3 posts
We benchmarked GPT-5.5, Claude Opus 4.7 and Gemini 3.1 Pro on Turkish workloads end to end: TR-MMLU and TUMLU benchmark numbers, a 50-prompt real-world test across legal, finance, code, creative writing and Q&A, an A/B in a Turkish enterprise, TL-based cost analysis and a decision matrix for picking the right model for each Turkish task. 35+ references.
A 2026 snapshot of the Turkish open-source LLM ecosystem: Trendyol-LLM, Cosmos-Llama, KanarYa, Kumru AI, the TÜBİTAK BİLGEM domestic model, and the T3 AI Baykar defense model. Detailed decision guide covering MMLU-TR and TUMLU benchmarks, licensing, tokenization gap, VRAM requirements, self-hosting needs, and which model to pick for which use case.
Large language models have become one of the most influential technologies in modern AI. Yet they are often explained too superficially, as if they were merely “text prediction engines trained on huge amounts of data.” While that description is not entirely wrong, it is far from sufficient. Without understanding transformer architecture, tokenization, self-attention, representation learning, and inference dynamics, it is impossible to understand how LLMs actually behave. This guide provides a systematic and technically grounded explanation of how large language models work, from tokens and embeddings to transformer blocks, attention, training, inference, and sampling.