Brief in progress — review the script and demo before recording.
This video introduces Product Engineering and, first and foremost, its practical application.
We need to lay a foundation for the entire week, while remembering that we are speaking to people who identify with their current role. Our job is to inspire them and open them to a shift.
- Definition: explain Product Engineering not with a description, but through examples of well-implemented features from popular products, including why some of them should not exist or how they could be better.
- Outcome over Output: take one feature from a well-known product and explain the difference between outcome and output, and where AI can help.
- Ownership: taking ownership is not obvious if no one expects it from us, and we are sometimes not given enough space to do so. With AI, we can start small and improve iteratively.
- Thinking: many programming activities no longer exist, have changed, or are now handled by agents. That does not happen automatically. We need to change how we think and work: issue tracking, writing specs, the scope of information we consider, and how we shape the environment agents operate in.
- Leverage: we are past the point where LLMs outperform us at most of the work we did for years, but they are still “trapped in a box”: they miss context and have a limited ability to interact with the world. We need to show how good observability, a testing environment, and dedicated machines can help.
We may want to showcase scenarios such as:
- An example of Kody’s killer feature(s) built with a Product Engineering mindset.
- A journey through implementing a feature — simplified if needed, but as practical as possible — while owning the outcome, not the output.