171–180 of 408 posts
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People can simultaeneously be reprehensible idiots while being a reliable expert on something they have personally invested billions of dollars into and operate at scale.
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People can simultaeneously be reprehensible idiots while being a reliable expert on something they have personally invested billions of dollars into and operate at scale.
He's lagging the AI race despite having tons of compute available, so he tries to make a narrative about how it's not that the model is behind, it's just smaller than the competition.
When discussing LLM pricing, people are missing the plot. The subscription token price is 10x-40x cheaper than API pricing. Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing. The second issue is that the quality of the model “operator” makes a massive difference in the outcomes. Highly skilled senior devs who know how to prompt and have high agency will outperform team peo…
> Lastly, there is a massive difference in capabilities, determinism, and error handling between 5T SOTA models like Opus What's your source for Opus being a 5T model? > and tiny distillations from DeepSeek that perform well only in benchmarks. I don't think you know what you're talking about. Local models aren't “distillations from Deepseek”. And they don't perform well “only in benchmarks”, Qwen 3.6 is a very decen…
From this paper
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Also, your local hardware is in no way capable of running the types of models that the cloud providers do, it’s just not economically feasible, and it never will be.
Depends on what you mean by "economically feasible". Even very cheap mini-PCs and laptops can run any of the models run by cloud providers, albeit at a much lower speed (i.e. with the weights stored on SSDs). Whether such a low speed is useful, depends on the application. For something like a coding assistant or bug scanning, an instant response is desirable, but certainly not necessary.
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Yes and no. Just take a look at the OpenRouter providers page: https://openrouter.ai/deepseek/deepseek-v4-pro/providers Deepseek v4 Pro is much cheaper when provided by Deepseek itself, likely as a combination of the loss leader strategy you mention and the desire to have more data flow through their pipeline for training. However, the same open weights model, provided by other providers, is somewhere in the $2-3/1M…
> Unless you mean that releasing open weights models is the loss leader, in which case, you might be right but I hope you're wrong. This is specifically what I meant. DeepSeek’s official service is trying to recoup some of the training and engineering costs too. The other providers only have to recoup their hardware costs and the cost of a team to run it. Even though DeepSeek’s official service is more expensive per…
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Depends on what you mean by "economically feasible". Even very cheap mini-PCs and laptops can run any of the models run by cloud providers, albeit at a much lower speed (i.e. with the weights stored on SSDs). Whether such a low speed is useful, depends on the application. For something like a coding assistant or bug scanning, an instant response is desirable, but certainly not necessary.
The SSD would wear out in days while the laptop generates two responses a day. This is like saying you could power your home with AA batteries, yes technically you could but in practice entirely infeasible.
Great article that reinforces my own opinion but adding the cleverness of adding low cost human labor into the equation. Nice. I spent a month comparing Gemini Ultra plan to using much lower cost DeepSeek v4 with open source coding harnesses and, spoiler alert: I was happier using the much cheaper and more environmentally friendly open models: https://marklwatson.substack.com/p/my-evaluation-of-ai-agent...
https://share.google/aimode/a0O95wzk2UUhIXLUI
https://cloud.google.com/blog/products/infrastructure/measur...
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> The quality of the model “operator” makes a massive difference in the outcomes. My hunch is that this is the source of much of the variability in outcomes upstream of HN commenters claiming extremes of, "This model changes everything!" to "This[same] model is crap." We haven't operationalized what it means to "be good at prompting," nor developed proxies/heuristics/shibboleths for accessing prompting skill. There's…
It's 100% this. Many people suck at prompting. It's likely that habits from search are ingrained. But in general some people are just so bad at it .
I have really been trying to get local models to work. I have tried different harnesses, tooling, skills, prompts, etc. But when I compare claude code with anthropic models or codex with gpt 5.5, vs qwen, glm or gemma and the same harnesses, the frontier models come out massively ahead. I am at the point where I just don't see the point of the non-frontier models, they waste more time than they save.
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its a private company, what exactly do you expect to 'see'?
Anthropic IPO's in less than 5 months and I guarantee you any company that officially is in the black will proudly shout it from the rooftops.
pure speculation. about as valuable as my linked wsj reporting i suppose. given thats the case, maybe you shouldnt claim so confidently that they are money incinerators.