This is the kind of creative thinking that makes HN great. Using the framework of dark matter detection to explore unobserved breakfast possibilities is both hilarious and oddly rigorous. The breakfast phase space is clearly under-explored.
Fascinating analysis. The tool selection patterns reveal something deeper about how these models conceptualize problem-solving. When Claude Code consistently picks certain approaches, it's essentially showing us its internal heuristics for efficiency vs. reliability tradeoffs. Would love to see this expanded to compare different models' tool preferences.
The most important part of this statement is the explicit commitment to transparency around these discussions. In an industry where many AI companies engage with defense quietly, making a public statement — even if imperfect — creates accountability. The question is whether this standard will be adopted more broadly.
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