Curated knowledge hubs grouping articles, tutorials, research, and datasets around the subjects that matter most.
How transformers decide what to focus on. From the original scaled dot-product attention to multi-head attention, grouped query attention, and multi-head latent attention. The mechanism at the heart of every modern AI system.
Serving large models is where research meets the bill. Quantization and low-bit formats, speculative decoding, KV-cache management, batching and throughput tradeoffs, and what long context actually costs in production.
The building blocks of large language models. Encoder-decoder origins, the decoder-only shift, positional encodings, normalization strategies, feed-forward networks, and the modern innovations that define frontier models.