The author demonstrates two practical techniques for programming with System One models (fast classifiers that pick from multiple choices): tiered goal-setting for sequential decision-making, and tournament sampling for choosing among many options. He shows these approaches working in Doom gameplay and Wikipedia navigation tasks.

• Tiered goals work better than single-pass decisions: periodic prompts asking the model to choose short-term goals (e.g., "kill enemies" vs "collect armor") make it perform more intelligently than just reacting to immediate game state every 200ms.
• Tournament sampling beats confidence scoring when picking from many options: feeding 100 links at a time, then narrowing down, found the optimal path in seconds, while trying to score all 1,000+ links failed badly.
• System One models offer a practical alternative to tool calls for real-time systems where you need predictable latency and don't need full language generation flexibility.

This issue covers building and maintaining AI harness code, Elden Ring’s simple goal-based NPC AI, and agent-driven development practices from ex-Meta setups to collaborative coding. It also highlights security and tooling updates—from Daybreak and Mistral OCR 4 to Mythos benchmarks, the fired Google Workspace CLI creator, and running GLM-5.2 locally.

• AI harness code needs different designs for production (locked-down, safe) vs. training (flexible, exploratory), and treating either as permanent breaks down as models evolve.
• Elden Ring's NPCs achieve rich combat behavior through a simple stack-based "Goals" system mixing randomization and hierarchy, not complex behavior trees.
• A former Meta L8 engineer replaced meetings and busywork with a voice-driven planner and parallel agent workflows to focus solely on design and strategy.
• A Google engineer was fired after his self-made Workspace CLI tool went viral, illustrating tension between corporate control and grassroots innovation.