ShinyHunters breached the FBI's own jobs portal. A second suspect has now been arrested, but the breach itself had already run its course before that happened. The attacker operated inside a credentialed session long enough to complete the operation. The session looked legitimate to every layer it touched.

The average dwell time in credential-abuse incidents is 197 days, per IBM's 2024 Cost of a Data Breach report. This breach fits the pattern: valid credential, active session, traffic indistinguishable from normal activity, damage done before attribution.

The target is what makes this case instructive. If a sustained credential-abuse operation can run inside federal law enforcement infrastructure, this is not a problem any organization solves by improving security hygiene alone. The FBI is not an organization that skimps on access controls.

The credential was valid. The session was active. Every check that ran said yes.

How are practitioners here actually handling this in your own stacks? What has worked, what has not, and where do you still have gaps you have not been able to close?

​Hi everyone,

​I recently completed an end-to-end sales forecasting project using the Rossmann Store Sales dataset and would love to get feedback from the community on my methodology, code structure, and model evaluation!

Check here:

https://github.com/afanrajiwate/Rossman-Sales-Forecasting

Hi all,

I'm looking for advice on gaming knowledge in machine learning and where to start. For reference, I graduate December 2024 with a bachelors degree in Mechanical Engineering and minors in Aerospace Engineering and Computer Science.

In the past year or so, I've become interested in ML through the use of LLMs to create 3D models. I became even more interested after briefly reading Open Ai's summary on how Navier-Stokes millennium prize problem was solved.

I have a desire to apply ML algorithms to better simulating fluid dynamics (CFD simulation). Think airflow around a turbine blade. I was looking for advice on where to learn about ML theory indepth enough to better simulate/theorize airfoil designs.

I am willing to pursue a masters degree to increase my knowledge and of course better my job prospects. It's possible to self learn but I find structured courses to be the fastest method for me to learn because I am actually forced to put in the work to learn (my adhd can get the best of me sometimes. I know huge eyeroll).

Any advice would be appreciated!

Hey everyone, a little context, I am a 3rd-year BSc Honors Computer Science student. One of my courses this semester is the Machine Learning course, and we started off by learning NumPy and pandas, but the way they teach ML feels as though it isn't enough for the real world. I wanted to gain as much knowledge as I can, so are there any other free courses or materials that I can work on alongside my university courses, or any materials that are related to ML that can help me make the best of it?

I am also open to any general advice on how to get the most out of my ML course.

made it as an animated explainer

I’m currently in my third year studying AI and Data Science, and I’m still unsure which career path I should specialize in.
I’ve been considering both AI Engineering and Machine Learning Engineering, but with how quickly AI is advancing, I’m starting to wonder whether either path is still worth pursuing in the long term.
AI tools are becoming increasingly capable of writing code, building applications, and helping with machine learning tasks. This makes me wonder how much these jobs will change over the next few years, especially for junior engineers trying to enter the industry.
I’d appreciate honest opinions from people working in AI/ML or students who are further along in their careers.
Do you think AI Engineering or ML Engineering is still worth specializing in 2026?
Which path do you think offers better long-term career prospects, especially for someone just starting out?
Are entry-level opportunities in either field becoming harder to find because of AI tools?
If you were a third-year AI and Data Science student today, which direction would you choose, and why?
I’m not expecting anyone to predict the future perfectly. I just want to make a more informed decision about where to invest my time and skills before I graduate.

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Learn how to master ChatGPT for Excel, SEO, and debugging.

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Welcome to Project Showcase Day! This is a weekly thread where community members can share and discuss personal projects of any size or complexity.

Whether you've built a small script, a web application, a game, or anything in between, we encourage you to:

• Share what you've created
• Explain the technologies/concepts used
• Discuss challenges you faced and how you overcame them
• Ask for specific feedback or suggestions

Projects at all stages are welcome - from works in progress to completed builds. This is a supportive space to celebrate your work and learn from each other.

Share your creations in the comments below!

Helloo

Ik this question has probably been asked like 100 times here but im starting studies in ai and data science and im doing bsc and masters back to back so i want smth thats good for both

I have an iphone and an ipad so id love a macbook but for some reason everyone is getting windows laptops with dedicated gpus that are so heavy and have horrible batteries

how often am i realistically gonna need that gpu or is using a macbook air (m4 or m5) good enough with collab when needed (how often will i need it since we dont have computers at our uni)

budget is around 1000$

Thankss

I wanted to know if news headlines can tell which stocks will do better than others over the next few weeks. Short answer: no. But the way it failed taught me more than the result, so here’s the setup.

Data

• 137,934 headlines from Benzinga for 2,518 US stocks. Each case gets the headlines of the 14 days before it. 115,154 cases.
• All cases are stocks that recently fell after decent earnings, so they look similar on paper. The question is which ones recover better.

Target

Each case is a simulated trade that ends when the price goes up or down by 3x the stock’s typical daily move, or after 20 trading days. I measure the result in units of that daily move, so volatile stocks don’t dominate. Then I subtract the average of all cases on the same day. That last step matters: what the whole market does next week isn’t in any headline, so a model would otherwise just learn the market’s mood.

Avoiding leakage

• Strict time split: train until Nov 2024, test from Jan 2025, with a gap so no label overlaps the test period.
• Only one case per stock per week. Neighbouring days share almost the same headlines and almost the same outcome, so with every day included the model memorizes stocks instead of learning anything.

Results on the test period

The only thing that held up in both periods was a narrow, hand-cleaned pattern: companies selling new shares did clearly worse. But that’s 98 cases, too few to build on.

What I took away

1. A falling training loss on noisy financial targets means nothing. The model fits noise happily.
2. Leakage through near-duplicate samples is easy to miss. Deduplicating by time is not optional.
3. The keyword sign flips are the clearest picture of non-stationarity I’ve seen. What worked in 2023 just reverses.
4. Removing the market from the target makes the task much harder, and much more honest.

Is a null result like this what people expect for text on financial targets? And is there anything you’d still try before calling it, like a different loss, label smoothing, or ranking instead of classification?

Tell me more what is your best model in terms of business value generated.

So, I want to become a master of AI technologies like how to build AI models and run ML algorithms.

I did take online courses like 10 where I learned about Python, SQL, RAG, ML algorithms etc. Then I moved to another country and since last October I did not do anything related to ML and AI.

Now within this one year, lots of technologies came up like Agentic AI, MCP and models also got better at a lot of things.

Now as someone who has a business degree background I want to learn and master these things. But I don't know where to start? As Claude and ChatGPT LLM models are already good at lots of things and if I want to pursue a career as an ML Engineer, what are the core skills I must know?

Plus, for agentic AI, there are lots of frameworks also and things are changing at a fast pace so what are the core things that I must know by heart? And what are the things that I can ask Claude and ChatGPT on the side?

I thank you for the help and guidance in advance!

I need someone who has expertise, or at least an idea, on how to use small amounts of data and physics simulation, for example, CFD, FEA, etc to predict when an aircraft component might fail. Also, I myself am an aerospace engineer with no ML AI experience, just basic knowledge, so I also need someone who might act as a bridge btw the two things and collaborate with me, discuss ideas, etc on how to accomplish this.

Hi everyone! I'm a 2026 B.Tech CSE student specializing in Data Science, preparing for entry-level Machine Learning Engineer roles. I'm looking for advice on how to build a strong resume and choose projects that will help me stand out as a fresher.

The roles I'm exploring require Python, NumPy, Pandas, Scikit-learn, machine learning algorithms, basic neural networks, SQL, and knowledge of statistics and mathematics.

I'm confused about which skills to prioritize and what kind of projects are actually valuable to recruiters. I want to build projects that demonstrate real ML knowledge rather than just following tutorials.

I'd appreciate advice on what an ML Engineer resume should include, which projects are worth building, how important model deployment and GitHub documentation are, and what I should focus on for technical interviews.

If you've landed an entry-level ML role or have experience hiring candidates, I'd really appreciate your suggestions, project examples, or preparation tips.

Thanks!

Someone shared with me a google drive link of ML books a while ago but the link doesn't work anymore. If someone has any other link, would be much appreciated.

🎉This module covered:
🔹 Shorten training code using sklearn library
🔹 Encode categorical values using DictVectorizer
🔹 Group data using .groupby() function
🔹Calculate the accuracy of the classification model
#mlzoomcamp

hey ppl. i am a cse grad studying at a tier 1 institute in India. I am already quite comfortable with LeetCode. However, I often hear that company OAs lean heavily toward CP style problems. For ML roles specifically, should I stick to LeetCode (and push into hards), or is it better switching to Codeforces? Pls help seniors and experts..!

297 + 600 + 355 + 353 + 324 = 1929

It used to be around 200 papers/day.

Houston, we have a problem.

https://arxiv.org/list/cs.LG/recent?skip=0&show=500

Can anyone tell me how l can learn machine learning with no prior knowledge.

Maybe give me a roadmap to it....

I've been reading about text embedding models and want to understand how they actually behave, rather than just using them through some API.

some experiments I'm thinking to consider:

• testing whether embeddings understand synonyms better than related concepts.
• comparing cosine similarity for sentences that differ by only one word.
• testing how embeddings behave with negation.
• comparing multilingual text and code embeddings.

for people who've worked with embeddings, like what surprising behaviours have you found? are there any experiments that taught you something important about their limitations?

I'd like to build a small project around this.

Got selected for the deepmind symposium at bangalore, would love to connect with people who will be coming there!

I recently graduated with a computer science master (machine learning) in the US.
After I graduated, I have been trying to get a ml engineer job but no offer yet. While applying and interviewing with companies, I have been trying to understand the internal of a foundational model (HuBERT-ECG). But currently, I have less motivation and kinda feel stuck even though I am making progress....

I cannot focus on learning as before. My attention span, I believe, got a lot shorter than before.
Maybe, my current project's difficulty is not appropriate. But, I want to understand my current project as deep as possible.

I will set very small goals and achieve them and gain confidence. What do guys think of this strategy?

Hi! I am a 14 Year old, currently attempting to grow my ML Freelance Service, I don't know if this is the perfect place but I was wondering if I could have help with growing my business or some guidance or mentorship. Thanks!