This argument falls apart when you look at Rust and Cargo. uv is literally trying to be "Python's Cargo." The entire blueprint came from a flagship FOSS project.
Rust's development used a structured, community RFC process—endless planning by your definition. The result was a famously well-designed toolchain that the entire community praises. FOSS didn't hold it back; it made it good.
So no, commercial backing isn't the only way to ship something good. FOSS is more than capable to ship great software when done right.
China is not the birthplace of so called '996'. Long before tech scene in China, there are a lot of investment banks doing that in HK especially for junior analysts. Calling 996 a China thing is just orientlalism. Everything bad is Chinese, everything good is western.
That's true. You would think LLM will condition its surprise completion to be more probable if it's in a joke context. I guess this only gets good when model really is good. It's similar that GPT 4.5 has better humor.
This actually makes sense because in the meantime Meta is ditching the open-source (open-weights) direction.
Before the national security narrative took over, the main argument was about "safe" AI, where releasing models as open weights was considered "not safe." Now that no major US AI players release premium open-weights models, the "safety" narrative isn't needed anymore—so cooperating with the US military is feasible again.
Deepseek and Alibaba just published their froniter models in open weights weeks ago. And they happen to be the leading open weights models in the world. What are you talking about?
Interesting that the output price per 1M tokens is $0.6 for non-reasoning, but $3.5 for reasoning. This seems to defy common assumption of how reasoning models work, and you tweak the <think> token probability to control how much thinking it does, but underlying it's the same model and the same inference code path.
This common feature requires the user of the API to implement the tool, in this case, the user is responsible to run the code the API outputs. The post you replied suggests that Gemini will run the code for the user behind the API call.
Also the "project" feature in claude improves experience significantly for coder, where you can customize your workflow. Would be great if gemini has this feature.
This may make sense if there is a centralized force to dictate how much these Chinese foundational model companies charge for their models. I know in the west people just blanketly believes that the state controls everything in China. However it can't be further from the truth. Most of the Chinese foundational model companies like moonshot, 01.ai, minimax, etc used to try to make money on those models. The VC money raised by those companies are in them to make money, not to voluntarily advance state competativeness. Deepseek is just an outlier backed by a billionaire. This billionaire has long been given money to various charities by hundered of millions per year before deepseek. Open-source SOTA models are not out-of-character move for him given his track record.
The thing is, model is in effect a piece of software that has almost 0 marginal cost. You just need a few, maybe even one company to release SOTA models consistently to really crash the valuation of every model companies because every one can acquire that single piece of software without cost to leave other model companies by themselves. The foundational model scene is basically in an extremely unstable state readily to return to a stable state of the model cost goes to 0. You really don't need the state competition assumption to explain the current state of affairs.
I have always suspected that the o1-Pro is some kind of workflow on the o1 model. Is it possible that it dispatches to say 8 instances of o1 then do some type of aggregation over the results?
What user experience are you talking about? Chatbot? Or software in general? Cause Tiktok beats Facebook out of water. Chatbot for English communities sure, I also prefer Claude over Deepseek in terms of project support and UI. But this is because they are focusing on Chinese communities, Doubao has much better features that is used by Chinese. It's not really comparable even if all US chatbots were accessible in China. Once LLM tech slows, I am sure Chinese chatbots would beat the American ones in terms of user experience.
Yes, it is fascinating that humans can have such seemingly fundamental differences in how they function 'under the hood.' I also have a friend who is highly intelligent—they earned a STEM PhD from one of the best universities in the world—yet they struggle to follow complex movie plots, despite having a photographic memory. It would be interesting to develop mirror LLMs (or Large Anything Models) for all these different types of brains so we can study how exactly these traits manifest and interact.
Before Deepseek, Meta open-sourced a good LLM. At the time, the narrative pushed by OpenAI and Anthropic was centered on 'safety.' Now, with the emergence of Deepseek, OpenAI and Anthropic have pivoted to a national security narrative. It is becoming tiresome to watch these rent seekers attacking open source to justify their valuations.
I usually add "upgrade": `uv tool install --upgrade --python python3.12 aider-chat`. So that it will upgrade the version to the latest if the current one is not already.
I think it will be more akin to o1-mini/o3-mini instead of r1. It is a very focused reasoning model good at math and code, but probably would not be better than r1 at things like general world knowledge or others.
Does this require nightly? If so, #[warn(clippy::wildcard_enum_match_arm)] will do the samething but no need for nightly, and from clippy instead of rustc natively.
Indeed, the blog mentioned in the other comment showed part of 3FS code was completed at least since 2019, when this was still a project of the quant funds. In HFT, you tend to dogfood a lot of the things to achieve low latency, high performance, sometimes just because HFT system just need to do one specific thing, and those off the shelf stuff usually cater for a lot wider scenarios where HFT doesn't really care about. Here you see similar case which they focus specifically on loading large amount of data during training, and implement that to the extreme.
I think these kind of open-source is really showing their objective of achieving efficiency in the industry. The reason is this kind of software benefits a lot to the big guys serving the model (competitors to Deekseek themselves if they are interested in being a provider) rather than to the general open-source community that wants to learn and tinker or serve model in consumer hardware.
My personal experience is that R1 is smarter than 3.5 sonnet, but 3.5 sonnet is a better coder. Thus it may be better to let R1 to tackle the problem, but let 3.5 sonnet to implement the solution.
It would be hilarious if this scenario played out.
OpenAI starts as a nonprofit, aiming to benefit all humanity. Eventually, they discover a path to AGI and engage in intense internal debates: Should they abandon their original mission and chase profit, knowing it could bring generational wealth? They ultimately decide, "To hell with humanity—let’s go for the money."
As they pivot to prioritizing profit, DeepSeek emerges. Staying true to OpenAI’s original vision, DeepSeek open-sources everything, benefiting humanity and earning global admiration. Unintentionally, this move tanks OpenAI’s valuation. In the end, OpenAI fails to become the hero or secure the massive profits they chased. Instead, they leave behind a legacy rebranded as "ClosedAI"
So this is an in-house benchmarks after their undisclosed partnership with a previous benchmark company. Really hope they do not have their next model to vastly outperform on this benchmark in the coming weeks.