A team member at Anthropic shared the exact LOOPS.md file Andrej Karpathy uses. When loaded into Claude, it shifted the model from generic replies to responses tailored to the user’s thinking. The approach highlights building a system prompt layer rather than chatting directly with the model.

This post shares a two-week-tested prefix prompt designed to make an AI respond as a world-class expert. It instructs the AI to verify every fact, use step-by-step reasoning, avoid disclaimers or moralizing, and deliver detailed, assertive answers with explicit confidence levels.

An Anthropic team member shared the internal Claude.md prompt template that Andrej Karpathy uses. Applying this file made Claude stop resisting and deliver exactly the responses the author needed.

The article argues that when you find yourself repeating the same prompts with AI agents, it’s smarter to build a “loop” – a system combining intent, context, action, evaluation, memory, and a stop condition – rather than manually steering each step. It shows how to calculate when a loop’s upfront cost pays off over repeated tasks and gives examples ranging from CI checks to goal-based agent scripts.

A former Meta L8 engineer shares his end-to-end agentic workflow, from terminal setup to prompt strategies. He outlines the tools (WezTerm, tmux, Neovim, Claude Code/OpenCode), voice input, and delegation techniques that let him manage AI agents like a dev team.

Hand-tuned natural language prompts make AI prototypes easy but lead to “prompt debt”—bloated, brittle instructions that slow development, confuse teams, and lock you into a single model. The author argues for replacing manual prompt fixes with measurable specs, automated prompt search, and testing to keep systems reliable and portable.

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.