> I expect it to cost somewhere in the neighborhood of an experienced dev because that’s the alternative
But there's two ways this could be true: Frontier models costs rise, or (more likely) experience dev costs fall
355 karma · joined May 31, 2015
But there's two ways this could be true: Frontier models costs rise, or (more likely) experience dev costs fall
Example from 15 years ago: https://stackoverflow.com/questions/7335920/what-specificall...
So it's partially AI, sure, but it's also because you're getting the summaries from what is an extremely stupid AI compared to the "good stuff" that's out there.
On the compute side, you can't load most individual experts into even high-end consumer-grade cards (e.g. RTX 5090) because the weights are simply too big.
On the latency side, you need to ship the activations and wait for the somewhat underpowered cards to actually do the matrix multiplications, then send the results back. This means each token takes hundreds of milliseconds or even more, which is borderline unusable.
Finally if you're running a P2P network, the only way to verify that peers aren't lying to you is to run duplicate calculations - I don't see how SHA-256 or signed model state help here unless I'm missing something (What are you calculating the hash of? What exactly is signing attesting to?).
This is one of the few places where borrowing ideas from cryptocurrencies actually makes sense - you could imagine a system where peers place bonds and forfeit them if they lie about calculations. You can look at projects like Bittensor and others for how these kind of things are currently implemented.
Yes, they say it will be a few milliseconds. This service is squarely targeted at algo trading firms.
I went an ungrad school that was top-5 in engineering. But my experience - and in the experience of other people I've talked to - formal undergrad education was, and always has been, a farce. At best, you learn through working on projects that are meaningful to you and learn "how to be an adult" (and later, you learn how to manage the enormous financial debt you acquired). But more typically, it's pure credentialism - no one cares what your grades were, only what school you graduated from.
The amount of actual learning that goes on from classes is minimal, but somehow we can't shift the overton window away from this silly game of grades that don't measure anything meaningful.
After graduating, I've was asked about my grades exactly twice in my life -- once when I applied to a master's program, and at one job interview (the company had a policy of asking about GPA for anyone who graduated less than 10 years ago).
I'm pro-education but anti-school, and all this nonsense makes me this way even more.
This doesn't make sense to me. "A clearer signal of leverage" implies an objective way to measure software engineering output, which has been the white whale of engineering management for the last 50 years.
The premise is just false. The parable might be true when comparing, say, lower class vs lower-middle-class, or lower-middle-class to middle class. But the difference between upper class and middle class is not "spending less money." It's a vastly different net worth that comes from inheritance, building / running businesses, investments, etc.
The boots theory focuses on the costs, but the real difference comes from the income & net worth
The language is not a feature!
I'm a C++ programmer, and even I don't care what language the applications I use are written in.
"Fast" is a feature, not that it's in C++
A transformer-based (but not LLM) chess model that plays like a human. The site right now is very rudimentary - no saving games, reviewing games, etc., just playing.
It uses three models: * A move model for what move to make * A clock model for how long to 'think' (inference takes milliseconds, the thinking time is just emulated based on the output of the clock model) * A winner model that predicts the likelihood of each game outcome (white win / black win / draw). If you've seen eval bars when watching chess games online, this isn't quite the same. It's a percentage based outcome, rather than number of centipawns advantage that the usual eval bars use.
Right now it has a model trained on 1700-1800 rating level games from Lichess. You can turn it up and down past that, but I'm working on training models on a wide variety of other rating ranges.
If you're really into computer chess, this is similar to MAIA, but with some extra models and very slightly higher move prediction accuracy compared to the published results of the MAIA-2 paper
Strong base salary plus quarterly cash bonuses depending on firm performance.
We're a digital asset hedge fund with a 7-year track record of delivering outstanding returns for our investors.
We're hiring a data visualization engineer to build C++ / Python / Qt applications for internal use by our researchers. Previous experience in real-time visualization of large data sets a plus. If you know what a DOM ladder is, that's a huge plus.
More details at https://kbit.pinpointhq.com/en/postings/3aa0f650-a80d-44e4-8...
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Runs on a local LLM, because even using GPT3 costs would have added up quickly.
Currently requires CUDA and uses a 10.7B model but if anyone wants to try a smaller one and report results let me know on github and I can give some help.
Yes, I think it is absolutely 100% fine.
> Noncausal mechanisms, including socioeconomic status and reverse causation bias, have been proposed as contributors to the KIHD findings (Kivimaki et al., 2015). Although differences in socioeconomic status may influence sauna access and opportunities for use, the robust dose-dependent associations observed between sauna bathing and sudden cardiac death, coronary artery disease, and cardiovascular events in the KIHD studies are indicative of genuine inverse associations (Laukkanen et al., 2015a). Furthermore, the KIHD studies were conducted in Finland, where sauna use is deeply rooted in the culture, and saunas are readily accessible (Laukkanen et al., 2015a). Similarly, whereas reverse causation bias figures prominently in observational studies and is a valid concern when investigating links between cardiovascular disease and lifestyle, the KIHD findings were adjusted for potential biases, including lifestyle factors such as socioeconomic status, physical activity, and cardiorespiratory fitness (Laukkanen et al., 2015a).