Thanks for the feedback, will look into it. I have too distinct interfaces for mobile and desktop so it could be that you are stuck with the desktop one that uses click (vs touch).
Thanks! The difficulty is based on the number of steps taken by my solver to solve the puzzle using backtracking and force moves calculations. Humans make better guesses, so it is not just luck ;).
I’ve been watching this space for a while and built my own puzzle with Cursor. Vibe coding speeds things up, but getting the idea, difficulty balance, and UI right is still tricky. Probably depends a lot on the type of puzzle (word-based vs. object placement, etc.).
Stochastic calculus is required to derive closed formulas and approximations used to calibrate SDE models. Similarly to deep learning, the secret sauce lies in the training, less in the inference. The code used by banks is closed source, and the research papers are missing said secret sauce. Calibrating models in a production environment handling correlation, multi-curves, stochastic funding, discrete dividends, etc. is not a solved problem. Interest rate derivatives modeling heavily relies on change of measure, even when using simple models.
Good point. On a serious note, I probably overreacted, sorry about that. I have been working as a derivatives quant for a decade and thought the claim that stochastic calculus was not used/useful was ridiculous.
The comment above is probably from a bot. You do need an extensive understanding of stochastic calculus to maintain quant models code, let alone explain what it does to regulators.