Andrej Karpathy walks through practical daily AI workflows in a 2-hour video, covering model selection, reasoning models, code execution, and multi-chat memory — techniques most people never use. Someone extracted these methods into a Claude-specific guide with ready-to-use examples.

• Most people use only 10% of what AI models can do; this covers the remaining 90%
• Specific techniques shown include choosing the right model, deciding when reasoning models justify the cost, generating full research reports from single prompts, and automating code execution
• The guide translates Karpathy's video into Claude-specific features with immediately applicable examples

The author argues that treating prompts as disposable is wasteful—instead, build modular "skills" (organized folders with instructions and scripts) that you improve over time and chain together. He breaks down 17 tactics from Anthropic engineers into 6 layers, showing how to structure workflows so improvements compound across tasks rather than starting from scratch each time.

• The author rebuilt a 600-line SEO prompt into six modular skill files (research, audit, metadata, internal links, GSC monitoring, title scoring), and improving one now improves every workflow that uses it.
• The core shift is from one-off prompting to building reusable "skills"—folders with a SKILL.md file, scripts, and references—that Claude runs identically every time and that get refined after each failure.
• Anthropic engineers' 17 tactics get grouped into 6 layers, with the top priority being: stop writing custom prompts for repetitive tasks and instead build slash-command-invoked skills.
• The lasting value comes from the infrastructure built on top of Claude, not from any single prompt, which evaporates once the session ends.

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.

• A supposed Anthropic teammate shared Karpathy's "LOOPS.md" file, claimed to be his personal prompt-engineering setup for Claude.
• The pitch is that using it shifts Claude from generic answers to step-by-step responses matching the user's own thinking style.
• The core concept: treat Claude as wrapped in a system layer (templates, token budgets, chain-of-thought triggers, multi-pass refinement protocols) rather than prompting it directly.
• Framed as urgent/scarce content ("save it before it disappears"), a hallmark of unverifiable social-media hype rather than a sourced claim.

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.

• A tweet claims an Anthropic team member shared an internal "Claude.md" prompt template attributed to Andrej Karpathy that reportedly makes Claude follow instructions more reliably.
• Karpathy reportedly joined Anthropic five weeks before this post.
• The file allegedly includes formatting rules, tone settings, and error-handling steps meant to reduce vague or evasive Claude responses.
• The claims come from secondhand reports (a "friend") rather than verified sourcing or a linked deep-dive.

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.

• The article provides no actual list of the nine habits, specific hooks, or countermeasures beyond vague category names—despite claiming precise percentages for each.
• The claimed source (a tweet/profile labeled "Mnimiy @Mnilax") doesn't match the detailed narrative about Boris Cherny, a podcast episode, and 400 hours of usage data, suggesting fabricated or unverifiable attribution.
• The specific statistics (73% total, 14% for CLAUDE.md, 13% for chat history, 11% for hooks) are presented with false precision but no methodology or source is given for how they were measured.