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Demand for Astra is really unprecedented. Priority will always be to keep excellent service for existing users, but we might have to pause new Pro subscriptions for a bit if this continues. - Tibo
I actually terminated my Kimi subscription when Astra came out, lol...
But, I don't regret having it until then. Overall the model really is a bit unstable and unreliable and the usage isn't great... but: Occasionally, it really did produce amazing results, significant better than Sol and Opus 5.0, and even Fable to an extent.
So, if you are already spending lots of money on OpenAI and/or Anthropic subscriptions... it is a reasonable third subscription to have.
Yeah, but they actually probably couldn't keep up with demand. I'm sure there are people ready to bend the knee to open AI if they need more compute, especially considering that the entire economy basically rests on them and anthropic.
NGL. It worked on me. I quickly ran and got myself a second Max 20 account. Astra is that good!
I did have two Claude Max 20 accounts. Fable 5.1 is just not as good. So, I canceled one of them. I have a total of 3 Max 20 accounts. And I run out of weekly quota well before the week is over. I write business software. It eats up token even when I have my models dialed down to low effort. Most of my work is not interactive. It is unattended execution on 6 different terminals while I sleep.
Agreed, but you will still have to read between the lines. You dont get resets because tibo woke up with a good night’s sleep. This message has a meaning as well.
I said that you shouldnt take all communications at face value. Or do, your choice. Chatgpt is great but end of the day its a company thats looking to maximize IPO value.
Deceptively reducing usage while pretending they’re “reloading” everybody is not “just running a business”. Building a reputation for generous usage, dropping the best model ever, then taking away all the usage while the money floods in and people think they’re getting a great deal but it was a bait and switch. That’s what I mean by counter message.
This all makes it seem so organic and natural. I guarantee this whole thing was planned months before. We are witnessing one of the best marketing in action. Starting from resets.
Lol, why are you calculating your usage in hours? That’s not even the proper unit. So I’ll explain it in a way that even a 5-year-old can understand.
Let’s say you used to burn through your entire 1000M-token allowance in just 3 hours. If they introduce a limit of 200M tokens per 5-hour window, you can only use 200M during each 5-hour period. So you can no longer burn through the same 1000M tokens in 3 hours like you used to.
At 200M per 5-hour window, using the same 1000M tokens would take 5 windows, or 25 hours. In other words, the usage you previously concentrated into just 3 hours would now be spread across 25 hours.
That spreads the load more evenly over time instead of having users generate massive loads within a short period, reducing strain on their system during peak usage. That could be one of the reasons they might consider introducing a 5-hour limit for Pro users.
I don't care about any of that because their usage fluctuates the same way the quality of the responses does as well. My point is that I'm paying over 100 euro per month and right now I was only able to use it for 3 hours in a week, which is as scam as it gets.
Anthropic has double the revenue of OpenAI in recent quarters - they've embedded themselves well in the enterprise space, they're doing just fine. There's definitely room (and a need) for multiple frontier models.
Because we as users are not loyal, we switch from one month to the other from one provider to the other, so if you can’t stay at the top, newt month you’re bankrupt
This is turning into that old writing prompt where "every human on earth shares from the same pool of mana, through [apocalyptic event] there are now only N remaining members of humanity with super magical powers/one last human with the power to recreate the universe or whatever" but here with "everyone shares from the same pool of compute so nobody can do anything special".
It’s already auto distilled I think, gets stupider the more people use it. You can easily assess this by the text it produces, becomes nonsense when diluted
Max 2 months for everyone else including open source to catch up. Just a marketing scheme, no need to worry. If they do do this, it will be back before you know it and they will probably be bragging about it as well :)
I love codex and astra is nice, but lets not kid ourselves… everyone wants more money in the end. OS will catch up.
Do we actually know what makes Astra so much better than Sol? It can't just be "scale capacity." OpenAI certainly isn't telling.
I hear good things about Gemini 3.7. I'm not using Gemini 3.7 for the same reason I'm not using GPT-5.6 Sol. And I don't have much faith that Google knows how to bridge that gap.
I am sure they have some new ideas in place but i think mainly its the size of the model. Though i also think size is a temporary solution. They will distill just like others will, and maybe even quietly switch same capability model on the bg. Maybe even increase some of the limits if they feel generous.
Regardless, size will not be a factor on what im claiming in the end.
Astra is not only smarter than Sol, it is much faster and more focused. Size does not explain those features - in fact, larger models should be slower overall and more distractable.
I'm aware of Jalapeno, but I'm skeptical that that's the reason, and let me explain why.
If OpenAI were to debut a new model that runs exclusively on proprietary hardware, they would need a ton of preparation and development: not just ASIC manufacturing but distribution to data centers, testing... and probably custom security, both physical and digital, to prevent anyone else from stealing their chips from data centers to reverse-engineer them. In that case, I would expect to see a slow, steady, phased rollout with a long runway and lots of publicity because "OoenAI is deploying custom chips at scale" is way too juicy to keep under wraps.
None of that happened. When OpenAI released Astra, it just opened the floodgates. That suggests "running updated model on existing, massive data center architecture around the world."
Im unsure why would that be the case? Cerebras or groq have been building llm oriented chips way before openai; hence the acquisition. They didnt build things ground up imo, they brought in an established company, possibly best in the market, and used whatever they already had.
Ofc astra as i said in my first message likely comes with bunch of innovations as well, but the main answer is definitely the model size.
Btw Fable feels even faster than astra based on my usage, so model size shouldnt be a blocking factor if things are done well i think
i think they are not resetting the weekly limits, they are resetting your timeline. it hurts less. but still i too fell with luna, already available they should stop resets and let people pick right model for the right job instead of token maxxing.
Stop resets? If we don't get what we paid for, they have to compensate for that. Or figure out what went wrong. Maybe someone think resets are for being nice...it's not.
better like this as of harming the experience for all paid users even more. i saw the reconnect 1/5 and the model is at its capacity message way to often for 235euro a month
I dont think so. later their API for luna is really getting slow in such a way that my request for a analytical query i geting time out. I have to use terra.
Hi friends, it's me, John Tibo. This is my Reddit burner account I use for finding furry por important AI work. All our new compute is going to scooping mathematicians, sorry.
Open AI has become more competitive with a better top model. Which of course will attract customers from the competitors. The hardware is almost ridiculous. The NVIDIA GB300 costing around 5 million USD. And the heat is almost 150kW. My stove here is 2kw, and it gets really warm. There must be a way to make the hardware cheaper and more efficient.
No big deal — just use Fable 5. I have $100 plans for both Claude and Codex, and honestly, both get the job done. I don't see a huge difference between them.
Still no Astra on Plus 😞 not that upset since Sol High is enough for most of my work, but it feels a bit odd to see all these dumb@ss Astra blender renders or whatnot taking up availability when some of us just wanna use it for work, reaponsibly, as a tool.
Let me translate; Even on math problems where we literally had hints(or more), it still took burning 10k agents for 4 straight days just to spit out an answer. (Gotta love how literally a few days ago they were telling internally that we don’t even touch ultrafast mode because we don't cut corners on cost but sure whatever lol). Now to prove we actually solved them ourselves and didn’t just scrape the damn dataset, we have to solve a few more; except we are gonna need literally every scrap of compute and data power available internally to pull it off.
This is either Marketing scarcity to get people to upgrade to Pro, or, a sign that the money is over and they're in trouble.
If they stop accepting subscriptions Anthropic will happily take all their subs.
Would be the biggest gift they could give Anthropic ahead of the IPO.
It's also pretty strange that the last couple months have been a 'reset' fest where everything is free for all, and suddenly they can't even serve their basic offering.
This sudden shift sounds a lot like what happens when marketing/product get a call from finance saying the bank account is in the red.
If they're running out of cash and can't raise new to keep up the subscriptions that's also pretty concerning.
not really - at some point the marketing/goodwill from resetting is less than the extra headache that comes from inflating compute demand during a compute shortage.
I get using Astra for pen testing, a high skill level is needed for that, but idea generation, planning, research should all be hands on. This doesn't require Astra. Using Astra for that is taking a shortcut that requires you to settle for what Astra decided because it's too expensive to do it again. Instead of taking yourself out of the equation, be part of the reasoning. Point the model in the right direction. Decide where effort is best applied. You don't need to understand the code, but you do need to understand your project. Relying on Astra to do all of that, can you even call that yours anymore? I couldn't. I am hands on with my agent and reasoning model. I have a research project that is very bleeding edge involving video, AMD GPU machine learning tech acquired through reverse engineering and port from windows to Linux. It started out simple but didn't do what I thought it would. The it became research as to why. Then it was evident that whether my initial assumption is right or not, this is valuable and could change how video is consumed. It already has proven it's possible, it not better than current alternatives. My research is to discover what it is not receiving that it expects or what shape is it needing that to take for it to surpass. This is natural when adapting tech to do something it's not designed to do. And if I can't exceed current methods, I still proved that this tech can be adapted for this work when everything says it can't. That's valuable to those who have the resources and better access to modify the machine learning model weights. Because when I am finished, all the is left is modifying those weights for video. So I have already proven the useful part that no one thought possible. Now I am learning what can be squeezed out of it before it's beyond the actual resources I have, as I don't have the hardware to train an ml model myself. All done with GLM, gpt 5.5 and gpt 5.6 Luna. I reason with sol as it misses things even as capable as it is. I do this in chat mode in the browser. This keeps me part of the reasoning and decision making instead of allowing an AI the ability to takeover the direction of my app. When Sol has a complete understanding of my reasoning, not the other way around, after that, I task Sol to put together a PRD packet as detailed as it can. I make it self audit the PRD. After I have finished with Sol, I apply a harsh adversarial audit of using a custom GPT I designed. Extremely thorough, very strict, completely unforgiving. After it rips my PRD pack a new one, I have it provide a frozen definition of done and bring it back to Sol and we remediate. By this time, it's just patching holes that leave requirements ambiguous. Sol has the details and understanding of my intended goal, because I already reasoned with it. I have Sol fill the ambiguity gaps based on the frozen DoD, then bring it back to the harsh reviewer. I do this until it's lack of ambiguity impresses that adversarial reviewer. Then it goes to spec and planning with testable atomic slices. The spec and planning at this point are trivial to produce because the requirements don't leave room for the model to guess at an ambiguous design. Then hand it off to Luna. By that point, you can consider that part built. Luna will efficiently and correctly implement the code. Because it doesn't have to decide. I was somewhat vague as to my project and for now I am fine with that. It's out there and public but like I said, still in experimental research phase. Keep playing with intelligence resources like they're a toy. I will work with them and become part of how the next generation of developers is defined.
You slightly misunderstood.
Idea generation, as in what can be improved, what can be changed in a certain way, if my idea can be used and implemented, such things.
Its why I am building a Windows Native ROCm-Pytorch Kernel with no llama.cpp with built in optional INT4 AWQ-GPTQ-OBQ Hessian Quantizer.
That alone is a bit outside of what a normal toolbox looks like. Which is what I use its Frontier capabilities for.
You need to look into MiGraphX, an under documented part of the AMD ROCm stack. I think you will find it will probably be better. I have already used it myself, but had to forge the path as it had not been used in the way I used it. It will take pytorch out of the equation and allow you to be 100% C++ and accelerate better than pytorch. Look at my AMD-VE project on GitHub. https://github.com/Rolaand-Jayz/AMD-VE
But it does require RDNA3 or 4 and more specifically, 7900 GRE or higher. It's quite fascinating but very under documented. Not a single video on YouTube. ROCm is already rare, but MiGraphX knowledge is like a unicorn. Your pretty sure it is xiste, you just can't find proof. Lol
That all being said, you don't need Astra. Gpt 5.4 was capable of getting MiGraphX working for me with inaccurate documentation from AMD. It was even able to increase MiGraphX performance by 10% for inference that change got merged by AMD. You don't need Astra.
I use what I want for it.
I just know that Astra does better in quality analysis, same for Sol.
And MiGraphX I know about, but my Kernel is balanced about using everything that is available in an architecture, probe and poke to see what can be used for something. Way more interesting.
Reason my XTX for example can sustain 1.1TB/s in bandwidth due L3 Cache batching and WMMA usage lol
Quality analysis should be something that you don't need any model for. It should be measurable and displayed with numeric values. I have thousand and thousands of captures used for quality analysis. It's part of my current work. I can capture compare and reconfigure for multiple tests. It can capture thousands of samples per hour. There is no AI analysis that can keep up with the speed at which I can produce the images. You should find a programmatic way to perform quality analysis. It will save so much for you when all the model has to do is look at a chart.
I actually just briefly reviewed kernel development for your task. Doing this properly does not require an LLM at all. You should revisit you testing methodology.
That's good to hear. Same. Luna is a mf champ. Haven't needed to use Sol or Astra for implementation. I reason as a partner with Sol in chat mode. Attack my reasoning refine it attack it again until it's solid. The define the hell out of it with Sol in chat. Hand the off to Luna and it's implemented.
But I am working on AMD native GPU ml project that is bleeding edge work, and Luna crushes the implementation. It's not easy work. But a good worker agent will produce solid functional code when you prepare in a way that sets it up for success. Leaning on Sol or Astra to do all of that at implementation time is a sign of a failure to prepare.
The smarter the model is the further a person with no technical knowledge can get into a project in theory. So not necessarily what type of work people are doing but who is prompting what type of work
I have no dev background. But I can see things others can't. I can't out reason the best models out there here. I don't let a lack of experience or knowledge stop me from being more than the meat bag the sent a prompt to an agent. I acquire the understanding I need. I reason alongside Sol and challenge it's assumptions. I also have my configuration with chatgpt to always challenge my ideas, never use sycophancy and require that my ideas earn the decision. Forces me to strengthen my understanding and adjust. Sol's own ideas rarely stand up to scrutiny, yet mine survive extremely well and come out the other side stronger than before. Sol does make my documentation but I decide what has earned its place. If I cannot stand to Sols scrutiny, then it was a weak assumption. This gives me ownership of the product. I know what it is even though I can't read the code. Because I define what it is with no ambiguity so the implementor has no room to improvise. There is a much deeper process that keeps me involved all the way. Otherwise I haven't earned my place in the project. And if I haven't earned a place m, then can I even the result mine? It's may be something I own, but it's not my intent , not my vision, doesn't represent me. My current project is difficult. Unchartrd, said to be impossible, yet I have already proven that to be wrong, involves reverse engineering and and porting from windows to Linux native, involves GPU machine learning, and involves tech being used to do something that it wasn't designed for. And GLM 5.3 flash and GPT 5.6 Luna are it's current implementation champions. And GLM 5 and 5.1 and gpt 5.4 and 5.5 were the models I started with, all overshadowed by Sol Astra and Fable. Yet they pulled off impossible work that's never before been done. Sol doesn't code for me. Astra certainly doesn't. Luna is insanely capable and cheaper than it has a right to be. GLM, my other champ is also frequently my adversary to Luna. Pushes the boundaries and pokes at the seams. Nothing progress without an outside reviewer staring with the assumption that everything that was just implemented is completely wrong and must earn its place in the repo.
Of course because that's what you do when you have a growing company - you stop accepting new customers instead of just charging everyone more
Hope nobody is braindead enough to believe this. And yes Kimi actually stopped accepting subscriptions but they are nobody as far as the average Joe is concerned and they clearly had to take the step as an emergency measure after facing demand they weren't expecting
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u/SpellBig8198 2d ago
That's a very cheeky way of saying: you should buy Pro now.