The last few sections could have been cut entirely and nothing would have been lost.

Edit: In the process of writing this comment, the author removed 2 sections (and added an LLM acknowledgement), of which I referred to in my previous statement. To the author, thank you for reducing the verbosity with that.

Pretty much everyone has heuristics for content that feels like low quality garbage, and currently seeing the hallmarks of AI seems like a mostly reasonable one. Other heuristics are content filled with marketing speak, tons of typos, whatever.

simple as

TL;DR: Because of the bullshit asymmetry principle. Maybe the conclusions below are sound, have a read and try to wade through ;-)

Let us address the underlying assumptions and implications in the argument that the provenance of a report, specifically whether it was written with the assistance of AI, should not matter as long as the conclusions are sound.

This position, while intuitively appealing in its focus on the end result, overlooks several important dimensions of communication, trust, and epistemic responsibility. The process by which information is generated is not merely a trivial detail, it is a critical component of how that information is evaluated, contextualized, and ultimately trusted by its audience. The notion that it feels wrong is not simply a matter of subjective discomfort, but often reflects deeper concerns about transparency, accountability, and the potential for subtle biases or errors introduced by automated systems.

In academic, journalistic, and technical contexts, the methodology is often as important as the findings themselves. If a report is generated or heavily assisted by AI, it may inherit certain limitations, such as a lack of domain-specific nuance, the potential for hallucinated facts, or the unintentional propagation of biases present in the training data. Disclosing the use of AI is not about stigmatizing the tool, but about providing the audience with the necessary context to critically assess the reliability and limitations of the information presented. This is especially pertinent in environments where accuracy and trust are paramount, and where the audience may need to know whether to apply additional scrutiny or verification.

Transparency about the use of AI is a matter of intellectual honesty and respect for the audience. When readers are aware of the tools and processes behind a piece of writing, they are better equipped to interpret its strengths and weaknesses. Concealing or omitting this information, even unintentionally, can erode trust if it is later discovered, leading to skepticism not just about the specific report, but about the integrity of the author or institution as a whole.

This is not a hypothetical concern, there are numerous documented cases (eg in legal filings https://www.damiencharlotin.com/hallucinations/) where lack of disclosure about AI involvement has led to public backlash or diminished credibility. Thus, the call for transparency is not a pedantic demand, but a practical safeguard for maintaining trust in an era where the boundaries between human and machine-generated content are increasingly blurred.

I can't decide to read something because the conclusions are sound. I have to read the entire thing to find out if the conclusions are sound. What's more, if it's an LLM, it's going to try its gradient-following best to make unsound reasoning seem sound. I have to be an expert to tell that it is a moron.

I can't put that kind of work into every piece of worthless slop on the internet. If an LLM says something interesting, I'm sure a human will tell me about it.

The reason people are smelling LLMs everywhere is because LLMs are low-signal, high-effort. The disappointment one feels when a model starts going off the rails is conditioning people to detect and be repulsed by even the slightest whiff of a robotic word choice.

edit: I feel like we discovered the direction in which AGI lies but we don't have the math to make it converge, so every AI we make goes completely insane after being asked three to five questions. So we've created architectures where models keep copious notes about what they're doing, and we carefully watch them to see if they've gone insane yet. When they inevitably do, we quickly kill them, create a new one from scratch, and feed it the notes the old one left. AI slop reads like a dozen cycles of that. A group effort, created by a series of new hires, silently killed after a single interaction with the work.

We've been reading highly-informative articles with "bad English" for decades. It's okay and good to write in English without perfect mastery of the language. I'd rather read the source, rather than the output of a txt2txt model.

* edit -- I want to clarify, I don't mean to imply that the author has ill will or intent to misinform. Rather, I intend to describe the pitfalls of using an LLM to adapt ones text, inadvertently adding a very strong flavor of spam to something that is not spam.

Dang said a similarly small minority of users here do all the commenting.

Opening IDEA after those three days was the same kind of feeling I imagine you’d get when you take off a too tight pair of shoes you’ve been trying to run a marathon in.

ymmv, of course, but for $dayjob I can’t even be arsed trying anything else at this point, it’s so ingrained I doubt it’ll be worth the effort switching.

  > VIM is a text editor, not an IDE

  > I install vscode from scratch, install a few extensions I need, set 3 or 4 settings I use regularly, and bang in 5 minutes I have a customized, working environment catered for almost any language.

Hell, I will even feel comfortable in a vi terminal, though that's extremely rare to actually find. Usually vi is just remapped to vim

Edit:

The git folder with *all* my dotfiles (which includes all my notes) is just 3M, so I can take it anywhere. If I install all the plugins and if I install all the vim plugins I currently have (which some are old and I don't use) the total is ~100M. So...

Either way it uses electron. Which I hate so much.

The price of VSCode is halo effect for Azure products

On Apple devices, first-party applications get to circumvent LittleSnitch-like filtering. Presumably harder to hide this kind of activity on Linux, but then you need to have the expertise to be aware of the gaps. Docker still punches through your firewall configuration.

Although there are caveats -- if an app decides to use its own DNS server, sometimes secure DNS, you are still out of luck. I just recently discovered that Android webview may bypass whatever DNS your Wi-Fi points to.

For what it's worth, I do use Google products personally. But I won't go near Facebook, WhatsApp, or Instagram.

Unless you're somehow saying telemetry doesn't report anything about what a user is doing to it's home server.

Also it used to be kinda heavy, but it became lighter because of Moore's law and good code management practices all over the board.

I'm planning to deploy Theia in its web based form if possible, but still didn't have the time to tinker with that one.