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All my notes are in latex.

Any way to use them, or do I have to go through markdown format?


What


Bad, bad architects.

They have a reason for this choice. I remember studying it at university—the professor said that when people have intimacy, there's no need for doors.

But what if you have a guest? And what if your poop stinks?

Incredibly low-IQ people.


Kindly suggest some books about RL?

I've already studied a lot of deep learning.

Please confirm if these resoruces are good, or suggest yours:

Sutton et al. - Reinforcement Learning

Kevin Patrick Murphy - Reinforcement Learning, an overview https://arxiv.org/abs/2412.05265

Sebastian Raschka (upcoming book)

...


I believe Kochenderfer et.al.'s book "Algorithms for decision making" is also about reinforcement learning and related approaches. Free PDFs are available at https://algorithmsbook.com


Scaling is not over, there's no wall.

Oriol Vinyals VP of Gemini research

https://x.com/OriolVinyalsML/status/1990854455802343680?t=oC...


He didn't say it's over, just that continued scaling won't be transformational.


Oriol Vinyals said that.



They can't solve the ambiguity about YOLO because the article exists only because of that inaccuracy.


YOLO IS NOT ULTRALYTICS


I know that, I’ve used it since darknet - but for all intents it was since the platform we were using had the ultralytics version. They claim that even using their model architecture requires a license.


> The YOLO series is developed and maintained by Ultralytics.

This is totally fake. There are a lot of different YOLO versions, each with a different licence.

This article is a total scam, it should be removed.

Read the first comment. https://www.reddit.com/r/computervision/comments/1gxce90/yol...


Unless I’m missing something, the Reddit discussion seems to indicate that the only maintained versions are the ones maintained by Ultralytics. Are there other maintained versions out there?

Even if this is not the case, that doesn’t mean the article is a ”total scam”.


Yes it is a scam because it says that YOLO is ultralytics. That's the wrong point. There are hundreds of different YOLO implementations. Just search for some bookmarks or look in huggingface. Paperswithcode is dead now, hugging face built something similar.


> Yes it is a scam because it says that YOLO is ultralytics.

How does that make it a scam? You know that “scam” doesn’t mean “incorrect”, right?

> There are hundreds of different YOLO implementations. Just search for some bookmarks or look in huggingface. Paperswithcode is dead now, hugging face built something similar.

Can you be more specific? The Reddit discussion you linked to described everything else as being pretty dead. What is being actively developed?


Who are you.

I'm not your tutor.


Agreed, this article is pure misrepresentation. However, are DETRs performing better than YOLO? That's the salient question, for me.


A small percent difference means nothing at all.

Your camera will be very different from the images the models have been trained on, so you'll have to do fine tuning. So the only thing that matters is this phase.

Plus, there are models that run device, on servers etc.


DETR model have outperformed YOLO models for a while, but they have been much slower making them impractical for real time detection.


Arduino merda.


Among the top 10 tech companies and beyond, they have the most successful open source program.

These projects come to my mind:

SAM segment anything.

PyTorch

LLama

...

Open source datacenters and server blueprints.

the following instead comes from grok.com

Meta’s open-source hall of fame (Nov 2025)

---------------------

Llama family (2 → 3.3) – 2023-2025 >500k total stars · powers ~80% of models on Hugging Face Single-handedly killed the closed frontier model monopoly

---------------------

PyTorch – 2017 85k+ stars · the #1 ML framework in research TensorFlow is basically dead in academia now

---------------------

React + React Native – 2013/2015 230k + 120k stars Still the de-facto UI standard for web & mobile

---------------------

FAISS – 2017 32k stars · used literally everywhere (even inside OpenAI) The vector similarity search library

---------------------

Segment Anything (SAM 1 & 2) – 2023-2024 55k stars Revolutionized image segmentation overnight

---------------------

Open Compute Project – 2011 Entire open-source datacenter designs (servers, racks, networking, power) Google, Microsoft, Apple, and basically the whole hyperscaler industry build on OCP blueprints

---------------------

Zstandard (zstd) – 2016 Faster than gzip · now in Linux kernel, NVIDIA drivers, Cloudflare, etc. The new compression king

---------------------

Buck2 – 2023 Rust build system, 3-5× faster than Buck1 Handles Meta’s insane monorepo without dying

---------------------

Prophet – 2017 · 20k stars Go-to time-series forecasting library for business

---------------------

Hydra – 2020 · 9k stars Config management that saved the sanity of ML researchers

---------------------

Docusaurus – 2017 · 55k stars Powers docs for React, Jest, Babel, etc.

---------------------

Velox – 2022 C++ query engine · backbone of next-gen Presto/Trino

---------------------

Sapling – 2023 Git replacement that actually works at 10M+ file scale

---------------------

Meta’s GitHub org is now >3 million stars total — more than Google + Microsoft + Amazon combined.

---------------------

Bottom line: if you’re using modern AI in 2025, there’s a ~90% chance you’re running on something Meta open-sourced for free.


OSQuery


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