So they spent $13B + existing $25B in Albany = $38B

For scale comparison I checked TSMC and they will spend ~$35B in R&D and capex in 2024 and it will only grow.

What are the most biggest challenges with frontend/backend hand-off? I'm exploring this area to build products, so curious to hear what you and your team experience.

You don't need to be good at selling.

If you are first starting out then what you really need to do is find/build a product that your customers want. You can't sell a product that no one wants. If you are a master seller and if you somehow manage to sell something no one wants then it won't scale.

Try to have calls with potential customers who are interested in your product. If your product is solving a problem then it should be easy to have calls and the customer will ask to buy it. If not, try to understand what they need and iterate.

Maybe this is just your way of procrastinating. I procrastinate from building a company because I tell myself I'm not a software engineer, but I could find a way to do it if I really really wanted to.

If you still feel like you want advice on selling, happy to offer it since I've been doing for my entire career at startups.

this is more incentive bias playing out. taking the opportunity to push google hangouts.

Switched to the duck 3 months ago. Don't really feel any pain really.

This is amazing! :) I took portions of it before but motivates me to go back and work on it again. Is it just me or the overview notes from Lesson 6 & 7 are the same?

Also wanted to add that as someone who hasn't professionally coded or have a degree in CS, the learning curve was a bit steep (which is why I took a break from taking the course).

Are there any resources or good ways to get up to speed on getting sufficient coding experience/knowledge so that I can really digest the content?

There's a lot of Python learning resources here: https://forums.fast.ai/t/recommended-python-learning-resourc...

My main recommendation is to try to do as many things as you can with code, instead of manually or with domain-specific tools. Even if it's slower at first, solving your own problems with code is the best way to become a productive coder.

I took half of ML before. The pace is a bit slower and topics are slightly different. ML goes into using random forests and focuses more on working on structured data. I think if you have programming experience and are more interested in Deep Learning than the Deep Learning course is great.

With current tax cuts, I can see a delay in down turn compared to what happened before

what was interesting was trump's comment the other day regarding paying down the debt using the gained taxes in gdp growth. I haven't done the math myself, but it's an interesting scenario to thing through if it's possible because I don't think it's one that many people are thinking about.