{
  "video_id": "reddit_1wcdisq",
  "channel_slug": "ClaudeAI",
  "channel_handle": "r/ClaudeAI",
  "title": "Anthropic says \"double-check your work\" is now an anti-pattern. I counted 125 of those lines in my own config and cannot tell which ones matter.",
  "url": "https://www.reddit.com/r/ClaudeAI/comments/1wcdisq/anthropic_says_doublecheck_your_work_is_now_an/",
  "external_url": null,
  "upload_date": "20260910",
  "published_at": "2026-09-10T08:50:45+00:00",
  "transcript": "Anthropic published a post about cutting cost and improving performance on their platform, and one part of it landed on me directly: the instructions we add to make a model try harder are now working against us.\n\n\n\nWe all do it. Double-check your work. Be maximally thorough. Older models needed that, because without it they cut corners.\n\n\n\nCurrent models already do it. So the instruction makes them work twice. It costs more and the answer comes out worse.\n\n\n\nThe examples in the post are concrete. \"Be maximally thorough\" sent a model through dozens of knowledge-base searches it did not need. \"Double-check your work\" made it redo work it had already finished. The one that stung was contradictory rules: a policy that allowed something in one place and forbade it in another, and four valid refunds never went out.\n\n\n\nThe reason is simple enough. Older models did only what you told them, so you had to spell out every step. Current models handle more on their own. Layer \"do it in this order\" on top of that and they stop doing what they would have done and follow your order instead. The phrase you added to help is the thing in the way.\n\n\n\nIt is not an argument for stripping everything. There is a separate control for how hard the model works. Too low and it answers before it has enough evidence; too high and it chews on easy things. What the post says to remove is the nudging, not the hard constraints.\n\n\n\nSo after reading it I counted my own. Across my rule files: sixty-six instances of \"must\", fifty-four of \"never\". A hundred and twenty-five in all.\n\n\n\nNot all of those are a problem. \"Never click the publish button twice\" is a hard constraint and it should stay. But I have never once gone through and split which of them are nudges and which are actual stops, and I suspect the nudges are the majority.\n\n\n\n[https://claude.com/blog/reducing-cost-and-improving-performance-with-claude-platform](https://claude.com/blog/reducing-cost-and-improving-performance-with-claude-platform)\n\n\n\nFor anyone running long-lived configs against Claude: have you ever audited yours for this? And if you did, how did you decide which lines were doing real work?\n\n\n\n--- Top Comments ---\n\n\n[1 upvotes] **TL;DR of the discussion generated automatically after 30 comments.**\n\nLooks like a spirited debate in here, OP. The general sentiment is that while Anthropic's advice is good *in theory*, it doesn't match the reality of using the models day-to-day.\n\n**The consensus is that you should absolutely still review the model's work, but you shouldn't ask the *same agent* to \"double-check\" itself.** The community's preferred method, by a landslide, is to spin up a **new, separate agent** to perform the review. This gives you a clean context, which is the crucial part.\n\n*   Many users do this with the same model (e.g., having a second Opus 5 agent review the first one's code).\n*   An even more popular suggestion is to use a different model entirely for the review (e.g., have GPT-5.6 review Claude's work and vice-versa) since they have different \"blind spots.\"\n\nA secondary theme is frustration with Claude's over-eagerness. Users complain that it \"builds a castle when you just want to look at a spire\" and will start implementing complex solutions from a simple query. The workarounds are to frame your prompts as a \"discussion\" or to explicitly add \"make no changes\" until you give the green li\n\n[72 upvotes] Yeah I’m still asking another agent to review its code. Asking the agent doing the work to “double check” their work is dumb imo. Let another agent do that\n\n[56 upvotes] Why is it then that every time I ask it to double check that it finds 5 things wrong? Are we straight up supposed to not understand how these things work at all now?\n\nEdit: obviously you can use a subagent or a different model. They will all find issues so its beside the point. Anthropic models are not able to communicate well because as always in RL its “just get the result I dont care how”. Its model bias not a context issue.",
  "transcript_chars": 4019,
  "ingested_at": "2026-09-10T13:30:06.431980+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 61,
    "upvote_ratio": 0.81,
    "num_comments": 33,
    "author": "Frequent-Ad-836",
    "is_self": true
  }
}