Machine Learning

Five local LLMs measured at the wall socket on an M3 Ultra. A 120B model costs five times less per token than a 27B dense model.

LLMs are stateless by design. These five architectural patterns show how to manage memory and state in production AI agents.

A step-by-step guide to understanding backpropagation in neural networks, built on linear regression intuition and the chain rule.

57% of enterprises have traced confident AI agent errors to bad context. RAG dominates, but the governed semantic layer to fix it isn't built yet.

OpenClaw bridges local Ollama models to messaging apps like WhatsApp and Telegram. This guide covers installation, context configuration, and Docker deployment.

A pure Python pipeline that compiles messy text notes into a linked, linted markdown wiki — no LLM calls, no embeddings, no external APIs.

Context engineering reframes how RAG pipelines work. Each brick emits typed pieces that converge on a single LLM call.

A step-by-step exploration of classical NLP methods—from Vowpal Wabbit baselines to stacked ensembles—applied to Kaggle's Spooky Author Identification task.

MCP standardizes how AI apps connect to external tools. Learn the architecture, request flow, and production security considerations.

Learn how agentic workflows and autonomous agents differ by examining who owns control flow — a human writing code, or a model reasoning at runtime.

Harness-1 separates query generation from state tracking to build a leaner retrieval agent. It outperforms larger systems across eight benchmark domains.

A single-agent text-to-SQL system failed on complex queries. Here's how a multi-agent pipeline fixed it.