591–600 of 797 posts
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I am on the opposite camp. Open models are starting to perform better. GPT 5.5 keeps on messing things up. On the contrary, pi + glm + DeepSeek… bliss. Fable was a different kind of beast though. Rip.
How are you running glm and deepseek? Local or hosted? If the latter, where do you run it?
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If you have no need for Anthropic/OpenAI's frontier model capability, you may be better served with an open-weight model that can't be taken away. Edit: > GPT-5 does the job. I bring up DeepSeek V4 Flash a lot on HN, but I want to mention that according to Artificial Analysis, it trades blows with GPT-5 (high) (from August, 2025) [0] [0]: https://artificialanalysis.ai/models/comparisons/deepseek-v4...
Unless you are hosting it yourself on your own infrastructure it absolutely can be taken away.
> Additionally, we’re introducing a new `ultra` mode that goes beyond the capabilities of a single agent by leveraging subagents to accelerate complex work. I'm curious about how does this work? Do the subagents also get to use the same tools? Will the client be flooded with tool calls? Why extra pricing for a new "model" when the same thing can happen in the client with more controls? And if it's an army of subagent…
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What is your definition of AGI that the current LLMs don't fit?
Autonomously Generating Income (which is why it will never be released to the general public)
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Why remove the code and binary artifacts, though? Don't you want to verify that the business logic is accurate and the processing is deterministic? In some circumstances there is no substitute for something that you know will produce the same answer for a given input, consistently. And that's before even considering the watts per response.
The AI is the business logic, and the processing, and all of it. The context window is effectively infinite, with layered context window depth and speed. Think of short and long term memory, or think of RAM vs SWAP. Dip into swap to pull needed data into RAM context. SWAP can be anything storage related, including a symbolic database or a best-encoded set of priorities. If a person knows 100 knots, but hasn't tied on…
Here is a trend I'm noticing: - GPT-5 mini costs $0.25/$2 and will be discontinued in December. - GPT-5.4 mini costs $0.75/$4.5 and is supposed to be the replacement. - GPT-5.4 nano costs $0.2/$1.25 and, while it ranks better in benchmarks than GPT-5 mini, it's not even close when you test it in real scenarios. So you're left being forced to go to GPT 5.4 mini if you use 5 mini today. The same thing is happening here…
If you have no need for Anthropic/OpenAI's frontier model capability, you may be better served with an open-weight model that can't be taken away. Edit: > GPT-5 does the job. I bring up DeepSeek V4 Flash a lot on HN, but I want to mention that according to Artificial Analysis, it trades blows with GPT-5 (high) (from August, 2025) [0] [0]: https://artificialanalysis.ai/models/comparisons/deepseek-v4...
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For all intents and purposes you'll be able to move an open weight model wherever you want. I really dislike this rhetoric, you sound like the FSF guys who are like "you're not free until you're running coreboot with zero binary blobs". Sure they have a point but also, most people are fine running regular linux.
Reading your comment made me realize that I love that the position of the FSF is held by someone, in the interest of stretching the Overton Window to that side.
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Funnily enough, pasting your comment straight into Jimmy leads to a... Funnily suboptimal answer that does not answer the question. As someone else already contributed, this is driven by a Canadian startup taalas that basically makes chips that are llms, so everything is very fast but also, baked into the chip. Once this kind of stuff is a commodity in like 10 years, our world will be very, very different.
Taalas HC1 AI uses Llama 3.1 8B, but takes up a massive 53B transistors and 815mm2 on TSMC N6 (nearly at the reticle limit of 858mm2). N2 is a little less than 3x as dense (110MTr/mm2 vs 313MTr/mm2). This chip would still be 272mm2 on N2 which is an eye-watering $30k/wafer and bigger than a 9950x or Nvidia 5070. This just isn't feasible. Some of the latest-gen LLMs seem to have 5-10T parameters or about 1000x more. I…