If you're stuck on what to learn or build in AI, pick any one of these open ecosystems. Read its codebase first, follow its learning path, then start contributing or build useful projects with them.

Hi everyone!

A few months ago, I got accepted into an Econometrics bachelor's program. I've been studying for about three months now.

My courses include:

• Mathematics (Calculus, Linear Algebra, Probability).

• Statistics.

• Programming (C/C++, Excel, MS Access).

• Economics.

1. Is this a good foundation, or should I study something extra? I've been looking at MIT courses and wondering which external resources are worth my time.

2. Is combining Econometrics with Machine Learning a promising career path? I notice quite a lot of similarities between what I'm learning and ML fundamentals. Can these fields complement each other professionally?

3. How can I start earning money while studying? I'm not expecting a huge income. I just want to find my first paid opportunity, gain practical experience, and gradually develop my skills.

For those who started from a similar background: How did you get your first job or freelance project? What would you recommend focusing on?

I'd really appreciate your advice and personal experiences. Thanks!

UPDATE: I forgot to write that i will also have Python and R in my course, and also i will have deep statistics and data analysis

Hi everyone,

I’m a first-year CS student from Nepal, and I’ve decided to build a career in AI/ML. I started from zero a little over a month ago, and I’m looking for other beginners at a similar stage who want to learn together and keep each other going.

Where I am right now:

• Finished CS50P (Python) up to OOP, and I’m learning Git/GitHub

• Currently working through NumPy, then Pandas, Matplotlib, and my first Kaggle project

• Haven’t started the math side of ML yet (linear algebra, probability, statistics), so I’d love to learn that alongside others

• After that, I plan to start Andrew Ng’s ML course

• Using only free resources, studying around my college semester

What I’m hoping to find:

• Other freshers who are also starting out, so we can share progress and resources

• A study group or small community (Discord, WhatsApp, whatever works) where we post weekly goals and hold each other accountable

• People to work through the ML math together, like 3Blue1Brown, Khan Academy, or StatQuest, and explain it to each other

• People to do mini-projects or Kaggle datasets with, and review each other’s code

• Anyone who’s a few steps ahead and willing to point out what’s worth learning and what’s a waste of time

I'm building Incognition, a free site for learning machine learning from zero. You learn by playing with interactive visualizations first, then the maths and code.

16 courses, 300+ visualizations, 1,600+ practice problems, and Python runs right in your browser.

https://theincognition.com/

Honest feedback welcome. Which ML concept took you longest to understand?

What We Are Covering :

• Transformer Architecture: Deep diving into the foundational mechanics.

• KV Cache: Understanding memory bottlenecks and retention.

• PagedAttention: How virtual memory principles apply to LLM hosting.

• Continuous Batching: Optimizing throughput and reducing iteration-level latency.

• vLLM Architecture: A complete overview of state-of-the-art serving frameworks.

> more about foundation visit once: https://before-gpu-engineering[dot]github[dot]io/BEFORE-GPU-ENGINEERING/toc [dot] html

It’s a first principles study group aimed at beginner to intermediate levels, focused on deeply understanding how LLM inference and GPU optimization work under the hood.

I plan to wait until we have at least 10 participants before setting up the WhatsApp group and coordinating meeting times across time zones. I will keep you updated as more people join in DM!

PS: This all free and No, there's no paid course after this, this is not an edtech not a promotion or any other possibilities that fantasies can fetch. I as in the OP isn't the mentor here exactly, I am simply a junior asked to look out and post.

I already have a subscription but I ask about the quality and the time will spend is this good question ? Also what you recommend to take in this platform for AI engineering

Hey guys, I am familiar with ML and training models and stuff. But I don't quite get the intuition behind different loss functions or different activation functions.

Like I know theoretically that CE loss is used for multi-class classification where there's only one correct output, but then I learnt CE loss can also be used for soft labels, which is also what BCE loss can be used for as well? Like how do I get this intuition in my head just by looking at the problem to figure out which loss function or which activation function to use?

And this is not just limited to CE or BCE. There's more loss functions like Huber, Brier, RPS, KL Divergence etc which I understand theoretically but I cannot figure out when to use what. Any suggestions/courses you would recommend me to improve my understanding? Thanks!

I wanted to test how well Claude Fable 5.2 can teach, so I had it build an explainer on attention. It cleared up most of my own doubts about transformers, attention, qkv vectors etc. I had tried similar projects with Opus and GPT models, but they weren't intuitive enough for me.

https://attention-from-scratch.vercel.app

- 9 chapters, from one knob (y = w·x) to a small working transformer that runs in your browser

- You do each idea by hand first (turn knobs, drag arrows, pick words), and the official name comes after

- Covers loss, gradient descent, neurons and backprop, embeddings, dot product, softmax and attention

- Optional "In symbols" boxes with the real maths

I'd like honest feedback:

- Did attention click for you, or did you get lost? Where?

- Is anything wrong or oversimplified?

I kept seeing explanations of MCP that either hand-wave the concept or just dump a spec at you, so I wrote a module that does both: a plain analogy first (why a stateless server still needs something like a coat-check ticket), then the actual code that implements it.

It's part of a bigger free site, CSE Roadmap: 15 levels, 127 modules, meant to take someone from zero to a real capstone project in agentic AI. No signup, no paywall.

Site: https://cseroadmap.com

Would genuinely like feedback, especially on where the explanations still feel thin or skip a step.

Is AI just magic? No, it is a methodology. Learn the difference between AI, ML, and Deep Learning in under 10 minutes. Build smarter systems today!
#AI #TechStack #MachineLearning #Coding

Could I get a second pair of eyes on my CV for ML research roles? My PhD is in self-supervised learning and computer vision, and I spent nearly two years before it doing AI research and deployment for robotics on Horizon 2020 projects.

I’m weighing up two CV versions. For Research Scientist roles, I’d put first-author research up front. For Research Engineer roles, I’d lead with what I implemented and deployed. If you’ve applied to or hired for either role, what would you want to see in the first few bullets?

A candidate has an interview next week for an entry-level NLP engineer role at Hitech i2i, which specializes in document intelligence for real estate.

For a practice run before the screen, PracHub has material on entity extraction and evaluation. Try explaining how you’d extract fields from a messy document and check where the approach gets them wrong.

What topics would you focus on for the initial screening round?

A Denver city audit found employees at City Hall and Denver International Airport actively using AI tools the organization had never approved. There was no policy enforcement in place. There was no enterprise-level visibility into what was running. No one at the organization knew until the auditors showed up.

This is not a scrappy startup. This is a municipal government and a major international airport handling sensitive passenger data, infrastructure systems, and city records.

The audit found no formal process for evaluating or approving AI tools before deployment. The gap was structural, not a one-off lapse. The organization could not tell you which tools were active, who was using them, or what data they had touched.

For those working in regulated or high-stakes environments: how are you actually getting ahead of this before an external audit surfaces it? Not policy documents, but what has worked operationally at scale?

I've been curious about a particular learning gap.

Sometimes it's relatively easy to understand what an ML algorithm is trying to achieve, like finding similar items, making predictions, or reducing errors.

But then vectors, matrices, dot products, projections, or equations enter the picture, and suddenly maths we last encountered in school reappears in a completely different setting.

We might understand the purpose of the algorithm without quite understanding why those particular mathematical operations are needed in the first place.

I'm curious:

-Have you experienced this disconnect, even after learning to code or use ML libraries?

- Was the harder part understanding the maths itself, or connecting it to what the algorithm was doing?

- What eventually helped things click, if anything?

I'd love to hear different experiences, including from people who didn't encounter this difficulty.

How have you guys actually used PCA in practice? I hear we can use it in place of groups of variables as features for example! I feel like in uni its just a "step" for like k-means, but my DS friend was saying she build a scorecard model with it by grouping features!

I'm building evaluation adapters (small reward models) for Telegraph, a network that routes AI requests to miners and scores their answers. Disclosure: I'm competing in Telegraph Hackathon Season II, which has prizes.

A preference pair is a prompt, a better answer and a worse one. A reward model trained on pairs learns whatever separates the two answers. If something cheaper than the skill you want separates them, it learns that instead, and your accuracy number measures the shortcut.

The test. Before training, run rules that pick an answer without checking anything:

• pick the longer answer

• pick the answer with more words in common with the prompt (TF-IDF cosine)

• pick the answer with more of an obvious feature (alarm words, a verdict word)

If any of them is far above 50%, fix the pairs first.

Two cases from this week.

1. Another builder (AdversarialEval, in the Telegraph Discord) found a keyword-count judge got every one of their severity pairs right, because all the worse answers were downgrades. After adding inflations, the TF-IDF judge still won ~95%. They rebuilt the pairs so the two answers are identical except for the severity claim, drew wrong severities from the same distribution as true ones (either direction), and balanced which answer is longer. On 84 new pairs: length 50%, TF-IDF 50%, keywords 55%.

2. My own 300 weather-verification pairs: length was 49.7% overall but 17–88% within error families, because the verdict phrases differ in length and some families lean one way. TF-IDF was 78% overall. Part of that is legitimate (in a wrong-station pair, not matching the question is the error), but my digit-swap and fill-value pairs leak too. Those rows are already in the pool, so I'm reporting this as a limit rather than quietly fixing it.

What I'd do differently:

• write the worse answer by copying the better one and changing only the claim;

• balance verdict direction and length within every error family, not just overall;

• report the shortcut judges next to model accuracy in every run.

Script (standard library only) and the per-family table: https://github.com/fexx301/oathcast-telegraph-s2/tree/main/shortcut-judges-v1

I wanted to understand how neurosymbolic methods like DeepProbLog actually work, so I built a small version with no frameworks, just NumPy (about 700 lines).

The setup: a neural network sees two noisy digit images. It is never told what the digits are. The only training signal is the result of a symbolic program on them, for example their sum. To get the sum right, it has to work out what the digits are by itself.

- An MLP (hand-written backprop and Adam) outputs a probability distribution over digits for each image.

- A differentiable reasoning layer sums the probability of every digit pair (a, b) that the program maps to the observed answer. The loss is -log P(answer), and the gradient flows back through the reasoning step into the network.

- A small Datalog-style forward chainer then reasons over what the network perceived and prints a proof tree for each conclusion (e.g. why "carry" or "odd_sum" hold

| Training pairs | Neurosymbolic | Plain MLP baseline | Digit accuracy |

|---:|---:|---:|---:|

| 1000 | 95.3% | 10.8% | 97.6% |

| 2000 | 97.2% | 14.4% | 98.6% |

| 4000 | 97.7% | 26.9% | 98.9% |

The baseline is an MLP that takes both images and predicts the sum directly, so it has to rediscover addition from examples. The neurosymbolic model is given addition as knowledge and only has to learn perception.

- Below about 1000 training pairs it fails too (23% at 500 pairs). The answer-only signal is weak.

- The digits are synthetic noisy glyphs, not MNIST, and these are single-seed numbers.

- Exact inference enumerates all 100 digit pairs, so it doesn't scale to long programs without approximation.

This is a learning project, not a competitor to DeepProbLog or Scallop. The point is that every piece is small enough to read in one sitting.

Code: https://github.com/outlawsujith/neurosymbolic-arithmetic

Install: `pip install neurosymbolic-arithmetic`

Feedback welcome, especially on the reasoning layer and on what to try next (MNIST, or approximate inference for longer programs).

This year alone, I spoke at 9 major in-person AI events, posted a Youtube video almost every week, shared hundreds of shorts, and wrote my second book..

Here's the most important thing I've learnt.

[This will take 2 mins to read]

I've been creating AI content for the past 6 years.
Started with some random infographics on Instagram, just to understand AI concepts myself.
Without any plan and direction!

Then, I started explaining research papers on YouTube, one or two videos every week, for years.
After a year, a CEO saw those videos and hired me to build his AI team.
Then I built a Discord community, and it became part of Towards AI.

I tried a podcast too.
It didn't work.

Then I slowed down on posting because my PhD and Towards AI took all my time.
I eventually quit my PhD, and the content shifted towards what we build with clients and how we build it.

And finally, the content evolved into what it is today:
teaching people how to build AI that works in production, on YouTube, on stage and in a book.

None of it was planned, honestly.

So when people ask me:

- How should I start?
- What should I post about?
- What if I'm not good at it?

This is what I tell them:

Start by explaining what you're learning right now. “Learn in public”.
That's how I learned AI in the first place.
Making those infographics forced me to understand every concept.

And you won't be good at first.
I'm still not a natural on camera. Recording a video still feels hard to me.
Being bad at the start doesn't mean you'll stay bad.

My advice:
Figure out your strategy and niche along the way.
Consistency matters way more.

Share what you're learning,
Explain what you understood,
Show what you're building,
And say "I don't know" when you don't.
In a field that moves this fast, nobody knows everything.

Do that for long enough and you'll find people who care about your work, sitting in a different corner of the world.
Some of them will even recognize you at a conference.

It doesn't matter if you're a student, researcher, engineer or founder.
You should start creating content on some platform.

Even now, with Towards AI growing and taking most of my time, I still post on all platforms.
Nobody can share what a company builds better than its founders.

My first job, my company, my talks, all of it came from content.
Content will help you in ways you can't even imagine!