Deep Learning Fundamentals: The MNIST Dataset and the Cost Function
A neural network needs data to learn from and a way to score itself: the MNIST dataset, one-hot encoded labels, and the cost function that scores its weights and biases.
Machine Learning Basics: The Bias-Variance Tradeoff
A model's error splits into bias and variance, and reducing one raises the other. Learn underfitting, overfitting, and how to balance them via total error.
An Improved Perceptron: Understanding the Sigmoid Neuron
A perceptron's output flips between 0 and 1, so learning never accumulates. See how the sigmoid neuron's smooth activation turns that cliff into a gentle slope.
Beyond Text-to-SQL: Implementing OpenAI’s 6-Layer Context Data Agent and the Hidden Challenges
Explore the architecture of OpenAI’s in-house Data Agent. Learn how they leverage six layers of context, self-correction, and Evals to analyze 600 PB of data.
AgentOpt: How to Optimize Client-Side LLM Agents and Cut API Costs by 67%
Optimize your LLM agents with AgentOpt. Discover how this open-source, client-side framework uses MAB to find the best model combo and cut API costs by 67%.
ERL: Teaching LLM Agents to Learn from a Single Attempt Without Fine-Tuning
Boost LLM Agent performance with ERL (Experiential Reflective Learning). This framework extracts "Trigger-Action" heuristics from single attempts to solve Agent amnesia, increasing Gaia2 success by 7.8% without fine-tuning.
Harness Engineering: Building Self-Evolving AI Agents with Ralph Loop and MemRL
Master AI Agent system engineering with the Model + Harness + Context framework. Explore the Ralph Loop and MemRL algorithm for continual learning, memory optimization, and stable industrial-grade Agent deployment.