A space for my thoughts, research notes, and articles about artificial intelligence, technology, cybersecurity, and everything in between. Knowledge should be shared.
Un saggio personale sul recursive self-improvement, sul tempo umano e sulla possibilità che l'intelligenza artificiale acceleri più rapidamente della biologia, delle istituzioni, della cultura e del lavoro.
Read Thoughts →Building ML systems isn't like building regular software and trying to force traditional development methodologies onto ML projects is a recipe for frustration. Traditional Software Development Lifecycle (SDLC) practices are often too rigid for the messy, experimental reality of machine learning work. Here's the thing: ML is fundamentally iterative. You don't just write code, test it, and ship it. You experiment, learn from data, tweak your approach, and repeat often many times.
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