It's not cut and dry to differentiate between the act and the wager.
One issue is that prediction markets provide financial incentives to perform actions in the real world. For example, if I want a head of state murdered, I can wager lots of money that they won't be murdered. If somebody wants to earn that money, they can simply bet against me and then murder them.
It's not an dispassionate wager like betting on roulette, it's a wager that directly influences the real world, at least a bit.
Of course you could directly hire an assassin, but that doesn't come with plausible deniability.
I wonder if the training data for some languages has higher quality code. I can imagine some niche languages having a higher standard than, for example Python, which surely has a bunch of random buggy scripts in the mix.
On the other hand, even if that were true, I don’t know how important it would actually be since LLMs can generalise across languages well.
It might be best to pick languages where it’s just harder to screw up, the canonical example being to prefer typescript over JavaScript.
I know, but it could be AI-generated as well, because people can't tell them apart. The point was that even if AI could imitate Monet perfectly, it's not Monet. It's a worthless test.
AI agents have made me far more productive, but the work now feels like drudgery. The most intellectually stimulating parts of the job were automated away first, and I am getting increasingly sick of typing into a chat bot all day.
I got into software engineering because I was always fascinated by getting computers to do stuff, and I really enjoyed the manual task of programming. It's been a dream to earn a living doing something I would do in my spare time. I was pretty good at it too.
I'm not having fun any more, so I've decided to leave the field and become a teacher. I won't earn nearly as much money but I expect to feel more fulfilled, and I hope I can help make a difference to some young people.
I've had an extraordinarily privileged career, and many people never get the luxury of enjoying their work at all. But I'd rather try to enjoy what I do day to day than persist in something that's lost its spark.
I am still coding interesting and complex stuff manually. I just make the AI do the boring stuff like data processing scripts or setting up tests. So it works kind of well for me
That's what I'd like to do in my spare time. My job has become intolerant of that slow pace though now they've drunk the kool aid. I work at a startup and we're expected to produce game changing new features every day.
I'm curious, how do you think people around you there are taking it? I just can't help but feel like that is unsustainable and everyone is just going to burn out
I've had the same experience. I used to enjoy doing software dev, I was good at my job and liked it and did good work. Then the AI push happened and now whenever I'm typing code I think "I wonder if AI could do this for me" except using AI is infantilizing and boring and I don't want to do it. So I feel bad if I use it and I feel bad if I don't use it. So mostly I go post on HN or something instead of working and my productivity has tanked in the last year. Luckily I'm nearer the end of my career than the beginning so it won't be a big financial impact to me when I finally leave the industry, hopefully later this year.
yup! When i did an analysis last month, GitHub is up 89.3% on weekdays and 96.5% on weekends. Incidents touch 62% of weekdays and 11% of weekends. Claude shows the same pattern: 92.5% weekday, 97.8% weekend. Tuesday through Thursday is the danger zone. Sunday is practically a different service.
I had an occasion recently where I was working a lot of late nights/early mornings with AI use. And I'd be getting these instant, beautiful responses, and then, as soon as the sun started coming in the windows, it would take longer and fail more, and by the time the clock struck 9 AM, every LLM had turned back into a pumpkin.
Which service(s) were you using, if you don't mind sharing?
I'm curious if most of the big players including eg Google do this thing of nerfing models or it's limited to more "smart" (read: black box models like ChatGPT.
Inference results for Copilot are also a lot better during weekends than workdays. Its my personal experience so take it with a grain of salt, but I work on personal projects only on weekends mostly due to that brain drain mon-fri of copilot.
How far through did you get? I think it gets significantly better in season 2, and continues improving thereafter. Basically after they starting bringing in bigger overarching storylines.
I made a few false starts where I couldn’t really get through season 1, but after I persisted it was worth it.
Somehow it fizzled out for me somewhere in season 3. These days I can see myself powering through with some skipping, but I would probably rather rewatch The Wire.
It’s hard to tell exactly how much of this is true and sourced vs hallucinated, since it all looks the same. How confident are you that this is largely accurate?
I’m not sure I buy the methodology of “Monitoring 39 public signals”. Claude just loves to make up stats. I clicked the Methodology tab but lost interest quite quickly after realising it was typical overwritten Claude bs.
Less is more. This is a rather overwhelming presentation for something purportedly simple. What exactly am I supposed to care about here, without sifting through screeds of text?
I would have thought that the list of entities with access to Mythos would be hard to get a hold of, and really the only source worth any weight is Anthropic’s own statements.
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