alt Hacker News> i truly have tried to use llms before, just to try it out. nothing i was going to publish as research, but i really couldn't and it left me never wanting to use an llm again
>
> i don't know how more people don't experience this, but staring at an llms output be confidently wrong eventually to get there in the process, or looking at the commands its running and see some really stupid things its doing drives me crazy to the point i will just stop it before it answers, i cannot handle something being that confidently wrong (even though it'll probably still get there in the end)
When people say stuff like this, that they've used LLMs before and hated it, and then list off a bunch of stuff about how they're confidently wrong and all this other stuff, my only question is when? When was the last time that they used an LLM? three months ago, six months ago, one year ago, two years ago?
Replies
Last week and tonight I tried asking a frontier model a couple of basic questions about a language with 60-70 million speakers with that I'm an expert on. It gave absolutely brutal, confidently wrong answers. It was completely confused and about the basic structure of the language.
They are still confidently wrong often. It’s harder to spot with Opus 5.5 effort medium+ but AI search overview is rife with this
I think the places the average person starts out with AI do look like this. Most people are not paying a $20 a month Anthropic subscription just to try it out. They're using free chat apps, reading the garbage llm summary Google puts at the top of every search page, dealing with the low quality tools foisted on them in their email app or word processor.
Have all these gotten a lot better in the last year? Sure! But there's still a several-orders-of-magnitude difference in quality between whatever garbage tier of GPT-6 Luna you can use for free on `duck.ai` (just for one example) and Opus 5.5. Or even GLM 5.3 Flash. It's a way bigger difference than the improvement in ~free models has made up for in the last year, even though those improvements have been substantial.
I think a certain sort of person, if they do try to use something better along with a coding harness, as also immediately inclined to read along with the reasoning traces - and understandably gets freaked out by the nonsense it contains. That's a mistake, I think; not just because models behaving "reasonably" doesn't depend on the rationality of their reasoning traces, but also because I think if you transcribed my brain activity into a serial stream of text, I'm not convinced it would be more coherent.
Every time I get pulled into a conversation with someone who is an extreme AI skeptic in this latter half of 2026, it’s eventually revealed that their only experience is one of:
1. The AI box at the top of Google
2. The voice assistance on their phone when they tried it a long time ago. Some question they asked Siri in 2025 and got a bad answer for.
3. ChatGPT free tier.
For a while you’d find someone who got assigned an AI tool at work but their administrator limited them to the cheapest model for cost savings, but most companies are wising up.
I also talked to a developer last month whose company had never let them use anything beyond Sonnet. When I told him I used Opus for everything his eyes went wide because he assumed I was spending thousands of dollars per month. However now that the latest Sonnet is very good, even this category of exposure is decreasing.
I think in 2027 as advanced models become more common we’ll see less of these claims about AI not working well when they tried it. There will be holdout group of people who refuse to try anything new, but the world is moving on.
I'll call myself a late adopter. I tried early models and wasn't impressed, work has made some things available but wasn't forcing us so Ive been keeping my fingers on the pulse for a bit but watching from the sidelines. In October I decided to give it another shot and Opus 5.5 has blown me away, I've written a whole PR for a sparsely documented project and was like write the PR description for me with like one sentence of background, no Claude.md, no context other than the repo and it completely nailed the description. Most of my apps are in the tens of thousands of lines, so nothing huge but still the capabilities of the frontier models is crazy good. They've come a long way from even a year ago.
Why does that matter? The argument still stands, they are still dumb. We learned some ways to bypass the dumbness and automate some major workflows, and we still see them fail at random things.
This is always the first response to criticism of LLMs, and it's starting to feel banal, because it's a counter that you can pull reflexively from your back pocket at any time without engaging with the actual content of the criticism. I'm not saying it's wrong necessarily, but rather that the added information value of "have you tried GPT Cosmica 15" in a discussion on LLMs is approaches zero. Because it's such a generically applicable response.