I think parent is talking about the US COVID bills that provided liquidity to the stock market and checks to households.
Did UK also do something like that?
We are also still seeing supply chain problems here that started during COVID. Consumption didn't cease during COVID, but production did, so there is a massive backlog. Not speaking retail consumption, but industrial and government orgs also consume resources like equipment, machines, etc. And those assets are seeing persistent backlogs, especially domestic production as US is fully employed and no room to increase production.
I'm not sure how much of a point there is in treating this inflation as region-specific when the economy is clearly globalized to the point where anything affects everything and most countries are also seeing it.
The UK made its own relief checks, as did all EU countries afaik, but to a much smaller degree than the US. Some only to students or as free meal/vacation vouchers.
"YouTubeAI will give every 6-year old child the capability to be my boss, and the guidance to get there" - Jeff Dean, Head of Google AI
Youtube AI is the the most powerful AI model ever built, with 1 trillion parameters, trained on a staggering 1 billion internet videos and the entire google knowledge graph using 20,000 GPUs and 10,000 TPUs, taking 2 billion GPU hours, equivalent to over 226,000 years of continuous computing on a single GPU, and costing over $100 million to produce.
YouTubeAI is set to transform various industries with its groundbreaking capabilities:
Dynamically generated educational videos: that adapt to students' level and learning style to enhance education, such as using dynamic scenes to illustrate different fencing techniques for a 6-year-old beginner named Sophia.
Personalized video content that goes beyond traditional recommendations to bring you never-before-seen videos tailored specifically for you. With YouTubeAI, you'll receive customized content recommendations for YouTube and Netflix based on your viewing history and behavior.
Automatic caption and description generation for videos to make content more accessible, such as transcribing the audio of a breaking news video for a news media organization called The Daily News.
Adaptive and personalized gaming experiences that adapt to user behavior and preferences, such as increasing the difficulty of a racing game as the user gets better, for a gaming company called Game On.
Interactive and engaging marketing experiences that go beyond traditional advertising, such as offering a virtual tour of a Burger King kitchen for users who frequently watch cooking videos.
Real-time video analysis for sports analytics such as analyzing live video feeds in real-time to deliver more accurate and comprehensive sports analysis, for a sports analytics company called ScoreMax.
1. Creativity can be achieved at faster speeds than humans can consume.
2. We have no evidence to say AI and the techniques behind it wont keep improving (there are already so many low hanging fruits, alot of it is infra problems and the other half that we can't even imagine can probably be solved by AI better than humans can)
Caveat - The best type of mentors for founders are other founders. VCs, Incubators, are not optimal mentors, rather those are key folks to have in your pocket.
My suggestion for all founders - find a mentor who is a founder & builder.
Let's say you're one of the hundreds of thousands of solo devs/founders making some money - but less than $1000/month on your SaaS startup.
You don't want VC, you just want growth and the ability to do your startup full-time. Why would someone mentor you in that case? What's the benefit to them?
"paying it forward"? Getting personal satisfaction from helping others? E.g https://www.indiehackers.com/ ? I haven't spent a lot of time there but it seems to be a community of exactly the kind of founders you describe trying to help each other out.
You can overfit your data by coming up w/ a model that is very specific to it. Consider an n dimensional data set where one variable is a boolean.
A researcher might notice that the True and False cases are different enough to warrant their own algorithms. Say they now fit a lower dimensional line to the True and False cases.
They've just performed gradient descent but the grad student was the gradient. It's a common joke in CS/ML departments.
(This is an obviously simple example, but the point is that hyperparameter tuning and algorithm selection _are a part of your algorithm_ and the data you're looking at while doing so is part of your training set)
I have a similar usage pattern. I have 200+ essays (1000+ words) on interesting thoughts. Often they are more incomplete than I would like. These aren't necessary notes but more ideas sparked by recent literature I have read.
Lets take this offline? - Would be delighted to chat.
