5 ms·
Haven't seen a jump this large since I don't even know, years? Too bad they are not releasing it anytime soon (there is no need as they are still currently the
by sourcecodeplz 6mo ago
Haven't seen a jump this large since I don't even know, years?
Too bad they are not releasing it anytime soon (there is no need as they are still currently the leader).
- ru552 6mo agoThere's speculation that next Tuesday will be a big day for OpenAI and possibly GPT 6. Anthropic showed their hand today.
- enraged_camel 6mo agoThat does not sound very believable. Last time Anthropic released a flagship model, it was followed by GPT Codex literally that afternoon.
- cyanydeez 6mo agoYa'll know they're teaching to the test. I'll wait till someone devises a novel test that isn't contained in the datasets. Sure, they're still powerful.
- swalsh 6mo agoMy understanding is GPT 6 works via synaptic space reasoning... which I find terrifying. I hope if true, OpenAI does some safety testing on that, beyond what they normally do.
- notrealyme123 6mo agoThat's sounds really interesting. Do you have some hints where to read more?
- levocardia 6mo agoOh you mean literally the thing in AI2027 that gets everyone killed? Wonderful.
- Turn_Trout 6mo agoAI 2027 is not a real thing which happened. At best, it is informed speculation.
- mgambati 6mo agoFunny if you open their website and go to April 2026 you literally see this: 26b revenue (Anthropic beat 30b) + pro human hacking (mythos?). I don’t think predictions, but they did a great call until now.
- Turn_Trout 6mo agoI agree that they called many things remarkably well! That doesn't change the fact that AI 2027 is not a thing which happened, so it isn't valid to point out "this killed us in AI 2027." There are many reasons to want to preserve CoT monitorability. Instead of AI 2027, I'd point to https://arxiv.org/html/2507.11473 https://arxiv.org/html/2507.11473.
- deleted 6mo ago[deleted]
- arm32 6mo agoOh, of course they will /s
- tyre 6mo agoFrom the recent New Yorker piece on Sam: “My vibes don’t match a lot of the traditional A.I.-safety stuff,” Altman said. He insisted that he continued to prioritize these matters, but when pressed for specifics he was vague: “We still will run safety projects, or at least safety-adjacent projects.” When we asked to interview researchers at the company who were working on existential safety—the kinds of issues that could mean, as Altman once put it, “lights-out for all of us”—an OpenAI representative seemed confused. “What do you mean by ‘existential safety’?” he replied. “That’s not, like, a thing.”
- actionfromafar 6mo agoAmusing! Even if they believe that, they should know the company communicated the opposite earlier.
- HDThoreaun 6mo agoNo chance an openAI spokesperson doesnt know what existential safety is
- Barbing 6mo agoI did not read the response as... >Please provide the definition of Existential Safety. I read: >Are you mentally stable? Our product would never hurt humanity--how could any language model?
- stratos123 6mo agoThe absolute gall of this guy to laugh off a question about x-risks. Meanwhile, also Sam Altman, in 2015: "Development of superhuman machine intelligence is probably the greatest threat to the continued existence of humanity. There are other threats that I think are more certain to happen (for example, an engineered virus with a long incubation period and a high mortality rate) but are unlikely to destroy every human in the universe in the way that SMI could. Also, most of these other big threats are already widely feared." [1] [1] https://blog.samaltman.com/machine-intelligence-part-1 https://blog.samaltman.com/machine-intelligence-part-1
- 6mo ago
- coppsilgold 6mo agoLikely an improvement on: > We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space. Our model works by iterating a recurrent block, thereby unrolling to arbitrary depth at test-time. This stands in contrast to mainstream reasoning models that scale up compute by producing more tokens. Unlike approaches based on chain-of-thought, our approach does not require any specialized training data, can work with small context windows, and can capture types of reasoning that are not easily represented in words. We scale a proof-of-concept model to 3.5 billion parameters and 800 billion tokens. We show that the resulting model can improve its performance on reasoning benchmarks, sometimes dramatically, up to a computation load equivalent to 50 billion parameters. <https://arxiv.org/abs/2502.05171 https://arxiv.org/abs/2502.05171>
- deleted 6mo ago[deleted]
- varispeed 6mo agoSounds like a good opportunity to pause spending on nerfed 4.6 and wait for the new model to be released and then max out over 2 weeks before it gets nerfed again.
- SparkyMcUnicorn 6mo agohttps://marginlab.ai/trackers/claude-code-historical-performance/ https://marginlab.ai/trackers/claude-code-historical-perform...
- codezero 6mo agothe performance degradation I've seen isn't quality/completion but duration, I get good results but much less quickly than I did before 4.6. Still, it's just anecdata, but a lot of folks seem to feel the same.
- refulgentis 6mo agoBeen reading posts like these for 3 years now. There’s multiple sites with #s. I’m willing to buy “I’m paying rent on someone’s agent harness and god knows what’s in the system prompt rn”, but in the face of numbers, gotta discount the anecdotal.
- coldtea 6mo agoYeah, why trust your actual experience over numbers? Nothing surer than synthetic benchmarks
- refulgentis 6mo agoStrawman, and, synthetic benchmark? :)
- codezero 6mo agoYou're probably right. It's probably more likely that for some period of time I forgot that I switched to the large context Opus vs Sonnet and it was not needed for the level of complexity of my work.
- Jcampuzano2 6mo agoA jump that we will never be able to use since we're not part of the seemingly minimum 100 billion dollar company club as requirement to be allowed to use it. I get the security aspect, but if we've hit that point any reasonably sophisticated model past this point will be able to do the damage they claim it can do. They might as well be telling us they're closing up shop for consumer models. They should just say they'll never release a model of this caliber to the public at this point and say out loud we'll only get gimped versions.
- quotemstr 6mo agoThis is why the EAs, and their almost comic-book-villain projects like "control AI dot com" cannot be allowed to win. One private company gatekeeping access to revolutionary technology is riskier than any consequence of the technology itself.
- frozenseven 6mo agoCouldn't agree more. The "safest" AI company is actually the biggest liability. I hope other companies make a move soon.
- FeepingCreature 6mo agoNo it isn't lol. The consequence of the technology literally includes human extinction. I prefer 0 companies, but I'll take 1 over 5.
- scrawl 6mo agoHaving done a quick search of "control AI dot com", it seems their intent is educate lawmakers & government in order to aid development of a strong regulatory framework around frontier AI development. Not sure how this is consistent with "One private company gatekeeping access to revolutionary technology"?
- quotemstr 6mo ago> strong regulatory framework around frontier AI development You have to decode feel-good words into the concrete policy. The EAs believe that the state should prohibit entities not aligned with their philosophy to develop AIs beyond a certain power level.
- lumost 6mo agoIs this even real? coming off the heals of GLM5.1's announcement this feels almost like a llama 4 launch to hedge off competition.
- m3kw9 6mo agonot much of a jump 94.5% / 91.3%
- enraged_camel 6mo agoActually, going from 91.3% to 94.5% is a significant jump, because it means the model has gotten a lot better at solving the hardest problems thrown at it. This has downstream effects as well: it means that during long implementation tasks, instead of getting stuck at the most challenging parts and stopping (or going in loops!), it can now get past them to finish the implementation.
- kkoncevicius 6mo agoWe can look at the same numbers in different way: Error with 91.3% = 8.7% Error with 94.5% = 5.5% Error reduction = 8.7% - 5.5% = 3.2% So the improvement is 3.2% / 8.7% = 36.8%