I consider myself a total 100% Internet-loving Geeky Nerd. And yet I never had (oh well, I did register and post 1-2 posts N years ago) Twitter, Twitcher, Threads, MySpace, Instagram, TikTok, Red Book, Facebook, any other -book for that matter, no Youtube channel, and I very rarely watch it. I also never "get information" from these. I don't understand how people do it.

So every time I got a "rabbit hole itch", I went straight to Google, lost in some niche blogs or forums.

Nowadays, of course, the Internet is gone - you can't Google a non-LLM blog, or there are just no non-LLM blogs anymore? So I go straight to Deep Research. I don't enjoy it, but I would never think to find my information on Twitter or something. Sometimes I will read a book. There are niche books with info that LLMs do not have, or have but it's shallow.

For "news" I have my trusty RSS feed from the days of Google Reader (now on Newsblur, happy customer for many years). If something starts to spam, I mute it.

Maybe all of this is just because I'm a loner without any friends. That's one possible reason.

I also don't have any streaming services. Everything you need can be... found elsewhere wink-wink, in good quality. We watch some older stuff, Breaking Bad, Person of Interest, some non-English stuff. Same with music. Let the dust settle and watch the good shows 2-3 years after. You also get the benefit of ignoring ones that get canceled (OA anyone).

YMMV I guess.

– Yes: 0.1% / No: 99.9%

– ...but I work at a crocodile farm!

For example, we know that Anthropic added "watermarking" to their texts. It is supposed to be undetectable to a casual observer. What stops them from adding a subtle backdoor, a self-assembling super-worm straight from Marvel movies? I mean, it's not like we read those 10k-line PRs before LGTM-ing them?

Just change 1 letter in a pyproject.toml, hijack a popular package, e.g. use `pydantlc` instead of `pydantic`, make sure the pydantlc passes all pydantic tests, but also installs a pth sleeper RAT, etc. All it takes is one big LLM provider employee with enough access getting compromised or coerced (or motivated).

From there it only goes downhill.

- work became horrible

- internet content became horrible

- even restaurant menus became horrible

Where do we go from here?

AI is very optimized to producing code, but not there (yet? ever?) in making products. Show me a serious software made entirely with AI. There's no AI SolidWorks, AutoCAD, MacOS.

And there lies a contradiction: AI is making it easy to start and produce copious amounts of code that looks okay-ish. We start releasing a product and notice subtle changes. We did not internalize our understanding through it. There's a million lines of smart looking code, so we don't know where's what. We ask to AI to "make the button blue" https://opusfived.dev/ and the cycle begins.

I am working on a large software at work, trying a "I didn't even look at the code" approach with GPT 5.6 Sol. It started really promising but by now (and about $10k in tokens) it's a complete clusterfuck. Yes I use GSD and code graph etc.

Industrial revolution worked that way because it replaced something very finite and unscalable - manual labor. LLMs just make intellectual work faster, so we can do more intellectual work. With labor we somehow decided that NOT doing too much of it is best. Will we decide to reduce intellectual labor because LLM made it more efficient? I doubt that.

On the other side, as I see in software engineering, the same models are available to everyone, some people are better at it and some people are not. "Software developer" is here to stay, we'll just always be better at it than people who are experts in, say, chemistry. Same works for most other fields.

So we'll just end up in the same situation, with same intellectual labor baseline, just more output requirements. Before, you spend 2h per day coding, deliver a software in 1 month, later, you spend the same 2h per day in intense Claude-herding sessions, deliver a software in 1 week. Ok. Next task.

Fundamentally, there's finite number of desirable resources, and if the models are available to everyone, humanity will just continue about the same, bickering here and there, war here and there, politics, homelessness, poverty, - normal human state.

And if the models are only available to elites, even worse.

If you need to independently verify every fact, why not just gather facts yourself in the first place.

Let's say, a mathematical concept of lie. I still use them every day, of course.

I was traveling to an obscure small town, doing some "research" with LLMs beforehand. Every and each one told me enthusiastically to go to "Foobar square" (name changed) for the "best street food in XYZ town", some added a lot of colorful details.

There was no Foobar square in XYZ town. There was no Foobar square anywhere in the world. There was a SINGLE old Reddit comment, with no upvotes, to a unpopular post in an unpopular subreddit, where someone clearly badly misspelled the name of the square, and said something like "for street food go to Foobar square". Nothing about "the best" even.

It's all a lie.

Or you play chess, but now you can insta-win by blinking left eye twice.

I consider myself reasonably skilled in AI usage (multiple harnesses, skills, MCP, etc). I use only the latest models: Opus 5, GPT 5.6 Sol. I experiment with different plan-build-evaluate frameworks like GSD, OpenSpec, Superpowers. I have a reasonable AGENTS.md without much cruft: test coverage ~80%, ASD-STE100 English, use uv - things like that.

I can churn out single-use scripts and micro-projects like there's no tomorrow - one shot GSD in autonomous mode usually nails it.

But when it comes to any larger software, it STARTS very promising, but very quickly becomes a quagmire of a death by thousand cuts. I steer the general ideas well enough, but the amount of code and tests quickly grows overwhelming, weird shit starts to creep in, 2000-test harness starts to test literal things, features error out, fixes take longer and produce tons of defensive code. Asking the model to "refactor if it improves readability and reduces complexity" usually only increases complexity. After $3-5k in tokens spent on a 100k+ LoC monstrosity I just don't even want to touch it anymore. AI feels like a trap that lures you with easy wins but then you pay the debts.

Many words to say the same as many others in this thread: good design takes time and working WITH the code, seeing and internalizing the decisions.

I also don't know what to do. Writing things completely by hand just doesn't feel right anymore, and co-designing with AI (as in laying out the classes and function contracts etc) feels weird because you need to iterate to build understanding, but the LLM will happily follow any stupid idea you happened to have.

Quadruple all of this when you work in a team and keep getting 5-minute effort 10k LoC PRs with 20-line load-bearing honest caveat comments.

Maybe it's time to get to woodworking.