That slowly changing landscape killed SO. Every year a higher percentage of answers were wrong because they got outdated, and removing duplicates made it worse.
I don’t think anyone in the org wanted to face the fact that the underlying assumptions in their model was flawed. The older an upvote the less reliable it becomes.
This is part of the problem with AI, and probably the vast majority of sites online... even citations to textbooks (god knows how many papers have been superseded or are outdated, also).
I feel that people are not getting that is one of the larger issues with AI and pretty much everywhere people interact online.
Many of these places and projects were things we truly poured our energy into. But people change, just as operating systems and commandline executables do. At the same time, places like that, once they are gone, lose their weight... so any site that was really large or particularly represented a version of the zeitgeist of whatever subgroup, once it goes defunct, is kind of written out of any sort of AI historical lookup.
Also, there's so many answers on SO that a lot of people just use the Search function to find answers, if they choose to use SO instead of google or whatever. It is very useful for older and historical answers.
It is hard to describe the feeling of seeing things getting archived in real life. It will be very sad once SO is deleted from the web. When I needed a good description of a new computer stuff I usually go to Anandtech and now the whole site is now gone and all the knowledge on it. The future generation can't even dig it out of the ground like what archeologist are doing right now to piece some of our history. One day SO will be deleted from the web and all the knowledge it holds just like the old Usenet and Yahoo/Google groups with vast knowledge just deleted.
Consistently catching errors is vastly harder when you’re not the one doing the legwork. Send the wrong file, do a bad copy and paste, or skip one verification step, etc is all it takes for most people to majorly mess up.
For most professionals it’s just safer to avoid having output in your workflow. Keep using AI, but stop copying and pasting AI output.
There’s models created by people with heavy right wing affiliation, but it’s harder than you think to bias models consistently because political beliefs are so broad reaching.
Calling out bias however is a standard practice for groups whose beliefs don’t match reality. You see that on political extremes, but also revisionist history, many religious groups, conspiracy theories, etc.
Elite early education minimal little long term benefit, people tend to revert to the mean over time. The education system has plenty of slack for intelligent individuals to excel, so play the long game.
Health and wellbeing pay the best dividends as you can maximize the nurture aspect of intelligence early and build resilience against unexpected hardships.
The data doesn’t support that assertion. Elite collages do favor elite high schools, and elite high schools do favor elite middle schools etc but each link in the chain dilutes the impact of the steps further up the chain. Kids have many opportunities to jump up the chain before college and many to fail to keep up with the elite track. Which is why statistically elite preschools have such a minimal impact.
Of course that’s adjusting for the role of the parents which impacts attendance at elite schools but is obviously not impacted by their kids attendance.
I don’t know why humans even bother talking to each other anymore. Go ask AI why your argument is wrong.
I already did it for you: the numbers I gave you are already conditioned on all that. You’re making the false assumption that moving from each tier is a low probability event. It is not. The retention from elite elementary to elite high school is very high. It’s not some hyper improbable funnel.
You didn’t give any numbers, just stated things very confidently without actually reviewing any of the relevant literature.
> the false assumption that moving from each tier is a low probability event
Doesn’t need to be a low probability events, multiply probabilities and the result becomes less likely every single time. And that’s of course ignoring the underlying reasons why the probability at each stage isn’t low in the first place. A smart kid at 6 is unlikely to turn into a dumb kid at 16 etc.
LLM’s are terrible at following the rules of Chess when playing a game. Determinism isn’t helpful because valid moves are so dependent upon the total state of the board.
Programming state gets way more complicated, but code is generally written to help people understand it greatly simplifying the process. Feed in “cursed” code leveraging obscure language features in creative ways and LLM’s and most people fail.
LLM's on their own might fail, but coding agents will succeed because they will be able to write scripts which simulate the states correctly.
You're right that it's worth considering in the same way as the letter counting tricks, since coding agents can get around that in the same way. But I would say the letter counting has the advantage that it at least is easy for humans.
I am just as interested in making the language hard for LLMs as for LLMs+coding agents though.
LLM's were terrible at chess last year. Today, they can't beat stockfish but they can play competitively with the top 2% of chess players. Next year, who knows.
Look at the “legal %” field on that chart. That’s utter trash even counting per move vs games without errors. Further they outperform vs bots who stick to main lines more than normal players, actual performance vs humans means both lower ELO and more errors.
Note: * = Anchor player (fixed rating, used for calibration)
HOA’s kill any character an area has. The fees more than offset the economic benefits of joining one. The damming economics largely comes down to local governments skipping road maintenance without giving you a discount on your taxes.
Net effect you lose money living in a worse location.
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