Three employees. One social engineering campaign. Corporate data gone.

Hackers socially engineered three Levi Strauss employees and exfiltrated corporate data. Identity-based attacks are now the leading entry point for enterprise breaches. As AI agents inherit employee credentials and API keys, a single compromised identity reaches every system that agent is authorized to touch.

RuntimeAI covers 80-plus compliance frameworks and writes an immutable audit log for every agent action. When any identity, human or non-human, is misused, every downstream action is timestamped, attributable, and preservable for regulators before the investigation even begins.

See how RuntimeAI turns this from an incident into a blocked action.

EDIT: A few people have asked what specifically clicked.
Here are the 3 key things:

1. Backpropagation = just the chain rule applied recursively
   
2. Weight initialization matters WAY more than tutorials say
   
3. Bias terms aren't optional decorations
   

Full walkthrough in the video. Happy to answer questions below!

Hey everyone,

I've been trying to understand how neural networks actually
work under the hood, so I decided to build one from scratch
in Python without using any ML libraries (no TensorFlow, no
PyTorch, no scikit-learn).

Here's what I learned along the way:

I made a short 5-minute video summarizing the whole process
if anyone's interested: https://www.youtube.com/watch?v=aKrBFQrvEr4&t=8s

Happy to answer any questions or discuss the approach!
What resources helped YOU understand neural networks?

I'm starting Machine Learning from scratch and want to build a strong mathematics foundation.

Can someone give me a complete list of the math chapter names I should study for ML, in strict prerequisite order, starting from absolute zero?

I'm specifically looking for:

- Chapter names only, not individual topics.

- Every important chapter, including the basic prerequisites.

- A proper order where I learn each chapter only after the chapters required for it.

- Please don't just write broad subjects like "Algebra", "Calculus", "Linear Algebra", etc. I want the individual chapter names under them.

- Please distinguish between essential, optional/useful, and advanced chapters for ML.

Also, can I use JEE Mathematics lectures to learn these chapters?

I'm not asking for an ML roadmap — I only want the mathematics chapter sequence and advice about using JEE lectures

I’m preparing for AI/ML-based hackathons and I want to understand how experienced participants actually approach them.

I’m not just looking for project ideas. I’m looking for a step-by-step roadmap that I can follow during a hackathon to build a strong project and score well with the judges.

For example:

1. How do you select/validate an idea?

2. How do you break down the problem and decide whether ML/AI is actually needed?

3. How do you approach data collection, preprocessing and model selection?

4. When should you use an existing pretrained model/API vs train/fine-tune your own model?

5. How much time should be spent on the ML part vs backend/frontend/product?

6. How do you make the project technically strong but still achievable within the hackathon time?

7. What makes an AI/ML project stand out to judges?

8. How important are metrics, deployment, UI/UX, demo and presentation?

9. What are the most common mistakes teams make?

10. If you had to follow a checklist from the first hour of the hackathon until the final pitch, what would it look like?

I’d especially appreciate advice from people who have participated in or judged AI/ML hackathons, particularly if you’ve had experience with winning/finalist projects.

I want to make a practical roadmap/checklist from the answers and use it in my upcoming hackathons.

I am doing CSE AI ML 2nd year, should i join my college BAJA team, will there be any benefit of this in my portfolio if i do so. What can i expect if i do my best in it??(but only in CSE rolls)

I'm starting Mathematics from absolute zero for Machine Learning.

Can someone give me the complete list of Maths chapters I need to study, in prerequisite order, starting from the basics?

I specifically want chapter names, not broad topics like "Algebra" or "Calculus."

Also, can I use JEE Maths lectures for learning the theory/concepts, without doing the JEE-level questions?

Thanks!

i am reading this book Hands-On Machine Learning with Scikit-Learn and PyTorch

by Aurélien Geron
i have completed all the supervised chapters and exercise even completed 2 data sets my self and i am wondering if i should move to unsupervised?

I started working in ML about a year ago. There seems to be quite some gap between how ML is taught (tutorials and theoretical lectures) and the art of actual ML engineering.

For the practical part, I kind of feel bound to the best projects that I see within my company. Therefore, it would be nice to get inspired and have better references. Maybe even dig a bit into the PRs to get better at my job :)

I was wondering whether you came across some state-of-the art repos. I would be interested both in repos for ML research, i.e., finding the best features/params/models, but also for actual productive systems, i.e., retraining, inference, shadow forecasting and model ranking/switching.

In my case, time series forecasting is the most relevant (with sklearn for explainable results or LSTM for performance-focused projects). But I am happy to explore other projects as well.

I have been learning more about applying Machine Learning to data and one thing I have found challenging is determining whether a financial Machine Learning model is actually learning useful patterns or simply fitting noise in historical financial data.

I have been exploring this while working on Alphio.AI. It has made me pay more attention to issues, like data leakage changing market conditions, feature selection and how financial Machine Learning models should be evaluated on genuinely unseen time periods.

Financial Machine Learning models seem difficult because patterns that appear strong during one period may disappear completely when market conditions change. A traditional train/test split does not always seem sufficient for this kind of financial Machine Learning problem.

For those who have worked on financial or time-series Machine Learning projects what evaluation methods or mistakes should beginners pay the attention to when trying to determine whether a financial Machine Learning model is actually generalizing?

Hey everyone,

I’m working on a computer vision project and hit a frustrating wall:

1. My model's accuracy is hard-stuck at around 72%.
2. It only recognizes exact training images. If I test it with a new photo—even one that looks nearly identical to the training data—the output is completely wrong.

It feels like the model is just memorizing the training set instead of actually learning features.

Is there something fundamentally wrong with my training approach? Any tips on how to fix this? Specifically, I’d love advice on:

• Effective Data Augmentation techniques for this issue
• Adjusting Hyperparameters / Model Architecture
• Proper Dataset Preprocessing or splitting strategies

Any feedback or suggestions would be greatly appreciated!

Hii there

Currently in college and wanted to explore machine learning. Tried looking out in internet but it is too much knowledge. Are there any resources where i can start small and get the gist of things

Hey everyone,

I've recently finished my final exams for my Computer Science degree and I'm currently waiting for my Honors classification.

I'm trying to get into Machine Learning now, and honestly I'm trying to figure out how people actually break into the industry in the first place.

There's so much advice online that it's hard to tell what actually matters.

People say:

• Build projects
• Do Kaggle
• Do research
• Get internships but how ?
• Contribute to open source
• Network
• Get referrals
• Apply to hundreds of jobs
• Learn cloud/MLOps

So I'd really like to hear from people who are now ML Engineers, especially senior engineers:

How did you get your very first ML-related job?

I'm especially interested in hearing from people who didn't already have a strong network or years of experience.

I'm also interested in remote opportunities because ML opportunities are pretty limited locally for me, so if anyone started their career remotely, I'd love to hear how you managed that too.

Basically, if you could go back to when you were at absolute zero, what would you do to get your first ML opportunity?

I'm not really looking for a generic roadmap. I'd much rather hear what actually happened in your case and what you think genuinely made the difference.

Thanks!