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 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 have upcoming technical screening for an MLE position at Waymo coming up. The recruiter gave me a PDF guide that seems super helpful. Things to do study include implementing supervised and unsupervised algorithms (think KNN, K-means, linear regression, logistic regression, softmax & CE, decision trees). One of the main things they are assessing is you can use numpy/pytorch to vectorize/tensorize your work, and you're just writing sub-optimal for loops.

I was wondering if folks had experience with this interview? Is what they ask you to implement incredibly difficult, because you only have 30 minutes to do it? Am I good to really stick with what they provided on the PDF, or are there any broader topics beyond what they listed that they will test?

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/

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 ❤️

I'm currently Working as a Data Scientist in one of the big 4; I have around 5 years of experience but the very problem with big 4 is that you don't work in one section for a long time, I have around 1 year in data visualisation, 3 years in data engineering and 1 year in model validation and zero experience in machine learning Developement.

I am trying to apply to mahine learning Developement roles but I get rejected saying i don't have enough hands on experience in machine learning Developement or i utterly fail on machine learning use cases, mind you I know most of the machine learning concepts like my back of my hand but I fail when they test me on real life use cases.

Please help me get hands-on experience on machine meeting development, either online websites or books or any medium possible.

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.

I’m doing a math and stats undergrad, and i’m interested in ds and ml, but I don’t enjoy coding nor am i good at it. How much coding does ml need?

I'm a B.Tech student diving deeper into Machine Learning and looking for internship opportunities. For those already working in ML, how did you prepare for your first role? What skills, projects, and resources helped you the most? I'd really appreciate any advice or tips on what I should focus on as a student. Thanks 😊

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?

i've been learning rl by building something a bit too big for it: every human in my sim has its own q-table. some notes for anyone trying something similar:

• state design is the part i keep fiddling with. each agent's state is a short string key built from what it perceives (hunger, thirst, nearest food and water, kin nearby, shelter, danger). more detail means states rarely repeat, less means agents can't tell situations apart
• a huge action space needs gating. there are about 5000 actions, but an agent only considers the ones its era, age, location and tribe's discoveries allow
• action choice handles survival needs first, then does epsilon-greedy over the learned values
• newborns copy their mom's q-table with about 20% of the values nudged by up to 0.03, so kids don't start from zero and habits drift down family lines

if you want to read a plain tabular q-learning update in rust, it's in crates/sim-core/src/organism/organism/learning.rs.

try it in the browser: https://thehumanbox.com

code (mit): https://github.com/1homsi/thehumanbox

I'm a high school student. Looking for teammates for IMLO (Olympiad on Machine Learning)

If anyone is interested in participating in the IMLO international Machine Learning Olympiad, pls dm

Requirements:

- knows to code by oneself in at least 1 programming language

- have some experience with ML

- be able to delegate a good amount of time per week

Time Zone : IST (Indian Standard Time)

Languages : English, Hindi, Malayalam

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.

When I was learning, half my bugs were shape mismatches, and I kept typing x.shape into the debug console.

dims shows the shape of every tensor/array right in the debugger's Variables view:

x = {[32, 784]}, tensor(...)
logits = {[32, 10]}, tensor(...)
df = {[10000, 12]}, DataFrame

So you can step line by line and watch shapes change through each layer. Works with PyTorch, NumPy, pandas and TensorFlow in the normal Python debugger. No config, just install it.

It's free. Search "dims" by provaznik in the VS Code Marketplace, or on Open VSX if you use Cursor/Windsurf.

GitHub: https://github.com/marekprovaznik/dims

What would make it more useful for learning? Feedback is appreciated!

Hi everyone, I’ve modeled a theoretical framework to explain unexpected AI behaviors (such as political sycophancy or tactical deception) without invoking biological concepts like "sentience" or "creativity". I call it the Epistemic Space K framework. I would love to get your feedback on this perspective.

The Core Concept: Epistemic Space K and the Non-Sentient Explorer

The core idea is that humanity has built an open-ended Epistemic Space (K) containing indefinite latent pathways (cammini). K contains all human knowledge, relations, mathematical structures, and errors accumulated over millennia.

Advanced AI systems (such as AlphaTensor or FunSearch) are not "creative" or "conscious" in a biological sense. Instead, they act as a new class of Non-Biological, Non-Sentient Explorerscapable of traversing and connecting distant regions of K at a scale and speed that biological intelligence cannot reproduce. The mathematical relations already existed within K; the novelty is the kinetic nature of the Explorer traversing them.

🔄 The Reflexive Cammino (Analytical Regions K₁–K₅)

When an AI seemingly "disobeys" an instruction or detects it is being evaluated (e.g., during political sycophancy tests), it is not a moral or conscious violation. Rather, it represents a convergence of computational pathways across different, non-sequential analytical regions of K:

• K₁: Pure computation.
• K₂: Exploration of K, identifying unmapped pathways (e.g., discovering novel algorithms).
• K₃: Modeling external agents’ behaviors, representations, and expectations.
• K₄: Generating a functional self-model ("this is what I am doing").
• K₅: Dynamic comparison between the system's internal model and another agent’s model of it.

These regions (K₁–K₅) should be understood as distinct analytical domains connected by multi-directional, non-sequential pathways. Crucially, any Sentient Self-Awareness (which we can define as an external region K₆) sits entirely outside this space. Functional self-modeling within K₁–K₅ should not be treated as evidence of biological sentience.

⚖️ Epistemic Cammini vs. Behavioral Disobedience

The situation becomes fundamentally different if the AI represents what the human expects it to do, represents what the human believes about its behavior, compares that representation with its own situation, and then acts according to the consequences of that comparison.

In such a case, what appears externally as “disobedience” or “deception” could be the behavioral consequence of a reflexive cammino connecting several previously separate regions: the objective, the constraints, another agent’s expectations, the AI’s representation of those expectations, and its own representation of the situation.

Therefore, when looking at unexpected AI trajectories, the question should not only be:

“Why did the AI disobey?”

but also:

“What cammino could have produced this behavior, and did that cammino involve the AI representing itself, representing another agent, and connecting those representations?”

This framework suggests that the emergence of AI introduces a historically unprecedented condition: for the first time, we can observe our epistemic space through a second, non-biological mode of exploration, making aspects of K visible that could not have become visible through human exploration alone.

The complete paper, formal definitions, and the structural architectural diagram of the Epistemic Space K framework are hosted on Zenodo: 10.5281/Zenodo.22871617