But I think training models using copyrighted content is stealing in the first place. It's not fair use, so it should be banned entirely.

Many stay-at-home moms (including my mom) seemed to enjoy her visit. She and my mom talked a lot, sometimes for hours (I still can't figure out how she completed her job when she spent so much time with one person). They chatted about recent events, like the daughter of the fisherman gave birth, the great-grandpa of the liquor shop died of cancer, a newly opened restaurant in the nearest town sucked, and sometimes shared even personal struggles or family matters. It really helped a lot of people combat mental struggles caused by the isolation of being traditional stay-at-home wives in a super rural area. The only downside was anything you shared with her would be spread in the entire village before dawn.

https://www.vibekanban.com/

  > Surely they know the risks, and surely people will be just as responsible with AI

  > The critical difference between AI and a tool like a calculator, to me, is that a calculator's output is accurate, deterministic and provably true.

In current situation, by vibing and YOLOing most problems, we are losing the very ability we still need and can't replace with AI or other tools.

  > The moment you hand them something fuzzy, though, like ...
  > “we should probably think about scaling”,
  > that’s when you see the difference.

I used Python's Instructor[1], a package to force the model output to match the predefined Pydantic model. It's used like in the example below, and the output is guaranteed to fit the model.

    import instructor
    from pydantic import BaseModel

    class Person(BaseModel):
        name: str
        age: int

    client = instructor.from_provider("openai/gpt-5-nano")
    person = client.create(
        response_model=Person,
        messages=[{"role": "user", "content": "Extract: John is a 30-year-old"}]
    )
    print(person)

    class MathAnswer(BaseModel):
        value: int
        reasoning: str

    answer = client.create(
        response_model=MathAnswer,
        messages=[{"role": "user", "content": "What's the answer to 17*4+1? Think step by step"}]
    )
    print(f"answer={answer.value}, {answer.reasoning}")

    67, First I calculated 17*4=68, then I added 1 so the answer is 69

Turned out, because I defined MathAnswer in that order, the model output was in the same order and it put the `reasoning` after the `answer`, so the thinking process didn't influence the answer like `{"answer": 67, "reasoning": "..."}` instead of `{"reasoning": "...", "answer": 69}`. I just changed the order of the model's properties and the problem was gone.

    class MathAnswer(BaseModel):
        reasoning: str
        value: int

ETA: Codex and Claude Code only said how shit my prompt and RAG system were, then suggested how to improve them, but it only made the problem worse. They really don't know how they work.

[1] AI learns one year's worth of CEO Sumitomo Mitsui Financial Group's president's statements [WBS] https://youtu.be/iG0eRF89dsk