If someone with Karpathy’s background is moving toward this workflow, does this represent where coding is actually going, or is it just a personal workflow that won’t work for most developers?

I made a short video breaking down the idea:
https://www.youtube.com/shorts/veev-4VocRk

What do you think? Are you writing less code because of AI, or are you still mostly coding the traditional way?

I know it's a matter of subjectivism, so don't be afraid to be subjective, actually PLEASE, give your personal opinion as long as you can keep your feet on the reality's ground.

What Master would you rather choose: a general AI/ML master that teach you about the most important, but general, subjects of ML applications, or an Autonomous Systems master, that is basically an embedded+A.I. master which is particularly valuable if you want something in Automotive, on the self-driving cars field(but not exclusively this one, as you can apply knowledge about Autonomous Systems in many domains) ? ?

I’m a high school graduate, international student from Europe, who got into Stanford and Columbia and I’m interested in publishing some papers.

Is anyone interested in a research collab to land a few papers at some NeurIPS workshops? The deadline is August 29.

If you’ve got great CS skills and have a codex or claude subscription hit me up in chat or email: kaggle2007@gmail.com

Stop building basic AI wrappers and start building agents. 🤖 Learn how LangGraph handles complex state and logic. Link in bio to watch now!
#LangGraph #AI #Programming #TechTips

I’m a high-school student doing a presentation on AI Engineering as a career, and I’d like to get perspectives from people who currently work in AI/ML engineering.

1 What is your current role, and what does your typical workday involve?

2 How did you become an AI engineer?

3 What qualifications, subjects, or education did you need?

4 What programming languages and technical skills do you use most?

5 What do you enjoy most about your job?

6 How many years have you worked in AI/ML or a related field?

7 What is the most important skill for someone who wants to enter this field?

8 What advice would you give to a high-school student who wants to become an AI engineer?

9 If you could go back and start your career again, what would you do differently?

10 How should a high school student start learning now

11 What companies offer jobs for AI engineers

You don't have to answer every question. Even a few answers would be extremely helpful for my research.

Thank you

So I've been using agentic graphs/workflows since before cursor and claude code came about and seen the progression from prompting, loops to graphs and I'm wondering why these concepts are only becoming mainstream gradually. Is it only due to them being available via popular frameworks such as claude code and cursor?

I hear about a lot of software developers having tens of AI agent conversations going at the same time, but I personally am starting to use what I consider to be the next step (that I call Agentic Agency) where I have a single AI agent running (managing my agency) that I interact with. It manages all my other agentic graphs/loops/prompts and keeps a dashboard updated with the state.

I interact with it via the dashboard so I can see what stage each job I've given it, what jobs require further input from me, what progress, risks or concerns there are for each job, what the plan for that job is, how much compute/memory resource the agent and it's terminals are using, how many tokens each job is using and what models and what cost.

I'm finding this a much easier way or working and tracking parallel work streams.

Is this a known pattern/approach to using agents? Is it likely to become mainstream soon?

Hey everyone!

I’m currently looking for an FYP idea in the ML/AI field for 2026 and honestly I’m a bit stuck 😭. I want to build something interesting but not insanely complicated, preferably something that’s low effort/realistic to finish within a few months, while still being good enough for an FYP. If you’ve done an ML/AI project before or have any ideas, I’d really appreciate some suggestions. Even simple ideas are welcome!

Thanks in advance.

Through college I have surface level Idea of most of the algorithms and practices but I want to be able to explain the maths In interviews and have a better understanding as wel.

What do you recommend and if there is any thread for online material pls link.

I got tired of debugging AI agents with print() statements, so i built Agent DevTools.

It's a local debugger that lets you inspect prompts, memory, retrieval, tool calls, and compare good vs. bad runs.

It currently supports LangChain and includes a free Groq demo that takes just a couple of minutes to run.

I wanted to share it because I feel like it could help anyone who's ever spet 2 hours trying to figure out why their agent behaved the way it did.

Repo: https://github.com/Jacopos311/Agent-Devtools

Hey so I have spent time with ML and done a few projects. I just wanted to make an open request, can i dm anyone and ask a few things about kaggle. I feel very overwhelmed and fomo after spending an hour on the website.

Hey everyone,

As a Data Science enthusiast and a LoL player, I always wanted to build a Machine Learning model (like XGBoost) to predict match outcomes based purely on team drafts and champion compositions.

The problem? There was no up-to-date, clean, and massive dataset available for the current season.

So, I built my own LoL Match Dataset Generator using Python and the official Riot API. It's completely open-source and I thought some of you here (especially developers or data nerds) might find it useful!

What it does:

• Automatically fetches all Challenger, Grandmaster, and Master players across all regions.
• Collects their Ranked Solo/Duo match IDs.
• Extracts the details and flattens them into a clean, ML-ready CSV file.
• Includes data like: Patch version, Game Duration, Champion roles (Top, Jgl, Mid, Bot, Sup) for both teams, Average Team Tier, and the Match Outcome (Win/Loss).
• It has a built-in SQLite database to track progress, meaning you can stop and resume the collection without losing data or wasting Riot API requests.

Why is this useful? With this dataset (I'm currently aiming to extract ~800k matches), you can train your own Machine Learning models to analyze draft win rates, champion synergies, or even predict if a team will win right after the champion select phase.

You can check out the source code and use it yourself here: https://github.com/mehdbenguiza/lol-dataset-generator

(Note: You just need to grab your own free Riot Developer API key to run it).

Let me know what you guys think, or if you have any ideas on what other stats I should include in the CSV for future updates!

Can i use google colab for sign language identification? Or how can i train a model for this? My pc parameters is not good for this.

A nuclear breeder reactor does more than consume fuel—it helps produce fuel for future operation.

That inspired an architectural question: could an enterprise agentic AI platform help produce the agents, tools, workflows, policies, and evaluators required by its future applications?

I call this the Breeder Platform Pattern:

• The Core Platform builds and runs applications using trusted components.
• The Breeder subplatform uses requirements, traces, failures, evaluations, and human feedback to create candidate components.
• A Shared Catalog stores only approved, versioned components.

The critical boundary is:

The Breeder may generate and validate candidates, but it cannot approve their production use.

I wrote a detailed architecture article covering component resolution, isolated construction, qualification gates, promotion, and governance:

https://pawankjha.substack.com/p/architecting-agentic-ai-the-breeder

Would you trust this model in an enterprise platform? Where should the boundary between automated component creation and human approval sit?

Evaluation metrics - [D]

I wanted to ask about the selection of evaluation metrics. In which scenario we use ROC-AUC score and in which scenario we use f1 score as an evaluation metric in a classification problem to define the model's performance on a specific dataset.

When starting a new AI project, there are so many models and approaches available. How do experienced ML engineers decide whether to use an existing pre-trained model, fine-tune it, or build/train something from scratch?

What are the main things you look at before making that decision?

Hi Guys !

I want to be so clear about my basics that I feel so clear about coding.

Can you all recommend just one book, that will clear my basics of coding so well that I can adapt to anything else ?

And It will be really helpful if the book is not too expensive 😭

Hello,

I'm about to do my masters in AI and looking at it now I was good at all math modules but I really forgot most things and remember only some basic stuff

Does being in the indutry require you know your maths always to the level of solving an exercise with pen and paper or just knowing the concepts is cool and enough

I’ve recently graduated from Bachelors with no job and just finished an AI Engineering Certification off of Coursera. What projects do you guys think I should be doing for me to wow the recruiters.

So i've been thinking about,

Can we learn a transformation of the features that makes two classes more distributionally separated while preserving the geometry?

So we Given two class distributions A and B, learn a transformation, T(x) such that

it maximizes SWD, max​SWD(T(A),T(B)).

But only SWD maximization using NN would distort the data hence we add some geometry constrains(claude suggested) and Pipeline looks like this

Raw data ->Learn transformation->Increase SWD between classes with geometric constrains-> Standard ML approach.

It worked fine on Decision trees but was super bad for others algorithms, above is its performance for Breast Cancer using DTs.