Engineering is full of self-expression: the problems you choose, the way you decompose them, what you optimize for, what you find elegant, what you refuse to tolerate.
@edmundmcmillen 1. A quick start option so you can jump in fast and pick your starting cats
2. Better breeding visualization with tabular, sortable data so you can actually analyze and better experiment with genetics
3. A new mode that strips breeding out entirely
trying to use topological data analysis to map the shape of my x bookmarks through mapper + embedding extraction and generated 3 views:
- density: where attention keeps gravitating
- pca: the dominant axes of variation
- centroid: center vs edge (typical -> outlier)
o design de um MVP é fundamentalmente diferente do design ideal, não é uma versão reduzida, é outra coisa. Com AI a limitação técnica cai e os produtos que surgem não são versões melhoradas do que existia, são coisas que antes nem chegavam a sair do papel
antigamente ideia grandiosa = risco alto, porque a versão incompleta ficava muito inferior ao produto final. aí veio o culto ao MVP, minimalismo, conceito mínimo. parte disso era sabedoria (valide antes de investir), e parte era só limitação técnica disfarçada de filosofia
In a world where AI codes better than seniors, the scarce human skill for innovation isn't taste. It's the orientation that drives someone to keep playing with different abstractions and models of the system.
@unclebobmartin Agreed, but semantics do more than raise abstraction, they change the nature of the work itself.
So it is not a neutral upgrade. Each step shifts the mental model and the working process itself, favoring different personalities rather than just making expression easier.