Most GenAI was trained on material they had no right to train on (including plenty of mine). So I’m doing my small part, and serving known AI agents an infinite maze of garbage. They can fuck right off.

Now, if we’re talking about real AI, that isn’t just a server park of disguised markov chains in a trenchcoat, neural networks that weren’t trained on stolen data, that’s a whole different story.

Let me know when we have some real AI to evaluate rather than products labeled as a marketing ploy. Anyone remember when everything had to be called “3D” because it was cool? I missed my chance to get 3D stereo cables.

It’s a glorified crawler that is incredibly inefficient. I don’t use it because I’ve been programmed to be picky about my sources and LLMs haven’t.

It’s a tool with some interesting capabilities. It’s very much in a hype phase right now, but legitimate uses are also emerging. Automatically generating subtitles is one good example of that. We also don’t know what the plateau for this tech will be. Right now there are a lot of advancements happening at rapid pace, and it’s hard to say how far people can push this tech before we start hitting diminishing returns.

For non generative uses, using neural networks to look for cancer tumors is a great use case https://pmc.ncbi.nlm.nih.gov/articles/PMC9904903/

Another use case is using neural nets to monitor infrastructure the way China is doing with their high speed rail network https://interestingengineering.com/transportation/china-now-using-ai-to-manage-worlds-largest-high-speed-railway-system

DeepSeek R1 appears to be good at analyzing code and suggesting potential optimizations, so it’s possible that these tools could work as profilers https://simonwillison.net/2025/Jan/27/llamacpp-pr/

I do think it’s likely that LLMs will become a part of more complex systems using different techniques in complimentary ways. For example, neurosymbolics seems like a very promising approach. It uses deep neural nets to parse and classify noisy input data, and then uses a symbolic logic engine to operate on the classified data internally. This addresses a key limitation of LLMs which is the ability to do reasoning in a reliable way and to explain how it arrives at a solution.

Personally, I generally feel positively about this tech and I think it will have a lot of interesting uses down the road.

Mixed feelings. I decided not to study graphic design because I saw the writing on the wall, so I’m a little salty. I think they can be really useful for cutting back on menial tasks though. For example, I don’t see why people bitch about someone using AI for their cover letter as long as they proofread it afterwards. That seems like the kind of thing you’d want to automate, unlike art and human interaction.

I think right now I just kind of hate AI because of capitalism. Tech companies are trying to make it sound like they can do so many things they really can’t, and people are falling for it.