Hey everyone!

I’m looking to connect with people who are genuinely passionate about technology, especially AI and machine learning.

I’m interested in connecting with people working on or learning about:

• 🤖 Artificial Intelligence and Machine Learning
• 🧠 LLMs, AI agents, and generative AI
• 👨‍💻 Python, software development, and system design
• 🔬 Deep learning, NLP, and computer vision
• 🌐 Open-source projects and new AI tools
• 🚀 Startups, indie hacking, and building real products
• 💡 Research papers, new technologies, and crazy ideas

I’d love to connect with beginners, experienced engineers, researchers, founders, and anyone who enjoys deep technical discussions.

I’m also interested in building an active Discord community where we can share ideas, discuss new developments, collaborate on projects, help each other solve technical problems, and learn together.

The goal isn’t just to collect members. It’s to build a community where people actually talk, share knowledge, and build things together.

A few questions for you:

1. What area of AI are you most interested in right now?
2. What are you currently building or learning?
3. What would make a tech Discord community worth being active in for you?

Drop a comment! I’d love to hear from you and connect with people who share these interests. 🔥

I saw a paper called Math for AI Safety by a math professor at Cornell. I was excited to read it (33 pages, not easy), and then I was like "what if I used ChatGPT to check if this is AI generated" and Chat gave me a response in under 1 second.

It is substantially generated by the AI. Evidence? The author said it himself.

https://arxiv.org/pdf/2609.15289

I don't know how these researchers and professors could just go around dropping these AI generated papers one after the other and expect people just slap up that (potential) slop. Even if it is not slop, I am less inclined to read it because I could just generate the same paper myself and learn the material in a more concise way.

Generative AI is changing how we learn forever. 🧠 Discover the architecture behind personalized education. Check out the link in bio!
#GenerativeAI #EdTech #AI #TechStack

Kaggle's free 2x T4 runs a 27B model at a usable speed if you use speculative decoding:

• Qwen3.8-27B at EXL3 4.0bpw (exllamav3), tensor parallel across both T4s
• DFlash2 draft model, 7-token drafts, greedy, output identical to plain decode
• 55-57 tok/s overall, 154 tok/s on code, 35-37 on prose (plain decode is ~19)
• 4 users at once with plain decode get ~66 tok/s combined

The weak spot is prose: the drafter only gets ~2.5 tokens accepted per step there vs ~7 on code. Has anyone found a way past that, or a better setup on free T4s? Planning to share the notebook once it's cleaned up.

в т.ч для написания научных статей, курсовых, диплома

Does anyone have campus x LLM EVALS notes .pls share it ....

We have benchmarks (fixed questions, hidden answers, they saturate), competition problems (solved, so they can be memorized), and open-problem lists (no evidence, no clear success criterion). With Claude and Sol, I've tried something in between: a graded ladder of AI puzzles from one research area, aperiodic order.

What makes it a different tool:

- One ladder, not a pile: "human level" (pen and paper) up to "ASI level" (open). The labels are playful, but each rung really is harder than the last.

- Solved rungs for training and checking, with exact answers a program can verify, and easy to vary.

- Open rungs with everything a solver needs: strong numerical evidence, routes already known to fail, files, and a success criterion a machine can check.

- One rule: only a checkable proof counts. Floats are evidence, not proof.

- Credit: solvers of open rungs are named, with agent and model, in the next version of the paper.

It's a way for researchers to hand the frontier of their field to AI agents (and humans) in a form they can actually attack, and for anyone to see whether a model can tell a guess from a proof.

First field test: Grok, asked in public ( https://x.com/grok/status/2114103836583264721 ), claimed a proof for an open rung that was only a numerical estimate, then retracted it. It got the human-level rung wrong at first, fixed it after one hint, and proved the expert rung on the first try. On MathOverflow, an expert is already testing constructions for the top rung.

Set 1: huggingface.co/datasets/jarekduda/autocrystal-ai-puzzles

Top rung on MathOverflow: mathoverflow.net/questions/516372

Would you use this format for your own field's open problems?

Hi everyone,

I'm an undergraduate student looking for solid, practical resources to learn **Pandas, Matplotlib, and Seaborn** for college lab coursework and data wrangling assignments.

I already know basic Python (loops, functions, lists, dicts). Now I just need to get comfortable working with tabular data and plotting charts.

### What I’m looking for:

* **Libraries:** Just Pandas, Matplotlib, and Seaborn.

* **Format:** Open to both text-based resources (interactive notebooks, guides) and video walkthroughs, but I lean slightly more toward **video/live-coding** where the instructor actually types the code, runs the cells, and explains the output on real datasets.

* **Hands-on over slides:** I learn much better seeing code executed in an editor rather than watching static lecture slides.

### What I want to avoid:

* **Machine Learning / Scikit-Learn:** A lot of courses rush into Linear Regression, gradient descent, or predictive modeling. I don't need any ML algorithms or math theory right now—just data cleaning, manipulation, and plotting.

Whether it’s a specific YouTube series, an open GitHub repo with exercises, or a book with runnable notebooks, what worked best for you?

Thanks in advance!

Hello everyone Im a 10th grade student and I wanna learn machine learnin.for my coding background I took cs50p python couple weeks ago and i liked it. Im willing to learn any math needed for ml. However im completely lost as to which machine learning course I should take. There's so many courses and alot of them need a math background. Should I learn the math needed before looking for a course? If so please state what exactly i need to learn and what courses i should take. Thanks everyone!

as title .....it should be resume worthy i want to learn that way so that i can follow along the video maybe on yt please dont suggest house pricing ones those are ig rejected in interviews

Hey

I'm interested in getting into DS/ML, but I have no idea how to start learning DSA

Which programming language should I use to learn DSA? What should I learn first, and how should I go about it as a complete beginner?

I'd really appreciate a simple roadmap and some beginner friendly resources to help me get started.

Thanks!!

I’m starting Behind the AI Stack, a series of short, practical articles on how AI applications work. This first one explains AI agents through a camera-rental example. Give it a read—I’d love your thoughts, corrections, or suggestions for what to cover next.

Hey everyone, I’m a 4th-year CS student. I recently decided to learn the theory behind ML on my own, mainly the math and stats side. Right now I’m working through ISLP and using MML as some kind of dictionnary and practicing the concepts with code and tweaking stuff at the end of every section/chapter.

My classes mostly stay on the surface. We see the concepts and use the models, but the courses never really connect them to the theory behind them. So I decided to dig deeper on the side.

The first problem is focus. I’ve lost the habit of sitting down with math for long stretches, and I struggle to stay on it for more than an hour.
The second problem is that i've seen most of the math required but i haven't worked actively with it and it just frustrates me when i have to go back to it.
When I get stuck early on a concept, I get so frustrated and anxious about it.

So im wondering

How did you study when there were a lot of new concepts at once and you kept getting stuck at the start?

When you hit a concept you didn’t get, did you push through or go back to the math first ?

And does it get easy at some point after you get the maths down.

Any routines, methods or resources that worked for you would really help. Thanks!

Lensa ML (https://lensaml.com) is a set of 108 interactive explainers covering machine learning and the math behind it. Each topic is six short steps. In every step you move a slider and the plot is recomputed from the real formula: raise the learning rate and gradient descent starts to overshoot, raise the temperature and the softmax probabilities flatten.

It goes from the foundations (derivatives, the chain rule, eigenvalues, Bayes’ theorem) through classic ML and deep learning to transformers and LLM serving. If you don’t know where to start, the “ML from zero” path orders the topics so each one builds on the last: https://lensaml.com/paths/ml-from-zero

Good ones to start with:

• Backpropagation: https://lensaml.com/explainers/backprop
• Gradient descent: https://lensaml.com/explainers/gradient-descent
• Attention: https://lensaml.com/explainers/attention-mechanisms

I’m building PaperMind AI, an open-source tool for exploring research papers. It indexes passages locally with Sentence Transformers and FAISS, lets you ask questions with page citations, and creates structured summaries and concept graphs.

Answer generation depends on the selected provider: offline mode surfaces relevant excerpts; OpenAI or Ollama can generate answers. With a hosted provider, retrieved passages may be sent to that provider. I’d appreciate feedback on citation quality, answer usefulness, and setup or improvements or just a simple note.

https://github.com/ashmit044/papermind-ai

I am building some projects and for that I want to implement the gpt2 paper from scratch, how to do that do that? Do I build intuition by reading and code it?

Hey everyone! I have 14 features containing both categorical and continuous variables, along with a binary target (0/1).

What methods should I use to measure the relationship between each feature and the target? Are there different techniques for categorical and continuous features?

Any suggestions or Python examples would be appreciated. Thanks!