In software development, there's this concept of agile. You want to get a product in front of users as soon as possible for their feedback. If you think your biggest risk is something else, such as your application falling over when it processes a large amount of data, then you probably want to prioritize a prototype on large data before showing it to customers. Sometimes the biggest risk is technical risk. Like, can you actually build a thing to do what you claim? Not so much will users like it.
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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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