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GLM 5.2 vs. Opus

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201–210 of 367 posts

Re: GLM 5.2 vs. Opus

#201
My understanding was that n-shot prompting just referred to the number of examples included in a prompt, not the number of prompts to achieve the desired result.

"Build a 3D platformer game from scratch, in raw WebGL, with no game engine or 3D library" would be a zero-shot prompt.

Re: GLM 5.2 vs. Opus

#204
post #66

GLM 5.2 has one big issue that will limit its meaningful success and that's the value of their coding subscription. Yes, in terms of API pricing, GLM 5.2 outperforms the competition. But the only people that use API billing for their coding work are large corporations, where these highly subsidized subscriptions are being fazed out. At the same time, none of these companies will use a Chinese API for their employees.…

Taking a view from outside the USA, European companies just had Fable taken away due to US export controls, and before that Anthropic announced it is holding their data for 30 days. There is immediate value to these firms to build their infrastructure around an AI that won’t be pulled away from them. And outside of Europe, other countries are more price sensitive and don’t have the same fear of building relationships…

There is no such thing as a relationship with "chinese companies". In China there is just the State, and that is it.

If the world needs any more evidence of Europe's short-sightedness, it would be them running to China to spite the US (instead of creating fertile grounds for their own tech).

Re: GLM 5.2 vs. Opus

#205
post #82

Earlier quoted context omitted.

And that prompt will basically be 2000 page spec Bible à la IBM circa 1960, see waterfall. Unless AI develops mindreading (and advanced mindreading at that), single prompt creation of actual complex software products will never happen. You'll one shot a simple non scientific calculator, but not Excel or Vim or Nginx.

Why not? Given a proper spec, you should absolutely be able to one-shot Excel, particularly if we put it at the level of complexity of, say, Excel 1.0 for Mac. Current models aren't capable of that, but that doesn't mean it's not possible.

Seems like this would be a good time to use this famous quote:

> given the sufficiently smart compiler

For those unaware, this is a similar quote used by compiler proponents. The first full compiler was created in 1957 (+/- 70 years ago) and the "sufficiently smart compiler" never happened, hand written code from the best coders still is faster. Now, that doesn't mean that compilers didn't do the job well enough, we just accepted that 90-95% of the top speed was enough for almost everything.

To the LLM one shotting point, it took 30 (40?) years for compilers to be good enough for the mass market. Caveat early adopter and investor.

Plus what pyrale said.

Re: GLM 5.2 vs. Opus

#206

I seriously dont' know all this big hullabaloo about one shot prompting. by definition, a single prompt wont' constitute the complexity of a software project. ergo, what you'll get is a series of assumptions made by the model based on preexisting code in its training corpus. I'd rather see a coding agent that can follow steps in a plan file to a T while following guardrails and adhering to the proper coding conventio…

IMHO, It's not the oneshotting. It's the "starting from empty slate" greenfield that's the real problem. We used to make fun of Engineers who follow a README on a framework, test it on an empty project, and say "this framework is the best for our 10 year running production app". Greenfield mentality is always the solution to all problems and problem to all solutions. One should still measure oneshotting, it's an impo…

At least they did some analysis. I've couple AI slop "X is the best tool for the job" that didn't even try it. (Worse, we are already using QT which has a tool for the job, and the QT tool works with the rest of the QT ecosystem unlike whatever AI told them)

Re: GLM 5.2 vs. Opus

#207

I signed up for GLM 5.2 yesterday to try it out because Anthropic kept throwing 529 Overloaded I like it, but the lite plan ate 22% usage of my 5h reset window in a single session after 2 prompts on xhigh of GLM 5.2 [1m] Result was satisfactory, I think stuff is decent, I'm happy to use either, wish there was a combined subscription plan where I could get both

I may be biased and interested as I'm going to give you an affiliate link, but really honestly Synthetic LLM provider is a beast! They provide perfect GLM5.2, awesome token/s, TTFT and price.

Coupled with a local Headroom (https://github.com/headroomlabs-ai/headroom) you'll be able to use a LOT without hitting your 5h window :)

Definitely the best $ value for me considering the reasonable performance of GLM5.2.

They provide a rolling window quota, so you're never really out of quota contrary to other providers, you can adjust day to day.

Check it out if interested : https://synthetic.new/?referral=kwjqga9QYoUgpZV

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Docs & all models : https://dev.synthetic.new/docs/api/models

Re: GLM 5.2 vs. Opus

#208
No one has really talked about hybrid and using Opus to plan and orchestrate GLMs work both through initial build and code reviews. That’s a true best of both worlds and there doesn’t need to be a winner.

Re: GLM 5.2 vs. Opus

#209
post #202

How are people running this locally? I just checked llama.cpp and it appears unsloth has a version but it hacks a bunch of things to make it work and isn't optimal. https://github.com/ggml-org/llama.cpp/issues/24730

No one is doing that for a model this size it would have to be so heavily quantized that it wouldn’t be useful - or you’d need to spend a half million dollars on hardware. People use hosted APIs. Open weight means cloud vendors can host it.

Re: GLM 5.2 vs. Opus

#210
post #173

I seriously dont' know all this big hullabaloo about one shot prompting. by definition, a single prompt wont' constitute the complexity of a software project. ergo, what you'll get is a series of assumptions made by the model based on preexisting code in its training corpus. I'd rather see a coding agent that can follow steps in a plan file to a T while following guardrails and adhering to the proper coding conventio…

It's a proxy for what you actually want to measure. Note that after the model generated a bunch of (intermediary) code, they still have to have it tested and get bugs fixed (via the agent/harness). In this "one shot" you still have agent loops against human defined objectives. And these toy examples give some insight as to how the model performs. If the test were "here's some code written by $corp, please take these…

As someone who works at $corp - there is a massive different in tickets. I've seen "The is not spelled 'teh'", and I've seen some other service is writing to memory causing a crash in my service (the later took months to track down since our code was correct and nothing gives a hint of where to look). Both problems are important to fix, but the first is so simple I don't care how good AI is (the hard part is getting it through the process)
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