#AKATU

1. Beloved Friends!

Indeed You Are Waiting For The Boomerang To Return! Do Not Be Concerned With The Timing Of The Return, But Know It Most Certainly Will!

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... 😂😂😂

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.

1/ Throughout my startup career, I’ve seen many founders have an aversion to sales. This is a mistake that can kill your startup.

2/ Sales is often viewed as either a saving grace or proof that the product isn’t good enough (because it should sell itself). Neither are ever true. Some common mistakes that result in...

3/ Mistake 1: Hire a sales rep before reaching product/market fit to get your initial batch of customers. This is a mistake because founders need to work through their MVP with early adopters to truly understand what it is they’re selling.

4/ Mistake 2: Reach product/market fit, need to scale, and rely entirely on self-serve. For enterprise products that require big commitments and internal shifts, almost no product is self-explanatory enough to sell itself.

5/ Mistake 3: Make a first sales hire who isn’t scrappy enough to help mold the sales process from scratch. Some salespeople are amazing at their jobs, but not cut out to establish the processes that others end up following. This skillset is what @rdedatta calls a “sales ninja”.