I disagree completely. It is very obviously LLM-written, and I would much rather read grammatically poor English than LLM-written text, which has a dystopian vibe and just makes me depressed.
I flew from central US to western asia (via moscow) and it was an interesting experience for the reasons you mentioned. I think I left early Saturday morning local time and arrived Sunday evening local time. I saw a sunrise, sunset, sunrise, sunset in 18 hours of travel time.
We all admire your absolute mastery of your own habitual reflexes and mind. For the rest of us, there is a daily battle of wits, desires, weakness, and habit.
If I could snap my fingers and break toxic habits and patterns, I would have done so decades ago :)
In my experience the LLMs work better with frameworks that have more rigid guidance. Something like Tailwind has a body of examples that work together, language to reason about the behavior needed, higher levels of abstraction (potentially), etc. This seems to be helpful.
The LLMs can certainly use raw CSS and it works well, the challenge is when you need consistent framing across many pages with mounting special cases, and the LLMs may make extrapolate small inconsistencies further. If you stick within a rigid framework, the inconsistencies should be less across a larger project (in theory, at least).
I've visited this museum and it was the highlight of my trip to the netherlands. I also wondered, for hours, about how cool it is to hook up modern hardware to these old systems. Can you imagine playing one live, similar to how an artist would play a synthesizer kit?
This technique is surprisingly powerful. Yesterday I built an experimental cellular automata classifier system based on some research papers I found and was curious about. Aside from the sheer magic of the entire build process with Cursor + GPT5-Codex, one big breakthrough was simply cloning the original repo's source code and copy/pasting the paper into a .txt file.
Now when I ask questions about design decisions, the LLM refers to the original paper and cites the decisions without googling or hallucinating.
With just these two things in my local repo, the LLM created test scripts to compare our results versus the paper and fixed bugs automatically, helped me make decisions based on the paper's findings, helped me tune parameters based on the empirical outcomes, and even discovered a critical bug in our code that was caused by our training data being random generated versus the paper's training data being a permutation over the whole solution space.
All of this work was done in one evening and I'm still blown away by it. We even ported our code to golang, parallelized it, and saw a 10x speedup in the processing. Right before heading to bed, I had the LLM spin up a novel simulator using a quirky set of tests that I invented using hypothetical sensors and data that have not yet been implemented, and it nailed it first try - using smart abstractions and not touching the original engine implementation at all. This tech is getting freaky.
I recently learned that buybacks and short selling historically have not been legal, its only in recent history that they've been standard practice en large (1982 is when buybacks were legalized, I think)
It’s part of Reaganomics. Buybacks weren’t explicitly illegal beforehand though, it just wasn’t clear when they would count as stock price manipulation.
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