thomashop

I'm also a fan of the Yamanote Line. I made a psychedelic AI audio-visual collage inspired by it.

https://www.youtube.com/watch?v=xwUSzUvShqcaa

I made field recordings during my last stay in Tokyo. From those, I made a song for each station of the Yamanote line, using the Jingle in the prompt. The visuals were made similarly.

Used mainly Suno, Udio, Runway and Ableton Live.

i thought the agent can execute real ffmpeg to compare

I don't think it's correct that we destroyed everything that isn't us. If we take all living beings, we have destroyed only a small percentage.

Ah understood i misread

Why is privacy a concern? You can run them in your own infrastructure

I'm also a fan of the Yamanote Line.

I made a psychedelic AI audio-visual collage inspired by it.

https://www.youtube.com/watch?v=xwUSzUvShqcaa

I made field recordings during my last stay in Tokyo. From those, I made a song for each station of the Yamanote line, using the Jingle in the prompt. The visuals were made similarly.

Used mainly Suno, Udio, Runway and Ableton Live.

I have done quite a bit of game dev with LLMs and have very rarely run into the problem you mention. I've been surprised by how easily LLMs will create even harmful narratives if I ask them to code them as a game.

The rest was fine without citations, but the part you disagree with needs them?

I have the impression that the thinking helps even if the actual content of the thinking output is nonsense. It awards more cycles to the model to think about the problem.

I use AI to simplify code. My manifesto has always been code is debt. Works really well too.

I don't care that much.

This level of knowledge about me can also be easily found on the internet.

I'm also working almost entirely on open-source software so I'm happy if the AIs know more about my projects.

But this, of course, only applies to me.

Interesting

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Who runs python code on mobile?

The AI is not paying for watching videos yet

But aren't they getting this data already at a much larger scale? GPT is still one of the backbones in many coding assistants, even Windsurf.

I'm pretty sure the model is cached with the system prompt already processed. So you should only pay extra tokens.

"I cannot emphasize this enough: ChatGPT is not generating meaning. It is arranging word patterns. I could tell GPT to add in an anomaly for the 1970s - like the girl looking at Billy’s Instagram - and it would introduce it into the text without a comment about being anomalous."

I asked ChatGPT to introduce the girl looking at Billy's instagram. The response:

"Instagram didn't exist in the 1970s. Do you want to keep the setting authentic to the '70s or update the story to a contemporary timeframe where Instagram fits naturally?"

It always feels like people are irrationally critical of AI assisted stuff. Does the typical Hacker News comment have more substance?

- Informally benchmarked against 4 specific competitors: Gemini, OpenAI, o3, and Claude

- Identified two concrete features: URL content ingestion and integrated search

- Noted specific limitations: search engine occasionally misses key resources

- Provided a real-world test case: consulting business analysis where it found new opportunities other models missed

Why sorry? So what?

I often write things I want to post in bullets and then have it formulated better than I could by an LLM. But its just applying a style. The content comes from me.

My wife is dyslexic so she passes most things she writes through ChatGPT. Also not everyone is a native speaker.

It feels like we've been moving in the opposite direction, where more and more models from various countries are state-of-the-art.

The idea that there will be one model to rule them seems very unlikely.

Sure. You can always write more tests. That's not a problem specific to AI.

I'd also do code reviews on the code AI produces.

Why not write tests with AI, too? Since using LLMs as coding assistants, my codebases have much more thorough documentation, testing and code coverage.

Don't start when you're already in a buggy dead-end. Test-driven development with LLMs should be done right from the start.

Also keep the code modular so it is easy to include the correct context. Fine-grained git commits. Feature-branches.

All the tools that help teams of humans of varying levels of expertise work together.

And that's just hooey.

Well, I mean Google animism. It's hooey from your point of view.

I'm one of those people. Very happy to associate myself with animism and anthropomorphize animals and machines. I think one of the biggest mistakes with Christianity and the western world is that we see ourselves as something greater than animals and other things.

Animism is the belief that objects, places, and creatures all possess a distinct spiritual essence. Animism perceives all things—animals, plants, rocks, ...

Ask it to plot the graph with python plotting utilities. Not using its image generator. I think you need a ChatGPT subscription though for it to be able to run python code.

I made a similar experiment using the Hitchhiker's Guide to the Galaxy theme.

Have you ever argued with a neurotic elevator? Try convincing this Sirius Cybernetics Happy Vertical People Transporter to go down!

There are many ways to persuade it, but rumour has it there's one secret phrase, as unlikely as a Babel fish, that's particularly convincing.

https://sirius-cybernetics.pollinations.ai/

Don't Panic! (I made this. The source code is also provided)

New research shows that by extending instruction tuning to handle visual tokens, LLMs can simultaneously learn image understanding and generation with minimal changes. The most intriguing finding is that visual generation capabilities emerge naturally as the model gets better at understanding - requiring only ~200K samples compared to millions typically needed.

It suggests current LLM architectures might already contain the building blocks needed for unified multimodal AI.

We have moved 2 orders of magnitude in the last year. Not that unreasonable

Maybe I mixed up that paper with another but the one I meant to post shows that you can read something like a world model from the activations of the layers.

There was a paper that shows a model trained on Othello moves creates a model of the board, models the skill level of their opponent and more.

You can never trust the outputs of humans to be correct but we find ways of verifying and correcting mistakes. The same extra layer is needed for LLMs.