Local optimization is almost always easier than global optimization. Backwards chaining is often easier, but maybe has the danger that you're only limited to sort of what good example you can find or what thing you can start off and then modify forward chaining. Maybe it's more challenging on average, but maybe it can kind of solve a broader range of problems.
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What is risk-driven development? How should we weigh advice, best practices, and common sense in a domain? What makes some feedback loops better than others? What's the best way to take System 2 knowledge and convert it to System 1 intuition? What are forward-chaining and backward-chaining? When is it best to use one over the other? What are the advantages and disadvantages of centralization and decentralization?
Satvik Beri is a cofounder and head of Data Science at Temple Capital, a quantitative hedge fund specializing in cryptocurrency. He is a big believer in the theory of constraints, and he has a background helping companies find and eliminate major development bottlenecks. Some of his interests include machine learning, functional programming, and mentorship. You can reach him at satvik.beri@gmail.com.
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