doginasuit
I've been working on a project for the last 18 months, and over that time LLMs have advanced to the point where they've become useful. I can write a section of UI and ask the LLM to write all the front end and backend services. There are enough examples that it writes exactly the code I would have written myself. It can build entire sections of code quickly and efficiently because there aren't very many questions without an answer somewhere in the codebase, which it is very good at finding. If it finds any gaps, it can raise it as a question. It analyzes, asks questions, and then completes the work.
The impression that I'm starting to form is that you do not want to use AI very much early in a project, except to ask questions and check your code. But it's usefulness at assisting with code scales with the size of the project.
I believe the next few years are going to see a lot of new and innovative tech products. Maybe this is how we build the internet we actually want.
Investment in product quality, "Don't be evil", everything that set a company apart and allowed it to rise, it lasts only as long as it is preserved in a way that resists decay.
Is there anything left of the rowboat? I'm at the point where I am actively trying to shut Google out of my life to the degree that it is possible. And yet it is still not Microsoft. I catch a glimmer of the old google in a product release now and then.
May we take this as an opportunity to recognize what a bad deal we get from most employers and explore better ways to organize labor. It's never been more possible. UBI is unlikely to happen and uncertain it would even be beneficial. If AI can lead to a better world, this seems like the most promising path.
It seems like the question is where to fit it in to the pipeline. The team responsible for reviewing submissions could leverage it to speed up the process, and still respond personally.
I remember these! I could not figure out why it never seemed to make the computer faster and actually made it slower. TURBO was even written in italics to imply speed.
I suppose for some short time it may have actually overclocked the CPU, until the default clock speed caught up. After that, the turbo button was a lie.
Edit: Maybe I have it backwards, it sounds like on some machines it might have been in the underclocked condition when it was off. I just remember disappointing results with the machine that I used.
While it might get workable results, this seems like a miserable way to work with code. You spend the vast majority of time thinking about the problem domain of working with agents instead of your project, and for mediocre output. You are transforming your role into a dumpster diver of code.
The approach that proves to be fastest in the long run is to stay close to the code. Lay out the base structure, and use an LLM to check your work and fill in the gaps. The beauty of this approach is that it scales very well with the size of the project. When the LLM has plenty of high quality examples to follow, they start to write the same code that you would. Where it diverges, create documentation.
Perhaps the single best property of LLMs is their ability to consume and apply examples and documentation.
> It is amazing what you can accomplish if you do not care who gets the credit.
--Harry S. Truman
https://en.wikipedia.org/wiki/Zizians
Beyond that, Effective Altruism got its start partly on Lesswrong. Whether or not that counts as harmful would depend on how you see that movement, but one of its biggest proponents is now in jail.
https://en.wikipedia.org/wiki/Sam_Bankman-Fried
There's a famous thought experiment that came out of Lesswrong that has resulted in a lot of distress and questionable life choices from people who take it too seriously. A lot of the AI discussion I've seen on the site has the same overtones, including this particular post.
Lesswrong is one of the smellier armpits of the internet. My impression is that there are clearly a lot of smart people who post there, but they've collectively embraced a lot of delusional thinking. There have been examples where it has escaped containment and harmed people.
Everything that comes from an LLM is a human expression, on some level, but if you asked a sampling of humans the same question I wonder if it would come out the same way. How would you explain the difference? What makes that answer more probable?
The software had a quantization feature which he showed us how to use but he warned us not to use it on everything, because it makes music sound mechanical and less human. I guess that philosophy did not survive in the music industry.
Social media definitely didn't invent this. I'm not overly familiar with what is popular on social media, but it seems like the internet has greatly expanded the viability of different styles and niche music. When I get together with friends, we all have tons of music than most of us haven't heard before.
Icons can genuinely help convey meaning/understanding. They can also be clutter. It comes down to how you use them.
I think you are close to every effective presentation format, but the cramped space for the text was the biggest friction. The moving visuals, as helpful as they are, also became a little distracting as I was trying to absorb the meaning of the text. You could solve both issues with a toggle to hide or expand the visual.