EP 01·13 min
We Cut 80% of Claude Code's Prompt: Boris Cherny on Radical Simplicity
Narration: WOW Mentor
WOW Mentor breaks down the strategy of Boris Cherny, the creator of Claude Code who, timed to the release of Opus 5, deleted more than 80 percent of the tool's system prompt and made the model smarter in the process. Each time a new model lands, he argues, you should delete your prompts and code and use ablation to add them back one line at a time, treating the model like a living creature and handing it a slightly-too-hard task alongside a way to verify its own work. From a model that no longer falls for prompt injection, to a language rewrite finished in eleven days, to a task still running by itself after two weeks, to thousands of agents that maintain the codebase on their own — the through-line is always the courage to delete and the discipline of the empirical.
Show notes
When a new model ships, delete your system prompt, CLAUDE.md, skills, and hooks first. Most of the rules you stacked up for older models are dead weight now, and deleting them often makes the model smarter.
Don't refill deleted rules by guessing. Run the product for real, and add a line back only when the model stumbles over the same thing repeatedly. Ablation is an eval where you delete things to measure each line's impact.
Treat the model like a living creature, like a coworker. Each generation has a different personality, so don't port over what worked on the last model — try, observe the result, and adjust empirically.
Give a task that looks slightly too hard. Instead of dictating steps one through four, describe the task, the guardrails, and the exit criteria, then let the model cook. Over-specification hobbles modern models.
Verification is the single most important lever. Hand the model a hard task, but always give it a way to check its own work — then it won't get stuck and will run for days or weeks on its own.
Chase the product overhang. Keep throwing the newest model at old problems, and experiment creatively even when there's no obvious commercial payoff. The model already holds abilities no one has elicited yet.
Orchestrate thousands of agents in sequence and parallel with dynamic workflows, and hand repetitive work to local loops and cloud routines so the codebase maintains itself.
Evals outlive code and prompts, but only by a generation or two. Keep appending, expect saturation, and treat throwing an eval away and building a new one as part of the process.
Transcript93 sentences
Transcripts are for members.
Ask this briefing
AIAsk this briefing anything
Members can ask questions and get grounded answers from the transcript — like “How do I apply this to my company?”
Unlock free for 7 days