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I don't know - I've done physical therapy before, and a surprisingly big component includes neurological exercises (vs exclusively strengthening and stretching, like you might expect).
It's feasible to me that thinking/visualizing a motion could improve your ability to recruit those muscles
I wonder how much state-sponsored corporate espionage there is. I'm not an OpenAI employee, but my mental model of tech startups involves a lot of openness/trust (it's one of the reasons people work there).
I have to imagine many techniques/plans/figures are open secrets to nation states.
These are neat - and the source of the many Jev clones we've seen. I think their recent funding round is in part because of their algorithms/data. It remains to be seen if that's a big enough edge to be worth 1 billion+ dollars
I think that extreme prosperity like this (another example - Baby Boomers in the US) is a lagging indicator of geopolitical/industrial power.
Britain was prosperous in the 1800s because of colonialism and the industrial revolution. The US was prosperous in the second half of the 1900's because they capitalized on not being decimated by WW2.
IMO Britain's success in this era was actually sustained prosperity driven by the working class, and the failure to maintain global leadership followed from the victorian attitude of the elite.
I think Jev also innovated on data & algorithms, but it remains to be seen if it's enough to be meaningfully better than traditional LLMs + a few tweaks.
It's hard enough to build them in the US that multiple companies are unironically spending 10s of millions of dollars to try to build them in space
Which cynics could say is marketing hype, but I tend to believe it's extremely difficult to build anything land/energy intensive in the modern United States
I agree it's overhyped, but the transition to a general purpose classifier (vs a narrow scope classifier) is new and noteworthy.
Ie the famous "Hotdog" clip from Silicon Valley [0]
https://www.youtube.com/watch?v=ACmydtFDTGs
You're better off going directly to artificial analysis, this is a feature-poor/misleading/outdated repackaging
Ex. this type of price estimation is quite naive - some models can require 2-3x the number of tokens to achieve the same level of intelligence. Artificial Analysis' own cost per task is a more fair estimation of cost.
I've had Gemini model API use degrade the most out of OAI/Anthropic/Google (often "over capacity" vs true failures)
Not sure on consumer/product use though
It'd be nice if we could change the bureaucratic process, not the interface
I'm interested how to mitigate risk without over-regulating & stifling innovation from exactly the people who are most thoughtful about the risk (which is who we should want innovating & leading).
Even though this article is new, I'm not sure it has materially new ideas vs what some other labs have been claiming for years.
Enforcing safeguards that apply globally to good and bad actors alike so far as proved quite difficult - compute is essentially the only lever, and that's tenuous at best.
Unlike nuclear arms (where functionally there's a limit at MAD), so far we believe that with enough compute, energy, and algorithms, intelligence will continue to scale (and not only scale - but compound)
Do you still believe this considering those companies live and die by the CCP? Would you feel differently if scale was reversed and Meta was the upstart trying to compete with Xiaomi/etc?
I'm right there with you questioning Meta's value for society, but I do believe it has less opportunity for abuse/manipulation
I've been showing people chatjimmy for months - it's incredible. Both reasoning and tool use generation scale with TPS. Imagine 100x more reasoning on a model, or 100x parallel tool uses.
I'll hijack this point to talk about robotics - I think we agree 1) It's not ideal for China to control production for _all_ of the things we use here.
2) What they already are good at they'll continue to improve at, because of domain knowledge & ecosystem
3) The reason they are ahead in the first place is labor cost & the QOL workers will tolerate (and intentional government investment)
4) The only way around this is for the US to invest into robotics (labor) & subsidize american manufacturing (ecosystem)
Without the singularity, I think Frontier labs will offer intelligent model blends. They'll have their own versions of "cheap" models and be expert at using the appropriate amount of compute for a task.
Frontier models need to do everything for everyone. It's expected (though not often done) that smaller models fine-tuned on specific tasks can approach frontier performance on a specific area. [0]
Post-training/fine-tuning is not trivial and having it as a service might make it more accessible.
[0] https://surgehq.ai/blog/training-on-complexconstraints
In my experience everything that affects engineers affects an agent. Good abstractions, reasonably sized methods, good names, principled (intra & inter) service architecture, unit tests, etc.
All of these things have historically been the job of engineers, because it helps other people contribute to the code.
Now it helps other people and other agents contribute to the code.
Related - "The Electric Slide" [0] from NotBoring (a techno-optimist news letter) talks about production of batteries and other parts of the "Electric Stack", and explains where the US/China are relative to each other and the rest of the world, and why China has such a big lead.
[0] https://www.notboring.co/p/the-electric-slide
Basically MCP is little more than a brand name for "APIs LLM's can use". This means more services are creating APIs, because xyz company who's never been super tech forward doesn't want their tools to be obsolete when everyone uses agents.
Overall, I am in favor of this goal. I'm not sure this is the protocol I'd choose to accomplish it, but it's the one people hear about, and the one they're using.
> If you’re going to use an LLM to write me an email, I’d much rather you just send me the prompt; at least then I’d have an idea of what you actually meant to say.”
I'm excited for when Github starts letting me check in the chain-of-thought that produced a line of code, and git blame it like I can with commits.
In a post Claude Code world that's the job of engineers - the engineering is designing good abstractions, scalable systems, and things that are easy to contribute to. This is what the highest leverage senior engineers have always done, the audience has just changed
Engineering has moved up another layer of abstraction (just like we moved past managing buffers & writing machine code)
I think a more realistic model is not fully open source, but apps with extremely open/flexible APIs and data models that allow arbitrary front-ends (likely with a default one provided by whoever provides the API). Kind of like Stripe's model, but the audience of "developers" is bigger since anyone can be a "developer" with Claude Code
Or maybe it will be the more established open source model where the code is free but the maintainers offer hosting/some default product
I'm really interested in how LLMs will enable more customizable, personal software. Our PMs & Designers are writing a lot of code now, and our engineers are spending time figuring out how to make a system that's easy for PMs & designers to extend/add to.
It's not a big leap to apply that model to a company and its customers, where the company builds a well-abstracted, easily extensible base that 1) Customers can easily extend/customize for their workflows 2) Customers can self-host or run fully isolated, much easier (probably not quite there yet, but is a possible world)
The "open source" part is the wrapper on top (up to you if you believe that's meaningful here)
Good engineering has always been about minimizing the amount of effort it takes for someone to understand and modify your code. This is the motivation for good abstractions & interfaces, consistent design principles, single-responsibility methods without side-effects, and all of the things we consider "clean code".
These are more important than ever, because we don't have the crutch of "Teammate x wrote this and they are intimately familiar with it" which previously let us paper over bad abstractions and messy code.
This is felt more viscerally today because some people (especially at smaller/newer companies) have never had to work this way, and because AI gives us more opportunity to ignore it
Like it or not, the most important part of our jobs is now reviewing code, not writing it. And "shelfed" ideas will now look like unmerged PRs instead of unwritten code
I've found it really valuable to pair with people, sit at a computer together while they're driving and using AI. It's really interesting to see how other people prompt & use AI to explore the problem.
Interesting thought (I think recently more than ever it's a good idea to question assumptions) - but IMO abstractions are important as ever.
Maybe the smallest/most convenient packages (looking at you is-even) are obsolete, but meaningful packages still abstract a lot of complexity that IMO aren't easier to one-shot with an LLM
I hear this often, but I think this discounts the fact that this was mostly true for the US/Western Europe at a time where they enjoyed unilateral super-powerism as a result of winning WWII. I'm not sure that kind of prosperity is normal (though I hope it could be).
I'm worried the harsh reality for most humans is that life is often not that easy. And if it is, it won't be for long