Didn't sfcompute launch with a vision of something like this and then cancel it because CFTC decided it was running an unregulated futures exchange? How are you getting around regulatory issues like this?
After being in the workforce for decades, this whole issue is just so incomprehensible to me.
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.
> Some engineers, given a fixed token budget, generate exponentially more (and better) output. Other engineers waste their tokens. The variance is enormous, and unlike most performance variance, it is now directly measurable. HR has never had a clearer signal of leverage.
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 100-point buckets are fine-tuned on blitz games from users at that Lichess rating. Lichess ratings tend to be a bit high compared to FIDE/USDF/chess.com ratings. There's a good post at https://chessgoals.com/rating-comparison/ comparing them
> The reason that the rich were so rich, Vimes reasoned, was because they managed to spend less money.
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
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
"We believe that continuous exposure to transportation support systems like cars may lead to the natural human tendency to over-rely on their engines, leading to travelers becoming less motivated, less focused, and less responsible when riding horses."
Kbit | Data Visualization Engineer + 2 other roles | Full time | 100% Remote, Hybrid available in certain locations if you prefer
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.
A Twitter filter to take back control of your social media feed from recommendation engines. Put in natural language instructions like "Only show tweets about machine learning, artificial intelligence, and large language models. Hide everything else" and it will filter out all the tweets that you tell it to.
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.
The best course by NVidia looks like "Fundamentals of Accelerated Computing with CUDA C/C++" which I think used to be publicly available, but is now offered "By invitation only"
> Do you really think it's fine blowing 400 watts because you can't be arsed to think or do not have the creative intelligence to get over the blank page syndrome and have to lean on a crutch?
This seems pretty interesting, do you have links that provide more details on this? Is it all functionality that's available by default on pihole or did you use some mods/custom blocklists/etc?
Want to bet? Literally, I mean. I'll bet you $100 that they will not hire all Rocky contributors in under 3 years and turn it into a rolling distribution.
> 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).