#LAOPALA -294.35

Objective is to cross ATH

#Probability

Oleg Karpov pisze, że gdyby Siergiej Sirotkin zdobył w pierwszej części sezonu więcej punktów od Strolla, to jego kontrakt z automatu zostałby przedłużony na kolejny sezon. Tak się nie stało i Williams ma możliwość szukania innych opcji i to właśnie robi. #F1pl

Czego w artykule brakuje, to informacji, że SMP prawdopodobnie przekazało Williamsowi część środków na sfinansowanie sezonu 2019. W przypadku zakończenia współpracy ekipa z Grove będzie musiała zwrócić te środki. #F1pl

To tłumaczy wysokie kwoty jakich Williams ma oczekiwać za fotel od nowego kierowcy. Jest pewnie próg opłacalności i dopóki nie zostanie osiągnięty, to zmiana z finansowego punktu widzenia nie będzie się zwyczajnie opłacała. #F1pl

Tyle można znaleźć w oświadczeniach prasowych... 😂😂😂

Shy people are either overthinking or highly observant of the social setting.

And in many cases it can be both.

'So what do I do?'

You learn the 75/25 law.

'What is that?'

The law of charisma...👇

Let's address the over thinkers first.

Do you have any idea why you overthink?

'Yea. Because others are judging how shy I am.'

Incorrect.

Others are thinking about themselves, not you.

But the fact that you think they are thinking about you, you fuck up the interaction.

Now you stay in your head too much & avoid the present moment.

A major no no in the social world.

Your tendency to think that others are judging you for being shy, coincidentally makes you more shy.

And that's when you start giving off a strange vibration to the social setting.

This is when you begin attracting comments like 'why are you so quiet?'

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.