? Complete disaster.
The hype around AI agents is real, but let's cut through the noise. After spending the last six months building and deploying…
Artificial Intelligence models – especially large language models (LLMs) and vision transformers – have transformed how businesses automate tasks, generate content, and…
This comprehensive guide organizes over 150 specialized libraries and tools that every LLM engineer should know about in 2026.
As someone who's spent countless nights debugging code, I've always dreamed of a more intuitive way to build software. The future I'm…
Diving into the world of Large Language Models (LLMs) like OpenAI's GPT-4 and Anthropic's Claude has been both exciting and a bit…
Hello there! I'm Alex Bobes, a tech expert and CTO with a decade of experience. Today, I'll be taking you on a…
Over the years I have encountered many captivating concepts in the realm of technology. One area that has particularly piqued my interest…
As a developer, the ability to build intelligent systems that can make decisions on their own is one of the most exciting…
Neural networks are powerful tools for pattern recognition and prediction. They're not magic, and they're not going to solve every problem. But when you have the right data and the right task, they're incredibly effective.
