MATTEHWHOU

My current workflow: I describe the component in plain English with specific constraints ("a data table with sortable columns, sticky header, and virtual scrolling for 10k+ rows"), let the LLM generate the first pass, then manually fix the edge cases it always misses.

The key insight I've found: LLMs are great at generating the 80% scaffolding but terrible at the 20% that makes UI actually feel good — animation timing, scroll behavior, focus management, accessibility edge cases.

So I've stopped asking them for "production-ready" components and instead ask for "the boring structural parts" so I can focus on the interaction details that users actually notice.

There's a version of this argument I agree with and one I don't.

Agree: if you use AI as a replacement for thinking, your output converges to the mean. Everything sounds the same because it's all drawn from the same distribution.

Disagree: if you use AI as a draft generator and then aggressively edit with your own voice and opinions, the output is better than what most people produce manually — because you're spending your cognitive budget on the high-value parts (ideas, structure, voice) instead of the low-value parts (typing, grammar, formatting).

The tool isn't the problem. Using it as a crutch instead of a scaffold is the problem.

This is one of those projects that sounds impossible until you realize CUDA is basically C++ with some extensions and a runtime library.

The hard part isn't the language translation — it's matching NVIDIA's highly optimized libraries (cuBLAS, cuDNN, etc.). If BarraCUDA can hit even 80% of the performance on common ML workloads, that's a game changer for anyone who bought AMD hardware.

Curious about the PTX translation layer specifically. That's where most previous attempts (like ZLUDA) hit a wall.

I've been A/B testing the big three (GPT-5, Claude Opus 4, Gemini 3.1) on a real codebase migration this week.

Quick take: Gemini 3.1 Pro's long context is genuinely better now — I fed it a 200k token codebase and it could reference files from the beginning without losing track. That was a real problem in 3.0.

For pure code generation though, Claude still edges it out on following complex multi-step instructions. Gemini tends to take shortcuts when the task has more than ~5 constraints.

The exciting thing is how close they all are. Competition is working exactly as it should.

The interesting thing about llms.txt isn't the file format — it's the incentive shift.

With robots.txt, you were telling crawlers to go away. With llms.txt, you're inviting them in and curating what they see. That's a fundamentally different relationship.

I've been experimenting with this on a few projects and the biggest lesson: your llms.txt should NOT be a sitemap. It should be the answer to "if an AI could only read 5 pages on my site, which 5 would make it actually useful to end users?"

The projects where I got this right saw noticeably better AI-generated answers about our tools. The ones where I just dumped every doc link? No difference from not having it at all.