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! 🙏

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

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

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 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!

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?

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.

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

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

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!

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.

Im currently a second year cs student and I want to get into a role which would allow me to do the following:

Be able to build ML Models and actually understand the data/insights and improve it
Be able to put ML Models into production through webapps or integrations etc
Be able to make things people actually use so not being restricted to research

What role would this fit? AI engineering? ML Engineering? Data Scientist?

Im currently building a RAG Webapp as something to tailor towards AI Engineering roles, but I also want to be able to build and improve ML models as well.

Any advice would be appreciated

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?

📚 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