closetheloopdev

This is impressive! It'd be both impressive and scary if Astra could edit a Qwen so that that Qwen itself could do this on another Qwen.

I remember when Opus 5.5 came out, we had a discussion about whether it was nerfed a week later [1]. It's not possible to do so with Gemini 4 models since none has been made available the day it is announced.

Without us being able to access Carbon when it is announced, there's no way to know the Carbon we get a few weeks later is the same thing as what has been benchmarked here. I think benchmarks should verify what is benchmarked is exactly the same as what's been released.

  [1] https://news.ycombinator.com/item?id=49901736

The problem is that these names are random and never-ending. They are not categorized like Claude and GPT, both of which have four classes of models each. That's eight classes that I can remember.

There is no way I will be able to (nor do I want to) remember more than four names just for Google or any other single provider, and there's no value in doing so for me. There's nothing unique or distinguished that Gemini does that neither Claude nor GPT can.

I used to land on SO questions and answers through Google. Nowadays I ask ChatGPT or Claude instead of Google, and then go to the links they give me to verify. The links are either to the official docs or some blog posts. Never have I seen LLMs recommend any SO links.

Despite that move, the gravitational pull was quite strong on their stock today after the earnings call.

True. And if Carbon (6) is better than Argon (18), wouldn't that make Hydrogen (1) the ultimate Gemini model, or will the next model jump to another random element?

  O, B, Sc, U, Re?
  Ra, Nd?

I agree. Announcing a new model without immediate availability or a definite release date seems confusing than just doing it later. When Carbon is made GA, OpenAI or Anthropic might just release their new model, and we'd be like: Carbon what?

Having said that, I don't see people excited about using Argon so far, so that might be why Google is announcing this to hype Gemini up a bit.

Tailscale's Response: https://www.reddit.com/r/Tailscale/comments/1xeoj1l/recent_t...

I created a proxy using the 6 compromised eMudhra CA keys (2 of the 8 are code-signing and not for TLS). I initially tried just one (the one with d=4) like in the commit, but it did not seem to work for anything. Once I added all 6 keys, I was able to intercept some.

I was mainly interested in testing which IoT devices are affected and protecting them, not trying to sniff their traffic since it's been a long day. My proxy thus just intercepted and forwarded the traffic upstream using a fresh TLS connection that does not use the compromised keys.

Thank you! Working on the proxy now ...

After my shock and anger subside, I can think more clearly now. I see many have mentioned using this to perform MITM on the IoT devices.

On the other hand, can we not utilize that to also protect the IoT devices? Can we not create a MITM proxy on our own network that proxies all of our IoT devices's traffic and then forwards them to the trusted HTTPS, MQTTS, ... endpoints upstream? Since the proxy already revokes these bad certificates, the upstream traffic should be protected. We should be able to revoke any other bad certs in the the future as well.

After my shock and anger subside, I can think more clearly now. I see many have mentioned using this to perform MITM on the IoT devices.

On the other hand, can we not utilize that to also protect the IoT devices? Can we not create a MITM proxy on our own network that proxies all of our IoT devices's traffic and then forwards them to the trusted HTTPS, MQTTS, ... endpoints upstream? Since the proxy already revokes these bad certificates, the upstream traffic should be protected. We should be able to revoke any other bad certs in the the future as well.

Do we call this human's slop? Should we be so hard on AI when humans do something like this? I'm pretty sure LLMs did not and would not allow such things to happen.

On the bright side, hopefully with AI we won't see silly slops like this.

I'd need to see a real video of such a thing first :-)

Do you have a link to it? I could not see the landing well in any of the videos since there was a lot of white cloud when it landed.

Write-up: https://closetheloop.dev/blog/flying-car

Code: https://github.com/closetheloop-dev/flying-car

I'm not affiliated with Tesla and happy to answer any questions you have about this project.

The most surprising thing to me was how much I had to be involved to correct Opus's work. I thought it would be just a sprint and did not expect a marathon vibe coding session :D

Anthropic did have some great results with the Jacobian Conjecture [0] and the Riemann hypothesis [1], so I think Anthropic was aiming to solve one of the Millennium Prize Problems, but OpenAI did it first.

If OpenAI did it purely for marketing, I still think it was well worth it. It marked an important milestone in the history of computing (and also human history). It is on the same level as when IBM's Deep Blue defeated Garry Kasparov in chess, or when DeepMind's AlphaGo defeated Lee Sedol in Go. The search for winning moves in those games is not dissimilar to the search for paths towards mathematical proofs, and AI has finally proven that it is capable of carrying out deep, complex, and intricate research that exceeds human capabilities.

Besides, a Super Bowl ad costs about $8M, so it is not absurd if they spent a lot of money on this and became the first company to definitively demonstrate AI can surpass humans in mathematics.

[0] https://x.com/__alpoge__/status/2079028340955197566

[1] https://www.anthropic.com/research/riemann-zeta

It was not just about models, but also techniques, and mainly the human guts to trust their models and follow through to solve one of the hardest math problems many experts had been musing over for more than 26 years since the million-dollar prizes for these problems were announced. For me, it was an amazing moment to witness indeed!

> Is that Wonder or Wonder Full (barf)?

I think Meta did a wonderful job of naming this model :D

So far only OpenAI has a model that solved a Millennium Problem, while Opus 5.5 is a star at coding. It's exciting to have many more to join the frontier, but I think the top two labs still have a significant lead over everyone else.

I'm not sure if that does any damage to anyone else's reputation other than Google's.

Nearly two weeks since Gemini 4 Argon was announced and I still haven't seen any pelicans from Simon Willison for it. Nobody is currently using it either. What's the point of announcing Gemini 4 and then throwing it into the black hole?

One thing that AI can give me: time. Would I like to see all the rest of the Millennium Problems solved during my lifetime? Absolutely.

Also how many math and scientific discoveries do I want to see during my lifetime? "Just a little bit more."

Hopefully the techniques and results here will be in the training dataset for the next models, so that each new release will give us more interesting techniques and results!

It seems that OpenAI has a proof machine that keeps multiplying fruitful proofs!

> They did check and it was in the trainingset

Do you have a source, especially of what was in the training data specifically?

As I mentioned, they would have to find out whether the mentioning of the approach is only present in Tristan Buckmaster's chats and not in other users', including free users', chats.

It's forced vs unforced Euler, so it's not exactly the same. Since OpenAI has access to their training data, they can probably scrub through the data to find whether there have been any mentions of the similar approach, and whether it only comes from Tristan Buckmaster or if it is in the training data before that. They'll probably have to kick off another fleet of agents to scrub through the training data to answer that.

To be clear, I only talked about the mentioning of the approach to solving it and not of the proof in the training data.

I'm not here to judge since I don't have all the facts, but from what they announced: they tried all 6, found a probable lead to Navier-Stokes, concentrated efforts in that direction, and found a solution.

I hope the next solved Millennium Prize Problem will have less drama.

To be fair, the first solved Millennium Prize Problem, the Poincaré conjecture, also had its fair share of drama!

From my reading of the announcement:

- There are at least two versions of a model more powerful than Astra at OpenAI at the moment.

- The less capable version was used to solve the unforced Euler problem (while the one solved by Levent Alpöge and Tristan Buckmaster was forced Euler) with 100 agents.

- The more improved version was used to solve Navier-Stokes, given the results of the unforced Euler problem from their earlier attempt, with 10000 agents.

- OpenAI initially tried a shotgun approach against the 6 Millennium Prize Problems until it emerged that Navier-Stokes was the most likely to succeed.

So the timeline was:

Shotgunning 6 open Millennium Prize Problems -> solved unforced Euler problem with 100 agents -> concentrating on Navier-Stokes with 10000 agents -> solution.

If so, that is fantastic development and a huge success (despite all the drama surrounding it)! Congratulations!