The human-in-the-loop is tired
pydantic.dev
July 17, 2026
10 min read
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56/100
Summary
Programming with large language models (LLMs) provides both utility and instability for developers. Pydantic offers tools for data validation, AI agent development, and system observation in production environments.
Key Takeaways
- Programming with LLMs is both useful and destabilizing, leading to developer burnout if the challenges are ignored.
- Developers experience fatigue from the need to supervise and review AI-generated code, which often lacks coherent intent despite being plausible.
- The gap between the promise of AI tools and their actual performance has narrowed, creating a sense of unease among developers.
- The integration of LLMs into workflows has resulted in an overwhelming volume of AI-generated pull requests that require human judgment and oversight.
Community Sentiment
Positives
- Using Claude as a code generator has been a game-changer for me; it’s all about planning, executing, and refining — a much smoother process than I expected.
- Many users find that LLM-assisted programming leads to more polished and consistent outputs, making it easier to focus on a single project and achieve satisfying results.
- Building with AI feels nostalgic and enjoyable for some, reminiscent of the early days of programming with just tutorials and books, bringing back the thrill of creation.
Concerns
- The cognitive load of reviewing AI-generated outputs has shifted the experience from rewarding to exhausting; the small victories of coding by hand are lost.
- Some users feel the solitary nature of programming with LLMs can be isolating, lacking the collaborative energy that comes from working with others.
- The AI-written content can be tedious to read, and it seems to lack the human touch, making the reading experience feel laborious and uninspired.
