LLM 비판이 맞다는 걸 압니다. 그래도 씁니다

The LLM Critics Are Right. I Use LLMs Anyway. 2026-07-15 I almost agree with all of the LLM critics, yet I still use LLMs a lot. I know this sounds like I am delusional, and I also feel like that sometimes because of this dissonance, but I don’t think I am alone with it. This week I was at Local-First Conf in Berlin, and the dissonance was everywhere. Armin Ronacher had just given a talk about building machine entities . He created Flask and was one of the early team members of Sentry, so he is clearly a good software engineer. Just recently he founded his company Earendil, which builds Pi.dev, an “open-source coding agent harness”. After the talk you could ask questions via Discord, which would be read out loud on stage, and I asked: are you accepting PRs for Pi, or how do you handle the flood of PRs from LLMs? He replied, live on stage, in front of the entire audience, that they auto-close almost all PRs and issues. But that one shouldn’t be discouraged from opening PRs, because the human will always shine through. So it is not only me, but apparently some pretty clever engineers too. The people building a tool for working with LLMs are themselves flooded by their own creation, and in order to protect themselves they are auto-closing it all. On their purpose page it says: In a world hurtling towards AI, we believe humans are the best agents. Again, dissonance. When I was sitting in the audience, I could see a lot of people having Claude Code open. And then the speakers would say these critical things about LLMs, and they would get this big round of applause. Even from the people with their Claude Code open. And again, this dissonance. I spoke at that conference myself, and when I later talked to some of the people, they described the feeling as pretty similar to mine, which is a relief, because I know I am not alone with this. So this article is me trying to describe it. I’ll start by going through all of the fair and valid concerns about using LLMs, the things that would get the big round of applause. Then I will explain what makes me still use LLMs. And I’ll finish up with some of the patterns I found, in the hope that by giving concrete examples, others can step in as well and describe their experiences, so we can all come together and get a better understanding of this dissonance. A few impressions from yesterday‘s talks 📸 @adamwiggins.com @martin.kleppmann.com @stevenruiz.bsky.social @jakelazaroff.com — Local-First Conf ( @localfirstconf.com ) July 13, 2026 at 3:44 PM LLMs are bad Just by listening to people or the talks or reading HN, I think I got a pretty good sense of why certain people refuse to use LLMs. And what makes it extremely weird is that I agree with almost all of their points! It is full of copyrighted materials, yes. It is bad for the environment, yes. All the ethical problems, yes. And this whole NVIDIA, OpenAI, money-moving circle-jerk is not going to end well. It is a bubble, and it is definitely going to burst. Let me go through the biggest ones one by one. Let’s start with the most common critique “LLMs produce a lot of slop”. Yes, they do. Definitely. If you look at open source software, you will see more and more repos and projects either straight up refusing all types of contributions or trying to put some kind of filters in front of it. Similar to what Armin and Earendil are doing with their auto-closing. I think the core issue here is trust. You should never trust random people on the internet anyway. But before LLMs, there was this base thing: creating a proper PR with proper descriptions would require at least some human time, so it would keep trolls and low quality submissions out. Or at least you could easily filter them out within a couple of seconds. So even if a new person came in, you could trust that this person would have at least spent a couple of hours on that. And then it was probably worth taking a closer look at it. That base thing is not true anymore. Everyone can simply create a new GitHub account and let their LLM loose, and as a maintainer you cannot easily tell whether someone put a lot of time into the PR (and maybe used Claude for just the PR description), or whether it is just an OpenClaw machine acting on its own. Projects like Zig or Gentoo are already refusing to accept LLM generated PRs (which I don’t think is the solution, because how would you even tell?) I think LLMs might have serious potential to kill OSS, if we don’t find ways to restore that trust. One idea could be to only allow a small set of verified people to contribute to a project, and in order to get verified you would need to go to a real-life meetup or something. And then there is the situation about junior engineers. There are actually two different points in there: a) you cannot trust the effort behind your junior’s code anymore, and b) seniors have no incentive left to teach juniors. Let’s start with a): Senior people have always corrected and fixed the code of junior people. And juniors have always written some pretty bad code (my worst code was written in the before-LLM times). It is just now that as a senior while reviewing you don’t know if that junior just vibecoded it in 10 minutes, or if he sat there for a couple of hours but is genuinely lacking some good insights. And b), the teaching, aka “How do we teach new people?”: previously, there was this balance aka “the junior does some pretty mundane tasks, but for this the senior reviews it together with him and helps him to grow”. Now as a senior, you don’t need juniors anymore. The mundane tasks, at least I find that a lot of people agree with that one, can be fully outsourced to an LLM. So why hire juniors at all? And then there are the geopolitical tensions. What happens if China or the US cut us off overnight from these technologies? Just a couple of weeks ago the US government showed it was able and willing to cut off non-US citizens from Anthropic’s latest frontier model . I don’t want to be a doomsayer here. I think Martin Kleppmann described it on stage in his talk pretty well: the probability of a conflict between Europe and the US is still very low. But last year, it was zero. Last but not least, even when just researching with LLMs, they have the natural tendency to silently sneak in the thoughts of the majority of the training materials, or sometimes even the political convictions of the ones who created the model. It is like two humans talking to each other: over time, their opinions will slowly merge. Ever noticed that one single friend who just keeps using this one weird word, and then a couple of weeks later your entire friend group is using it? It is like that, just with opinions. And one of the participants of the conversation is not a human. LLMs are good But we cannot make them go away entirely anymore. They are here, and they are here to stay. And instead of going against the flow, we can go with the flow, and then control it and shape it as well. For example by ensuring models can run on your laptop. They are already getting better, and they make us programmers independent of these big corporations. And when the subsidies end and the prices rise, it is the open-weights models that keep the large vendors in check. And a model that runs locally on your own hardware cannot be cut off overnight by any government either. I would even make the argument that when the bubble bursts, there is going to be a lot of damage to the world economy, and a lot of companies will topple. But the open weights models are not going anywhere, so we programmers can fall back on them. Even at the conference, the talks that were about AI mostly took local models seriously. Imagine: you would have this Sci-Fi AI running in the background, and you could always ask it questions, and it would always answer you. Like in Star Trek or something. In many of the talks, AI appeared only as an aside: “we built this with Claude Code.” Some of the speakers even openly sa