> Unlike the previous GPT-5.1 model, GPT-5.2 has new features for managing what the model "knows" and "remembers to improve accuracy.
Dumb nit, but why not put your own press release through your model to prevent basic things like missing quote marks? Reminds me of that time an OAI released wildly inaccurate copy/pasted bar charts.
Businesses do whatever’s cheap. AI labs will continue making their models smarter, more persuasive. Maybe the SWE profession will thrive/transform/get massacred. We don’t know.
If the claims in the abstract are true, then this is legitimately revolutionary. I don’t believe it. There are probably some major constraints/caveats that keep these results from generalizing. I’ll read through the paper carefully this time instead of a skim and come back with thoughts after I’ve digested it.
His specific thesis is that pods fundamentally clean worse than powder because they're inherently single-stage releases of detergent in machines designed for two-stage releases. Despite this, he still explicitly says that pods have their uses. So I'm unclear on how his goal is "proving that everyone is wrong." Did we watch different videos?
I would bet that it's far lower now. Inference is expensive we've made extraordinary efficiency gains through techniques like distillation. That said, GPT-5 is a reasoning model, and those are notorious for high token burn. So who knows, it could be a wash. But selective pressures to optimize for scale/growth/revenue/independence from MSFT/etc makes me think that OpenAI is chasing those watt-hours pretty doggedly. So 0.34 is probably high...
This might be a dumb question but like...why does it matter? Are other companies reporting training run costs including amortized equipment/labor/research/etc expenditures? If so, then I get it. DeepSeek is inviting an apples-and-oranges comparison. If not, then these gotcha articles feel like pointless "well ackshually" criticisms. Akin to complaining about the cost of a fishing trip because the captain didn't include the price of their boat.
You're right! China is presently terrified of involution. They're dealing with wage deflation and immense debt right now. Beijing is telling its companies to scale back subsidies and stop price wars. The flood of cheap batteries, solar panels, etc is about to change.
Is it possible to run Cursor entirely with local models? My Mac can comfortably run relatively massive models. I would experiment so much more with AI in my codebases knowing that I won't slam into a brick wall due to quotas, connection issues, etc.
That's absolutely wild. I've been loving using the 96GB of (V)RAM in my MacBook + Apple's mlx framework to run quantized AI reasoning models like glm-4.5-air. Running models with hundreds of billions of parameters (at ~14 tok/s) on my damn laptop feels like magic.