In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.
[5] L. Liang & D. Hale: “A stable and fast implementation of natural neighbour
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The very first step was research. I dove into market analysis to understand the demographic I wanted to serve and studied competitors closely to see where the gaps were. I also began sourcing manufacturers in Italy to determine whether the product could realistically be produced at the quality level I envisioned. Before investing, I needed to validate that the idea was truly viable, so I began taste testing lots and lots of pasta.
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