djx22 · HackerLangs

yes, I understand the usecase. Where I'm coming from is quantized vs. unquantized. 4bit quants are lobotomizing the model heavily to the point that it's better to invest in some capable hardware than keep fighting the limitation. Refurbished server grade hardware is accessible. For the price of an RTX 6000 PRO you could probably get much more VRAM but 1-2 generations older.

djx22

dündiscuss

similar experience, yes. I cancelled my subscription.

djx22

dündiscuss

Not sure what everyone's experience is but I find 5.6 Sol to be a great liar. Reported success on a half done job and left things in a broken state after having quite a few back & forth followups on the initial prompt to clarify the plan. Didn't experience this with 5.5. Opus 4.7 and below sometimes did it but they fixed it in Opus 4.8. So, overall, the initial experience has made me think that this model will be a lot more stressful to work with just because the level of trust that it actually completes the task is now much much lower.

djx22

7 gün öncediscuss

in my experience running models that have been heavily quantized(q4) or altered to some extent has never made me say “wow, this is so amazing”. On the contrary, the model ended up in the thrash bin after a few prompts. I have an RTX 6000 PRO with 96GB, and what I can run comfortably is Qwen 3.6 27B or MoE, Gemma 4 31B. This is as far as it goes when you run the model at full precision and maximum context length. They perform well and you can use them for coding, doing research on the internet and what have you. So if you do the math and you see yourself spending more than the $2400/year to Anthropic, then it might make sense to get one of these cards but accept the quality drop. Otherwise, will humans even be coding in 5 years from now?

djx22

5 ay öncediscuss

Don't let AI write code for you unless it's something trivial. Instead use it to plan things, high level stuff, discuss architecture, ask it to explain concepts. Use it as a research tool. It's great at that. It's bad at writing code when it needs to be performant or needs to span over multiple files. Especially when it spans over multiple files because that's where it starts hallucinating and introducing abstractions and boilerplate that's not necessary and it just makes your life harder when it comes to debugging.

Imagine if every function you see starts checking for null params. You ask yourself: "when can this be null", right ? So it complicates your mental model about data flow to the point that you lose track of what's actually real in your system. And once you lose track of that it is impossible to reason about your system.

For me AI has replaced searching on stack overflow, google and the 50+ github tabs in my browser. And it's able to answer questions about why some things don't work in the context of my code. Massive win! I am moving much faster because I no longer have to switch context between a browser and my code.

My personal belief is that the people who can harness the power of AI to synthesize loads of information and keep polishing their engineering skills will be the ones who are going to land on their feet after this storm is over. At the end of the day AI is just another tool for us engineers to improve our productivity and if you think about what being an engineer looked like before AI even existed, more than 50% of our time was sifting through google search results, stack overflow, github issues and other people's code. That's now gone and in your IDE, in natural language with code snippets adapted to your specific needs.

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