No, it isn't. The private data is largely private data, created by highly-specialized, highly-paid contracted teams of experts for domains finance, swe, consulting, etc.
Reddit data is just not that interesting, that deal is worth like $60m/year. Labs spend 10x as much on computer-use RL environments.
labs invest multiple billion dollars a year each in private data, and that number is growing. internet training data is not where frontier capabilities come from, this view is outdated
it is certainly possible and being done all over the place.
there's a black market that chinese labs use to buy frontier american llm trajectories by the millions through US intermediaries. they're not even particularly shy about it, i have been offered $0.7 per opus 4.8 call
there's also a market for chinese labs sending checkpoints to US companies to be trained on US compute and sent back
i'm surprised that so many people take chinese tech reports about how they train their models at face value tbh
The benchmarks are now the equivalents of SAT/ACT/other standardized exams for humans. They are directionally quite predictive, but with plenty of outcome variance on the margins
Yes, they did. You could also find this information easily.
A company like Andon creates value by exposing interesting AI failure modes, so it makes perfect sense for them to move on to harder problems when the previous ones get saturated.
I think you're just being overly cynical.
You could just look it up on their website leaderboard? The newest Claude model makes over $10k profit over a simulated year of operation, after starting with $500
I don't think this was a simple assumption. LLMs used to be much dumber!
GPT-3 era LLMS were not good at grep, they were not that good at recovering from errors, and they were not good at making followup queries over multiple turns of search. Multiple breakthroughs in code generation, tool use, and reasoning had to happen on the model side to make vector-based RAG look like unnecessary complexity
Kinda funny how we went full circle with you calling me ignorant and illiterate on the basis of not using your preferred terminology, as opposed to the actual, obvious meaning of the subject
one underrated approach more and more people are finding success with:
apple watch ultra as a primary device (optionally with a case for a more phone-like factor)
you can do most things an iphone does, but you can't doom scroll. you don't have to eject out of apple ecosystem, you get payments, 2fa, navigation, notifications. your iphone can remain as a backup that's always in sync for when you need it (e.g. traveling)
> In nearly every case of RTO, there was no recorded dip in productivity associated with the move to remote work.
I am extremely skeptical of this. On the contrary there is a mountain of direct evidence that people barely work when working from home. People have been openly bragging both on the internet and in person about how they do laundry and watch netflix and mow their lawns while looking productive
All you need to do is look at the crowds in the park or lines at the grocery store on any given friday to gauge how much work is being done on wfh days
the funny part about the backlash is that the travel budgets are likely somewhat pareto distributed and under the not-personalized pricing strategy it is the little guy who is probably overpaying for plane tickets, not the corporations and the wealthy individuals
I find the very popular response of "you're just not using it right" to be big copout for LLMs, especially at the scale we see today.
It's hard to think of any other major tech product where it's acceptable to shift so much blame on the user.
Typically if a user doesn't find value in the product, we agree that the product is poorly designed/implemented, not that the user is bad. But AI seems somehow exempt from this sentiment
The tech industry unfortunately screwed up a basic social contract
It is well-understood in every other industry - if you want to be at a prestigious firm, make top compensation, sit in a nice office, work with top-tier coworkers and enjoy excellent perks, you must hustle hard and be unreasonably competitive every day to continue reaping those benefits.
I'm not even talking about back-breaking work - this is true for law, medicine, financial services, entertainment, sports, academia, and everything else I can think of.
After a decade+ run of cheap money and strong demand for talent, returning to broader reality may feel very unfair for many. But that doesn't make it so
I'm thinking a lot about these tools, and the biggest challenge in the space seems to be figuring out the right unit of work for AI to take on.
A signle class/function is too small to be that helpful, a whole app is too big and complex. A whole ticket is also still too big. What's inbetween? If you could divide projects into units of work of consistent complexity calibrated to AI's abilities, then you could probably get really good results.