This article shows how to replicate Stanford’s STORM research workflow inside Claude with four simple prompts. You generate five expert perspectives, map their contradictions, synthesize findings, and peer-review the results—all in five minutes instead of 40+ hours.

Ponytail is a plugin and ruleset for AI coding agents that enforces a six-step minimal-code ladder—skip unnecessary code, prefer stdlib or native features, then one-liners—to produce only what each task needs. Benchmarks on Claude models show 80–94% less code, 3–6× faster runs, and 42–75% lower cost. Installation covers Claude Code, Codex, OpenCode, Gemini/Antigravity CLI, Copilot, ClawHub, and more.

This article traces the evolution of AI loops—small programs that run, check, and re-prompt coding agents—from early ReAct and AutoGPT examples to today’s durable, multi-agent orchestration with scheduling and self-verification. It shows why loop management, not model calls, is now the biggest cost in AI coding and outlines best practices: cap iterations, build reusable skills, and include feedback checkpoints.

The article shows that when an LLM evaluates if text meets a given criterion, the answer already sits in its hidden state before any token is generated. By capturing the hidden representation at a designated seed token and training a small MLP head (with optional LoRA sharpening and isotonic calibration), you get a fast, calibrated classifier that accepts arbitrary English criteria without per-criterion retraining.

Boris Cherny breaks down nine common habits that burn most of your Claude tokens before the model even sees your prompt—loading CLAUDE.md, rereading chat history, forgotten hooks, and more. He shows how each pattern eats into your limits and why complaints about “Claude getting dumber” usually miss the real culprit.