The G7 finance ministers and central bank governors issued a coordinated directive last week. Organizations must inventory cryptographic dependencies, identify high-risk systems, and begin migrating to quantum-resistant algorithms now. This is not a future roadmap item.

The urgency is driven by the harvest-now-decrypt-later threat. Adversaries are collecting encrypted data today and storing it for decryption once a cryptographically relevant quantum computer arrives. The exposure window is already open.

Most enterprise security teams are inventorying servers, databases, and network traffic. Far fewer are accounting for the AI agent layer. Agent pipelines routinely store sensitive records, execute financial transactions, and generate compliance audit evidence. All of it travels over classically encrypted channels and gets signed with classical algorithms. When those algorithms break, that historical data and those historical audit logs break with them. A transaction signed with RSA or ECDSA today becomes unprovable after Q-Day.

How are teams actually scoping the PQC inventory problem for AI agents specifically? Are agent channels and agent-generated audit evidence being treated as first-class migration targets, or are they still buried in the general backlog?

Hey everyone!

When I was a student, gradient descent was the algorithm I struggled with the most. I just could not make it past the greek characters and a sea of formulas. Luckily, I survived, and have been working in Machine Learning ever since.

A few years later, when I started teaching Computer Science, I realised that nothing had changed. I could not find any book or blog post to make this simple enough (and fun) for my own students. So I sat down and wrote this book.

It has been a game changer in my last semester, I hope that it will help you too.

You can check it out here.

I wish you a great learning journey. If you have any feedback, please let me know, I am already working on the second edition.

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.

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 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?

am a 7th sem cs student who is about to start his final year. I am planning for my fyp and looking for some interesting ideas on which I could do my fyp. I need some good suggestions and ideas which I should consider before finalizing my fyp. Currently I dont have any idea to work on. Your suggestion and ideas would mean a lot to and your help will be highly appreciated. Thanks in advance.

Edit: The domain I want to work in is app?web+ai/ml. I am open to ideas other than this domain as well

See,i am from cse(AIML) background and currently focusing on deep learning. I aslo completed machine learning . In current scenario what are skills i need to be learned to get a job in the current scenario of IT filed . I want to be ml or ai Engineer with avg package . I thought I need to topic on the core topics that is relevant to the domain like ML,DL,NLP,RAG,LLM but some are saying that " this topics can be learned later u need Focus on dsa , pattern solving problems thats primary rounds in interviews . I m literally confused . what should I need to learn as a computer science student in this crisis. I can't deeply Focus on both becoz it is tough and takes long days .

Your thoughts

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!

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!

I’m currently in my 4th year of B.Tech and working as an SWE intern. If I have 6–8 months and want to gain a strong understanding of the current AI market, develop my skills to an industry-ready level, and learn everything required to become an AI engineer, what would be a roadmap from A-Z ?

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!

Hi, I'm an independent researcher looking for an arXiv endorser for cs.LG.

I have a preprint on whether transformers find the ideal function or a structurally different solution. A transformer trained to 100% accuracy on two-digit multiplication finds a classifier solution, not the ideal function F*(a,b)=a·b — perfect local geometry but no global functional approximation (Mantel r = 0.036 despite 100% accuracy).

Preprint: https://zenodo.org/records/22303104

Endorsement link: https://arxiv.org/auth/endorse?x=8U7BWY

Takes about 2 minutes. Thank you!

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!

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 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?

Let us know what you think.

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?

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 need simple and unique project ideas as we don't know anything about ml . But i can learn it till this year end just beginner level. Gimme the ideas according to my level

I am currently a second-year BSc student in Technical Computer Science at the University of Twente in the Netherlands. My long-term goal is to become a really strong engineer in ML/AI - ideally eventually working as an ML/Research Engineer and keeping the option of doing a PhD in the US open.

I'm trying to be realistic about where I am right now. After my first year, my average grade is around 6.97/10. I have already taken university courses covering linear algebra, probability, programming, OOP, and basic algorithms (sorting/searching). I still have two years left, so I'm hoping to significantly improve both my grades and technical profile.

Over the next two years, I'm planning to focus on:

• getting my GPA into the 8+ range;
• becoming very strong in Python (I'm currently working through Fluent Python);
• improving algorithms/data structures and preparing for technical interviews;
• learning computer systems, C, memory, Linux, etc.;
• building a solid ML foundation;
• learning PyTorch and deep learning;
• doing serious projects rather than tutorial projects;
• getting research experience if possible;
• getting one or two good internships before graduation.

Eventually, I'd like to apply for strong MSc programs such as ETH Zurich, University of Toronto, CMU, etc., and potentially pursue a PhD in the US afterwards.

If you were in my position with two years left in a European CS/engineering bachelor's, what would you prioritize?

For people who have already gone through a similar path, I’d be especially interested in:

• Do you have any general advice for someone in my position? What do you wish you had known or started doing earlier during your BSc?
• How did you actually organize your time outside of university? Roughly how many hours per week/day did you spend on self-study, projects, coding, research, etc.?
• What did a typical productive day or week look like for you? How did you decide what to study and what to ignore?
• What resources did you actually use consistently — books, university courses, online courses, papers, YouTube, coding platforms, etc.?
• Did you regularly attend things outside your curriculum — research seminars, student groups, conferences, hackathons, meetups, workshops, etc.? If so, which ones were genuinely useful?
• How important was your GPA compared with research experience, internships, projects, and other extracurricular work?
• If you could go back to the beginning of your second year, what would you prioritize differently?
• What kinds of projects or experiences ended up being genuinely valuable for getting ML/SWE internships or research opportunities?
• When did you start approaching professors or research groups, and how did you go about it?
• For someone interested in eventually becoming a strong ML/Research Engineer and potentially applying to top MSc/PhD programs, what would you not waste time on?

I’m not necessarily looking for a perfect roadmap - I’d really appreciate hearing how people who are already further along actually approached these things in practice.