The reason Claude code is so popular is because it’s really good at taking super vague human prose “Claude build me a million dollar SaaS”-type prompts and spitting out thousands of lines of code which cover tons of surface-level edge cases, build in tons of functionality, etc

The smaller/open models are less good at that. But that’s not how software development is done. You don’t prompt a whole app and be done with it. If you’re using it as an aid to traditional software dev, iterating on small, targeted functions, GLM works as good if not better than Claude. Anthropic expects low quality prompts. If you rubber duck GLM, you get absolutely pristine output in most cases.

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Gigachad an hour ago | parent | next [-]

Personally I've found Deepseek v4 Flash to be as useful to me as Opus. But I don't do these silly one shot tech demos. I have the technical understanding to ask for exactly the change I want with the right terminology. I loaded up some credit on openrouter and it took me ages to hit $1 in spend.

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wps an hour ago | parent [-]

I’m the exact same way. $15 on openrouter lasted me so long it would’ve got me fired at FAANG. Despite this, my number of commits is dramatically higher. Small, beautifully scoped changes is just good software development, and good for the wallet as well.

I think the issue is that no one is content with incremental progress. We all know one shots are mostly possible, so the age of the personal project is kind of over. There’s no motive to invest dozens of hours getting a working prototype when Claude can give you something right now. So you can’t make small changes until you have that codebase in place already. You’re forced to make sweeping changes if you use AI from the beginning. And it’s not like it matters, there’s no personal attachment to any one part of the code, it’s not even seen!

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Gigachad an hour ago | parent [-]

I can see why Anthropic is freaking out right now. China has undermined their whole business. AI models will be basic commodities where hosting providers earn a tiny margin over the raw costs rather than the predicted fortunes from being the gatekeepers to the technology.

	▲	majormajor 4 minutes ago \| parent \| next [-]
		Their business model always seemed to be "become IBM, gods of the mainframe and server-side-computing" and that always seemed a bit unlikely to last forever considering how thoroughly the PC disrupted that by being "good enough" without the lock-in. IMO because they speed-ran it so much (raising orders of magnitudes more, pushing out new stuff orders of magnitudes faster) it will also lead to the rest of the world catching up faster and shrinking the pool of people internal to them who continually benefit compared to what actually went on with IBM. There's only so much upside when you join a company with hundreds-of-billions valuation. I guess the other part of their business plan bet was "become literal god of AGI". Maybe it'll still happen!
	▲	basch 40 minutes ago \| parent \| prev \| next [-]
		the writing should have been on the wall when meta did it with llama. it was very apparent that one or two entities could release a "good enough" product into the wild commoditizing the frontier of yesterday.
	▲	cootsnuck 20 minutes ago \| parent \| prev [-]
		[dead]

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drnick1 an hour ago | parent | prev [-]

Frontier models like Opus 5 are also extremely good a "research" in an academic sense, including combining elements from different fields or literatures and creating something quite novel sometimes, publishable even, from vague/speculative prompts. This is on top of implementing known methods in about 1/100th of the time it would take by hand, allowing for fast exploration of ideas. They can also roll things out from scratch, removing essentially all third party dependencies from your code base, other than Numpy or Scipy. This really buys you a lot of clarity and/or performance sometimes.

	▲	majormajor 8 minutes ago \| parent [-]
		Yeah, the top-end models still feel better at synthesizing and especially simplifying design. Any of the current GPT-5.6 models can overengineer their way around edge cases. Figuring out how to get the same thing without the overengineering and the often-performance-overhead is harder and something that seems to be still more limited to the bigger models. But even the top-end models tend to overdo it first, then only note that it could've been a fifth the code with twice the perf if you push on it to make it realize that some of those edge cases or situations aren't actually relevant to this particular code. But the situations where you need that are narrowing every release. The "Composer 1" era of low-end models is pretty far away now.