Given these models could not have been trained in the first place if they had to license every line of random fan fiction on the internet, I think distillation also being fair game is a tradeoff everyone should be willing to take (unless they want to decelerate, but that's a different conversation).
This is my number one complaint about the M-series MBP line. Especially true of the cutout in the middle that has points so sharp they can cut you if you accidentally scrape it with your hand.
Why haven’t we seen any queues or the like over the past week then? If it’s truly a capacity limitation why not just boot subscription users to a lower priority queue or limit usage to outside peak hours?
Can you include GPT 5.5 non-pro (extra high thinking I guess) in your comparison? GPT Pro is the "I am willing to torch cash for a sooometimes slighty better result" option, not the one people are actually expected to use daily. That's probably part of the reason it's not in Codex
OOM on CUDA GPUs is relatively graceful (the process crashes). However, on macOS if torch MPS tries to allocate too much memory, the whole kernel will simply lock up and the only option is to reboot the computer. I have no idea why Apple doesn’t reserve memory for stuff like the OOM/kernel watchdog, but it seems they either don’t or there is a bug.
Love me some JSD. Here is a problem most people don't consider with generative modeling (e.g., AI text, image, music, video models): basically all standard pre-training algorithms for generative models (i.e., cross entropy, basically all diffusion/flow formulations) are closer to a Forward KL divergence. In other words, given limited capacity the model will try to stretch itself to cover every mode. This gives you a jack of all trades (lots of knowledge and diversity), but a master of none (you get blurry images and text filled with nonsense).
The real magic in generative modeling comes from the post training process that comes after, which usually (e.g., RLHF) approximates Reverse KL (given limited capacity, try to perfectly cover what you can, but it's fine to drop the rest entirely). This gives amazing results, but is also the cause of AI oddities like the "AI Image Pixar Look", many of the verbal tics of LLMs, and all AI music using the same small set of voices. Jensen-Shannon Divergence sits right in the middle of Forward and Reverse KL and is what many GANs are claimed to approximate. Ideally, it is a better trade-off between diversity and fidelity.
Seems pretty clear, Claude and Codex were getting a lot of free publicity by instructing their models to do the same and MS wanted similar results. However, a bug caused this to be applied to all commits instead of all Copilot-influenced commits.
I bumped from $20 -> $100 today but the Codex CLI lacking code rewind and "you can change files but ask me every time" mode from Claude Code is quite annoying. Sometimes I want to code, not vibe code lol.
My point is you should consider creating truly undetectable audio end to end with AI to be effectively impossible for the foreseeable future (i.e., I would bet money it is still trivially detectable five years from now). It won't be detectable to humans, though, only models.
I train music generation models. They are very trivial to detect. In fact, detecting them then training them to evade detection by the detection model is a big part of training them! But the detectors win instantly without some hardcore regularization. Simply turn that off and you've instantly got a perfect classifier.
This isn't like text classification, the signal many orders of magnitude higher bitrate and so many more corners need to be cut. It's likely going to be nearly impossible or at least not remotely worth it to generate an audio signal that is truly undetectable in the foreseeable future.
What's your reasoning effort set to? Max now uses way more tokens and isn't suggested for most usecases. Even the new default (xhigh) uses more than the old default (medium).
Many of the games that actual kids spend time on are the purest expression of gaming slop (half-broken microtransaction gambling hell with schizophrenic flashing colors). Roblox and Fortnite's Islands system are both guilty of this. The problem is kids don't know any better and don't yet understand the value of money. The obvious response is "parents should handle this" and while I agree, there is no system to let them say "here are Robux/V-Bucks you can spend on quality content (e.g., Fortnite's Battle Pass is very well designed, quality content), but gambling slop is disabled".
In my experience, the Epic Games Store downloads faster, installs more efficiently, and launches games faster than Steam. The social features I actually use (i.e., add a friend, join them in a game) work fine. I'm not aware of any features Steam has that EGS lacks that I actually use frequently (Valve's VR, streaming tech, and Proton are great, but I don't use those frequently). It's not just me, many indie game developers are also big fans of EGS (most recent example that comes to mind are Jeff Kaplan's remarks during his 10 hour stream a week or two ago). Gamers' vehement defense of what is effectively a monopoly continues to confuse me.