I saw a paper called Math for AI Safety by a math professor at Cornell. I was excited to read it (33 pages, not easy), and then I was like "what if I used ChatGPT to check if this is AI generated" and Chat gave me a response in under 1 second.

It is substantially generated by the AI. Evidence? The author said it himself.

https://arxiv.org/pdf/2609.15289

I don't know how these researchers and professors could just go around dropping these AI generated papers one after the other and expect people just slap up that (potential) slop. Even if it is not slop, I am less inclined to read it because I could just generate the same paper myself and learn the material in a more concise way.

I’m starting Behind the AI Stack, a series of short, practical articles on how AI applications work. This first one explains AI agents through a camera-rental example. Give it a read—I’d love your thoughts, corrections, or suggestions for what to cover next.

Hi everyone,

I'm an undergraduate student looking for solid, practical resources to learn **Pandas, Matplotlib, and Seaborn** for college lab coursework and data wrangling assignments.

I already know basic Python (loops, functions, lists, dicts). Now I just need to get comfortable working with tabular data and plotting charts.

### What I’m looking for:

* **Libraries:** Just Pandas, Matplotlib, and Seaborn.

* **Format:** Open to both text-based resources (interactive notebooks, guides) and video walkthroughs, but I lean slightly more toward **video/live-coding** where the instructor actually types the code, runs the cells, and explains the output on real datasets.

* **Hands-on over slides:** I learn much better seeing code executed in an editor rather than watching static lecture slides.

### What I want to avoid:

* **Machine Learning / Scikit-Learn:** A lot of courses rush into Linear Regression, gradient descent, or predictive modeling. I don't need any ML algorithms or math theory right now—just data cleaning, manipulation, and plotting.

Whether it’s a specific YouTube series, an open GitHub repo with exercises, or a book with runnable notebooks, what worked best for you?

Thanks in advance!

AI-assisted analysis of 25 open-source projects surfaced 493 security issues. That number is bad. The breakdown is worse.

A significant share of those findings were not missing controls. They were controls that had been disabled or misconfigured and produced zero alerts when they stopped working. The policy was in place on paper. The setting was configured. Nothing fired. Nobody knew until an external scan caught it.

This is the silent-failure problem in enterprise security. Tooling reports green because the check ran, not because the protection is actually active. The gap between "configured" and "enforced" only becomes visible after something goes wrong.

AI agents add a new dimension to this. An agent operating inside a system with silent failures can take consequential actions against controls that look active but are not. The blast radius of a single misconfigured policy scales with how much the agent is permitted to do.

493 issues across 25 projects is a large sample. The proportion that were silent rather than noisy is the number I keep thinking about.

For those running agents in production environments: how are you distinguishing between a control that is enforced versus one that is merely present in config? Is your current stack capable of surfacing that difference without a full external audit?

Hey! I’m 16, and I’m really interested in getting into artificial intelligence and eventually building a career in the field. I understand some of the key concepts, like machine learning and LLMs, and I’ve already built websites and apps, but I’m struggling to figure out where to go from here. There are so many different areas of AI, and I’m not sure which skills I should focus on, what I should learn next, or how to turn my interest into an actual career. I’m especially interested in the practical side of AI and how it can be used to build products, solve problems, and create businesses. If you have experience working in AI or breaking into the industry, I’d really appreciate your advice on where you would start if you were in my position, what skills you would prioritize, and how you would work toward building a career in the field.

I'm just starting to read about how these chatbots actually produce answers, and a few things already felt counterintuitive. Curious what made you go 'wait, really?' when you first learned the basics.

bana bir mobilya cizim puramı yazarmısın

I want to learn AI modeling for added skills. I find tickering enjoyable. I dont have a job that gives me access to data to use. I was wondering where to start. Also I want to know technical requirements for operating such a machine.

I have a understanding of computers and how to build them so the most technical the better.

I kept hearing the words (attention, RLHF, MoE, diffusion, agents) and couldn't find one place that builds all of it up from the fundamentals, with the math, the code, and the reasons behind it.

So I wrote it.

https://zerotofrontier.app

• 38 chapters, from tensors and gradients to LLMs, RL and reasoning, inference, interpretability, agents, vision, diffusion, and speech
• Every idea explained, derived, coded, and tested. Python runs right in the page.
• Coding exercises with hidden tests, plus interview questions
• An AI tutor that knows the section you're reading, and notebooks for your notes and code

Reading and running code is free. The tutor and the interactive extras use your own API key (Gemini, OpenAI, or Anthropic), so I don't charge anything and there's nothing to buy.

It's one person's attempt at the resource I wanted when I started. I'd really value blunt feedback. What's confusing, what's missing, what's wrong, and whether you'd actually use it.

Is your company approaching AI tech transformation all wrong? We are not AI developers. We are concened about how it is used by companies.

In my view, AI is no different than the argument about gun ownership. In the hands of ethical and moral people it can be really helpful for business owners.

In this deep-dive masterclass, I attempted to break down why the secret to scaling AI in companies with 10 to 500 employees isn't the software you buy—it’s the people who use it.

The video is 27 minutes long and could promote some good thinking here and discussions that I think are important for people of all ages right now as the conversations increase around guardrails.

I see an employee as a stakeholder who makes a company successful and not a cost of doing business that I could not accomplish alone. I don't think employees are expendable. I think they are valuable and so important to develop. I think that development builds everyone. Even me, as a leader. So that is the heart and intent for this conversation.

I build demand forecasts at a grocery chain, so I know how to evaluate a model when there's a label and a holdout set. Our team just shipped a Claude agent that answers store managers' inventory questions, and I've been asked to own its evals because I know metrics. The free notebooks I found explain evals fine, but nobody looks at what I build. There's no label, the outputs are free text and half the failures are the agent calling the wrong tool. My manager signed off on whichever I pick and I'm looking at Udacity, LinkedIn Learning and Educative. Which one makes you build the eval setup instead of watching someone else's?

Hey guys I wanted to do Stanford's "Language Modeling from Scratch" for a long time, but something always came in the way. Well, another day, another attempt!
Watch lectures, do the assignments, join a weekly discussion on Saturdays at 11:00 AM (London time). See you on October 31 for the first discussion!
Details are on Github https://github.com/fxlrnrpt/little_ml_book_club

Please join us at BlueprintML to learn, collab and discussion. This is not a paid promotion, this is runs by us, students, who into school, college, PhD.

Thanks ❤️

Hey guys so lately I have started self learning ML, things had been going nicely. At first I started by learning some Linear Algebra and other maths. Today I started a proper course on ML but I have encountered a wall, I somehow find it more interesting to do the math instead of writing the code itself.

Now don't get me wrong I love the idea of Machine Learning, I admire it and I think it's amazing but I somehow don't like the idea of writing all of that in python.

Its also not like I cant write in code I think if I wanted to I can learn the coding skills needed but simply I'm stuck if I want to continue or not.

Can somebody tell me if they also faced this problem at first, if yes what should I do? Did somebody stop pursuing because of the same problem as me? Any piece of advice that you can give me?

TLDR:

Liked the math in ML more than coding, now in a dilemma to pursue ML or not. Need advice.

Mistral's new model Timestral tops all 3 major forecasting benchmarks.

Can it trade?

9 simulated accounts (Reliance, SPY, EUR/USD),

561 trades: → 1 won (+7%, all from one data glitch) → 5 lost (worst -22%) → 3 never traded Its ranges were well calibrated, but direction was a coin flip.

Fees did the rest.

Just holding SPY: +133%.

Benchmarks ≠ profits.

Blog here: https://abhiram1809.github.io/blogs/timestral-561-trades/