Browsing: layer

Linear attention replaces the unbounded KV cache of softmax attention with a fixed-size recurrent state. This cuts sequence mixing to linear time and decoding to constant…

Your AI agent is smart but forgetful. Every new session starts from zero — no memory of who you met, what you read, what you decided…

TL;DR debugging the same crash, I stopped blaming the model. It was always the same three problems:broken structured outputs, silent validation failures, and pipelines that looked…

TL;DR a full working implementation in pure Python, with real benchmark numbers. Most teams evaluate LLM responses by reading them and guessing. That breaks the moment…

Running AI agents in a local script is straightforward. Running them reliably in production across teams, across restarts, with isolated environments per context is a different…

, a learner messaged me about a wrong answer. She had asked the tutor about a concept from one of my Generative AI tutorials. The response…

TL;DR RAG retrieved the right document. The LLM still contradicted it. That is the failure this system catches. Five failure patterns: numeric contradictions, fake citations, negation…

NaNs don’t originate where they appear — they silently propagate across layers torch.autograd.set_detect_anomaly is too slow and often misleading for real debugging A forward hook–based detector…

TL;DR: a controlled four-phase experiment in pure Python, with real benchmark numbers. No API key. No GPU. Runs in under 10 seconds. As memory grows from…

In this tutorial, we build a universal long-term memory layer for AI agents using Mem0, OpenAI models, and ChromaDB. We design a system that can extract…