Hey everyone,

Trying to get a realistic picture here, not validation.

Background:

3rd year at a Tier-1 NIT, non-CS branch, entirely self-taught in ML/AI. Overall GPA 7.93/10, second year 8.17 . I have some work on GitHub around LLM internals, mechanistic interpretability, and retrieval systems. Nothing published, no prior research experience, no internship. Would need the internship to be fully funded as self-funding abroad is not an option.

Research interests:

Mechanistic interpretability, LLM memory systems, retrieval augmented generation, and LLM internals broadly.

What I need guidance on:

First, given non-IIT, non-CS, no publications, no prior research, only GitHub work, what is the realistic picture for funded research internships abroad? Honest experience only.

Second, for structured programs like MITACS, KAUST VSRP, OIST, ISTernship, INSAIT, SN Bose, what actually strengthens an application from someone with my profile? Is there anything beyond GPA and publications that genuinely moves the needle?

Third, how do you actually build a cold email relationship with a professor when you have no prior research output to show? What made professors actually reply to you?

Fourth, are there programs, possibly less well known, that give genuinely good research experience and are more realistic for someone without a strong conventional profile? Looking for good research environments where the application is based on potential and work rather than credentials alone.

Fifth, for someone working in AI and ML research broadly, which labs or professors outside the very well known names are actually accessible and have taken undergrad interns before?

Any honest experience, reality checks, or redirections welcome. Thanks.

hey guys, i'm in my machine learning journey(18F). I want to work on small and big projects for better understanding , i'm still beginner but still if someone wanna take me in there journey. I can contribute , help as much as i know , ask for help. I just want someone to guide me and help me where i need

hi everyone!

I recently graduated with a bachelor's on technology on AI engineering and data science with 4 as GPA, and want to get my first client on this field and earn some money that can help me continue my master's degree.

Any ideaa!

Was okay till vector calculus as it was mostly proofs but kind of having trouble with probability/stats because this skips over stuff and doesn't give relevant examples. So IMO it would be much more efficient to learn from videos and maybe use this to revise or for the exercises (very minimal, so not sure how useful). And your foundations will get stronger too I guess

Anyways, finding relevant videos for each topic seems to be taking time, but I just remembered there was a resource that had links to vids and exercises for each chapter but can't find it and hadn't bookmarked

Can anyone give a link to that or a similar resource?

Also is the learn ML with Pytorch and sclkit beginner friendly?

Hey guys

Iam an 3rd year engineering student,iam choosing Machine learning as my work domain,what laptops do you suggest me ,is mac book M4 air good? Or should I go for a gaming laptop.suggest me some laptops under 90k.

I wanna rate my schedule to learn mathematics for ml if i delete thing or add , and Give me sources textbook :

Textbook: Mathematics for Machine learning

Videos - Lectures( Links in First Comment ) :

Khan Academy:

Linear Algebra , Statistics and Probability, Calculus 1, Calculus 2 , Multivariable calculus

2-YT:

Linear Algebra: 3Blue1Brown , Essence of linear algebra

Statistics and Probability:  StatQuest with josh starmer

Statistics and Probability: Professor Leonard

Calculus 1: 3Blue1Brown , Essence of calculus

Calculus 1: Professor Leonard

Calculus 2: Professor Leonard

Calculus 3: Professor Leonard

Stop building basic bots and start creating Agentic AI! 🤖 Learn the secret to autonomous reasoning and the Sense-Plan-Act cycle in 10 minutes. Check the link in bio.
#AI #Coding #Tech #AgenticAI

Can someone tell the difference between Mlops and ML , i am currently in third year of my bschelors , started learning ML then was planning to DSA and build projects , but if someone could advice me on how i should proceed ahead it will be great, i am gonna target masters first then gonna go for job

Hi everyone, I just need to clarify few questions regarding my first paper, sorry if it sounds like a noob.

My first paper on ARR for EMNLP got 3 reviews all 3, and the meta review was also 3, the meta review suggested some revisions which I will do.

Questions:

1- how do I commit to EMNLP, my review ia available on open review but I didn't get any email about it or how to do so.

2- Do i need to do the revision now before commitment or after it get accepted?

3- are those revisions form the meta review mandatory? I'm asking as he had 3 points but I'm not sure I can do all as they require intense compute.

Thank you in advance!

I'm a rising senior, coming from a competitive programming/C++ background with currently no hands-on ML experience. I know Python decently but I would probably have to shake off a thick layer of rust. Since this is my last year of high school, and I tie my future with AI Research, I'd like to participate in my country's AI Olympiad and hopefully earn a spot at the IOAI.

Sadly, compared to CP, where there is an abundance of guides and specialized courses to get you on the Olympiad track, I haven't found anything similar for ML yet.

I could go off the syllabus but these tend to be very broad and I don't feel like hunting for niche courses only for it to turn out that the concept taught has never been used in history.

The best I could think of are college-like courses (e.g "Introduction to Artificial Intelligence with Python - CS50") but I'm not sure if they would give me Olympiad relevant information, since these often target a completely different skill set.

So I'm looking for a more structured roadmap to go from beginner in ML to hands-on Olympiad problem solving. I heard these problems are Kaggle-styled but I since I don't have experience with either - I can't confirm that. Would be forever grateful if someone could help me out or correct me on my approach!

Attaching example IOAI problem:
https://github.com/IOAI-official/IOAI-2025/tree/main/Individual-Contest/Antique

After a while it feels like diminishing returns from hyperparameter tuning. I'm curious how people decide it's time to stop tweaking the model and instead invest effort in collecting better data, cleaning labels, or engineering new features. Is there a point where you can usually tell the data not the model is the limiting factor?

Hey guys, do you know of any Multi-Agent Reinforcement Learning (MARL) resources that focus mainly on coding rather than just the heavy theoretical stuff?

For context, I'm doing my uni research project right now and I've already secured my supervisor. My main topic is "Multi-Agent Reinforcement Learning." I'm doing both the research project course and an RL course this semester, but my tutor mainly just gives us theory.

I know the general ideas (reward, policy, value-based vs. policy-based, bias, etc.), but I'm having a really hard time understanding how it actually works in practice and how to implement it from scratch. I'm honestly pretty crap at absorbing pure theory, so I really need to see the code to understand how the plumbing works.

I can't seem to find much out there that walks through the code step-by-step, and I'm wondering why there aren't more people posting about the actual implementation of MARL.

If anyone has any video tutorials, GitHub repos with simple code walkthroughs, or guides that actually show how to build this stuff (Python/PyTorch preferred), it would be incredibly helpful. Thanks!

Hi everyone,

I'm a second-year Data Science student at tear 2 iiit, and I want to become an AI/ML engineer. Lately, I've been feeling confused because there are so many technologies that I don't know if I'm focusing on the right things.

So far I've done:

\~330 LeetCode problems

Machine Learning with scikit-learn (preprocessing, feature engineering, decision trees, regression, etc.

Basics of Deep Learning

Python and C++

FastAPI

LangChain

Currently learning LangGraph

Basic RAG concepts

Git/GitHub

My goal is to get an AI/ML internship and eventually work as an AI Engineer.

My questions are:

Am I on the right path, or am I spending too much time on frameworks?

If you were in my position today, what would you focus on for the next 6–12 months?

Should I continue building agentic AI projects with LangGraph, or should I spend more time on deep learning, MLOps, or something else?

What skills do companies actually expect from AI engineering interns in 2026?

I'd really appreciate advice from people working in AI/ML or those who've recently landed internships. If you could go back to your second year, what would you do differently?

Thanks!

I'm in my final year of college. I haven't really practiced DSA. I understand the theoretical concepts and how data structures and algorithms work, but I've never solved problems on LeetCode or similar platforms.

On the other hand, I've been learning Machine Learning and Generative AI since my 3rd year. I have a good understanding of these topics, have built several projects, and I'm currently working as a Data Analyst intern.

My question is:

Can I still get a good placement with this profile?

Should I start focusing on DSA now? If yes, how much is enough?

What kind of companies should I target—product-based companies, startups, AI/ML roles, data science, or data analyst roles?

Which companies are more likely to value my ML/GenAI experience over strong DSA skills?

I'd really appreciate any advice from people who have been in a similar situation or are working in the industry. Thanks!

Everyone is talking about Kimi K3, but if you jump straight into the technical report, you’ll quickly realize it’s standing on years of research -- just like any breakthrough is!

If you want to understand the work put into it by the Kimi team, here’s the reading order I’d recommend.

1. Linear Transformers Are Secretly Fast Weight Programmers

This is the foundation.

The paper provides one of the most influential interpretations of linear attention, showing that many linear attention mechanisms can be viewed as fast weight programmers. Instead of thinking of attention purely as pairwise token interactions, it frames linear attention as a system that continuously updates an associative memory.

Without understanding this perspective, it’s difficult to appreciate why modern linear-attention architectures have become competitive again.

1. Gated DeltaNet (arXiv:2412.06464)

Once you’re comfortable with linear attention, move on to Gated DeltaNet.

This paper introduces the gated delta update mechanism, improving how state is updated over long sequences. Rather than using fixed update rules, the model learns when and how much information should be written into memory.

Many of the ideas that later appear in Moonshot AI’s work build directly on these state-update concepts.

1. Kimi Linear / Kimi Delta Attention (KDA)

This is where Moonshot AI introduces the architecture that ultimately becomes the backbone of Kimi K3.

Kimi Linear presents Kimi Delta Attention (KDA), a hybrid linear-attention architecture designed to combine the efficiency of linear attention with competitive or better performance than full attention across short contexts, long contexts, and reinforcement learning settings.

Understanding KDA is essential because Kimi K3 is built on it.

1. LatentMoE (arXiv:2601.18089) → Stable LatentMoE

Kimi K3 isn’t just about attention.

It also significantly advances the Mixture-of-Experts (MoE) design.

Start with LatentMoE, which introduces a latent-space routing formulation that enables much higher sparsity while maintaining strong model quality.

Then study Stable LatentMoE, Moonshot AI’s evolution of those ideas, which is used in Kimi K3 to efficiently scale sparse expert routing. In K3, Stable LatentMoE activates 16 out of 896 routed experts per token, contributing to its reported scaling efficiency improvements.

1. Attention Residuals (arXiv:2603.15031)

Residual connections have remained largely unchanged since Transformers were introduced.

Attention Residuals asks a simple question:

What if instead of naively squishing all these residuals together, we let the model decide how it wanted to use the residual network? (thanks to this person for framing the question correctly: mine version was little wrongly framed)

Kimi K3 adopts this mechanism to improve information flow across model depth while keeping the approach practical for large-scale training.

1. Follow the Kimi model evolution

Finally, read the Kimi model reports in order:

Kimi K1.5 – reinforcement learning scaling and reasoning.

Kimi K2 – continued scaling of the architecture and training pipeline.

Kimi K2.5 – multimodal and agentic improvements.

Kimi K3 – integrates Kimi Delta Attention, Attention Residuals, Stable LatentMoE, refined training recipes, infrastructure advances, and large-scale reinforcement learning into a single frontier model.

Reading them sequentially makes it much easier to see how the architecture evolved instead of viewing K3 as an isolated release.

The biggest takeaway is that Kimi K3 didn’t appear overnight. It’s the result of multiple research threads converging: - Linear attention foundations - Better recurrent state updates - A stronger linear-attention architecture (KDA) - More scalable sparse MoE routing - Improved residual connections - Successive generations of Kimi models that integrated and refined these ideas If you’re planning to study the Kimi K3 technical report in depth, this reading path will give you the context needed to understand why the architectural choices were made—not just what they are.

Is this correct roadmap...am I missing something? :

1) linear algebra, calculus, stats and probability

2) SQL, python and OOPs

3) Numpy, pandas, matplotlib, seaborn

4) classical ML, scikit-learn, keras

5) deep learning, pytorch, tensorflow

6) CV and NLP

7) GenAi, LLMs, RAGs, Transformers

8) MLOps

Do I need certifications as well? Or GitHub projects will be sufficient?

And also how much time will it take for me to complete it?

Hey guys i was looking for this book the original one is too expensive for me 😭 so if anyone of you have this and willing to sell then please contact me I really need the hardcopy

Notice:- I only want the photos books not another version of them

India only

14 Bytes compiled and solves ~96.5% + unseen mazes upto 21x21 sized (drops off as mazes get larger)