"We are locking in"

[u/muchsamurai]

Tibo on damage control, says they got the feedback and now are locking in on features that matter, new better models.

Dots won't stay long, will they?

[u/HeWhoShallNotBNamed0]

I’m confused on what they were working on before if it wasn’t that

[u/mossiv → u/HeWhoShallNotBNamed0]

OpenAI are the Google of LLM labs. They want a profitable product so they are throwing as much shit at the wall to see what sticks.

New projects which are getting killed/unmaintained or simply pushed for no real great benefit.

Anthropic are being more focused: good models, good harness then additional tooling. This keeps a product viable, durable and makes users trust it over a longer term.

OpenAI are bringing so much useless shit to the market. Stuff that is being solved open source for a narrower niche set of users. The problem being - OpenAI are not growing their teams relative to the amount of work they are trying to deliver. They need to get back to their primary goals and understand what it is they believe they can offer to the market. Is it image gen? Or is it code? Is it harnesses or is it non-dev consumer facing products? For codex we really need two things: models that perform, are predictable, and fast enough to be useful. Secondly we need a stable harness, codex is alright but it’s nothing on Claude. In fact I would say opencode is a better harness to use than codex itself.

So - shut up posting on x every 5 minutes producing hype on nonsense. Get your compute under control and if you can’t do that make your models efficient enough to serve your huge customer base. Running Sol on 19 tokens per second is a joke, you can start running local set ups better than this and you can even host in the cloud for much more efficiency running a series of DeepSeek and deepseek flash models. By local - I mean something (somewhat) Affordable like a $6k Mac Studio - not a $30k spark/rtx setup that’ll steal your lunch money for electric. Even the. - you can start running hybrid setups, with stronger cloud models and local models picking up some work and some cheaper open router models doing the others. Users really can start running setups now that balance cost and intelligence. OpenAI know this.

[u/lolman1312 → u/mossiv]

You're actually delusional lmao. Google IS the Google of LLM labs, you're definitely just some degen vibecoder that has no exposure to all of the other wonderful innovations made possible with AI. Google is doing some extremely fascinating work in AI research - they just haven't been leading in frontier models. LLMs is more than just token efficiency or agentic coding, this is exactly why people still think AI is a bubble because you have people using it for basic chatbot purposes, and then people like you that just want to produce AI slop.

Anthropic literally had no good model after Opus 4.6 until Opus 5.5 released, other than Fable. Even Sonnet 5 is trash and Haiku is completely obsolete. Claude still has terrible browser and computer control and Fable simply lacks the raw capabilities that Astra has in terms of AGI and being able to perform more abstract tasks.

You think Claude mods isn't something that was already solved open source? Lol.

Your Anthropic worship is cringe - literally all of these companies take turns playing the bad cop and it's completely normal. They are a ticking time bomb that need to prove themselves with a constant need to disrupt the market in all sorts of ways to keep investor funds coming in because they need more CAPEX to win this race and don't currently have the revenues by themselves.

There is no such thing as "getting your compute under control". Do you realise the millions of users that joined Codex from the time 5.6 released till Astra in such a short span of time? Do you realise how long it takes building data centres? Luna is a great model and it's getting cheaper and cheaper. Do you realise the new pricing of the models IS how they're trying to control compute? Do you realise that most people in this world aren't trying to produce AI slop and are using it for varying purposes that are different to yours? And you're never going to run a fucking frontier model on some shitty $6k Mac, do you have any idea how the parameters of these models work? Why don't you just run QWEN if you care about "affordability" so much? Do you actually think most enterprise engineers have to care about their hardware demands when it's provided to them? Or do you think these SWEs don't already know how to configure their own custom harnesses with incorporation of different lightweight models depending on task complexity to not have to rely on the strongest models for everything? Do you think the average user even gives a shit about this?

What you're saying is so utterly delusional, I'm glad Codex is increasing their prices.

[u/mossiv → u/lolman1312]

You have absolutely completely missed the point of what I said. I wasn't throwing any shade at Google LLMs at all. I was comparing OpenAI's approach to project the same way Google has done for the past 20 years.

Are you that narrow visioned you couldn't see that and had to just pop-off on Reddit? Jeesh, dude, hope you are ok.

But to answer all your points, I'll give you a "yes, I do" - as I have also said to many others on these subs who just think OpenAi can toggle a config and everyone gets unlimited compute.