I support this. There is a mass psychological effect emerging out of this AI-content-witch-hunt where people are manufacturing "flaws" to beat the AI-slop allegations.
To list out a few personal examples:
1. Several students I knew at GaTech in 2024 taking the algorithm class (which is notoriously hard) started submitting assignments with sub-optimal/brute-force algorithms cuz the TAs kept reporting them for academic misconduct on optimal solutions.
2. I've started avoiding "em-dashes" in all my writing
3. Junior engineers leaving "typos" in their code reviews or submitting code-reviews with absolutely 0 comments (LLMs love to leave verbose comments)
> A general pattern for LLMs is that they look really good at things you are bad at.
Naah I disagree with this. I think LLM's are good at gas-lighting you into thinking that good writing only comes in one flavor. And LLMs prefer a very "textbook/technical-manual" coded flavor of writing because maybe that way they are more useful to us humans. But human writing is not just about crafting the most elegant sentences. Sometimes great writing is just this doggo-drawing meme:
That was actually the first thing I tried. It did a good jov at explaining the code base mess and the architecture. Then I ran 3-4 refactor attempts. Each one broke things in ways that were harder to debug than the original mess. The god object had so many implicit dependencies that pulling one thread unraveled something else. And each attempt burned through my daily Claude usage limit before the refactor was stable.
And I'm sure the rewrite is going to teach me a whole different set of lessons...
Go reads fine whether the architecture is good or bad, and I couldn't tell the difference until I was in trouble. Rust is harder to read but harder to misuse. The borrow checker would have caught that data race at compile time. I've also just written more Rust. That familiarity matters separately.
+1 on Open 4.7 involving the user a lot more. Rn I'm trying to get to a state where I can codify my design + decision preferences as agents personas and push myself out of the dev loop.
Partly, but the order matters. The CLAUDE.md constraints only work if you designed the architecture first. They're just how you communicate it to the AI. The mistake I made wasn't writing bad skills files, it was not designing anything at all and expecting the AI to make coherent structural decisions across 30 sessions.
The rewrite is me sitting down with a blank doc and drawing the boxes before any code exists. Then the CLAUDE.md enforces what I already decided. Whether that actually holds up as the project grows, I genuinely don't know yet.
I personally know people who look down upon people who use LLMs to write code. There is a lot of hate in some of senior developers that I talk to. I don't know if this growing tendency to be suspicious of AI usage is good or bad.
For example, towards the final semester of my bachelors degree, my algorithms class started reporting students for academic misconduct because they the TAs started assuming that all the optimal solutions to assignment problems were written by LLMs. In fact, several classmates started purposely writing sub-optmial solutions so that the TAs at least grade them without any prejudice.
I worry that because LLM slop also tends to be so well presented, it might compel software developers to start writing shabby code and documentation on purpose to make it appear human.
Curious to know if others are seeing a similar uptick in AI slop in issues or PRs for projects they are maintaining. If yes, how are you dealing with this?
Some of the software that I maintain is critical to container ecosystem and I'm an extremely paranoid developer who starts investigating any github issue within a few minutes of it opening. Now, some of these AI slop github issues have a way to "gaslight" me into thinking that some code paths are problematic when they actually are not. And lately AI slop in issues and PRs have been taking up a lot of my time.
Motivation is that large-language models have a very straight-forward task of predicting the next token and the dataset is easy to get. With this app I aim to do two things:
1. Gather a fairly large dataset that captures the brush-strokes for various art prompts.
2. Bootstrap an algorithm / model that can decompose any image/art/illustration into brush strokes.
A longer-term goal for this app is to build an auto-complete (Co-pilot or Grammarly equivalent) for art.
FAANG is to be blamed for rapid rise in incompetence in tech. So many engineers are grinding on leetcode and bagging jobs that pay them insanely high salaries. These engineers know next to nothing when it comes to real world engineering challenges. I knew a guy who had a 200 day leetcode streak but didn't know the difference between a process and thread.
We have essentially collectively decided that these stupid hiring processes are an accurate proxy for finding real talented people.
I know so many colleagues who are here just to virtue signal that they work for a "cool" company. And it has become super easy to get into these companies and coast.
free will kiddo