axpence

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...

Let's say i want to predict an output `C` by multiplying two distributions `A``B` = `C`.

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?

Can you elaborate with specific examples?

This is such a great point because it underscores the importance of viewing investments with a longer time scale than I am used to.

Are there any commercial applications your team has discovered?

I find it interesting that this tool exists for the Facebook platform and not Instagram. Instagram has a meaningful amount of FB’s ad spend.

Two things:

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".

100% agree.

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.

Can you share some insight into how you are counting mentions of coins on Reddit? For example, the coin Golem has the potential for overlap with the character Golem in Lord of the Rings. Are you browsing a pre-set number of subreddits?

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!

Very insightful; many think of Startup Studios as a recent phenomenon, however (as illustrated in this book) they've been around for a while :)

I am so impressed with your consistency over the years. Kudos and best of luck.