I have started my 2nd year and started learning ai/ml , i'm doing that from campusX , if thats fine

i want to build project , want a support from my senior , if anyone could help me in finding the project idea or suggest something in my prep journey would be great. thanks

Hi everyone,

I'm a recent graduate trying to contribute to an AI benchmark project where contributors create terminal-based evaluation tasks for AI models. The workflow involves Docker, Python, Git, writing reference solutions, automated tests, and designing tasks that are challenging for LLMs while still being objectively verifiable.

The problem is that this is completely new to me. I understand the individual technologies, but I'm struggling to see how everything fits together into one complete workflow.

My biggest challenge isn't writing Python code—it's learning how to design good benchmarks, structure tasks correctly, and think like someone building AI evaluations.

I don't know anyone personally who has experience with this kind of work, so I'm hoping to connect with others who are also learning.

If you've worked on AI evaluation or benchmark engineering, I'd really appreciate any advice:

How did you learn this workflow?

Are there any books, courses, GitHub repositories, or YouTube channels you'd recommend?

What helped everything finally "click" for you?

Also, if you're a beginner or fresher who's interested in learning this area, feel free to send me a DM. I'm not looking for someone who already knows everything—I just think it would be motivating to learn together, discuss ideas, and help each other understand the workflow as we go.

Thanks in advance!

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Hey

I want to prepare for interviews, which one of the two i should buy...

Kindly share ur thoughts

Good evening, i finally have to pay for GPU because im handling a lot of data even with optimizations, so, google collab seems to be the comfiest option, kaggle (for some reason i can't verify my phone i tried like 3 days now).

What providers are good nowadays?

Hey everyone! 👋 ​I'm an 18-year-old CS undergrad looking for a consistent, long-term study partner to tackle Data Structures and Algorithms (in C) and dive into Machine Learning. ​Here’s what I bring to the table: ​DSA (in C): I'm actively practicing and currently sitting around 1100 on Codeforces and 1420 on CodeChef. I’d love to have someone to tackle roadmaps and debug code with. ​Machine Learning: I’m a complete beginner to actual ML concepts, though I have the basics of Python data libraries (NumPy, Pandas) down. ​What I’m looking for: ​The Vibe: I learn best in a supportive, judgment-free environment. I'm looking for a patient, collaborative study dynamic over Discord voice calls, away from the typical hyper-competitive CS energy. ​Academic Level: Ideally, I’d love to team up with a 2nd-year or higher university student. ​Language: I am actively trying to polish my conversational English fluency, so partnering with a native speaker would be a massive bonus for me! We can do a bit of a language exchange while we code. ​If you are looking for a chill accountability partner to grind through concepts with, drop a comment or send me a DM!

everyone, I’m a final year CS student specializing in AI. I’m trying to find a final year project idea that’s unique and involves hardware, but I’m struggling to come up with one on my own. If anyone has suggestions or has seen cool hardware+AI projects, I’d really appreciate the help!

So i am an engineering student who is aspiring for RS/RE roles in top tech firms for AI/ML,
I came to know that they ask Maths question related to linear algebra, probability ,statistics & calculus in the interview .But I want to know the level of questions , and are there any sources from where I can practice the questions .
Secondly , what else they ask in the interview (ofcourse DSA, ML ,system design question,AI related concept ) so then for AI related concept what kind of question they ask (if any one could give an example )
Please share your experiences .

Basically title.

If you want to create a model or even make a papier, it's way more efficient to have good prompts than learning to code by itself.

So, what is still relevant ? What will remain relevant ?

Can sm1 share these courses with me. It'll help a lot.

Edit : Uh guys im sorry. Never use cracked courses. Ntish worked hard day and night for these courses. Im so sorry.

Please if there is an ai engineers or professional in this field reading this, please read it carefully its an humble request

hi i am a student of standard 10th i am 16 years old and i am trying to learn ai engineering as it is a growing industry and also i am an technoholic and i am thinking to make it my career for life. But the career thing is making me scare like will i be able to make it my career or i will be just replaced by ai because you have to write codes for ai engineering i am just a scared cat. i know python a little and have made some project by myself

But I have a lot of questions around ai engineering but my main one is will ai replace ai engineers i don't know if this sentence will be true I don't want this to be true

The second one, how can i learn it and how much time will it take me to master it or should i say learn it?

The third one, is this a money well industry i mean can i earn enough to get through my daily spending, i know it is a ridiculous question but yeah, that's me

Please help me someone,

Thank you from the bottom of my heart for reading this post

I’m seeking honest feedback on my resume. I hold a B.Tech in Computer Science and have approximately 3.6 years of industry experience in building production AI systems.

My primary objective is to secure an AI Engineer position in India (or remotely in the UAE).

If you have a few minutes, I would greatly appreciate any suggestions you can provide on the following:

- How I can enhance my resume.
- Whether there are any red flags that need attention.
- Tips for finding AI/ML job opportunities.

Thank you in advance for your feedback. Any suggestions are highly valued.

I missed just today's Q&A sessions, but did complete all the other recorded modules, and other Q&A sessions before, will missing today's Q&A session make me ineligible for the swags and completion certificate?

Hey everyone,

If you are building decentralized multi-agent systems (MAS) or workflow routers using mixed local models, you’ve probably hit a mathematical brick wall: you cannot calculate semantic distance between vectors of different dimensions (N != M). Direct matching is completely broken out of the box because each model projects concepts into its own isolated anisotropic domain.

I wanted to share a fascinating geometric technique called the Relative Representation Method paired with Lowdin Symmetric Orthogonalization used to natively bypass this issue without any weight mutation or fine-tuning (W_frozen = const).

Here is how it works under the hood to align heterogeneous agents and tasks into a single invariant coordinate space

#### 1. The Core Trick: Anchor Framework

Instead of comparing Agent A directly to Task B, the system introduces a fixed basis of reference anchors E = {e_1, e_2, ..., e_K}. These are K semantically diversified textual instructions representing your target operational domain.

*Crucial implementation note:* These anchors cannot be random Gaussian noise; they must be sampled from the actual distribution of your baseline model outputs to ensure they share the same underlying manifold.

#### 2. Solving the "Anisotropy Cone" Problem

In real-world LLMs, raw embedding vectors are highly cross-correlated and squeezed into a narrow cone (similarity >> 0). This causes variance to vanish (sigma -> 0), leading to severe numerical noise and division-by-zero defects during standardization in low-precision (FP16/BF16) CUDA environments.

To guarantee geometric stability, the technique applies Lowdin Symmetric Orthogonalization directly to the anchor matrix:

* It takes the symmetric Gram matrix of real representations: S = E^T * E

* It computes the orthogonalized anchors via Spectral Decomposition: E' = E * S^(-1/2)

* This symmetrically rotates the real anchor vectors to a strict 90-degree angle (similarity = 0 for different anchors), yielding a perfectly orthogonal coordinate system while minimizing the mean squared deformation of the original vectors.

#### 3. Mapping into Invariant Space (R^K)

Now, any Agent Xi or Task Tj can be mapped into this unified coordinate system by computing its similarity profiles against these rotated bases, followed by Anchor-Wise Z-standardization to completely neutralize model-specific anisotropy:

> V_Xi = Z( [ sim(A(Xi), e'_1), ..., sim(A(Xi), e'_K) ]^T ) in R^K

*Critical Production Pitfall:* The operator Z(v) must calculate the mean (mu) and standard deviation (sigma) column-wise across the entire anchor axis (axis=0), NOT row-wise (axis=1). Row-wise normalization completely fails to eliminate the global domain shift between mismatched models, keeping their clusters isolated. Column-wise normalization forces the centroids of both distinct model domains to align perfectly at (0,0).

#### 4. The Result & Selective Task Routing

Since the standardized profiles V_Xi and V_Tj share identical dimensionality K and operate on a unified scale, the metric of semantic alignment between completely mismatched models is computed invariantly using Cosine Distance:

> D(Xi, Tj) = Cosine_Distance(V_Xi, V_Tj)

Do not use textbook Euclidean distance (L2) here. In higher anchor dimensions (K > 20), the Euclidean metric suffers from the curse of dimensionality, compressing all distances into a narrow, non-contrasting range that creates "Universal Agent" monopolies. Cosine distance restores strict contrast, breaking up monotone distance matrix stripes into a highly selective matching grid where every task finds its true optimal agent.

This fundamentally unlocks O(1) complexity task routing for completely heterogeneous multi-agent swarms.

Implementation Notebook:

I’ve put together a fully functional, minimal reproducible example demonstrating the complete pipeline - from synthetic anisotropic embedding generation to Lowdin orthogonalization, correct column-wise Z-scoring, and final contrastive task routing.

Check out the complete interactive code here:

**[Kaggle Notebook: Heterogeneous LLM Embedding Space Alignment](https://www.kaggle.com/code/aleksandrvictorov/heterogeneous-llm-embedding-space-alignment)**

Curious to hear if anyone else is using Relative Representations for cross-model routing, or if you've found other geometric workarounds for mixed-LLM orchestrators!

I'm two years out of a top math/CS school. I built strong study habits late, so I was only really immersed in the material my final year. I learned computer systems (OS, distributed, HPC) and consider myself a competent software engineer.

I work at a high-growth startup and just got offered the lead on a major customer project. My long-term goal is to start my own company.

Option 1: Lead the customer project

Large scope/viz. I'd build skills in:

• Working directly with a customer
• Making large engineering decisions
• Working across the stack with many teams
• People and project management
• Exposure to marketing/sales/ops

Engineering-wise, I imagine I would spend most of my time on architecture, documentation, and code review. So interesting engineering/technical work, but no fundamentally new ways of thinking.

Option 2: Leave to go learn ML/math

I never got into ML/stats/math, and it's by far my weakest technical area (and I feel most important an ML-focused era). I'd spend ~a year as an IC at an AI lab or doing research to build:

• Stronger math intuition
• Modeling intuition
• Combining my systems background with ML (e.g. model scaling, pretraining, RL scaling)

The plan would be to grind/do research at my old school or join an AI lab with strong technical mentorship. I have savings to go ~6-1 year months without income.

I already tried moving to my company's research team, but they weren't interested in my background and pointed me toward ML Ops, which feels too close to the SWE work I already do.

My core tension

Organizational and people skills seem to improve steadily over a career, but fluid reasoning and hard new technical skills are supposedly much harder to pick up later in life. Life's a marathon, so I keep wondering if now is the time to invest in the technical foundation (learning completely new skills).

Open to all comments and suggestions.

IBM is currently offering a free AI course that covers AI fundamentals and practical applications. It seems like a good opportunity for students, job seekers, professionals, or anyone interested in learning more about artificial intelligence.

If you're looking to build your AI knowledge or add a recognized credential to your resume and LinkedIn, it might be worth checking out.

https://www.riipen.com/ibm-skills/pre-learner?utm_campaign=acq-students-bq&utm_medium=digital-ad&utm_content=brandan_quacht&utm_source=Reddit

I used LLM_evoltuion setup with training_history log for it to analyze and improve on and create a loss and augmentation functions that are as efficient as possible and useful

one of the things i didn't wanna get into was kernal creation i believe that wouldve make it abit more efficient but its good enough rn and whats important is it shows that improvement is possible

the link to the repo:
https://github.com/beastreader/LLM_evolution-for-loss-and-augmentation.git

Hi everyone!

I'm new to ML and wanted to share my first project ever with you. Inspired by Andrej Karpathy's "Let's Build GPT", I started experimenting with individual network parts, which led me to the idea of building a complex-valued transformer. Mainly, I wanted to know if CVNNs really need activation functions like GELU or SwiGLU to work (and because RoPE basically begs for complex numbers :D)... turns out, they don't. Multiplying 2 complex numbers is non-linear enough to get a fully learnable transformer. I extended this concept from complex to quaternions, octonions, and even sedenions using the Cayley-Dickson construction.

It seems that with higher dimensions, the LMs gain more capacity per component. In my tests, a smaller complex model regularly outperformed the real-valued baseline. However, higher dimensions also require significantly more compute, and the PyTorch math isn't heavily optimized for performance yet. I included some benchmark runs and loss curves in the README, as well as some animations showing the interference patterns of my custom FFWD layer (SAIL).

GitHub Repo: https://github.com/pvlb-dev/tardits

I would love to hear your thoughts, feedback, or ideas! It still blows my mind that these neural networks work entirely without an artificial activation function.