#CHALET 239 . 11% up. Power of Stan Weinstein strategy.

For fun I made a Robinhood account. It took me 5 minutes to sign up, get approved and transfer funds for trading...

Here's some details... Read on.

...it took me another 5 minutes to get qualified for options.

If I share a sign up I get "free shares" and I can share as much as I want up to 500$ worth.

The main UI has a prepopulated "list" of stocks/crypto to watch and links to news.

Clicking a stock opens a "trade" button that looks like this.

Notice options is first, buy second.

Options took me to an approval process (which took about a minute) and now away I go trading Baba options.

Buying stock looks like this. It emphasizes fractional shares.

So, I want to talk about how sexual harassment and fair pay are linked at Google (and beyond), because I think that's an angle that isn't being highlighted enough in the coverage of these walk-outs. I'm going to frame it largely around my personal experience there. Thread.

When I was sexually harassed by the director of the area I was working in, I was afraid to report it because I was worried that "getting him in trouble" would result in the subtle retaliation of missed leadership opportunities.

I wanted to continue working on the team I was on, because I'd gained a lot of very deep knowledge and expertise in that area, as well as reputation and camaraderie with the other folks working in that area. I didn't want to make the situation more "difficult."

To get promoted at Google, several need to happen: 1. you need opportunities for ownership and leadership above your current level (basically, opportunities to show you're working at the next level you're trying to get promoted to). The work you're "assigned" has a big impact.

2. You need glowing reviews from peers, *at or particularly above the level you're hoping to get promoted to.* Basically, you need people a lot more senior than you to say you're doing awesome work.

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.