[atrilla]

I see lots of common points with the "lean" method here (at the expense of technical debt), but can't forget Knuth's advice that premature optimization is the root of all evil.

To me, the utmost important aspect to take into account is the "key" of the business. I had a similar story with my PhD research (AI+NLP): I wanted to focus it on adding value to commercial products. I worked on the core of the implementation, I built it with Java, with a configurable pipeline, plus an online app with servlets. I did attain getting noticed by some companies, but at the moment of closing future lines with them, my adviser told me this was not what he expected from me, and I was forced to get back to the non-useful-goal-centred research of academia. I am still glad I did what I did, regardless of how useless it was eventually, but my knowledge grew, and I learned how important it is to know your business model before you do anything at all. I learned how unbalanced is the academic market, always relying on public funds to survive instead of worrying about building useful appealing stuff. I realized where I wanted to be, so I dropped out and joined the private industry where I now feel very fulfilled.

My present approach goes from small to big, little by little, getting as much feedback as I can so I can fix mistakes asap and prevent them from getting bigger and more difficult to manage. My agreement with your words.