Diffusion models, GANs, variational autoencoders, image/video/audio generation.
7 articles
AI is making generalists increasingly valuable, especially in smaller organisations. But greater capability should not become an excuse to turn one role into an entire department.
The AI revolution over the past few years has been dominated by massive foundation models, trillion-parameter counts, and futuristic chat interfaces. Yet inside engineering teams…
If your event marketplace is mostly software, not operations, you can push gross margins above 90% by combining Telegram Mini Apps for zero-friction distribution, PHP for a lean backend, and AI only where it directly improves conversion, moderation, or discovery.
Build an autonomous function-calling AI agent on a standard LAMP stack by using PHP for orchestration, MySQL for memory, Gemini Flash for reasoning, and cPanel for deployment.
In production systems, "hallucination" is a catch-all term for when Large Language Models (LLMs) generate plausible-sounding but incorrect or unintended output.
I thought the biggest challenge in building an AI system would be the model. I discovered the real bottleneck starts much earlier, with the data.
A deep dive into hierarchical retrieval, hybrid search, reranking, and production trade-offs in modern RAG systems
