sha_r_roh

Yes, this was my misunderstanding of the AGPL license. Makes sense now.

Congrats to anyone who worked on it! However, I'm guessing the cost of just running this team be quite large and not significantly different from the savings (6M), and add on top of it the overhead of maintenance. Payments would not likely be a long-term bet as well, so kind of interesting why teams take up such projects ? Is it some kind of sunk-cost with the engineering teams you already have?

Nice work, looks like a lot of effort would have gone into building something this comprehensive. But what are the problems you are trying to solve, as far as I can see that forma is a mix of few things - it offers google for works features and then some other tools to automate processes. But does it target any specific problems? I think the tool might be too generic for SMBs. From my limited knowledge most popular tools focus on one or two problems, before expanding.

I can add some perspective. One and a half years back I took a 80 percent pay cut to join a 10 member startup. My salary now has improved to almost 60 percent of my previous company. I don't really regret that decision though and here are few things I clearly thought about before making the decision.

1. Learning and growth opportunities at the new place

2. Autonomy around my work

3. People

I think people derive meaning when they see that their ideas are appreciated and users care about the product. I liked the people at my previous job, however the first two items were somehow missing.

It also helped that one of the co-founders at the startup was a friend from undergrad.

> "Slamming hands in drawers hurts; one should not do it."

This is a fact and don't think this is what the author is trying to say. I guess he's more focused on the kind of theories which people throw around as some sort of self-evident truth, when in fact it's a personal truth which they learned through trial and error.

But I guess there's a point in learning from some of these "self-evident" truths (this is more rare though)

This. Any good data science person needs to understand how distributed systems work. They need to be decent at applied statistics. I think an average engineering grad can easily work with simple statistics they learn in Linear algebra and intro to ML. The good software engineering part is what has been brought on in the last few years.

I once saw some code written by a "data scientist". The overall code was non-complex, but the Java/Scala code was the worst of my nightmare. Additionally, I think other engineers have also matured enough to understand that underneath the veneer of data science, the fundamentals do not change much.

I'm Asian (Indian to be specific) and yes I ignored that line as fluff. Marketing though influences a lot of our decisions, sometimes inadvertently.