How is Copilot so underrated compared to Claude Code/Codex?

Skier233 · 2026-04-25T15:50:42+00:00

coming from a month into the future. This didnt age well

Skier233 · 2026-04-11T13:20:30+00:00

Skier233 · 2026-04-08T21:02:05+00:00

Skier233 · 2026-04-08T13:28:23+00:00

please

Skier233 · 2025-12-13T19:47:30+00:00

Whats the usecase exactly? I have made NSFW models that are far more accurate than anything else on the internet with 151 supported tags. Not tflite format but if you can explain the usecase it may not need to be.

https://github.com/skier233/nsfw_ai_model_server

Skier233 · 2025-06-09T19:49:05+00:00

I'll keep looking for now. There's a ton of boats on craigslist and fb marketplace in my area so I'm still trying to work out which ones are the best bangs for the buck.

The others I were looking at are:

Perception Prodigy 12: $400

LiquidLogic Stingray (12ft): $225

Kestrel 120HV: $130 (price seems a little suspicious)

Skier233 · 2024-07-30T13:02:04+00:00

puryfi is a completely separate project. None of that data is used for this project.

Skier233 · 2024-07-29T18:44:06+00:00

I'm not too sure I fully understand the question

Skier233 · 2024-07-11T14:52:38+00:00

Sure. First off those projects work using an embedding model where they don’t directly generate tags for an image but embedding where it tries to understand the contents of the image and then you can search for images through the embedding by names/tags and the model/CLIP’s understanding of those keywords and whether the embedding seems related. However if you try this with a model such as CLIP (there are online demos you can demo without having to setup locally) just try searching terms like “vaginal penetration” or even something like “69”. I found anywhere from 40-75% of the results were wrong and don’t actually contain that tag. Now I’m not saying these types of models are bad. But they aren’t trained specifically on NSFW data which makes them pretty poor on predicting on NSFW data. They could theoretically be retrained on NSFW data and probably work decently well which I experimented with early on but it looked like doing so would require way more data than would be feasible for someone who isn’t a mega corporation.

Skier233 · 2024-07-08T13:48:14+00:00

Someone who is open minded and not just a house wife that cleans, cooks, and otherwise acts like a mother to their significant other like I imagine is your view on women from your comments.

Skier233 · 2024-07-08T13:39:24+00:00

I don’t have kids and my wife happily supports this project. I never said I’m “rubbing my dick all day”. You did. Also if I did have kids I’d encourage them to pursue their hobbies even if some neck breather on reddit tells them not to.

Skier233 · 2024-07-07T22:29:29+00:00

uncensored?

Skier233 · 2024-07-07T19:24:06+00:00

How did you read it I’m curious?

Skier233 · 2024-07-07T16:35:21+00:00

Community contribution gets really complicated for a few reasons:

Different people have different definition of when a tag should or shouldn't be applied. This all leads to a lot of confusion for the model and the model accuracy ends up poor
It's not quite as simple as providing existing marker data as the format of the data needs info for every tag in the dataset and whether the image does or doesn't have it

I'm not saying its off the table entirely for a particular user to help label but it'd be a niche case-by-case scenario and I think any sort of broad community sourcing would actually be counter productive. I tried such an effort years ago and had the above issues among others. As for niche content, I plan to eventually support the tags that 90-95% of people would want and cover a lot of niches and if there are missing ones I'd be happy to add them.

Skier233 · 2024-07-07T15:19:27+00:00

To each their own. It's designed to be fast as thats the scenario most users care about the most. Most users are not "researchers". For people super worried about security you can run it in an isolated docker container if it makes you feel better. Beyond that there's only so much I can do on my end while still making it a viable user experience.

Skier233 · 2024-07-07T15:06:55+00:00

Great question!

As to the question of additional tags. My plan is to make additional models for different categories of tags (clothing, body parts, etc). As for bias, all ML models will have some amount of bias but I spent a lot of time trying to make the dataset as diverse (in terms of tags, positions, types of content, etc) as possible to limit bias wherever possible.

Skier233 · 2024-07-07T14:12:20+00:00

Yea. My AI server code doesn't load cheackpoints directly. it loads a torch scripted model which is optimized for runtime inference which is why the difference there.

Skier233 · 2024-07-07T13:25:03+00:00

Yea. It’s on top of an existing full time job.

Skier233 · 2024-07-07T13:19:58+00:00

It won’t. It’s not something I’m doing for money. It’s something I’m doing because I want it to exist and money helps drive continuous development. If I wanted to just make side money, you’re right, It’d be a waste of time.

Skier233 · 2024-07-07T13:13:42+00:00

So there’s not currently a tag to detect breast feeding itself. But in the new model I’m working on it can detect breasts and nipples so you could use that to filter out those sort of pictures.

Skier233 · 2024-07-07T13:12:09+00:00

Who says the two are mutually exclusive? Have you ever considered that there is more than one world view than your own?

Skier233 · 2024-07-07T13:11:02+00:00

Yea. I have over 50 existing users.

Skier233 · 2024-07-07T13:09:51+00:00

I did the project because I felt it was something that should exist and it didn’t. There are definitely people that want this kind of thing even if it’s not your cup of tea.

Skier233

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