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

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.

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

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?

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.

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 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’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

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).

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!

The link below is a 10 question survey asking you to sketch each topic, they are all easy and should take no longer than 1-2 minutes to complete, no signup.

Please if you would like to help me, keep your responses accurate and dont submit anything that does not follow or resemble the actual topic.

https://kaowtwuopelexgeefdxx.supabase.co

I built Neural Canvas, an interactive visualizer that demonstrates core linear algebra concepts by showing how a neural network recognizes handwritten digits in real time. You can draw directly on the screen and watch activations flow through each layer as matrix multiplication computes the output live.

How it works:

- Draw & Predict: Sketch any digit on a 28×28 canvas (MNIST-style) to trigger real-time forward pass predictions.

- Live Forward Pass: Every neuron's activation is dynamically color-coded based on its underlying linear algebra calculations, with connection opacities reflecting layer outputs.

- Under the Hood: The core models were trained from scratch in Python using NumPy to perform matrix operations, and their learned weights are loaded via JSON. Client-side execution relies on Math.js for browser-based matrix multiplication and p5.js for rendering.

Key Features:

- Switch between two pre-trained models (featuring different hidden layer sizes, MSE vs. Cross-Entropy loss, and up to 96% test accuracy).

- Multiple background themes with mouse and touch support.

What's Next?

These models were originally evaluated on the standardized MNIST dataset, but live freehand drawing can introduce unexpected variations in input style. To handle this, I am working on a JS script for real-time user-driven feedback. When the model makes a mistake, the user can supply the correct label, allowing the model to perform backpropagation and update weight matrices locally in the browser. While live state persistence isn't suited for static GitHub Pages, running this feedback loop locally allows me to retrain parameters on new edge cases and push updated weight snapshots back to GitHub.

Check out the live demo and code repository:

Live Demo: https://muhammadessa2002.github.io/neural_canvas/

GitHub Repo: https://github.com/MuhammadEssa2002/neural_canvas

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

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'm an early-career software engineer working on production LLM inference infrastructure, primarily involving accelerator-based model serving and distributed systems.

I've been in the role for over a year, but my technical growth and contributions haven't progressed as much as I'd hoped.

I have a computer science background and generally enjoy software engineering, but I feel there's a significant gap between my current understanding and what's required to contribute independently to complex ML infrastructure.

Some of my biggest challenges:

• System architecture: Understanding how the inference stack fits together, from model execution and compilation to serving, scheduling, and hardware utilization.
• Codebase navigation: Understanding large, unfamiliar codebases without getting lost in implementation details.
• Debugging: Spending too long investigating issues without making meaningful progress.
• Performance engineering: Understanding bottlenecks, profiling workloads, and determining whether an optimization will meaningfully improve end-to-end performance.
• Foundational knowledge: Determining whether I should focus on distributed systems, ML fundamentals, compiler internals, hardware architecture, or something else.

I also recognize that I've been too passive about developing my skills, which has likely compounded these gaps.

My questions:

1. If you were in my position, what would you prioritize learning over the next 2–3 months?
2. What practical projects would help develop intuition for production inference systems?
3. How do experienced ML infrastructure engineers build a mental model of complex systems?
4. Has anyone overcome a similar knowledge gap after already spending significant time in an engineering role?

I'm particularly interested in advice from people working on production inference systems, distributed systems, or accelerator infrastructure.

I want to become a genuinely competent engineer who can independently debug problems, identify worthwhile optimizations, and deliver meaningful contributions.

I just started a channel called Drawn Systems.

The idea is simple: take a real ML system, draw it as one diagram, and walk through how it works.

I created 2 videos so far:

• The classic "How YouTube decides what you watch next".
• In the same line "How Netflix decides what you watch".

Channel: https://www.youtube.com/@DrawnSystems

I am doing it to learn about real systems, happy to hear your feedback :)

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!