Code can be art, and copyright/plagiarism is real. It sort of boils down to how much it bothers us.

Not all image models (or sampler settings) do this, but with gpt it's very obvious.

With this in mind, I misjudged 3 images on the default game.

It sounds completely trivial and likely I'm wrong here, but could it be that opus saw the reference image squished? That might explain the sharper horizontal curvature

Is there nothing out there that does this? I'm paying for kagi and I can see that it has an api, is that maybe sufficient if configured properly?

(I switched to using local models as usage limits, api instability and the concept of paying per token stresses me out)

https://gistpreview.github.io/?815466e3208746488d47679949b68... - 33170 tokens

https://gistpreview.github.io/?815466e3208746488d47679949b68... - 18125 tokens

https://gistpreview.github.io/?815466e3208746488d47679949b68... - 12960 tokens

Scale 35 felt a bit noisy and incoherent, but 30 seemed nice. (they use the same seed, but idk how reliable seed in llamacpp is)

I use a python test script that captures the answer and renders it to a html page along with the llama-cli log, launch parameters, the chat log, and the python script itself for maximum transparency. :)

ThinkingCap is a 3.6 27b finetune that claims to halve thinking tokens while maintaining the same output quality. I've used the model a lot and I'd say it holds up. Since 3.6 has the same architecture as 3.8, the lora can be applied.

With the prompt "create a fancy circle in html", these are the results for xhigh, medium, low and xhigh + thinkingcap lora

https://gist.github.com/CapsAdmin/b0ea64006f942c5a96a56dba78...

(Note that the gists are bloated because they contain the full chat and launch params in text/plain script tags for transparency)

I'd say xhigh looks a little better than xhigh + lora, but the lora variant has 40% less thinking tokens. Both seemed to take the same approach with adding random details that weren't explicitly specified.

Medium and low (no lora) are close to each other but are much simpler results.

This is just me testing a single turn. I haven't tested this on multi turns and whatnot, but I thought the result was interesting enough to share anyway.

My total sleep time is about 6 hours. deep sleep is about 1 hour and 30 min. rem sleep is about 1 hour and 20 min.

I usually go to bed around 11pm and wake up at 6 am (give or take, I'm married)

It does however warn me about bad sleep if I've been drinking, have a fever, traveling across timezones, or staying up very late for some reason. One of these things may happen every 2 months or so.

I feel mentally good, I'm in shape and in general good health according to yearly checkups. I've been drinking coffee for about 18 years now.

It doesn't matter if I have 1 cup in the morning or drink coffee all day. As long as I have it in my system from the previous day, it's very easy to wake up the next day.

I think it's useful when evaluating "model+intended harness", but I'm more interested in seeing raw model improvements than harness improvements.

I've had to use the aws cli at work a few times (not an expert), but I could see aws trying to compete here if this becomes a thing.

Almost everything was possible given you put in the effort, but few people possess the will to put in the effort AND pursue a malicious goal.

I think an obvious example is all the fake ai content flooding the internet made to trick people in exchange for money (ad revenue, scams, likes, etc). This existed before ai, but I think it's fair to say pumping out content now requires less effort than it did before.

Most physical locks are an example of security through obscurity/effort. You can after all just pick a lock if you go through the effort to learn the skill. But once a universal lock picker is made available to everyone, you will simply see more locks getting picked.

So I guess I kind of prefer seeing helper functions at the top and the main logic at the bottom most of the time.

The way I see it, a script kiddie is someone who buy weapons with the intention to cause harm. They don't know or care how the weapon works, as it's just a tool for their malicious plan.

A non-script kiddie would be someone who build weapons.

If you assume weapons have recreational purposes, the builder/non script-kiddie is less likely to be malicious as they love the craft of building weapons. For example recreating historical weapons, or just being nerdy about machinery of a gun works.

A builder with malicious intent as first priority can exist, they are more likely to just be interested in building.

So basically, nerds are perceived to be more ethical than non nerds.

However people will claim that the artist had an intention, it was just not obvious to the artist at the time. The art was also shaped by their experiences, etc. So in contrast, AI does not have this intention.

For example, I had never thought or cared where the trash can in my office came from. I forgot where I bought it from. Now that I'm thinking about it, I'm a little curious, but is it worth knowing? If I knew the designer of the trash can was a bad person, should I get get a new one and make sure the designer of that trash can is a good person?

I think logically and ethically yes, but I'm not willing to spend my time on this.

It's an extreme example, but I think the same applies to music, art, movies, etc, just to a lesser degree for people who claim they care more about the outcome than the human author.

I'm not too familiar with visual arts, but I know most jazz musicians make a living off of teaching music and doing concerts where the attendees are just other musicians. It's very much centered around technique and human performance. (the term musician's musician is also used here)

Meanwhile, the average person think jazz is noise.

So my point is, saying "What makes music meaningful..." sounds more like an elitist jazz musician take. (I'd say jazz musicians tend to be more self deprecating than elitist, and often make light of the genre and how people perceive it!)