Software is going more and more from "heavy code" to "no code" environments. This is a cool project that seems to strike a middle ground. A true example of how Software Engineers underestimate how powerful Spreadsheets + APIs can be...
13 karma · joined November 11, 2012
Assuming I am just guessing at the distribution of `A` and `B` (Uniform? Bernoulli? Geometric? Log-Normal?), would I get a better estimate by just multiplying `mean(A)` `mean(B)` ?
Point values suck. However, predicting the mean is often possible/realistic. And I feel like I am taking wild guess when describing a distribution of a data set to be honest.
TLDR: What results in better prediction/guestimate? multiplying incorrect probability distributions? Or multiplying more-correct means/point values?
1- Find solace in the fact that "senior" engineers often spend a significant amount of time searching the web/stack overflow just like you do.
2- Have respect for the code that came before you. Be generous when passing judgment on architecture or design decisions made in a codebase you've adopted. Approach inheriting legacy code with an "opportunity mindset".
Likewise, I think adopting a "how can I create a stack trace/error message" mindset is incredibly important.
Can you add a breakpoint to a certain piece of code? Could you add a try/catch statement somewhere to catch the error?
Far too often good engineers do not have an "active" mindset in hunting for stack traces/error messages, instead, they wait for them to fall like manna from heaven.
Likewise would love some clarity on how you're collecting twitter stream data and properly tagging the data to a respective coin--are you using preset tags?
Would love to see an API endpoint for this type of thing. Kudos!