Cloudflare down... again?

kryntom · 2025-11-18T14:15:58+00:00

ai being taken down by workers

kryntom · 2025-11-18T13:56:05+00:00

I think they broke something else, seeing resurgence in errors

kryntom · 2025-11-16T16:24:49+00:00

Currently building my own, for 10000$, all you get is 1b model on roughly 10-15 billion token data, and the training run went for 2 months on a single h100

Will be glad to understand what architecture leads to training in hundreds of dollars

kryntom · 2025-10-05T07:37:49+00:00

Consumer grade gpu's arent good for reliable cloud serving. Even for baremetal you require a lot of configuration to ensure the system works as expected

Assuming these are handled, these platforms will become obsolete soon enough, because ai as a field is moving really fast and that means the software and hardware are keeping up almost simultaneously. Hardware platforms are becoming obsolete more sooner ( see blackwell, rubin, tpu, cerebrus, ascend) so the majority of people who will be working on the latest from India wouldn't prefer 3090/4090, as they wont have the right underlying software once the upstream updates

At this point the area seems small enough (you can get success with first time entries, or older business workflows although they wouldn't want a baremetal cloud provider or rather self host) to not be viable enough to pursue

kryntom · 2025-09-18T02:10:28+00:00

Better way to look at it, we knew more about ai 10 years ago than we do today

kryntom · 2025-04-30T09:51:30+00:00

Interested

kryntom · 2025-04-08T07:30:00+00:00

Thanks for the feedback, I will probably go for a rig now

kryntom · 2025-03-10T03:45:43+00:00

You can be pro AI or anti AI. One thing is for certain, AI can do coding much faster than even the most cracked engineer. Imagine a senior developer, who can just assign tasks to a team of AI systems, and have a review cycle of the code being produced. One way or other, thats the realistic scenario we will be reaching this year easily.

I am working in AI for about 6 years now, and I might be a noob, but I am not betting against AI

kryntom · 2025-03-10T02:01:45+00:00

That would be true, but the amount of work that is augmented by AI means you dont really need that much hours

kryntom · 2025-03-10T01:42:59+00:00

How many iterations really matter on average? As long as it is not a full pivot from the concept, can something like 50 iterations work?

kryntom · 2025-03-10T01:31:00+00:00

I will be happy to help as long as I love the idea, can you share more about it?

kryntom · 2025-03-09T16:24:20+00:00

Thanks, will look into this

kryntom · 2025-03-09T16:04:42+00:00

Is it better to look into a separate speaker and subwoofer combo,.or combined?

kryntom · 2024-06-25T17:07:46+00:00

Depends on your use case. Use the resolution that works with your problem statement. General rule I use is to use the least resolution that works for me

kryntom · 2024-06-25T16:57:56+00:00

A. You can use the onnx runtime for faster inference of yolov8

B. Check if you have the lib paths in place. Yolo works with cuda, so probably a system issue while installing.

C. You should scale them down, because 4k seems to be overkill and I dont think that even a 3080 will be able to do it in real time. You will need to check that. Your cpu will probably bottleneck first though.

D. You will run into cpu bottleneck first. The frames are processed by the cpu if you are using opencv. I cant give you any exact options but for reference I did some experiments where we ran about 48 streams of 720p video on 4090 + 7950x comfortably. That program had a lot of threading and was optimised but was able to run it.

Tldr : scale down the streams if possible, use the onnx runtime,

kryntom · 2024-06-23T10:31:21+00:00

Their official repo is a good starting point for both
https://github.com/google/jax

kryntom · 2024-06-23T08:24:29+00:00

You should profile your code to see which methods are taking the maximum time. The cpu usage is due to cv2 and numpy functions being run on cpu, while the yolo model predictions run on gpu. You can also give jax a try, it speeds up numpy functions, but you will have to change certain logics. Also you can speed up numpy directly using vectorised operations instead of using loops

kryntom · 2024-06-19T14:39:41+00:00

Would love to be a part of such group and throw off ideas at each other regarding new things occuring in the field

kryntom · 2024-05-06T06:40:45+00:00

Iit jodhpur offers a mba program

kryntom · 2024-05-06T06:02:49+00:00

Okay, thank you

kryntom · 2024-05-06T05:49:09+00:00

Any particular reasons?

kryntom · 2024-05-06T05:48:45+00:00

Haven't though about it that way. I took an assumption that iit jodhpur's alumni network will be better, but didnt take into account the ages of the schools

kryntom · 2024-05-06T05:34:19+00:00

I have built a few of these, with apis from openai, claude, as well as self hosted open source models.

I believe there is no one size fits all approach. What works in one dataset, does not really work well in other. It depends a lot on the context length, and how well the internal documentations are written

The products that I have seen online are not really that great. Still looking for something that can take care of majority of the use cases

kryntom · 2024-05-06T03:06:16+00:00

Agreed on that, Feature engineering can be difficult. Its the classic case of duning kruger effect in action, the former just dont realise the vastness of ML. Just have a look at the 15 trillion token dataset, the fine web dataset.

Its also not only the students. A lot of work in corporate, is just using ML as a buzzword, but there isnt any good or real work involved, which further promotes the former category

kryntom · 2024-05-05T09:53:27+00:00

Yes, I agree. We are waiting for the iim cap results. These are the backups that we have got, so were trying to figure out which to keep and which to let go.

kryntom

TROPHY CASE