Inside the AI Workflows of Every’s Six Engineers
Source: https://every.to/p/inside-the-ai-workflows-of-every-s-six-engineers Author: every.to Date: 2025-10-29
Summary
Profile of how six engineers at Every.to (AI writing/thinking tools company) actually use AI in their daily work. Diverse perspectives: ML engineer, product engineer, frontend developer, etc. Useful as a ground-truth sample of real AI tool adoption.
Key Claims
- Universal adoption: all six engineers use AI tools daily, but for different tasks.
- Most common uses: code generation, code review, documentation, and “rubber duck debugging” (explaining problems to the AI helps clarify thinking).
- Divergence: ML engineers trust AI for boilerplate but not for model architecture decisions; frontend engineers trust AI more broadly.
- Trust calibration: everyone has developed implicit trust models for which outputs need close review and which can be accepted.
- The meta-observation: the best AI users are those who know when NOT to use AI — they reach for it selectively, not reflexively.
Concepts
- Coding Agents — practitioner usage patterns
- Cognitive Debt — trust calibration as cognitive debt management