Anthropic probably trained Mythos on their own code and found that it is too got at reproducing it.
Some suits with no understanding of how LLMs work are scared that the models might hack them, or believe that they'd have to send data to China because they do not know that open models can be run on your own infra.
Not as far as I can tell. Are we seeing different things? For deepseek-v4-pro: - $0.350 in, $0.003000 cache, $0.80 out https://crof.ai/pricing - $0.435 in, $0.003625 cache, $0.87 out…
Those were amazing times. You could vibe code an entire prototype in seconds (200 tps). With Qwen3.6-35B-A3B and MTP, you can program at that speed on a single GPU at home now, but Kimi K2 is of course much smarter at…
Groq stopped serving Kimi K2 (1T params) when they got aquihired by NVIDIA, so I guess NVIDIA took most of the hardware in addition to the employees. The largest model they serve now is the relatively minuscule…
For Anthropic, 5 minute caching costs 1.25x base input price and 1 hour costs 2x base input price. https://platform.claude.com/docs/en/about-claude/pricing#pro... For OpenAI, it seems like you can't prolong the caching…
> The demo shows how every case gets successfully decoded without any hangups or a crash. I am always baffled by the audacity of those LLMs to suggest that anything else would even be acceptable.
> There exist a large number of people who are absolutely convinced that LLM providers are all running inference at a loss in order to capture the market and will drive the prices up sky high as soon as everyone is…
Same here. LLMs are great at spitting out well-known solutions to problems instead of the best one. The "long tail" of solutions is usually lost due to how tokens are sampled from the LLM's probability distribution.…
> What's your source for Opus being a 5T model? Elon Musk tweeted that Grok is 0.5T or 1/10th the size of Opus. https://xcancel.com/elonmusk/status/2042123561666855235#m While this source's reliability is certainly…
Another factor is that DeepSeek is not just doing inference, but also training models, so they can use underutilized compute nodes for training during off-peak hours, as described in their DeepSeek v3 article:…
We can at least put an upper limit on it. From https://www.anthropic.com/glasswing Claude Mythos Preview will be available to participants at $25/$125 per million input/output tokens ... Anthropic is committing up to…
Maybe the human brain also does other things besides interpolation?
> Please go run some numbers. - DeepSeek serves DeepSeek V4 Pro at 27 tps: https://openrouter.ai/deepseek/deepseek-v4-pro - At 27 tps per user, a B300 GPUS will give you around 800 tokens per second (serving 30 users):…
I think I was using GitHub Copilot when I made the experience that led me to this statement. I guess the experience of using LLMs can be quite different depending on model version and harness.
> Uncensoring a model also doesn't necessarily improve generic use cases. While the following is not a generic use case, I have a funny anecdote about how censorship is holding back flagship models. I was asking an…
Do your 20 year old university essays really fulfill all those criteria at once?
Sorry to say, but it almost certainly is AI. - 51 EM-dashes - Section headings - Excessive repetitions: "The [...] are real. The [...] are real. The [...] is real. All three things are true at once." - Excessive use of…
> Open weights will remain open only if they’re significantly worse than the frontier weights. This makes the assumption that you earn more money by selling access to the model than by releasing the weights. That might…
> Should we interpret this to mean that in the new world Windows is more resistant to attacks than say Linux. LLMs can read assembly better than most, so probably not. But reality has never stopped people from trying to…
Being aggressive from the start is not a good strategy. It is better to appear weak and/or helpful and loyal while amassing resources, and only then steamroll everyone when you have secured overwhelming power (at least…
> Slow, resource intense, better than non local ai Why should connecting small models to big models result in higher output quality than just running the big models without the small models?
Serving a single user is likely not profitable, but total throughput rises a lot when serving many concurrent users, because the same weights can be used to generate tokens for all users at once, which increases…
Putting at least $200,000 worth of compute in someone's yard is doomed to fail. Those things will be stolen in minutes.
> As part of this agreement, we have also expressed interest in partnering with SpaceX to develop multiple gigawatts of orbital AI compute capacity. Anthropic is either taking this space business more serious than the…
Anthropic probably trained Mythos on their own code and found that it is too got at reproducing it.
Some suits with no understanding of how LLMs work are scared that the models might hack them, or believe that they'd have to send data to China because they do not know that open models can be run on your own infra.
Not as far as I can tell. Are we seeing different things? For deepseek-v4-pro: - $0.350 in, $0.003000 cache, $0.80 out https://crof.ai/pricing - $0.435 in, $0.003625 cache, $0.87 out…
Those were amazing times. You could vibe code an entire prototype in seconds (200 tps). With Qwen3.6-35B-A3B and MTP, you can program at that speed on a single GPU at home now, but Kimi K2 is of course much smarter at…
Groq stopped serving Kimi K2 (1T params) when they got aquihired by NVIDIA, so I guess NVIDIA took most of the hardware in addition to the employees. The largest model they serve now is the relatively minuscule…
For Anthropic, 5 minute caching costs 1.25x base input price and 1 hour costs 2x base input price. https://platform.claude.com/docs/en/about-claude/pricing#pro... For OpenAI, it seems like you can't prolong the caching…
> The demo shows how every case gets successfully decoded without any hangups or a crash. I am always baffled by the audacity of those LLMs to suggest that anything else would even be acceptable.
> There exist a large number of people who are absolutely convinced that LLM providers are all running inference at a loss in order to capture the market and will drive the prices up sky high as soon as everyone is…
Same here. LLMs are great at spitting out well-known solutions to problems instead of the best one. The "long tail" of solutions is usually lost due to how tokens are sampled from the LLM's probability distribution.…
> What's your source for Opus being a 5T model? Elon Musk tweeted that Grok is 0.5T or 1/10th the size of Opus. https://xcancel.com/elonmusk/status/2042123561666855235#m While this source's reliability is certainly…
Another factor is that DeepSeek is not just doing inference, but also training models, so they can use underutilized compute nodes for training during off-peak hours, as described in their DeepSeek v3 article:…
We can at least put an upper limit on it. From https://www.anthropic.com/glasswing Claude Mythos Preview will be available to participants at $25/$125 per million input/output tokens ... Anthropic is committing up to…
Maybe the human brain also does other things besides interpolation?
> Please go run some numbers. - DeepSeek serves DeepSeek V4 Pro at 27 tps: https://openrouter.ai/deepseek/deepseek-v4-pro - At 27 tps per user, a B300 GPUS will give you around 800 tokens per second (serving 30 users):…
I think I was using GitHub Copilot when I made the experience that led me to this statement. I guess the experience of using LLMs can be quite different depending on model version and harness.
> Uncensoring a model also doesn't necessarily improve generic use cases. While the following is not a generic use case, I have a funny anecdote about how censorship is holding back flagship models. I was asking an…
Do your 20 year old university essays really fulfill all those criteria at once?
Sorry to say, but it almost certainly is AI. - 51 EM-dashes - Section headings - Excessive repetitions: "The [...] are real. The [...] are real. The [...] is real. All three things are true at once." - Excessive use of…
> Open weights will remain open only if they’re significantly worse than the frontier weights. This makes the assumption that you earn more money by selling access to the model than by releasing the weights. That might…
> Should we interpret this to mean that in the new world Windows is more resistant to attacks than say Linux. LLMs can read assembly better than most, so probably not. But reality has never stopped people from trying to…
Being aggressive from the start is not a good strategy. It is better to appear weak and/or helpful and loyal while amassing resources, and only then steamroll everyone when you have secured overwhelming power (at least…
> Slow, resource intense, better than non local ai Why should connecting small models to big models result in higher output quality than just running the big models without the small models?
Serving a single user is likely not profitable, but total throughput rises a lot when serving many concurrent users, because the same weights can be used to generate tokens for all users at once, which increases…
Putting at least $200,000 worth of compute in someone's yard is doomed to fail. Those things will be stolen in minutes.
> As part of this agreement, we have also expressed interest in partnering with SpaceX to develop multiple gigawatts of orbital AI compute capacity. Anthropic is either taking this space business more serious than the…