Get the latest tech news
Working on complex systems: What I learned working at Google
This is why recognizing whether a system is complicated or complex is so important: it shapes how we should approach problem-solving.
The components interact in non-obvious ways, and local optimizations can create ripple effects that are difficult to predict, sometimes leading to negative outcomes at the system level. Well-chosen metrics shouldn't just confirm the success of a local change; instead, they should help us make better decisions and ensure meaningful improvements at the system level, not just isolated areas. Indeed, unlike static heuristics, ML models can continuously adapt based on feedback loops and learn from real-world data rather than relying on rigid, predefined logic.
Or read this on Hacker News
