- What Is an Agent, Really?
- From Vectors to Symbols — The Binding Problem Inside Neural Networks
- Reasoning Is More Than Architecture — Where Extra Computation Lives
- Preference Rankers — Learning Which Answer Is Better
- Build a Production AI Agent From First Principles: The Complete Reference Architecture
- Advanced Agents From First Principles 24: How Do You Release Agent Behavior Safely? Add Behavioral Contracts, Compatibility Checks and Promotion Gates
- Advanced Agents From First Principles 23: Your Infrastructure Is Healthy. Why Is the Agent Getting Worse? Detect Behavioral Drift and Roll Back Safely
- Advanced Agents From First Principles 22: What Happens When One Dependency Starts Failing? Add Circuit Breakers, Bulkheads and Graceful Degradation
- Advanced Agents From First Principles 21: What Happens When Too Many Agents Compete for the Same Resources? Add Admission Control, Quotas and Backpressure
- Advanced Agents From First Principles 20: Can Your Agent Coordinate Across Machines Without Duplicating Work? Use Leases, Idempotency and Fencing
- Advanced Agents From First Principles 18: What Should Your Agent Observe Next? Use Expected Value of Information
- Advanced Agents From First Principles 17: What Is Your Agent Actually Uncertain About?
- Advanced Agents From First Principles 16: Where Should an Agent Spend Its Compute? Build a Dynamic Budget Scheduler
- Advanced Agents From First Principles 15: How Do You Optimize an Agent Policy Without Turning It Into Another Black Box?
- Advanced Agents From First Principles 14: Can Your Agent Learn From Its Own Trajectories Without Learning the Wrong Lessons?
- Advanced Agents From First Principles 13: How Do You Debug an Agent That Made the Wrong Decision? Add Trajectory Observability
- Advanced Agents From First Principles 12: Is Your Advanced Agent Actually Better? Benchmark It Under Equal Budgets
- Advanced Agents From First Principles 11: Which Advanced Agent Architecture Should You Use? A Practical Selection Guide
- Advanced Agents From First Principles 08: Is Your Agent Spending the Same Compute on Every Task? Build Adaptive Agents That Escalate Only When Needed
- Advanced Agents From First Principles 25: Can You Reproduce an Agent Run Months Later? Add Deterministic Replay and Provenance
- Advanced Agents From First Principles 10: Are You Combining Every Agent Technique Into One Monster? Build a Mixture-of-Agents Runtime
- Advanced Agents From First Principles 09: Can Your Agent Actually Learn From Previous Runs?
- Agents From First Principles 00: What Is an Agent, Really?
- Which Model Should You Use? MR.Q, EBT, SICQL, HRM, Tiny and PACS Compared
- Inside Tiny — Residual Blocks, Attention and Sparse Autoencoders
- Tiny — Recursive Reasoning With a Small Neural Network
- MR.Q — Building a Neural Quality Model From Two Embeddings
- The Model Inside the Model
- PyTorch Zero to Hero 10: Build a Small GPT-Style Language Model From Scratch
- PyTorch Model Not Learning? A Systematic Debugging Guide
- PyTorch Zero to Hero 00: What Are We Actually Doing?
- 🔦 Phōs: Visualizing How AI Learns and How to Build It Yourself
- Writing Neural Networks with PyTorch
- Mastering Prompt Engineering: A Practical Guide