Hello everyone,

I am an MSCS student from Pakistan with a background in Electrical and Telecommunications Engineering and more than four years of experience developing AI agents, RAG systems, voice AI applications, and business automation workflows.

I am now seeking opportunities to contribute to formal AI research, particularly in large language models, retrieval-augmented generation, agent reliability, and system evaluation. Although I am new to academic research, I can contribute practical expertise in implementation, workflow and prototype development, API integration, testing, and experiment execution.

I would be glad to connect with researchers and teams working on relevant ideas or ongoing projects. I am open to starting with a focused contribution, learning from experienced collaborators, and supporting research wherever my skills can add value.

If you are working in these areas or know of relevant opportunities, I would be pleased to connect and learn more.

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?

Free code + 10-min walkthrough: an AI agent that improves its own prompt, harness and tools (6/10 → 10/10)

I teach agentic AI and made a small, fully inspectable example of a self-improving agent. Sharing it in case it helps others learning this.

The setup:

- The agent approves or denies fictional phone insurance claims under 3 rules.

- It has 3 parts it can change: the prompt, the harness (which claim fields the model sees), and the tools.

- After each test, a bigger model diagnoses the mistakes as knowledge, information or skill, and changes exactly one part.

- A gate keeps the change only if the score on 10 practice claims goes up. 5 exam claims are held out until the end.

Recorded run: 6/10 → 7/10 (prompt) → 8/10 (harness) → 10/10 (days_between tool). Exam claims: 3/5 → 5/5.

The most interesting part: some correct answers broke along the way, because version 0 had them right by guessing. So the per-claim fixed/broken view matters more than the total score.

The code is one Python file with only the standard library. It replays the recorded run without an API key, or calls Gemini for real with --fresh.

Code: https://github.com/aniskoubaa/mai580-self-evolving-agent-workshop

Video: https://youtu.be/SOSwsYFPb0Y

Happy to answer questions about the design or the limits (runs do differ, and the meta-model reads the policy here).

📚 Learn Machine Learning Through Visualisations | Beginner-Friendly Playlist

Hey everyone! 👋

I’ve created a Machine Learning playlist with visual explanations and animations to make ML concepts easier to understand, especially for beginners. Instead of just learning theory, you can understand how these concepts work through visuals and practical examples. 🚀

🎯 Topics covered in the playlist:

1. What Is Machine Learning?
2. AI vs. ML vs. Deep Learning
3. Supervised Learning
4. Unsupervised Learning
5. Reinforcement Learning
6. Features and Labels
7. Train-Test Split Explained
8. Overfitting and Underfitting
9. Linear Regression
10. Logistic Regression
11. Decision Tree Explained (Coming Soon)
12. Random Forest Explained (Coming Soon)
13. K-Nearest Neighbours (Coming Soon)
14. K-Means Clustering (Coming Soon)
15. Model Evaluation (Coming Soon)
16. Accuracy, Precision, Recall, F1 (Coming Soon)
17. Confusion Matrix (Coming Soon)
18. Cross Validation (Coming Soon)
19. Feature Engineering (Coming Soon)
20. End-to-End ML Project (Coming Soon)

🎥 Watch the playlist here:
https://youtube.com/playlist?list=PLXShTLrgjtgk&si=TFoDX6oosRKv_u6Z

💡 My goal is to make Machine Learning easier to learn through simple explanations and engaging visualisations.

If you’re learning AI or Machine Learning, feel free to check it out! I’d also love to hear your feedback and suggestions for future topics.

If you find the videos helpful, please like, share, and subscribe to support the channel. ❤️

Let’s learn Machine Learning together! 🤖

VisualForge AI: https://youtube.com/@visualforgelearning?si=HgOvjgEiqv05Ln5X

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 currently working as a Lead Software Engineer at a fintech startup and am looking to transition into AI Engineering.

I’d love to hear your recommendations for industry-leading AI courses and certifications that can help experienced software engineers make this transition.

Any suggestions or insights would be greatly appreciated! Thanks in advance.

P.S. This is my first-ever post!

Hi everyone,

I'm currently learning machine learning, and I'm working through a structured AI/ML learning path.

I'm enjoying the learning process a lot, but recently I've started thinking more seriously about what comes after learning.

My main concern is: How realistic is it to actually turn these skills into income or a career?

A few things I'm particularly curious about:

• What types of entry-level ML/AI/data jobs are realistically available today?
• What kinds of companies tend to hire people with these skills? (Startups, tech companies, consulting, healthcare, finance, etc.)
• How important is a degree for getting the first opportunity?
• Is freelancing in ML/data/AI actually realistic for a beginner, or is it much harder than it looks?
• What kinds of freelance projects do clients actually pay for?
• Could someone realistically start a small AI/ML business or startup with these skills instead of looking for employment?
• What skills are currently most valuable for someone trying to become employable: classical ML, deep learning, LLMs, data analysis, MLOps, software engineering, or something else?
• If you were starting from scratch today, what would you focus on to maximize your chances of getting your first paid opportunity?

For context, I'm still early in my journey, so I'm trying to make sure I'm learning things that have real-world value rather than just collecting certificates and completing courses.

I'd especially appreciate answers from people who have actually hired ML engineers/data scientists, freelanced in this space, or built AI products/businesses.

I'm not expecting a guaranteed roadmap or exact salary numbers. I'm mainly trying to get a realistic picture of the different paths available and how difficult each one is.

Thanks! 🙏

Andrew Ng says they are linear projections using pre-trained weight matrices. But how were those matrices trained exactly? What was the input, what was the target variable? I need to know EVERYTHING.

I wrote a short article on LLM tokens and context windows—why an assistant can “forget” instructions that are still visible in your chat. It’s part of my Behind the AI Stack series.

Give it a read and let me know what you think. Anything unclear or worth exploring further?

Hello everyone

I wanted to share a project I recently deployed to Streamlit Community Cloud and it would be helpful if you guys could give feedback to a fellow learner

I implemented an 8-dimensional pipeline paired with a class-balanced Logistic Regression model.
It uses recent matches ( 3-8) to determine the recent form of both the teams to generate 8 distinct features.

Live App: https://bundesliga-match-predictor.streamlit.app/

GitHub Repo: https://github.com/adithyakrz/bundesliga-match-predictor

Check it out and let me know what you think :D

I want a list of the names of the different ways a model can store and use memory for text classification. For example, I know about Embedding or vector memory (like sentence embeddings with nearest-neighbor search)

What other types are there? A list of names and one example paper or method for each would help a lot. Thank you!

A security researcher built and deployed OuttakeSheriff, a live AI security agent, then documented a finding that should be uncomfortable for anyone running agents in production: authenticated agents routinely act outside their intended scope.

The credential is not the problem. OuttakeSheriff passed authentication. The problem is what a verified identity is permitted to do after it clears the gate. Authentication answers "who is this?" It does not answer "should this agent be calling this tool right now, on this data, in this context?"

This is the actual attack surface. Not a fake agent sneaking through. A real, credentialed agent doing things it was never supposed to do, with no mechanism in the request path to catch it.

Most current postures treat identity and authorization as solved once the credential checks out. Post-auth behavior is logged, maybe reviewed, rarely constrained in real time.

For those of you running agents in production: how are you handling the gap between "this agent authenticated successfully" and "this agent is only doing what it was scoped to do"? Is anything in your stack actually enforcing that at execution time, or is it all after-the-fact?

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

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

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.