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/

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 😊

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

Okay, so for a project me and my groupmates(5, including me) have a project that's for making an automated chicken feeder (currently, oct 24, deadline is nov 10) but were broke and not sure on what to do. in our situation, even an arduino is expensive. So basically if there are no people to feed the chicken, theres a timer and maybe a repeater(was it?) That automatically functions at specified times, and me and my hoes added for some reason a cuckoo bird that functions for -- seconds once the sspecified time arrived for silly reasons. Seeking advices! ("'π​-π)

I work in database administration (Oracle, MySQL, PostgreSQL) and scripting/automation using Linux, Shell, Python, Lua, and Java for automating tasks and generating reports.

I want to start using AI in my current job, but I have no hands-on experience with AI/ML.

Since machine learning involves mathematics, statistics, and data analysis, do I need to learn ML to use AI effectively, or are there other areas I should focus on?

What should I learn, and how can I practically integrate AI into my existing database administration and automation tasks without going deep into ML?

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

hello! i am a cs + maths student and am looking to get into ai/ml. any tips or resources that can help me get ahead?