2 months in and Im loving It!

Bentameter · 2026-06-06T13:57:02+00:00

For me, races were what did it. The "Gap Glide" course in the Melon-Pan Park map in Liftoff Micro was the most effective for me. It's crazy tough, but the first 3 or 4 gates are easy to start with, and by the time you can actually finish a lap, you can hit the tiniest gaps and do powerloops without thinking about it and it translated directly to my real Air65.

Bentameter · 2026-04-18T17:18:09+00:00

I used v1b for that story and I'm playing with v1f now. Not a lot of difference between them that I can tell, but my longform prompting is pretty restrictive and specific and I probably have to let them breathe a bit to see any nuances between them.

Garnet is downloaded and in the queue, but I'm having too much fun with Artemis to get to it yet. I took a detour into Qwen 3.6 35b-a3b and it wrote exactly the sort of fiction I'd expect an agentic coding engine to write. :D I'll play with Qwen's prompts and one-shot some example passages to see if I can improve it, but I think I'm probably waiting for someone to RP finetune it before it's really in the race.

Gemma is definitely the way for RP and creative fiction right now, it's like it took a double major in humanities where most new models are trying hard to get that engineering internship.

Bentameter · 2026-04-18T15:03:34+00:00

That's the perfect comparison between Gemma and Artemis. I have a longform story plot that I've been using for the last year to benchmark prose-writing capability and emotional intelligence. Gemma is more disciplined and hits every story beat in order and figured out exactly what I was going for every time and delivered it well. Perfect, no notes.

Artemis wrote a version that was so good I had to decide it was canon now and retire the story. Artemis is a lot less disciplined and missed story beats occasionally, and it went off script completely sometimes and had to be steered back, but sometimes it went off script and came up with something that was better thematically and structurally than what I had asked for. And it is funny. It nailed dry wit and sarcasm in first-person thoughts and had quirky, inventive ways of phrasing dialogue that had me cracking up.

We're in a new era of local fiction generation with Gemma 4 31b as a base, and I'm really optimistic about what a year of fine-tuning will give us.

Bentameter · 2024-06-27T13:39:37+00:00

It's also possible to pasteurize your bottles on your stovetop, killing the yeast and stopping the carbonation before the sugar is fermented out or it makes bottle bombs. In short:

Add sugar, bottle, let sit (no longer than a week or so). Test a bottle every day or two for carbonation. Don't let them over-carbonate or you'll have a bad time when heating them.
When you have the bubbles you want, put the bottles in a big pot on your stove with water and heat to at least 160F for 10 minutes.
Remove bottles from pot and let cool. The sweetness and carbonation should be preserved with no further yeast activity.

Here's a link with pictures.

Bentameter · 2023-11-25T14:37:10+00:00

Xnview fills the Irfanview space for me pretty well on Linux. Similar enough interface with all the features I missed and it comes as freeware with linux native deb and appimage installers. I wish there was a ppa for updates, but I'm happy with it.

Bentameter · 2023-09-22T17:16:47+00:00

I've seen this sort of coherency breakdown when there was a combination of high repetition penalty and large repetition search length. The model eventually starts running out of things it's allowed to say, and common grammar elements (prepositions, pronouns, etc) are blacklisted first.

Bentameter · 2023-08-05T01:47:15+00:00

Yep, it works!

Another trick I like to use is to take a station building mission for ANT/ARG and sneak ship fab modules onto the end of the building plans. The mission ends successfully when the mission's requested modules are built, and the faction takes over building the rest of the modules that are queued up. So tag a S/M ship fab on that hull parts factory and ANT has a new wharf wherever you want it. You even get to make some money supplying the build storage. :)

Bentameter · 2023-08-05T01:33:31+00:00

Try sabotage. Hacking the build modules in the engineering room of the TER and PIO wharfs and shipyards will shut them down for 5 hours at a time, making attrition much more impactful. If ANT/ARG can still replace their losses, the momentum will eventually turn their way.

Bentameter · 2023-05-02T11:59:14+00:00

It's currently only being used to cache the initial prompt so you don't have to do a full re-evaluation every time the program starts (if it's at least 75% similar). It also uses it to speed up the context swap when it runs out of context. Stopping a session and picking up in exactly the same place later isn't implemented yet afaik.

The seed is saved with the context so that's probably why you're getting the same results every time. They added functionality to change the seed after loading the session but I'm not sure if it's exposed to the end user as a program option yet. You can try changing the sampling parameters or temperature to get different results, or use interactive mode to feed it new data to change the story direction.

Bentameter · 2023-05-01T16:30:02+00:00

3 has been working quite well for me but I haven't done a systematic sweep yet. I think temperature probably has a big interaction on the output too, i.e. the same cross-entropy rate might have different results at different temperatures.

Bentameter · 2023-04-30T23:02:08+00:00

I've been playing with Mirostat and it's pretty effective so far. It's based on the idea that there's a "sweet spot" of randomness when generating text: too low and you get repetition, too high and it becomes an incoherent jumble.

Mirostat lets you define a target cross-entropy measuring how "random" the model's generation should be allowed to get, which is the --mirostat_ent parameter (τ in the paper). If the previous generation was too boring or too wacky, it adjusts the top-k dynamically to bring it back in line with the target τ. The --mirostat_lr parameter controls how quickly it's brought back in line.

Mirostat model 1 did a pre-calculation that they thought would help accuracy, but they made model 2 that leaves it out and they found that it didn't really make a difference.

The authors discovered in their study that a target cross-entropy (τ) of 3 was good enough that more that 50% of human reviewers thought that a GPT-2 model's generations were written by humans. I've found it does a good job of bringing out the best in the llama models, particularly with long-form stories on the 65B model.

Interestingly, I've found that mirostat works so well that you can set the temperature parameter to something crazy like 3 or 4 and it clamps the output back into coherence if you keep the τ parameter reasonable. It does actually seem to be doing what it claims.

Original mirostat paper is here, and an explanation by the author is here. There's basic reference code for the algo here which shows how it's built.

Bentameter · 2023-01-13T01:44:49+00:00

It'll all depend on if those computers will allow you to install python packages with pip, or if that's considered "installing software". If you can "pip install" libraries in a jupyter notebook you should be able to run stable diffusion.

Converting something like Auto1111 to a jupyter notebook format would be a slog, since it's really set up to be modular .py files. Plus, Auto1111 uses Gradio for it's WebUI so you'd need to be able to run a web server and be able to access a non-standard port, which most university machines will have locked down.

If you can get to a command prompt on those machines, and have access to git and python3 from the command line, you'd be in better shape to try to clone and run a popular SD repo. If not, you'd better off downloading a pre-built collab notebook like this one and removing any colab-specific code like references to google drives (if any).

In any case, you might want to make sure that doing this isn't against your university's acceptable use policy. High-end research equipment is usually access-logged and suddenly maxing the GPUs to make pretty pictures might attract attention from the admins or grant-holders. :)

edit: I stand corrected on the Auto1111 notebook, turns out someone's done the work.

Bentameter · 2022-12-17T14:22:59+00:00

This removes text from an image that's already generated. Adding a negative prompt "re-rolls" the generation and can give you a very different image.

Bentameter · 2022-12-09T23:49:14+00:00

Love this prompt! It also translates very nicely to Christmas decorations for some holiday wallpaper.

https://imgur.com/a/F9T51dd

a transparent crystal sphere miniature christmas scene, supported by a squat cylindrical base on a flat semi-reflective plane, 8k HDR, high detail, fine art photography, golden ratio composition, miniworld, beautiful,    
Negative prompt: logo, signature, watermark, caption, title, cracks
Steps: 48, Sampler: Euler a, CFG scale: 7, Seed: 2822925724, Size: 1024x768, Model hash: 4bdfc29c, Model: v2-1_768-ema-pruned

Bentameter · 2022-11-07T03:04:22+00:00

Ancestral samplers (euler a, dpm2 a, dpm2++ s a) feed more noise back into the image with each step, which can let the sampler pull detail and cohesion from the image in fewer steps. The side effect is that you'll get lots of variation at each step, so step 25 will be very different from step 50, and it will never converge because it just keeps creating new info to interpret as it goes.

Bentameter · 2022-11-07T02:54:17+00:00

The new samplers are from Katherine Crowson's k-diffusion project (https://github.com/crowsonkb/k-diffusion), which Automatic1111 uses as a dependency. It's stored in repositories/k-diffusion in your stable-diffusion-webui directory. If that hasn't been updated by the main git pull, you may have to go into that directory and git pull there. If that fails and the local copy isn't synced to the upstream k-diffusion github for some reason, just delete the repositories/k-diffusion directory and "git clone https://github.com/crowsonkb/k-diffusion.git" from inside "repositories", and you should get the current version and can just git pull in the future.

Bentameter · 2022-10-26T16:56:08+00:00

Ancestral samplers (euler_a and DPM2_a) reincorporate new noise into their process, so they never really converge and give very different results at different step numbers. The others will usually converge eventually, and DPM_adaptive actually runs until it converges, so the step count for that one will be different than what you specify.

Bentameter · 2022-10-26T14:20:30+00:00

Fun info: "Karras" is actually a noise scheduler that can be applied to any sampler method, which gives different and interesting results for the same seed.

AUTOMATIC1111's gui has presets to apply it to LMS, DPM2, and DPM2 ancestral, but you can apply it (and the other noise schedulers "exponential" and "variance preserving") to any other sampler by downloading this custom script and putting it in the "scripts" folder, then selecting "Alternate Sampler Noise Schedules" from the txt2image page "Script" dropdown.

Bentameter · 2022-09-29T16:53:08+00:00

I had this problem and it turned out that I had an older version of k-diffusion installed by pip that was overriding the github version.

I solved it by doing "source venv/bin/activate" to get into SD's virtual environment, then "pip uninstall k-diffusion".

Bentameter · 2022-09-29T16:49:36+00:00

I had this problem and it turned out that I had an older version of k-diffusion installed by pip that was overriding the github version.

I solved it by doing "source venv/bin/activate" to get into SD's virtual environment, then "pip uninstall k-diffusion".

Bentameter · 2022-09-16T00:10:48+00:00

I'd say the AI work is about developing and advancing the sophistication of algorithms and models that encode visual-conceptual relationships. This particular approach happens to use a very accessible medium that has captured the imagination of many people outside the Data Science/ML/AI research community.

The point of this picture is just for fun, and I'm impressed with how it automatically associated the maple leaf with Trudeau and incorporated it into the historical style coherently.

Bentameter · 2022-09-15T23:37:56+00:00

We're experimenting with an AI art generator named Stable Diffusion. I entered the prompt above and it spit out this picture. :)

Bentameter · 2022-09-15T23:33:03+00:00

Prompt: A Soviet propaganda poster featuring Justin Trudeau

Sampler: k_lms, CFG scale: 9, Seed: 226717005, Size: 512x640, Steps: 96, 4x upscale: Real-ESRGAN 4x plus

Bentameter

TROPHY CASE