There's no cabal keeping small creators small. Small creators need to make better content, i.e. provide value to their audience, and they will grow.
Making content isn't about sharing every little thought. It's about being consistent, finding a niche, and providing value. It's just like running a business.
I feel like you're being a little myopic if you feel that all of programming is logical. Look into the probability stuff like machine learning. Read books from Nassim Taleb like Antifragile to understand that it's very easy for us to convince ourselves that logic and increasingly complex models solve everything. In reality there are many meta-strategies you can use like "via negativa" to still be rational, but make the world appear less crazy, and make the crazy in the world look more obvious, and avoidable.
Yeah, tree based models are great for tabular datasets that are primarily numeric, with only a few categorical variables. But as soon as you categorical variables have a 1000+ potential values that need 1-hot encoding or if you have any natural language text associated with your rows, deep learning almost always outperforms, especially if you have over 50K instances in my experience.
The major downside of DL is the slow training, and therefore slow iteration feedback loop. Couple that with an exponentially growing number or hparams to tune, and you get something very powerful but costly in terms of time to use.
But if you want the best possible accuracy, and data collection isn't expensive, DL is the way to go. Just expect to spend 10x the amount of time tuning it vs trees to get a 10% to 20% reduction in error.
>categorical variables have a 1000+ potential values that need 1-hot encoding
You typically do not need to 1-hot encode categorical variables as the common implementations like LightGBM and Catboost have native efficient ways to handle them. Googling around I can't easily find cases where people get better results with GBM+one-hot and I haven't either, though I haven't worked with 1000+ values categorical variables much.
>deep learning almost always outperforms
This doesn't the case in the article we are commenting on, nor on Kaggle but given that DL models occasionally (though rarely) outperform I'm willing to believe this is one of those cases. Any recommendation on which DL models in particular I should test this claim?
Just ignore them. On social media, ignoring a stranger is not rude. It's the default. And a perfectly valid strategy for dealing with the volume of people asking for your valuable time.
Obviously, when it's someone you know or important to you, give them the courtesy of a polite decline or say you're very busy and will let them know if anything changes in the future.
But for strangers: ignore if you don't want to interact.
Congratulations on getting your first 100 paying users. Most startups never make a single dollar.
How long did it take you to hit that number? Many of us devs want to do SAAS but don't understand what's a reasonable amount of time to expect until we can get "ramen profitable".
It took me around 1.5 years (at first part-time and then full-time) to get to 1.5k-2k€/month with https://webtoapp.design
Probably quite long but I had a lot of stuff to set up to get the product automated (and I did barely any marketing, basically just wrote a couple random blog posts for SEO).
I can't find it in the guidelines right now, but it's disallowed by both Apple and Google. I've personally seen Apple reject apps because of that before (not an app that I created though, since I follow the guidelines)
I found it, it's a restriction on "app builders" or "app generators". If I manually build an app for a client, it's fine for me to publish it in my account. Glad I didn't do anything wrong.
Hey Minimaxir again! This is really funny because I built an entire company around this idea: optimizing titles for YouTube videos: https://CreatorML.com.
Similarly, the user can use GPT-3 to generate titles, and a secondary ranker model scores them for estimated viewership. I use a regression model however.
Anyway, just funny that "great minds think alike" :)
Couldn't you use something like Huffman encoding to compress the entire will and print it on a single sheet of paper, then the estate could use OCR to scan and reverse the compression? If you're using ASCII characters only, I assume the compression could be very high.
Indeterminate pessimism also means they aren't taking economic risks, whereas many Gen-Z are becoming content creators or starting businesses with their savings, so it could be more determinate optimism. Possibly naive, but time will tell.
Making content isn't about sharing every little thought. It's about being consistent, finding a niche, and providing value. It's just like running a business.