bjorney

This seems more like a collection of examples than an actual attempt at a definition.

At its core, AI is a program that takes a given input and returns the output that, during it’s training phase, would be expected to minimize it’s error (or maximize it’s reward).

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lol. Did this in my old building - the dryer was on an improperly rated circuit and the breaker would trip half the time, eating my money and leaving wet clothes.

It was one of the old, “insert coin, push metal chute in” types. Turns out you could bend a coat hanger and fish it through a hole in the back to engage the lever that the push-mechanism was supposed to engage. Showed everyone in the building.

The landlord came by the building a month later and asked why there was no money in the machines, I told him “we all started going to the laundromat down the street because it was cheaper”

These people aren’t placing bets on who they want to win, they are placing bets where the house odds differ from the actual expected outcome. The people throwing big money on this are doing it based on actual data (amalgamating polls, etc), not just gut feelings.

If I think Kamala has a 45% chance of winning the election and the bookie is giving her implied odds of 40%, I should take that bet, because even though I think she will lose, I stand to make a 12.5% ROI on my bet. I can then hedge that bet on another bookmaker giving a 48% implied odds, and if enough people do this the bookmakers odds will converge on 44%

but either way I don’t think this “market” knew more than the mainstream media was telling us.

No, but it is a culmination of all the available public information (and some private information you won’t find elsewhere) in a single metric. If you read a single article you would assume there is either a 100% Biden drops out or a 0% chance - if you read every single news article in existence, aggregated all social media buzz, polls, etc, into a statistical likelihood, you would likely come out with a number that closely matches the odds.

Biden was only going to drop out once, so you can’t say how closely these odds matched the actual likelihood on this specific measure, but if you analyze hundreds of predictive markets like this, the implied odds pretty strongly correlate with the actual binomial outcomes

Too bad it still can’t figure out how to do dead ass simple things the old assistant had no problem with, like setting a reminder or alarm

You can just point your domain at your local IP, e.g. 192.168.0.100

Right? it screams wayyyy pre-y2k but MySQL was only release in 95

will it become a relic of the past?

Probably

why YEAR in the first place, who would actually make use of it?

Accounting systems in the 90s that needed to squeeze out every drop of performance imaginable

I expect it won’t

The year datatype is a 1 byte integer, but the engine adds/subtracts 1900 to the value under the hood and has special handling for zero.

If you need to store more than 255 years range, you can use a 2 byte integer, which doesn’t need that special handling under the hood, because with 2 bytes you can store 65000+ years

There are like 10,000 different solutions, but I would just recommend using what’s built in to python

If you have multiple versions installed you should be able to call python3.12 to use 3.12, etc

Best practice is to use a different virtual environment for every project, which is basically a copy of an existing installed python version with its own packages folder. Calling pip with the system python installs it for the entire OS. Calling it with sudo puts the packages in a separate package directory reserved for the operating system and can create conflicts and break stuff (as far as I remember, this could have changed in recent versions)

Make a virtual environment with python3.13 -m venv venv the 2nd one is the directory name. Instead of calling the system python, call the executable at venv/bin/python3

If you do source venv/bin/activate it will temporarily replace all your bash commands to point to the executables in your venv instead of the system python install (for pip, etc). deactivate to revert. IDEs should detect the virtual environment in your project folder and automatically activate it

Reddit has way more data than you would have been exposed to via the API though - they can look at things like user ARN (is it coming from a datacenter), whether they were using a VPN, they track things like scroll position, cursor movements, read time before posting a comment, how long it takes to type that comment, etc.

no one at reddit is going to hunt these sophisticated bots because they inflate numbers

You are conflating “don’t care about bots” with “don’t care about showing bot generated content to users”. If the latter increases activity and engagement there is no reason to put a stop to it, however, when it comes to building predictive models, A/B testing, and other internal decisions they have a vested financial interest in making sure they are focusing on organic users - how humans interact with humans and/or bots is meaningful data, how bots interact with other bots is not

Not with 64gb ram and 16+ cores on that budget

To compare every comment on reddit to every other comment in reddit’s entire history would require an index

You think in Reddit’s 20 year history no one has thought of indexing comments for data science workloads? A cursory glance at their engineering blog indicates they perform much more computationally demanding tasks on comment data already for purposes of content filtering

you need to duplicate all of that data in a separate database and keep it in sync with your main database without affecting performance too much

Analytics workflows are never run on the production database, always on read replicas which are taken asynchronously and built from the transaction logs so as not to affect production database read/write performance

Programmers just do what they’re told. If the managers don’t care about something, the programmers won’t work on it.

Reddit’s entire monetization strategy is collecting user data and selling it to advertisers - It’s incredibly naive to think that they don’t have a vested interest in identifying organic engagement

Look at the picture above - this is trivially easy. We are talking about identifying repost bots, not seeing if users pass/fail the Turing test

If 99% of a user’s posts can be found elsewhere, word for word, with the same parent comment, you are looking at a repost bot

I know everyone here likes to circle jerk over “le Reddit so incompetent” but at the end of the day they are a (multi) billion dollar company and it’s willfully ignorant to infer that there isn’t a single engineer at the company who knows how to measure string similarity between two comment trees (hint: import difflib in python)

If you have access to the entire Reddit comment corpus it’s trivial to see which users are only reposting carbon copies of content that appears elsewhere on the site

Reddit has access to its own data - they absolutely know which users are posting unique content and which user’s content is a 100% copy of data that exists elsewhere on their own platform

Reddit probably omits bot accounts when it sells its data to AI companies

Doing two timelines in parallel

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