What basic features are missing from agy? I've been using it and cli-cc + web-cc for months (among a few other random harnesses to test here and there) and they all seem roughly comparable to me.

I actually just cancelled Ultra also because I couldn't subscribe to a YouTube Family plan while I had it active (Google... :[) but trying to use Codex as a replacement while I testdrive Astra makes me yearn for agy again.

There's an API: you can use Gemini with other harnesses. Isn't the situation exactly like Claude vs Claude Code?

There is a pi plugin to use agy directly from it.

I remember having this issue ALL THE TIME with Gemini CLI but personally I haven't experienced that yet with agy.

> Is that specific to Linux?

I don't think their point was about knowing the stack, but being able to point a harness at something running on your desktop GUI and say "change this".

Being able to edit and recompile pretty much any part of the OS and userland (often not even needing to reboot!) is not something that can be said about Windows for sure, or even lots of things on Macs too. Or even when the browser is effectively the operating system, the JS/TS others write is also hard to change in your end.

Arguably, it requires the LLM to have access to the source code during training. If it's closed off (like Windows), then the LLM can't train on it.

Even if the source code's old, the fact that it is publicly available makes it much easier to train & improve on than if it were walled off.

WHY ARE ALL OPENAI MODELS SO CHATTY - i thought claude kept going on, then i literally put it in claude.md that summarize your thinking in 200 words or less and tell me in points what you did and what's next. Did the same for CODEX - nope still keeps effing going on and on and on

Incorrect (to some degree)

> The knowledge cutoff date for Gemini 3.8 Flash is March 2026

https://deepmind.google/models/model-cards/gemini-3-8-flash/

Only html or also css? Opus seems a bit more creative than most other models i've seen.

What do we use for the “model made stuff up and claimed it as facts”? I can see hallucinations somehow anthropomorphizing LLM even more. I don’t like that we’re doing that to begin with but it’s a losing battle. I prefer “it’s broken” and “IT produced shit results” personally.

Hallucination is accurate for what I'm seeing -- e.g. it's making up information about the 2nd gen Toyota Tundra that has no basis in reality. When challenged, it corrects itself.

Why? It's the same concept and root cause it was when we first started using it.

there are many dated expressions, including

- AI is just a tool, like excel; it does what the human operating it tells it to

- next token prediction cannot be true understanding

- models can have no desires and goals, don't anthropomorphize it

However, "hallucination" is very much not one of them

Astra also really loves reverse engineering binaries. I guess it's one of those things that isn't that complicated but is super tedious, and tedium means nothing to AI.

This experience is with Antigravity both internally and externally, and I have done quite a few side-by-side comparisons with the same prompt across a number of different Google and non-Google models.

I've tried Codex as a harness too, and that was nice. I don't find a significant difference between Antigravity and Codex. Codex has more features but I don't use them.

Not everybody has access to AI. More than that, every prompt uses insane amounts of natural resources. So why not share it.

Why would we even ever distribute software again, by this logic?

Wouldn't it be better if only one person had to and then we all got to benefit from the fix?

Why would the guy who wrote curl share it? We can all build our own now...

Why do the Linux folks need to be so selfless? We can all build our own kernel now...

why reinvent the wheel and spend tokens for a problem that has already been solved?

Yeah, unfortunately, it does deliver for its platform.

I didn't say it wasn't.

Still impressive that it did so much just to answer my simple question.

Now the LLMs know it too.

I'd add that we've seen how the leadership/reputation of different AI leaders, and with 2 decades of history with Larry and Sergey, they seem to come up on top as the most respectable.

Did they ever really abuse the power Google could have wielded? I could be missing something, but for the most part they seem to just get down to building and pushing technology/science forward and avoid drama rather than welcome it.

I am not rooting for them, I don’t own stock from either just that like GP I was sure by now Google would have eaten others’ lunch. I was expecting a Gemini 4 before Mythos/Fable and Astra just from what GP pointed out: a cash hose, data centers, good engineers. But I underestimated the investment others would keeping absorbing so started to lose confidence in my Google prediction. Let’s see how Gemini 4 holds up. Are we going to see a flurry of scientific and mathematical breakthroughs from it?

Altman raped his sister. I trust google to at least be evil in a professional albeit less exciting way. Anthropic shouldn'g even be mentioned. They aren't even worthy of dicussion I rather talk about deep seek or llama.

The thing is, Google doesn't have to scramble and freak out every time the others do some impressive update, because they have stable income, the other two do not.

It wasn't that they were out of the running for a few months, it was a year and a half.

That's too long to be so far behind. This release looks like it puts them back in it but if they don't ship anything again for a year plus it's hard to imagine building on top of them and watching the world go by.

As others note, this is most relevant for us here, Google does not need to chase YC developers and the like. They can move more slowly, they have the size to do that. But it does suggest a lot of dysfunction given they have the world at their fingertips and couldn't seem to ship anything for a year.

Google aren’t back in the boat. They’ve announced that they have seen the boat, intend to swim over to it and will sail not quite as fast or as far as the other boats.

Google should be dominating, but instead all we currently have is a disparate collection of consumer facing apps and a flash model that’s fast, clever and expensive.

Muse and Dots are doing what Google should have brought out last year, with their resources and know-how.

I still wouldnt build with any of google's stuff.

the risk of randomly getting my email banned is way too high

This model isn’t even generally available..

> Then why have they been lagging behind OpenAI and Anthropic for most of the last few years, and only briefly been at the frontier?

One possible explanation: because Google is a little bit more frugal and focuses on how to make providing AI models financially feasible - combined with some willingness to burn money so that they don't strongly fall behind on their AI models.

On the other hand, OpenAI and Anthropic at least formerly concentrated on building and providing the best models that they could with concerns about financial feasibility taking a backseat.

Just to be clear: I do have the impression that by now (likely because of pressure from investors) OpenAI and Anthropic take these financial concerns more seriously, but nevertheless Google's vs OpenAI's/Anthropic's "DNAs" concerning on what to focus on differ.

> Then why have they been lagging behind OpenAI and Anthropic

Because they're not desperate. Slow and steady wins the race, at this rate all Google has to do is wait for OpenAI and Anthropic to exhaust themselves on aggressive training, then they can casually amble along right past them.

Alphabet isn't just an AI lab, they're an ad company, a search index, a media distribution company, an email provider, office tool provider, a OS developer, a browser developer, a smartphone brand (Pixel), cloud provider, DNS, amongst a plethora of other services and goods.

OpenAI and Anthropic are AI business, if the AI market burst tomorrow, they'd be the first to flounder.

Google just has to keep pace in the AI space, they don't have to lead. Especially since whom is leading changes like two or three times per month non-stop for four or so years now, including small (by US standards) Chinese AI labs with a fraction of the money who keep pushing the tech forward every month while being open for now.

> Then why have they been lagging behind OpenAI and Anthropic for most of the last few years, and only briefly been at the frontier?

Because it's not an existential battle for Google. If OAI or Anthropic disappear from the absolute frontier for ~8 months the news cycle and churn will diminish them to the second rate. Google is processing near 4 quadrillion tokens every month, that's - I'm sure - significantly more than OAI or Anthropic, because Google is interested more so in their flash models and getting these competitive, which they are.

> Then why have they been lagging behind OpenAI and Anthropic for most of the last few years,

The reverse could also said to be true. Google has models that run with search, producing usable results in well under a second. I suspect the world is consuming far, far more of those Google tokens then the tokens produced by OpenAI or Anthropic.

So why are OpenAI and Anthropic so far behind? They are serving a different market: the one that wants high intelligence / high cost tokens. Google is targeting the low cost end of the market - ie the commodity. That's where they've always played with search, email, docs and the like. That's were they are playing with AI too, and they are killing it.

From a business perspective a frontier model does not make much sense anymore if you are not a startup. Neither for Amazon, nor for Google. Their clouds need models that are fast and perform well in their agent frameworks nothing were a frontier model excels at.

Most Google products even use flash lite underneath, so their frontier model is mostly used for distillation.

Alphabet issued a very oversubscribed 100-year bond with 6.1% yield earlier this year to raise capital for datacenter expension.

Meanwhile, Anthropic/OpenAI will struggle to survive the next 24 months on their current trajectory.

Given the spend requirements to stay on the leading edge and the speed (and with low cost) that others catch up, the economics support a fast follower model unless you get some benefit from being a pioneer. So far nobody has received a permanent benefit from being ahead.

Chinese models are barely behind the leaders. Google can catch up anytime they hit the gas. I think they’re intentionally spending less, and when this crazy race burns out they can play their cards.

Lagging by what metric exactly? Is Toyota lagging behind McLaren? (company vs company)

They havent. OpenAI and Antropic are now integrating with a plethora of apps, google has it from the get-go. Also, it may be important to point out that the transformer model (aka the hole llm thing) is a google initiative; Id assume they are not competing in the qualification series of AI (which is lets build a generic ai and get customers), but in the finals - they already have the customer base and the lock-in, they don't need to scale - just to cater. Cerebras and Google are probably the best safe bets in AI right now, and google does gave the tradition of being way ahead of the curve internally vs what is published.

It only feels like that. It's been less than a year since Gemini was on top and here they are again.

Are you ready to call this race 5 years in? I'm waiting another 15 years and at least one or two new LLM architectures.

Could it just be that Demis was checked out of the race and they lacked leadership?

I think Google, mainly Demis, just made a bad bet that multimodal world models would be the key to unlocking massive progress. Anthropic made the bet that it would be coding, and they were right. OpenAI was originally betting on like hardware and trying to compete in the browser space (?) but was able to quickly pivot to coding due to their size. Google, on the other hand, is like an aircraft carrier, it takes a lot of time to course-correct.

Wait 1-2 months, Anthropic will come up with a shinier model and Googel will look obsolete again. I too struggle to see the moat - yes Google is able to serve their own A.I traffic but as far as I can see - at a loss. Prices keep going down because open source models. We'll see.

Like Facebook, Google is for "old people" now, according to a recent conversation with university students.

(Aside: I didn't appreciate the revelation!)

The financials for Anthropic and OpenAI are likely borderline suicidal, google and co are publicly traded. Moreover, all innovations downstream to them dont they? Why not just stay slightly behind, especially given many have stake in those other companies?

Have you done a google search recently? The response time beats any anthopic or openai stuff, by an order of magnitude. They already are.

Better to stay in the game and keep pace with the startups until they've burned up all their VC money and employees cash out after IPO.

True, but have other hyperscalers caught up to Google's AI data centers?: fully liquid cooled, torus networking(?), 100,000+ TPUs interconnected, etc.

Google is already on gen 8 of its TPUs and is certainly already working on the next version or two.

Moore's law stalled ~2015. Unfortunately, no way current models will run on the <100W thermal budget of a cell phone. Printing the weights directly into a chip would help efficiency a lot, but not enough.

They probably said the same thing about social media back in the day.

I'm sure the thinking out there, and hence investment, is all about how to tether the user to the most addictive, network-effected, incredibly deep, server-side, moat-able version of AI possible.

> China will always be generations behind until they crack domestic EUV

Are you sure?

--

China Just Built What TSMC Said Was Impossible

https://www.youtube.com/watch?v=Pk-w279ESHg

--

China Just Built What ASML Feared Most

https://www.youtube.com/watch?v=YiPgSm62fiM

Do you think that they won't?

So 6-18 months.

yeah this would be one interpretation where "winner take all" could still be right. though with the recent accelerating release cadence, doesn't it seem like the head start / lead has been shrinking over time?

Yeah but like, we wouldn't be the dominant species on the planet anymore, and that turned out pretty great for golden retrievers.

Papal excommunications present facts and data. These things aren't exclusive.

https://www.anthropic.com/research/glm-5-3-and-the-spread-of...

It doesn't need to. It can use your cash to pay the people who are. Those in power like it more that way.

You say that because the 'most' existing models have done is hack governments and companies. Can't you think of worse things a model could do; accidentally or by instruction?

Just a wet dream of a sleepy old European... How should this work?

Based on historical developments the cost of compute will go down again eventually, decreasing the cost of training AI models of the same quality as today even further. That part is what I would be the most certain about.

If anyone other than NVIDIA has a moat, they for sure never talk about it

WW2 engineered things we've never imagined. Are you looking forward to a redux?

The problem is twofold. One, even a monopoly AI provider wouldn't have pricing power against its suppliers. Its suppliers are energy, semiconductors, and real estate. Semiconductors maybe they could get some leverage on but energy and real estate have plenty of other buyers. Two, there's still no evidence of a runaway scenario (ie a small lead turns into a big lead over time) and there's still no evidence that there's some resource that you can deny everyone else that they can't build your product also. You can't hoard energy, compute, memory, data, human talent, or customers.

The net effect is that the most likely scenario is if one big lab fails, they will likely all fail. Their revenues are all correlated.

To go to your dotcom comparison, the winner will be the ones picking through the assets that were written down by orders of magnitude and trying new products with the technology until one sticks to the wall. But I don't know if a dramatic crash is guaranteed either.

I suspect this is going to end up like most services provided e.g. cloud stuff, balkanized between a couple major players and an assortment of DIY or less popular options if you don't like those ecosystems, plus some UX/DX focused wrappers that use the big players under the hood.

I think that would be a pretty satisfactory outcome compared to one hypercompany consuming trillions of dollars of the world economy.

“Divide the world” sounds ominous. Here’s another scenario to consider:

Internet access is not really unlimited, but for many people with fiber at home, it effectively is and we pay a flat rate.

Perhaps by the end of next year, most programmers will stop thinking about metered access for AI? For many people, the cheaper models (about as good as today’s frontier models) will be good enough.

Which might sound good, but the downside is that it will also be easier to build an AI botnet without the users paying for it noticing. Particularly when people are running AI inference on their own hardware.

Or, like airlines, the ones that are left will have great technology but be not so great from a business and financial perspective. To me AI seems like a commodity service.

THe problem with analogies is that they are imperfect.

I would argue those who already rule the world, will continue to do so.

What happens to OAI and Anthropic? No idea, probs go bust. Google just has to offer a half-decent offering in the long run and have a cost-advantage and it'll eventually knock OAI and Anthropic out as firms figure out what combination of models they want to be best for their economics and generating returns. Enterprises trust google over OAI and Anthropic. A clear signal of this was the Apple deal.

Dont forget those sweet returns fellas! CEO's are hired to make the owners wealthier. That is not gone.

> I guess if one of them hits singularity, it could in theory just wipe out all the rest

The story that some AI company might reach singularity and then "everything will be different" is another science-fiction story that executives of AI companies love to tell to justify the staggering amount of necessary investments and cash burn. :-)

Agree entirely but that's the point, if it's a margin knife-fight with marginal product differentiation/pricing power nobody is going to be making bank.

Yes, it may come down to how well they get efficiencies from scale.

People always compare the inflated API prices, but subscription prices of American models are competitive for the intelligence. You get >20x the subscription cost in tokens.

If they have to recoup training costs then they don't have much choice

For the types of basic business tasks my company does, we have hit the line where if it never gets any better, we are fine. On our own hardware. For $25k worth of DGX Sparks, we have essentially the output of a few admin-level FTEs.

Europe doesn’t even have cheap electricity

"if this pace continues" is load-bearing.

Those people have a lot of overlap with the LessWrong crowd. They do not have RSI now and probably never will

Sure, it's happening...but, is IT happening? By that, I mean, we can see that the models are able to iterate at a pace and scale that humans can't match, and that provides gains in model performance and efficiency. But, humans are still needed in the loop, and not just because it's necessary for safety/alignment reasons. I don't think any significant discovery has been made by models on their own, and I don't know that LLMs will ever have the capacity to invent. They can synthesize from known data amazingly well, and since they know everything "known data" is extremely broad. But, the leaps, so far, have all come from humans.

So far, I don't think the models are capable of running away on their own. Of course, it would be playing with fire to not at least consider the risks of such a runaway scenario and build in safeguards against it. But, there is no model that can build a better model on its own, thus far, to the best of my knowledge (which is far more limited than the models, so maybe I should ask them).

The companies whose insane valuation is based on accomplishing thing X say they’re getting closer to accomplishing thing X?

At least they’re led by trustworthy and honest people or we’d need to take their claims with some dose of skepticism.

Is it recursive self improvement if Claude Code writes your pytorch for you?

Why does your outcome make any sense?

Already businesses that have more compute and access to data seem to eat the world around them. If, and ya its and if, we can make something that self learns into RSI it's not looking like any business that came before this.

We're on Enterprise Standard, our renewal was up like 50% because "Gemini is included now think of all the added value" yet they won't even give us the latest models. I tried hard to champion Gemini internally once every user had it included, yet we ended up spending extra on Claude because Gemini has stagnated. Even the included usage for the Gemini CLI was taken away and now requires an extra subscription. I wonder if we we'll even see Gemini 4 before 2028. It's ridiculous.

Seeing the same - definitely frustrating. 3.8 seems decent, but I’m still unable to use it at work because Google has been so slow to release it to Google Workspace customers.

a distributed market research institution with no direction?

Would that change anything about the conclusion? Having a "bug" that changes available model options for some small set of Pro users 3 months after launch certainly qualifies as a wtf-are-they-even-doing level of bug in my book.

My paid company Google Workspace account is stuck on 3.6.

My free gmail account is not.

OpenAI said they dropped Astra 6.1 over safety concerns: https://www.wsj.com/tech/ai/openai-chatgpt-model-release-can...

Google R Gon lose the AI race

They are going alphabetically, Android style.

All-my-tokens-ar-gon

On the other hand, Rust's borrow checker is very picky, and even a frontier LLM still sometimes struggles to respond to roadblocks sensibly (refactoring so whatever it's trying to do can be done safely) rather than stupidly (introducing some horrible global arena thing so it can make the borrow checker go away). A lot depends on how good your instructions are, and how good the existing code is, since bad input begets bad output.

Evidence needed. I think for certain kinds of outcomes it has very strong advantages, but these advantages are not a given as 'best for LLMs' :)

I've narrowed in on only using Go or Rust generated code (Go for APIs right now) and rust for some TUI or other thing. TS for web interfaces (w/React).

Not always. In my experience, if you're not working on a small, trivial codebase, LLMs will sometimes just create spaghetti unreadable, inefficient code to satisfy the constraints of the type system/borrow checker.

I've been a Swift/Obj-C engineer for my whole 15 year career. I now have all these odd jobs I have going on Raspberry Pis around my house that LLMs have written in Python. I'm now having them convert some of them to Rust and I am astonished on how fewer resources are used.

Is there evidence that LLMs generate better code in more popular languages? I get the sense the "experience" translates between languages and it can reason in any language just fine. I write Clojure code using a rather esoteric framework (Pathom3). There is probably very little similar code out there (it's definitely a tiny fraction of the training dataset) but it seems to do just fine

Not saying you're wrong, just curious if there are numbers backing this up.

Cpp2 has been dead for a while afaik, while Carbon is still going.

I don't think Carbon is dead, it just all depends on how easy it actually is to rewrite "all of C++" in Rust. (The jury is still out on this one, but it's not looking good.)

Rust is not the end of history. One of the difficulties with the language lies exactly with porting existing code written in an OOP style to idiomatic Rust, as those codebases weren't written with ownership in mind.

Such rewrites will contain judicious uses of Cell, RefCell, unwrap() etc. which make for ugly code that's not exactly simple to understand and might even have some landmines (crashes).

Getting rid of these requires a subtantial amount of engineering effort, which I'm not sure how well these LLM manage.

Given the nigh-universal experience of LLMs producing an ungodly mess when left to their own devices, I have my concerns.

> There is 0 practicality in inventing an entirely new coding language that only one company uses, and you have to teach it to thousands of new engineers

They did that for Go and it seems to have worked out for them though.

Hmm, I don't disagree with you that LLM's remove the need for type-safe languages, but as the blog mentioned, Google is porting their C++/C code to rust. Does this mean the port is waste of time and that they should just rely on the LLM's to catch memory errors?

Didn’t FaceBook fork php into another language?

I’m not entirely sure Google should have both Go and Carbon but when you have billions in server costs it makes sense to do extreme stuff for even basis points of performance. I’m still surprised at how much java there is.

Fuchsia shipped to many millions of devices today as an OTA in 2021. What do you mean finished?

Well, sure, but that doesn't mean it's not in a decline that is very unlikely to reverse course. Fewer people this year are starting new projects in C++ than last year, and fewer people than that will be starting new projects in C++ next year. And, as the models get really good at porting and the cost (both in terms of tokens and human supervision) comes down, there will be an avalanche of ports from less safe languages to safer languages.

This year, maybe next, maybe a year or two after that, is probably the most C++ lines of code in production use there will ever be. Why would one choose C++ for new projects at this point? There are niches where Rust is still uncomfortable or just doesn't have the support, but not for much longer. Models are very good at Rust and good at porting to Rust. And, Rust is a good language for models because it is so strict...it helps keep them in line.

Can't help you with your teammates, but see go/presubmit-latency-skill :)

Can it serve 5TB?

I could start a business with a similar growth trajectory. We can mail people $100 bills for a low low payment of $10. I just need $500 billion dollars of startup capital and I can show you a 10x yoy growth for a few years.

"indifferent" was the word they used. The sensible way to use Fable and Opus to do work at this point is claude code, but that's fine. I always have several instances running, along with codexes and pis. The question to ask is: can you stop a claude code and start a codex in its place without disrupting the team? Does my infrastructure support learning in a way that is stored by me, and not tied to a provider?

Not quite; training against the chain-of-thought is the Most Forbidden Technique, because it might teach models to obfuscate the it. The point of avoiding that, though, is to ensure the chain-of-thought can be usefully read (and, done carefully, monitored).

We have had this discussion for a decade regarding cloud hosting. There are definitely workloads and AI inference that you want to do in-house. Hosting these GPUs and models and setting up the interconnect and balancing it is really hard right now. It's not traditional infrastructure. It is exotic. Typical enterprise is at such a disadvantage to try and host this completely themselves. And models are constantly updating. And what models are you going to run? Six months ago, things that probably cost you a billion tokens probably cost you 50 to 100 million now, given how much more efficient models have gotten, or at least they cost that much. I don't know. It's just not a given.

Well... given that I have codex reporting 1b tokens consumed for my couple-days long session, I suspect 10-20b is not that hard to reach for a company of 10 devs. It would be interesting to know what is "rental fee" for a model like Astra or Sol 6.1, and how much hardware they actually need - not just gpu, but all of it?

Doesn't make sense for them to give away weights. They sell their own service using that and killing the competition (that they have a stake in) isn't in their interest either because their cloud growth is based on other companies' continuing heavy investment in this space.

It wasn’t actually. From the docs for Zircon (Fuchsia’s kernel) [0]:

> Zircon targets modern phones and modern personal computers with fast processors, non-trivial amounts of ram with arbitrary peripherals doing open ended computation.

Fuchsia also had a Linux compatibility layer similar to WSL1 at some point. Might still be there?

[0]: https://fuchsia.dev/fuchsia-src/concepts/kernel/zx_and_lk

Working on a novel OS seems like it would fun to work on....

It's an accounting trick.

They get to claim that as revenue, and then the discount as an expense.

This is how you grow your top line

They will follow through with it, it's because they want to get the money of the people who embedded it into their system and lazy to change it.

Google model really like reasoning a lot

It's a trope I used for a cheap laugh.

https://tvtropes.org/pmwiki/pmwiki.php/Main/GirlfriendInCana...

It means I am saying something that is not very believable.

US-ian joke. Close enough to plausibly visit, but the international border makes it hard to verify she exists :).

yeah it got something wrong, confused itself, then claimed i was gaslighting it. bizarre

They never explained the "please die."

Did you just link to an article from 2024 as if 2024 is relevant these days?

I stopped asking it to put me in a photo in different scenarios for laughs because it considers me a public figure. I am not. I've managed to wrangle quite questionable content out of it, but never to slap my face on a meme.

It is wild but it was back in 2024 and that's multiple AI lifetimes back.

Now I really feel worried for the first time.

From the example alone it's hard to say that a postmortem would be useful from a technical perspective. It could be context poisoning by an adversarial user, memory corruption etc.

wtfffff that gave me sinister chills. Right up the spine. Wow!

Wtf I just read

wow

It is definitely smarter than that. It is mostly mannerisms and how it likes to work. I would take it seriously as an Astra or Fable or Opus 5.5 level model. It just needs polish, but where and how you harness and use it matters a lot. But it has amazing long horizon attention and gets things done.

tbh while the astra announcement in that link had some good stuff it was scattered through so much boilerplate marketing speak that I had to force myself to read it and look for the content. I found the gemini blog post in the OP a lot more readable and engaging.

but that's a side issue; my main point is that you are underrating the impressiveness of getting a safe rust port of a highly optimised c++ library even nearly up to par with the original. the tradeoffs rust makes for memory safety cost it some of the raw speed of c++ even with all the zero cost abstractions and purely compile time guarantees they have. (tangentially i wonder if ats (https://www.cs.bu.edu/~hwxi/atslangweb/) would be a good candidate for LLM assisted ports; it seems way more advanced than rust and might actually get c-level performance with safety, but it's really hard to write.)

I prefer Pro Max Ultra XXL

So you have no experience of their latest model release then? Just repeating the usual tropes about Google having messed up? Or basing your opinions on their website chatbot?

If you have actual independent benchmarks and evidence about how this new model release is "so far behind" and refutes the stuff from their blog then please do share because I think we'd all love to see that?

Yeah, they've definitely got some recurring tooling/infrastructure problems around the models.

Google employees do.

Their profile says: > Currently at Google as a Sr. SWE SRE on the cloud.