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I have a few lightweight apps using deepseek api, and funny how the initial credit I topped up for using r1 is still left. Nothing makes the user happier than getting more for less. cc: anthropics with its fancy token-wasting claude code "features"
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I would say all benchmarks are inherently subjective. How is yours better? It seems to produce a little bit strange results. Opus 4.6 being worse than 4.5 for example. Or chinese models being rated too high. Kimi, Deepseek or GLM are all great in open source world, but I don't believe they are ahead of SOTA models from Anthropic, OpenAI or Google.
I agree that benchmarks are inherently subjective. but the fact that you cite your brief as your main argument is funny - you don't even have any inherently subjective numbers to justify what you believe, you only have "I don't believe".
It's easy to praise Deepseek for its results and generosity -- how they can keep up with frontier labs on Huawei chips for a fraction of the cost! -- but let's not forget a big part of their toolkit is heavy distillation of SoTA.
There are quite a few comments here about benchmark and coding performance. I would like to offer some opinions regarding its capacity for mathematics problems in an active research setting. I have a collection of novel probability and statistics problems at the masters and PhD level with varying degrees of feasibility. My test suite involves running these problems through first (often with about 2-6 papers for conte…
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This is only good advice if you don’t have the need to understand what’s happening on the edge of the frontier. If you do, then you’ll lose on compounding the knowledge from staying engaged with the major developments.
Not all developments are equal. Many are experimental branches of testing things out that usually get merged back into the core, so to speak. For example, I knew someone who was full into building their own harness and implementing the Ralph loop and various other things, spending a lot of time on it and now, guess what? All of that is in Claude Code or another harness and I didn't have to spend any amount of time on…
I don’t believe that top tier engineers just skip learning things because they might turn out to be dead-ends or incorporated into tools by someone else; in my experience they tend to be extremely interested in things that seem like minutiae to others when working on the bleeding edge, often implementing their own systems just to more fully understand the problem space.
If it’s a day job for someone and they are not ambitious, fine. But we are at hacker news. I would bet 99%+ of top tier software talent could tell you practical experience with ralph loops this year, or a homegrown variety, simply because they are an attempt to solve a very real engineering problem (early exit, shitty code/incorrect responses, poor context window length and capacity), and top tier software people expect more control of their engineering environment, and success using their tools than they’d get by just saying ‘meh, whatever, I don’t get this and I’ll just wait it out.’
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I feel like people suck at promoting Opus. Baseline, it's pretty on par with GPT 5.5. But if you prompt it well - give it the reasoning behind why you're asking it to do something - it pulls far ahead.
That's fine for procedural tasks, and I understand its value there. But these particular tasks I'm referring to occur on the front lines of research. You can't expect the prompts to be incredibly detailed, since those details are the whole challenge of the problem. I think there is value in having models that are capable of making really good preliminary insights to help guide the research.
Open Source as it gets in this space, top notch developer documentation, and prices insanely low, while delivering frontier model capabilities. So basically, this is from hackers to hackers. Loving it! Also, note that there's zero CUDA dependency. It runs entirely on Huawei chips. In other words, Chinese ecosystem has delivered a complete AI stack. Like it or not, that's a big news. But what's there not to like when…
> Also, note that there's zero CUDA dependency. It runs entirely on Huawei chips. That is a huge claim to make with no evidence. I researched what you said, and I have found no statement to that effect in their paper[0], on huggingface[1], twitter[2], WeChat[3], or in their news release[4]. They only mention as a footnote in only the Chinese version of their news release that they plan to reduce inference costs with…