I'm with you. I haven't materially been more satisfied with the code or reasoning with 4.8 than I was with 4.7. But I'm also not vibe coding, I'm reviewing all of the output. Maybe 4.8 has been making fewer mistakes that I otherwise would have corrected on, but I was perfectly happy going through a few iterations with 4.7 to get it over the finish line. This trend just has me startled and I'm now realizing that my workflow will need to shift to open-weight models very soon. They're cranking the costs and there's no way I can get my employer to cover what's apparently become $2k a day in token use.
Anecdotally this tracks what I've felt over the past month, though I haven't rigorously quantified it. I've just been burning through my quota considerably more quickly than I was a week ago. Hitting limits I didn't before. I hit my weekly max yesterday, it resets tomorrow so I asked my admin to add $50 to my overage limit so I could bridge the gap. Burned that in an hour, I was astounded. Two weeks ago that much bought me two days of work. I asked for another $10 so I could simply have Claude dump handoff notes that could be picked up by Codex, and got through 4 of the 10 agents I'd had running before running out. When it resets tomorrow I'm setting the default back to 4.7, my strong suspicion is 4.8 was designed to lighten the load by burning more quota (not necessarily tokens) on the backend. I don't understand the mechanism but they're clearly putting the squeeze on power users. Curious what others have experienced.
This view of the world puts everything on the individual. It might be worth reading up on structuralism to balance that perspective out a bit. I'm somewhere in the middle of the two extremes myself, but surely one must acknowledge that there are larger systems at play that can constrain an individual's ability to "optimize".
The automation one is so true! When I first deployed a huge job to MTurk, with so much money on the line I wanted to be careful, and I wrote some heuristics to auto-ban Turkers who worked their way through the HITs suspiciously quickly (2 standard deviations above the norm, iirc) - and damn did I wake up to a BUNCH of angry (but kind) emails. Turns out, there was a popular hotkey programming tool that Turk Masters made use of to work through the more prized HITs more efficiently, and on one of their forums someone shared a script for ours. I checked their work and it was quality, they were just hyper-optimizing. It was reassuring to see how much they cared about doing a good job.
I used MTurk heavily in its hey-day for data annotation - it was an invaluable tool for collecting training data for large-scale research projects, I honestly have to credit it with enabling most of my early career triumphs. We labeled and classified hundreds of thousands of tweets, Facebook posts, news articles, YouTube videos - you name it. Sure, there were bad actors who gave us fake data, but with the right qualifications and timing checks, and if you assigned multiple Turkers (3-5) to each task, you could get very reliable results with high inter-rater reliability that matched that of experts. Wisdom of the crowd, or the law of averages, I suppose. Paying a living wage also helped - the community always got extremely excited when our HITs dropped and was very engaged, I loved getting thank yous and insightful clarifying questions in our inbox. For most of this kind of work, I now use AI and get comparable results, but back in the day, MTurk was pure magic if you knew how to use it to its full potential. Truthfully I really miss it - hitting a button to launch 50k HITs and seeing the results slowly pour in overnight (and frantically spot-checking it to make sure you weren't setting $20k on fire) was about as much of a rush as you can get in the social science research world.
I got this one a few months ago and have been running it in my basement directly under my living room, separated only by the floor and a bit of insulation. Can't hear it at all. It's been working well and it's a fun low-investment hobby. I live on a glacial moraine so there are lots of unique rocks in my backyard, and my son enjoys digging for them. https://a.co/d/4HSnVVX
Hard disagree. I gave up a top-10 engineering scholarship and switched to liberal arts largely because my entire curriculum was predetermined in the former. Five courses in calculus and two slots for electives in your entire undergraduate schedule - that doesn't teach you how to think. Political philosophy, symbolic logic, comparative history, econometrics - having the freedom to explore and dabble and push yourself into new ideas instead of being fast-tracked into a pipeline, that's how you learn how to learn. And the "difficulty" is entirely what you make of it. Sure, if you show up to college and want to major in anthropology and put no effort in, you get nothing out. But I saw very quickly that with absolute unfailing effort applied to my engineering degree, I was still going to get exactly one and only one thing out of it. The liberal arts gave me a cornucopia of possibility. I've gone on a human trafficking sting op with the FBI, I've presented my research at the White House, I've been cited by the Pope - that's all wild shit that an engineering degree never would have enabled. Breadth of learning and soft skills matter. I'd be a shell of a person today if not for my liberal arts education. I owe everything to it, and the constant condescension towards non-STEM education in tech would frustrate me more if I didn't run laps around my peers.
Well that's kind of my point - liberal arts and humanities set you up with a very versatile baseline. With a proper education in those disciplines you learn how to think, and that's applicable to a wide range of fields. The woman I dated in grad school at UChicago studied war history and wound up being an analyst for a prominent wine auctioneering firm as a key researcher. My master's thesis was on the meaning of life, and now I'm running data science at a non-profit. So many of my fellow liberal arts grads have gone on to do incredible things entirely unrelated to their chosen subject of study.
Oh I agree with you on that wholeheartedly. I think our society would be substantially healthier if we required civics, philosophy, economics, etc in high school. But if we're already struggling to have evolution taught in schools and we have state boards of education removing references to the slave trade and founding fathers from history curriculum (https://www.theguardian.com/world/2010/may/16/texas-schools-...), expanding liberal arts in public education is a non-starter. Hell, half the country would love to see it wiped from post-secondary education. Best I figure we can do at this point is defend the idea itself to the extent we can - for instance, in Hacker News threads where the liberal arts are being dismissed as an unnecessary lesser-than academic pursuit.
For what it's worth, I have enjoyed a very successful career in data science and software engineering after taking some AP STEM courses in high school, followed by three liberal arts degrees. Many of the best engineers I've known have had similar backgrounds. A good liberal arts education teaches one how to think and learn independently. It's not a substitute for a highly-specialized education in, say, molecular biology, but it provides a really solid foundation to easily pick up more logic-derived technical skills like software development. It's also essential for an informed citizenry and functional democracy.
Are there any models out there for cleaning up an image, not just upscaling? I have a bunch of old photos taken on early low-res point-and-shoots that have JPEG artifacts etc and this seems like something a modern model could easily be fine-tuned to resolve, but every few months I look around and have yet to find anything
This is such a clever way of sampling, kudos to the authors. Back when I was at Pew we tried to map YouTube using random walks through the API's "related videos" endpoint and it seemed like we hit a saturation point after a year, but the magnitude described here suggests there's a quite a long tail that flies under the radar. Google started locking down the API almost immediately after we published our study, I'm glad to see folks still pursuing research with good old-fashioned scraping. Our analysis was at the channel level and focused only on popular ones but it's interesting how some of the figures on TubeStats are pretty close to what we found (e.g. language distribution): https://www.pewresearch.org/internet/2019/07/25/a-week-in-th...
I mean, that's definitely a huge factor, but there IS a limit to how much Amazon can pay their hundreds of thousands of workers and still remain competitive - and I have NO idea where that threshold is, which is why I'm fascinated to see how this shakes out. Either Amazon workers start getting treated better which is great, or the company collapses and turns into an unprecedented case study of what not to do. Either way, I'm grabbing some popcorn!