One of the key aspects of feature flag systems is enabling A/B analysis, measuring the impact of features on metrics like click-through rate, session duration, and revenue. This doesn’t seem to be mentioned in the highlights. Is the product targeting these kinds of insights?
A useful way to find ideas is by observing asymmetries. When something appears easy for some people but difficult for others, or vice versa, it often reveals underlying complexity, missing tools, or flawed mental models. Frustration can also be a signal. If something feels unnecessarily painful or awkward while others seem fine with it, there may be an opportunity to simplify, rethink, or build something better. These patterns often lead to meaningful insights or practical solutions.
In my experience, unit tests and logging code generated by LLMs tend to be overly verbose, miss meaningful assertions, and often produce boilerplate that looks correct but doesn’t test or log anything useful. It’s easy to get misled by the surface structure.
At my company, we went from using detailed RFCs to one-pagers, and eventually to writing no formal docs at all. Over time, RFCs turned into artifacts optimized for promotions rather than alignment or clarity. In many cases, people ended up spending more time crafting the perfect document than actually implementing the solution.
