a_wild_dandan

Guessing SH meant Steven Hawking, who kicked the bucket. Metaphorically.

Maybe everyone will migrate to Hugging Bay for downloading models via torrent?

Wow, you weren't kidding. I looked at their chart, and the cost-per-task for Fable is more than double Sol's. And DeepSeek absolutely stomps. Four cents per-task vs Sol's $1 and Fable's $3.

I might need to check out DeepSeek more. I had no idea the difference was this obscene. Makes me wonder if something's off with the benchmark. A 70x cost reduction vs. Fable seems too good to be true.

solve p=np make no mistakes

Backward compatibility with current meatspace tooling.

You’re not stupid. That’s terrible UX. The button is completely disconnected from its modal, and is placed in a bizarre/nonstandard location.

Speaking of tricks, does anyone here know how many angels can dance on the head of a pin?

Taiwan’s geopolitical position is vastly more complex than the fantasy where invasion would follow merely from fab parity.

> 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.

No. I like being able to ignore them. I can’t do that if people chop off their disclaimers to avoid comment removal.

Someone will make a killing on a rechargeable version of this. The ergonomics are a good idea.

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?

How does having management strategies over an alleged addiction imply that it isn’t an addiction?

Intelligence is whatever an LLM can’t do yet. Fluid intelligence is the capacity to quickly move goal posts.

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...

...but then Sora came out.

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.

Thank you for explaining. I was so confused at how AMD was improving Quake performance with duck-like monikers.

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.

> What am I doing wrong?

Providing a woefully inadequate descriptions to others (Claude & us) and still expecting useful responses?

GLM-4.5-air produces tokens far faster than I can read on my MacBook. That's plenty fast enough for me, but YMMV.

Oh absolutely, AI labs certainly talk their books, including any safety angles. The controversy/outrage extended far beyond those incentivized companies too. Many people had good faith worries about Llama. Open-weight models are now vastly more powerful than Llama-1, yet the sky hasn't fallen. It's just fascinating to me how apocalyptic people are.

I just feel lucky to be around in what's likely the most important decade in human history. Shit odds on that, so I'm basically a lotto winner. Wild times.

I'll accept Meta's frontier AI demise if they're in their current position a year from now. People killed Google prematurely too (remember Bard?), because we severely underestimate the catch-up power bought with ungodly piles of cash.

Right? I still remember the safety outrage of releasing Llama. Now? My 96 GB of (V)RAM MacBook will be running a 120B parameter frontier lab model. So excited to get my hands on the MLX quants and see how it feels compared to GLM-4.5-air.

"Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away." - Claude, probably

"You haven't contorted your comically simple query enough to make the brittle tool work. Throw the chicken bones better next time."

Huh? Grammar-based sampling has been commonplace for years. It's a basic feature with guaranteed adherence. There is no "carefully crafting" anything, including safeguards.