emmap21

At the end, Elon Musk put Dogecoin at the right place. This crypto is completely a joke to tell how superficial it is. Why do everybody put money on that useless tech rather something real, e.g. save people lives?

FAANG does not care about idea - they care about spreadsheet. If you have something that they wanna buy, it also should make their spreadsheet works much better.

Anyway, if you wanna get hired by those companies, just apply for a job.

They do send a rejected email. Anyway, the odd of success if you are selected by YC is not much higher. You might have a brand, but in startup world, name or ideas or noun do not matter only executions.

Differential equations vs Machine learning based model/data -- I am wondering about the trend of scientific progress in the next few years. Which one is better? Let's time tell us the truth. Science is not the truth itself, but a journey to find a truth.

From user experience, I personally don't see a strong benefit of crypto. It always looks like a Ponzi Scheme to me. When early users are people driven by greed than productivity, it is a HUGE problem.

This should be a responsibility of those who run algorithms. As a data scientist and growth hacker, I have seen so many situation that a platform has been manipulated by a dark force of marketing. However, a response from leaderships are too underwhelming.

Remote work fits to a small close-knitted team less than 5 people. More than that, there is so much that it's hard to fill.

You don't own the work itself, but a certificate issued by a third party for that work.

This means,

* Everybody still can copy and distribute it without your permission.

* No protection by Law/ Policy in Digital World. I don't know if NFT will do that for you on behalf.

* Like many others say, they trade NFTs, but not the work itself. Say, NFT of 50 Million valuation between two entities. Very fancy numbers ...similar to a board game.

This is more like a conceptual framework. Similar to apply social studies to learn about machine behavior. In this case, it's a fancy AI. This appears to me as a waste of time to kill the wrong problem.

Here is my three step process:

1. Accept that you feel bad about failures.

2. Accept that what happens did happen.

3. Re focus on the present. Today is today.

A few key notes:

* Get re-started despite how little you can do.

* Whatever it is, just do it.

* Be persistent.

This works.

It sounds so painful as someone all-in this area. But I have to agree on a task of overdoing with fancy models. Nevertheless, the most common ML algo in industry is still linear regression along with boostraping.

ML/DL is at the exploratory phase for most companies. I have no surprise when seeing this post. Nevertheless, this also open new opportunities in other domains and new kind of business based on data. I have no doubt.