cweill

Yes but if you're in a Jupyter notebook, you may not be directly connected to a DB. If you're using pandas, this unlocks some scalability before needing dask and a cluster.

If you ever need to join two large dataframes, but are OOMing on the join, write them to disk as parquet files then use DuckDB to do the join. It's amazing what you can do on one machine thanks to DuckDB.

Do you think he was on garden leave? Some kind of non-compete?

I also have this question. Is the RL MDP actually encoding cause and effect? Or just learning (bidirectional) correlations between states and actions?

I wonder if Pearl thinks that RL replicates his do-calculus under the hood, or if that's an innovation we're missing.

For reference, most gaming channels in the US have an RPM of $4-$5. I'm guessing their RPM is $1 because the channel owner is based in Europe. I imagine toy channels have higher RPMs in the US.

How far back can this go? Would you be able to do 1-3 years of daily data?

Having been on both sides as a customer and foundee, I love subscriptions.

Every business needs a business model. Subscribers is a very clear one. I'm untrusting of any service that's free and doesn't rely on ad-revenue, because they have incentive to make money in other shady ways like selling your data to third-parties who will use it against your best interests.

Subscriptions are a nice business model because they are predictable costs/revenue to both customers and the business.

What I don't like are subscriptions that don't grandfather you into higher pricing, and pay-as-you-go plans, because it's so easy to forget about then and their rules and get charged a nasty bill later.

As a customer IMO subscriptions are the lesser of all the evils, and the best alternative to ads, eg YouTube Premium.

Google colab is $49/mo to get an A100. Trust me when I say you can build multimillion-dollar ML companies with just that (and maybe $100 extra dollars per month of spot credits).

I disagree with this comment, and anyone reading it should take it with a big grain of salt. Let's go back to 2016 and replace "LLM" with "Reinforcement Learning". Everyone thought every problem could be solve by RL because it's a looser restriction on the problem space. But then RL failed to deliver real world benefits beyond some very specific circumstances (well defined games), and supervised learning is/was still king for 99% of problems.

Yes, LLMs are amazing but they won't be winning every single Kaggle competition, displacing every other ML algorithms in every setting.

So many accomplishments are the result of someone just putting one foot in front of the other with a vague idea of where they're going. Hopefully, their north star is something others find respectable once achieved. They're then labeled "geniuses" if it works out and "fools" if it doesn't. Accomplishments are only impressive with hindsight.

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.