Deep-dives, tutorials and essays on AI, automation & the future of the web — written by builders, for builders.
Research ExplainedOptimizationModels13 articles
Flash Attention achieves 2-4× speedup on attention by changing memory access patterns. Understand I/O complexity, tiling, and how to optimize matrix operations on GPUs.
Different optimizers suit different problems. SGD is stable, Momentum accelerates, Adam is adaptive. Understand why each optimizer works and pick the right one.
Vision Transformers apply Transformers to image classification. Patch embeddings convert images to sequences, enabling the same architecture as NLP models.
model.fit() hides the training loop. GradientTape exposes it. Use it when you need per-batch gradient manipulation, custom loss combinations, or training dynamics that Keras callbacks can't express.
RoPE encodes position by rotating query and key vectors in complex space. It extrapolates beyond training length, transfers across fine-tuning, and adds zero parameters — here's the math.
DataLoader is more than a loop — it's a parallel data pipeline. Build a correct Dataset, write a proper collate_fn, and understand num_workers to eliminate training bottlenecks.
LangChain wraps every LLM provider behind the same Runnable interface. Swap OpenAI for Claude or a local Llama model without changing a line of your chain logic.
Keras gives you three model-building APIs. Sequential is a dead end for anything non-trivial. Functional handles 90% of production architectures. Subclassing gives you full control when you need it.
Autograd is not magic — it's a directed acyclic graph of Function nodes. Understand how gradients flow, when retain_graph matters, and how detach prevents gradient leaks.
Prompt templates and output parsers are the input and output contracts of your LLM pipeline. Build them wrong and your chain breaks on every edge case.
TensorFlow 2.x made eager execution the default, but tf.function and AutoGraph still power production deployments. Understand when and how graphs take over.
Tensors are the foundation of every PyTorch model. Master dtypes, device movement, memory layout, and broadcasting rules to eliminate hours of debugging.
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