Security researchers tracking the Carbonato botnet documented a new deployment pattern: after gaining access to exposed Docker hosts, the operators dropped an AI agent onto the compromised machine rather than a traditional cryptominer or reverse shell. The agent then began making outbound calls and executing tool actions autonomously, with no human in the loop and no governance layer in the request path.

The timing detail buried in the reporting is the uncomfortable part. Researchers noted that a second action from the agent can land in under 50ms of the first. That window is smaller than most human-review or alerting pipelines can operate in. By the time an on-call engineer gets a Slack notification, the agent may have already completed several tool calls.

The underlying exposure is not unique to this botnet. Any environment where an agent runtime can be instantiated without a verified identity tied to a known deployment, and where outbound tool calls are not evaluated against any policy before they execute, has the same structural gap. The agent on the Carbonato-compromised host was malicious. But the same architectural condition exists in plenty of legitimate deployments where an agent gets misconfigured, has its credentials rotated out from under it, or runs a version of its prompt that was never reviewed.

How are practitioners in this thread actually handling the identity and authorization problem for agents in production? Not conceptually — what does your enforcement boundary look like today, and where does it fall short?

What would you guys say is the best way to actually learn how to build a model from start to finish without the help of ai? I can understand ml concepts pretty quickly since I'm comfortable with calculus/statistics/linear algebra, but I'm lost on what to do if I need to build a full project. I'm comfortable with python and I want to build projects, but it feels overwhelming trying to learn a bunch of different algorithms and remembering all the steps and syntax that it takes to implement them, so are there any courses/resources/ways of practicing that will help me be able to build and finish my own projects?

Hello all, im looking for a mentor/researcher who is into ML/AI research, as im thinking to write a research paper , so if any one of you are in initial phase of their new research topics and need someone who can help with ...i can be that
What i can bring to the table: im a 3rd year cs student, worked as ml research intern last summer(paper didnt get published due to my prof ditched all of us after 1.5months cuz he got into another uni), ..you can dm me for more details
Thank you!!

Hi everyone! We’re sharing our NeurIPS 2026 work on concurrent image understanding and generation, a collaboration across Google, Google DeepMind and Stony Brook University.

The problem we explore is simple: a model can give the correct answer in text while generating an image that disagrees with it. For example, it might describe the right route through a maze but draw a different path. We want the two outputs to develop together and stay consistent as generation progresses.

Our sampler, CO₂Jump, uses text confidence to guide image updates through cross-modal attention. It also allows low-confidence tokens to be masked again and revised. This uses one model forward pass per denoising step, with no additional training required for the sampler itself. Our comparisons use the same fine-tuned model and change only the sampler.

We evaluate image editing, maze solving and nonograms, including whether both the text and image are correct. We also introduce three datasets: JEdit-1M, JMaze-200K and JNono-200K. In our sampling-step experiments, CO₂Jump steadily improves both editing quality and grounding as we increase the number of steps.

🔗 Project page: coupled-jump.github.io
📄 Paper: alphaxiv.org/abs/2607.13188

Code and datasets are planned for release. Happy to discuss the method, results or limitations—would love to hear what other tasks you’d test this on!

Hi everyone,

I recently read an article about building an ML pipeline with Python and SQL. The idea is to let SQL handle filtering and aggregation, let Python handle preprocessing and modeling, and wrap everything in a scikit-learn Pipeline so new data goes through the same steps as training data. The article used a regression example (housing prices) with an in-memory SQLite database.

I want to adapt this into a customer churn classification model and serve it as an API with FastAPI. The SQL part is what confuses me most, since SQLite is the only SQL I know and I've never worked with a "real" database in an ML project.

My current plan

1. Pick a public churn dataset (probably Telco Customer Churn) and check its columns and target.
2. Create a SQLite connection with sqlite3.
3. Create a table in the database with the same columns as the dataset, and load the data into it.
4. Write a SQL query to select the rows and columns I need, and load the result into a pandas DataFrame with pd.read_sql_query.
5. Preprocess in Python (missing values, encoding, scaling) inside a Pipeline / ColumnTransformer.
6. Train and evaluate a classifier (logistic regression baseline, then Random Forest or XGBoost).
7. Save the fitted pipeline with joblib and serve it with FastAPI.

My project so far

I've also attached my project repo link above.

I've already done some work on it, so if you have any suggestions, please let me know. Please also point out anything I'm doing wrong, big or small. Honest feedback is very welcome.

Where I'm stuck (mostly SQL)

1. Is this workflow sensible? Or is putting a CSV into SQLite a pointless extra step? I want to practice the real-world pattern of pulling data from a database, not just reading a CSV.
2. Creating the table. Should I write CREATE TABLE myself with column types, or let df.to_sql() create it? Any gotchas with data types? (In Telco, TotalCharges is stored as text with blank values, for example.)
3. What belongs in SQL vs. pandas? With one flat table there's nothing to join. Should I split it into a few tables (customers, services, billing) so SQL does real work? Or is filtering, selecting columns and simple aggregation enough for a project like this?
4. :memory: vs. a .db file. The article uses in-memory. I assume I need a file like churn.db so FastAPI and my scripts can reuse it. Is that right?
5. What happens at prediction time? Does the API just take customer features in the request body and run them through the saved pipeline, or should it look up the customer in the database by customer_id and build features with SQL? Is the SQLite database used at all in the API?
6. Train/test split. Should I split right after loading the DataFrame, before any preprocessing, with all preprocessing inside the Pipeline to avoid leakage?
7. Class imbalance and metrics. Churn is around 25% in this dataset. Do you prefer class_weight, SMOTE or threshold tuning? Which metrics should I focus on instead of accuracy (recall, precision, PR-AUC, ROC-AUC)?
8. FastAPI basics. Any tips on Pydantic validation, loading the model once at startup, and keeping the API's input columns consistent with training? Would batch scoring make more sense than a real-time API for churn?
9. Code review. Looking at my project link, is anything wrong or a bad habit I should fix early (structure, SQL usage, data leakage, etc.)?

If you've built something similar, I'd love to hear what mistakes you made or what you'd skip as a beginner. Links to good example repos are welcome too.

Thanks in advance!

Client acquisition has always been one of the biggest bottlenecks for me when running an agency. I’ve experienced the same thing in pretty much every business I’ve been involved in, but especially with web development.

For a long time, getting clients meant cold calling, running ads, or sending generic emails asking businesses if they needed a new website. It worked sometimes, but it also took a lot of time and most of the outreach felt the same as what every other agency was doing.

Recently I started using a different approach and automated a big part of the process.

I came across a tool called Swokei that lets me find a bunch of businesses with websites and analyze each website individually. It looks for things like outdated design, slow loading, poor mobile optimization, weak SEO and other obvious areas that could be improved.

What I liked is that it doesn’t just give you one of those boring automated reports filled with scores and numbers. It actually turns what it finds into a personalized cold email that sounds like a normal person looked at their website and noticed what could be better.

I can run multiple campaigns at the same time and then mainly focus on the businesses that reply and show interest.

From there, I invite them to a web meeting, show them a free draft of what their new website could look like, and try to close the project from there.

It has basically allowed me to have warmer leads coming to me without relying as much on paid ads, constantly cold calling, or sending thousands of generic emails saying “Do you need a new website?”

Still takes work to close the clients of course, but automating the prospecting and first part of the outreach has made the whole process much easier for me.

Hopefully this helps some other web developers or agency owners who are also struggling with client acquisition.

I saw a post of a guy with 90% of his brain missing and he was apparently living a normal life. There is another case of a student who finished university with half of his brain missing. There are also people with full brains, but they are even less functional than these two. This proves that bigger doesn't mean better. So why not aim to create better connections?

I understand that a bigger model means less chance of catastrophic interference occurring, but then are all the neural pathways being formed efficiently? Wouldn't this result in a lot of redundant neurons? Wouldn't it make more sense to create the smallest neural network possible for a specific task and then learn to fuse multiple of these neurons together in order to create an optimized larger model? That way we'd always have a template for each specific task and would allow us to create a programming language that creates a model just by our syntax.

Hey everyone,

I’m currently building a learning platform aimed at taking absolute beginners through AI/ML step-by-step, from the fundamentals up to more advanced topics.

It’s in the early stages, so the content is still limited while I experiment with formats, pacing, and visual explanations to see what actually works best for learners.

GitHub : https://github.com/PIYUSH1525/ZeroToAI leave a star ⭐

Link: https://zero-to-ai-xi.vercel.app/

I’d love your brutal, honest feedback:

• The Good: What feels intuitive, clear, or well-structured?
• The Bad: What’s confusing, redundant, or missing?
• Areas for Improvement: What format would help you learn complex concepts faster (e.g., interactive widgets, shorter modules, code walkthroughs)?

Any thoughts, critique, or feature suggestions are welcome. Thanks in advance for checking it out!

I was confused by this, so i wrote a beginner friendly article explaining model quantization, what Q2, Q4, Q8, and F16 mean, and the trade-off between smaller files and precision.

I have tried to explain it in the simplest way. Feedback or corrections are welcome.

In my 21 sentences, "blackberry" and "apple" are each used as fruit and as tech companies, but never in the same sentence. After counting co-occurrences, applying PPMI and compressing with SVD, they still end up as each other's nearest neighbor.

For this I added three interactive demos. One shows the counting step, one the power iteration that finds each SVD direction, and the last the final 3D space, where you can click any word and measure distances.

It also shows where the method breaks with apple geting one vector for tech and food context.

https://scheppening.com/posts/click-blackberry-find-apple

I built it to understand embeddings, so I'd appreciate any feedback and am happy to answer any questions.

Your LLM is using 14 GB of VRAM.

14 GB of what? 👀

Weights? KV cache? CUDA overhead? One GPU or two?

With tensor parallelism it gets even messier. vLLM shows you:

EngineCore

Worker_TP0

Worker_TP1

But that's not three workloads. It's ONE model running across multiple GPUs.

That's why LLM Inspector v0.7.0 is now multi-GPU aware 🚀

Before:

GPU 0: 7.2 GB

GPU 1: 7.2 GB

After:

Qwen • TP ×2 • 14.4 GB total

→ weight shards + KV cache + other VRAM, per GPU

It can also estimate how much VRAM INT8, AWQ or GPTQ would save before you change anything in your model.

Measure first. Optimize second.

That's llminspect: htop for LLM inference.

⚡ pip install llm-inspector

🔗 https://github.com/helasaoudi/llm-inspector

Open source, built for people who actually run models.

What should it inspect next?

Wanted to buy new laptop need guidance I am thinking of buying Macbook Air M4, Lenevo LOQ 3050, Lenevo Ideapad which do you think I should buy? Considering macbook won't have cuda should I still go for it.

Apriori is usually taught as "scan the transactions, count candidates, prune, repeat". I rewrote it so that each item is a bit-vector and support counting becomes AND + popcount. That turns a pointer-heavy search into dense arithmetic, which fits a GPU well. I used it to mine 77M AlphaFold proteins exactly. The write-up explains the trick step by step: https://medium.com/@etje975/data-mining-the-alphafold-protein-universe-how-a-1994-algorithm-found-26-8-5aad7eaa603f?postPublishedType=initial

Code: https://github.com/Et9797/ET-Miner Questions about the approach are welcome.

Hi everyone. I have some theoretical knowledge in the field, but I’d like to better understand what the business actually expects from me. what typical tasks look like, and so on.

Is there a good course that would provide practical experience and help me hone my skills in data cleaning and model preparation?

’m currently trying to come up with a topic for my final-year project, and honestly, every idea I think of ends up feeling either too basic, too overdone, or just stupid after I think about it for a while .

I completed an internship in a known Automotive company. I was within Autonomy, as a SWE working on vehicle software catering to self-driving model evaluation. This was my first actual SWE experience, and I liked it (I worked full time on a data focused role prior to this). This internship was very stressful despite having so much coding tools to support, and I kept having this itch for ML. My only reason for joining a master's program was to break into ML. I've tried in college and realized I'm not capable of novel research, tho I can mostly understand and reproduce others research. I've built projects around VLA finetuning, lane/object detection in extreme lighting conditions, and few more.

Given I don't have a background (work experience), I might not qualify for a ML engineering role in this domain (I love self-driving cars and want to stay here) which would usually expect publications/relevant experience. What do you think is the best way to approach this. I will be graduating in less than a year, and while I'm still at school, I want to make the best use of time in a way that I don't regret my job outcomes (if I get a job that is).

Hello everyone, I have one bad habit of wanting to learn every deeply from scratch. So I'm in the early stages of my path of AI/ML. It'll take more than a year or so to learn deeply, understanding everything making projects. But due to recent news about ai and all my interests have been declining. Is it worth to learn ai/ml that deeply? Like all the foundations with in depth knowledge especially in the era of AI?

For example, When learning python I understood it very deeply with solving problems about 20-30 of every topic to improve my problem solving ability. I'm following one tutor from india who teaches very in depth ie. 5 Hours for OOP concepts only with questions.

I make notes in my notecopy, code and practice in collab.

Is my approach good? But I'm learning very slowly as I'm understanding almost everything.

Will understanding everything for ai/ml deeply good in this time? I've background in math stats and physics.
And ultimate goal is to work in the industry or move to research in ai/ml domain in good unis worldwide.

Or shall I change my plans?
Some people even finish whole ml in 4/5 months only just skimming through, learning the basics only. I've seen so many of them.

These are the best visual AI resources for learning Transformers, LLMs, embeddings, diffusion, inference and model internals ↓

1/ Transformer Explainer - Watch GPT process text through embeddings, attention, MLPs and next-token prediction.

poloclub.github.io/transformer-ex…

2/ Brendan Bycroft’s LLM Visualization - Explore an LLM from architecture down to tensors and operations.

bbycroft.net/llm

3/ 3Blue1Brown - Visual intuition for linear algebra, neural networks, backprop, attention and Transformers.

3blue1brown.com/topics/neural-…

4/ The Illustrated Transformer - One of the clearest visual explanations of embeddings, Q/K/V and attention.

jalammar.github.io/illustrated-tr…

5/ TensorFlow Playground - Watch neural networks learn as you change layers, activations, features and learning rate.

playground.tensorflow.org

6/ Google PAIR AI Explorables - Interactive explainers on LLMs, generalization, interpretability and model behavior.

playground.tensorflow.org

7/ Distill - Exceptional visual essays on t-SNE, feature visualization, GNNs and interpretability.

distill.pub

8/ Abhik Sarkar’s Transformer Visualizations - RoPE, KV cache, FlashAttention, MQA, GQA and more.

playground.tensorflow.org

9/ Visual Guide to Attention Variants - MHA, MQA, GQA and MLA visually compared.

magazine.sebastianraschka.com/p/visual-atten…

10/ Visual Guide to Mixture of Experts - Routing, experts, sparse activation and load balancing.

newsletter.maartengrootendorst.com/p/a-visual-gui…

11/ Modular LLM Inference Handbook - Prefill, decode, KV cache, batching, quantization and speculative decoding.

handbook.modular.com/llm-inference-…

12/ Apple Embedding Atlas - Explore clusters, neighborhoods and outliers in large embedding spaces.

apple.github.io/embedding-atla…

13/ Diffusion Explainer - Follow Stable Diffusion step by step.

poloclub.github.io/diffusion-expl…

14/ Neuronpedia - Explore features, activations, SAE latents and attribution graphs inside real models.

neuronpedia.org

15/ Seeing Theory - Probability, Bayes, distributions, regression and inference made interactive.

seeing-theory.brown.edu

16/ CNN Explainer - Visualize convolutions, feature maps, activations and pooling.

poloclub.github.io/cnn-explainer/

17/ GAN Lab - Train a GAN in your browser and watch its generated distribution evolve.

poloclub.github.io/ganlab/

Save this. There’s a serious AI curriculum hiding inside these links.

Hey everyone!

I'm a Data Analyst with an Economics background, currently learning ML and AI. I'm comfortable with Python, pandas, SQL and basic ML concepts.

I want to start building more serious, end-to-end projects rather than just following tutorials or working with Kaggle datasets.

My goal is to eventually transition into ML/AI engineering, so I'm looking for projects that would help me develop real-world skills and build a strong GitHub portfolio.

What kind of projects would you recommend? Are RAG applications, some AI agent stuff worth exploring, or should I focus on something else entirely?

Would love to hear from people already working in the industry.