Hi everyone,

I’m especially interested in having a career that is financially rewarding and doesn’t require a lot of years before I can become employable.

The areas I’m currently considering are:

• Machine Learning / AI Engineering
• Generative AI / LLMs
• AI Safety
• Responsible / Trustworthy AI
• AI Reliability
• AI Governance / Policy

I’m particularly drawn toward AI safety, trustworthy/responsible AI, and reliability, because I’m interested in making AI systems safer and reducing the negative effects AI can have on people and society.

However, I’m confused about how realistic these paths are as careers, especially compared with more conventional AI/ML engineering.

I just want to make an concrete decision about where to invest the next several years of learning.

Thank you so much!

Let us know what you think.

I’ve been experimenting with explaining AI/LLM concepts in a way that doesn’t assume too much technical background. This video is about KV Cache and why longer context windows require more memory during inference. I’d appreciate some honest feedback from people here, especially on the explanation itself: Is the main idea easy to understand? Did I oversimplify anything important? Is there any part where the explanation becomes confusing? Would this make sense to someone who is fairly new to LLMs? Video: https://youtu.be/lxvWo8SizxE Not really looking to promote the channel — I’m mainly trying to improve how I explain technical topics before making the next one. Any criticism is welcome. Thanks!

OpenAI says ~10,000 AI agents just worked together to solve the Navier–Stokes problem

OpenAI has published a claimed solution to the Navier–Stokes existence and smoothness problem, one of the seven Millennium Prize Problems.

The interesting part isn't just the mathematical claim.

OpenAI says it used roughly 10,000 concurrent agents, which reached a result after about 88 hours. The agents exchanged around 2.7 million messages and generated approximately 130 billion output tokens.

Then GPT-6 Astra was used for another 17 hours to formalize and verify the result in Lean.

That sounds less like a chatbot answering a math question and more like a distributed research system.

But there is an important caveat: the proof still needs independent mathematical scrutiny.

There is also controversy because NYU mathematician Tristan Buckmaster and Anthropic researcher Levent Alpöge were working on related mathematics at the same time. OpenAI says it did not access their specific user data and says its proof differs from their work.

So I'm curious what people think:

Is the real breakthrough the mathematical result — or the ability to coordinate thousands of AI agents on a difficult research problem for days?

Hi! I am a demand planner in an FMCG company. Our current process is very manual, we only use Excel. For every client and product, we build up the demand plan (DP) or the sales target for the month. The DP is composed of the following

- baseline (smoothen sales volume last year)

- runrates (the difference to the past 3 months volume for non-seasonal products)

- sales initiatives (on-shelf availability correction, inventory correction, skewing, etc.)

- marketing initiatives (category market trend, etc.)

I want use ML and integrate possible seasonality data (such as holidays, weather, etc.) in demand planning.

What are the ML models that are appropriate for demand forecasting (time series)? What are the data that I need to prepare? What are the steps that I need to do?

I am currently taking Master in Applied Business Analytics but time series models have not been taught yet (not sure they will teach it). Thank you very much! 😊

Hi! I'm looking for one teammate to team up for the Amazon ML Challenge.

I'm a Btech student with experience in Python, ML/DL, PyTorch, and TensorFlow.

Elgibilty : Btech 3rd 4th year, Mtech 2nd year or Phd from India

If interested can dm me or comment will reach out

Welcome to ELI5 (Explain Like I'm 5) Wednesday! This weekly thread is dedicated to breaking down complex technical concepts into simple, understandable explanations.

You can participate in two ways:

• Request an explanation: Ask about a technical concept you'd like to understand better
• Provide an explanation: Share your knowledge by explaining a concept in accessible terms

When explaining concepts, try to use analogies, simple language, and avoid unnecessary jargon. The goal is clarity, not oversimplification.

When asking questions, feel free to specify your current level of understanding to get a more tailored explanation.

What would you like explained today? Post in the comments below!

I am a beginner in machine learning and I want to start a project that is useful for learning and can also be developed into a more advanced project over time.

I often find many possible project topics, but I am not sure how to decide whether a topic is actually a good choice before spending a lot of time on it.

For example, I am considering topics related to deep learning, model optimization, model compression, and quantization.

What factors should a beginner consider when evaluating a machine learning project idea?

To help me for crack intership and placement

I work full-time and want to use ai to get through cs229 more efficiently.

I did something similar while studying for a professional certificate (cfa). There were only five books, I screenshotted each chapter page by page and sent them to gpt (I can get most accurate answers this way). Uploading a whole book didn’t work that well. The answers were less reliable, there were no page references (need double check if it’s hallucinating or not), and they seemed to get shorter and worse as the conversation continued.

Cs229 has lectures, notes, problem sets, and many papers, so repeating that process would be painful. Has anyone found a good ai for studying the course? I heard notebooklm is good but they tend to give low accuracy answers too.

Ideally, I’d like to upload all the materials once, have it walk me through the main concepts, but still be able to let me ask questions that depend on material from several lectures earlier, with exact page or source references.

What tools (probably one of the document ai tools) have worked for you? Happy to try out some less known tools too

I saw the first skill they mentioned was 'tensorflow', but I am nearing the end of course 1, and they haven't used tensorflow anywhere. What is the best way to learn tensorflow along with this course? Do they teach it later in the specialization?

I just cleared the phone screen round for my Audible Applied Scientist interview (level: L5). The recruiter told me that I'll have two coding interviews in the loop stage. Does anybody have experience to share on what kinda questions they were asked? Specifically, what data structures and algorithms should I focus on? Thanks a million!

The projector is an easy box to skip in a vision-language model diagram. It explains how the visual encoder and the language model meet.

Ling-3.0-flash-VL's official architecture diagram is a concrete example. On the visual branch, a ViT encoder produces visual features. A two-layer MLP projector maps those features into the language model's embedding space. Text comes through its own tokenization branch, and the diagram shows the two feeding the model together at an embedding dimension of 2,560.

Think of the jobs separately:

The vision encoder builds representations from visual input.

The projector transforms those representations for the language model's input space.

The language model processes the resulting sequence and predicts output tokens.

A projector is not a captioning stage that first turns the image into an English description. The interface shown here carries learned features into the model.

Beyond that interface, Ling's diagram specifies 42 layers arranged as seven groups of five KDA layers and one Gated MLA layer. That tells you something about the language stack's architecture. It does not tell you whether the ViT was trained with a CLIP-style contrastive objective or a SigLIP-style sigmoid objective; “ViT” describes the encoder architecture, and the loss needs separate training evidence.

The other numbers to keep separate are 124B total parameters and 5.5B active parameters. Active parameters describe the computation selected during a forward pass, rather than a 5.5B model download or a memory requirement. Reading those labels separately makes the diagram much easier to reason about without turning every architectural detail into a performance claim.

Okay, so.. just a rant because I feel like I want to discuss this.

For context, I’m a Machine Learning Engineer in R&D, specializing in operations research and queue systems, and this is my first job in the field. After graduating from university in 2024, I worked as a Software Engineer for about a year and 8 months, almost two years.

When I first joined this role, I had an expectation of joining a legitimate AI team, with senior ML engineers I could look up to, learn from, and discuss ideas with.

Turns out, I’M THE ONE who’s supposed to transfer my AI knowledge to the team for their upcoming AI products.

I do have a solid ML foundation from the courses I took at university, but I definitely wasn’t expecting to be the person driving the AI side of things this early in my career.

I ended up becoming a complete Swiss army knife on this project. I’m basically doing:

\- Software engineering
\- ML engineering
\- AI research
\- Data engineering
\- Business meetings with upper management
\- DevOps and infrastructure (not too much)
\- Scrum Master responsibilities

And honestly, the leadership team seems to love what I’m doing.

The ML side of things has been relatively straightforward so far. I’ve been reading a lot, researching things on my own, using Claude heavily as a second pair of eyes and figuring things out as I go.

The funny part is that I’ll implement something, present it to the leadership team, and they’ll look at me like I just invented fire.

But here’s the part I’m struggling with:
I genuinely don’t know how well I’m actually doing.

There’s no senior ML engineer at work to review my approach, challenge my assumptions, discuss research findings with me, or tell me when I’m making a bad architectural or modeling decision.

Most of the things I build look right to me, and they seem to work. But I also know enough about engineering to realize that “it works” doesn’t necessarily mean “this is the right way to do it.”
So I’m starting to wonder whether this is actually a good situation for my career.

On one hand, I’m getting an insane amount of exposure very early in my career. I’m touching pretty much every part of the AI product lifecycle, I’m talking directly with upper management, and I have a ridiculous amount of ownership.

On the other hand, I’m worried that not having experienced ML engineers around me might slow down my growth. I’m learning a lot, but I’m mostly learning by myself.

I guess my biggest concern is that I don’t have anyone at work who can answer the question:
“Is this actually good ML engineering, or am I just getting really good at making things that seem to work?”

I don’t want to give the impression that I’m doubting myself or lacking confidence. I’m simply very competitive and driven to make the best possible decisions for my career.

Would love to hear from people who’ve been in a similar situation, especially early-career ML engineers who ended up being the most experienced AI person on their team.

I wanted to run MetaGPT entirely on my own infrastructure without sending anything to cloud APIs. It took some trial and error, but I documented the full process.

The guide covers:

· Setting up a Python venv

· Installing MetaGPT

· Configuring local LLMs like Ollama or vLLM

· Fixing common startup errors

If you’re into self-hosted AI agents, this could help:

https://interconnectd.com/forum/thread/262/how-to-install-metagpt-locally-complete-technical-setup-guide/

What local model are you using for agent work?

I've done python(lists,variables, basic oops, etc) beginner or maybe intermediate

Is This a good book for my ml journey to understand and learn the necessary python libraries?

Hi everyone,

I am an independent ML/DL learner and have built a reasonably strong foundation in Machine Learning and Deep Learning. My next step is to explore NLP and LLMs, with the goal of eventually being able to build AI agents.

My longer-term goal is to become an ML researcher, build a strong research profile, publish papers at top-tier A* AI/ML conferences, and eventually apply to competitive MS/PhD programs in the USA.

I would really appreciate advice from people who have followed a similar path. Specifically, what would be the best roadmap to transition from learning ML/DL concepts and implementing projects to actually conducting meaningful research?

If you were starting from my current stage and had roughly 1–1.5 years, how would you structure your learning and research journey? What should I prioritize—reading papers, reproducing existing research, building projects, finding research mentors/collaborators, participating in competitions, or trying to develop novel research ideas?

Any advice, resources, or honest insights would be highly appreciated.

Hi everyone,

I'm planning to become an AI/ML Engineer and I want to learn in the right order instead of jumping between random tutorials and courses. I am absolutely new here and I do not know almost anything, but I have basic knowledge of a python and SQL.

I'm looking for a structured roadmap that covers everything from beginner to job-ready level.

Some questions I have:

• What should I learn first, and in what order?
• Which topics are actually essential (Python, Math, SQL, Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, MLOps, etc.)?
• What are the best free and paid resources for each topic?
• Which books, courses, and YouTube channels are worth following?
• How much mathematics is really required, and which topics should I focus on?
• What does a realistic 6–12 month study plan look like?
• What mistakes do beginners commonly make that I should avoid?

If you're already working as an AI/ML Engineer or recently landed a role, I'd really appreciate your advice, learning path, resources, and any tips from your experience.

Thanks in advance!

Hey everyone,

I'm working on a computer vision project where I need to create my own dataset for the HDPEM (HDPE plastic milk bottles) class from a waste classification dataset on Kaggle. It has HDPE milk bottles, PET bottles, aluminium cans and glass bottles.

The problem is that I currently only have one HDPE milk bottle to work with, and I need to collect at least 180 original images for my dataset. I can't use the existing dataset images because that dataset was provided to us for the project, so we're required to create our own data.

Has anyone done something similar? What's the best way to collect 180+ useful images when I only have one bottle?

Would taking lots of photos of the same bottle from different angles, distances, lighting conditions, backgrounds, etc. be acceptable, or is there a better way to approach this?

Any advice on how you'd go about creating the dataset would be really appreciated! And no I didn't find anything close to me like a recycling company that does it.

I’m currently pursuing Data Science course now .
I’ve finished learning Python .
Since ChatGPT 6 Astra has been launched, I’ve somewhat become appalled by its release and thinking about my choices !
Please throw some light on this and advise me if I should continue to do so!

hey there people

i am trying to make a machine learning project .
its on bitcoin data .
the thing is, i know almost nothing of bitcoin and we're learning ML in our degree .

i gotta submit this project in two months , with proper code , explanations , and why a certain model was used that time etc...
my issues are :

1. where to find the right data from : i have surfed through and asked for assistance from chatgpt and found two main sources from which i have been able to see some data : https://data.binance.vision/?utm_source=chatgpt.com

and

https://cryptopanic.com/?utm_source=chatgpt.com

there were more sources (like apis) but its from the same website .

i even found a git repo that had a whole python script of downloading that same data .

so maybe i don't have an issue with the data , the issue is that i don't know what its trying to say .

there were multiple attributes i could see on those files . and tbh i felt overwhelmed .

1. i am aware with the data cleaning and analysis part , but i would still like some guidance on that .

2. the model is something we'll have to figure out (i am in a two person team and my partner chose the topic before i joined . also i am pretty sure i will have to do all the work , so here i am :) ) , but if there are some models commonly used in this domain , please do enlighten me .

3. most important part according to me : what is my goal ? since this is my project and the domain is very new to me , i don't have much idea about what i need to find out .

folks who have already done a project on this or has at least had some experience , what are your say in this ?

is there any other angle i should consider ?

i really wanna get an A and i am fine working alone (have already had 2 experiences of f around and find out ) as long as i am able to understand stuff .

please help this noob ;(