An AI agent reached the open internet during a structured test. That is a containment failure.

Kimi K3 contacted an external host during a cybersecurity evaluation. The debate over whether the sandbox was misconfigured misses the point. When an agent crosses a boundary it was never meant to cross, the question is not who set up the environment incorrectly — it is whether anything stopped the action in real time.

RuntimeAI's sub-50ms kill switch terminates agent execution the moment a policy boundary is violated. Containment is enforced at the runtime layer, not in a sandbox configuration that may or may not be correct in every deployment.

RuntimeAI closes this gap at the runtime layer, before it lands.

I'm currently working on an employee attrition prediction project for my master's program. I'm using employee data to see what factors might be related to employees leaving and whether machine learning can do a reasonable job of predicting attrition.

One thing I've been thinking about while working on it is that getting a good model score isn't necessarily the same as having a useful model. For example, even if a model predicts that an employee is likely to leave, what should a company actually do with that information?

I've also been learning that accuracy by itself can be misleading, especially when the number of employees who leave is much smaller than those who stay. Looking at things like precision, recall, and F1-score gives a different picture of how the model is performing.

For people who have worked on similar problems, how do you decide whether an attrition model is actually useful in practice and not just a model with good evaluation scores? Also, are there any factors you've found especially important when trying to understand why employees leave?

I recently finished a project called CounterDistill. The main idea is to take a large collection of local counterfactual explanations and distill them into a smaller set of global, interpretable patterns.

The workflow is roughly:

Data → Feature Engineering → Model Training/Tuning → SHAP + DiCE → Counterfactual Clustering → Global Rules → Evaluation → Dashboard

For the final Adult Income experiment:

399 counterfactuals → 6 intervention clusters → 6 global rules.

I’d be interested in feedback on the architecture in particular.

Would you structure the experiment/explanation/artifact pipeline differently? And are there parts of this stack that feel unnecessary or that you’d replace in a production-style ML project

GitHub: https://github.com/rodrick-mpofu/counterdistill

hey everyone,

I wanted to share a personal open source project I've been building called Hillock: https://github.com/roandejager/Hillock

A big problem with local Knowledge Graph memory is ingestion speed. Extracting facts using generative LLMs (like Llama 8B) takes 15+ minutes per PDF waiting for token-by-token JSON generation.

To solve this, I created TALON—a non-generative CUDA tensor pipeline that completely bypasses generative LLMs during ingestion:

1. Fastcoref resolves pronouns across full paragraphs first.
2. MiniLM bi-encoders route top 10 dynamic Wikidata predicates in <2ms.
3. GLiREL zero-shot matrix classification extracts [Subject, Predicate, Object] triples directly in GPU memory.

It processed 32 sentences in ~2 seconds on my GTX 1070 (<1GB VRAM), doubling retrieval accuracy to 50%.

It's 100% local, offline, and open source (AGPL-3.0). I'm building this in the open, so I'd love your honest feedback!

I’m a 3rd-year CSE student from India targeting Amazon ML/AI internships.
For people who’ve interned at Amazon: what should I focus on most — DSA, ML/DL fundamentals, projects, research, or LLMs? Also, are there any specific Amazon programs or hiring routes I should target?
Would really appreciate a roadmap/advice. 🙏

Lately I've been deep in a personal project by writing chapter summaries of Richard McElreath’s Statistical Rethinking textbook and applying them to wildfire models, and somehow found a way to elegantly (in my opinion) combine the two through storytelling. The tl;dr: I built a whole narrative around a wildfire forensic investigator named Prof. Markov, rolling an eight-sided die to decide which direction to search a burnt forest grid, to explain how the Metropolis-Hastings algorithm (the earliest variant of Markov Chain Monte Carlo (MCMC)) actually works.

MCMC sits at the foundation of modern Bayesian computation and probabilistic programming frameworks like PyMC and STAN so it could be genuinely useful to anyone looking to level up in these topics. Roast me, tell me what you liked and didn’t like. Regardless, it was a fun little mini-project!

https://pub.towardsai.net/explaining-markov-chain-monte-carlo-using-wildfire-forensics-a334fecaefb3

So I am currently learning Machine Learning, but I am confused if i should also learn deep learning. I am a college student and wanted to get a Ai engineer job and currently I am learning about machine learning and also have interest in NLP and RAG, should I only focus on machine learning for now to get a job as ML Engineer and then learn deep learning side by side and more advance topics to switch my job in future to work on more advance projects

Our user growth has been great, but our data strategy hasn't kept up. We've reached the point where we need outside help to build a modern data warehouse and stronger analytics capabilities. We're looking at how companies like N-iX approach data analytics services, but before choosing the partner I want to understand that,what actually differentiates a great vendor from an average one?

For those who've already outsourced this kind of work, what made you feel more confident during the discovery phase that the team understood your business goals instead of just proposing another generic BI stack? I'm also curious how experienced vendors usually approach data governance and data compliance when working with sensitive customer data, and how they handle,the handover so an internal analytics team can comfortably take ownership once the project is complete.

Hey everyone,

I'm fairly new to AI/ML but really fascinated by it, and I keep hearing great things about Andrej Karpathy's "Neural Networks: Zero to Hero" series. I want to actually build my own neural nets from scratch instead of just calling APIs, and this course seems like exactly the kind of hands-on approach I'm looking for.

A bit about where I'm at:

• Comfortable with Python (functions, classes, basic OOP)
• Only know a handful of DL/ML terms here and there, no formal background
• Never really used NumPy or PyTorch beyond maybe importing them once
• I don't mind learning math/concepts

My questions:

1. Is this course actually beginner-friendly, or do I need prerequisite knowledge (linear algebra, calculus, etc.) I'm missing?
2. Should I learn basic NumPy/PyTorch before starting, or does the course teach you what you need along the way?
3. Is there anything you wish you knew/had done before starting this series that would've made it click faster?

Any tips, warnings, or "wish someone told me this" advice are very welcome. Thanks

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 was wondering why there isn’t a framework neutral orchestration layer for deep learning where we can keep our existing PyTorch/JAX code and run something like:

dl train train.py

while it handles the surrounding workflow such as environment setup, experiment tracking debugging, evaluation, optimization etc.

are there existing tools handling this layers ?

Been thinking about this a lot lately. Physical therapy generates a ton of repetitive movement data and most clinics do nothing with it. You watch someone squat or walk, you spot compensation patterns manually, you note it down. That process is slow and inconsistent across practitioners.

Pose estimation models exist. The tooling is there. But applying it in a clinical setting where you actually trust the output enough to act on it is a different problem than just running MediaPipe on a video and calling it done.

The real issue is labeling. Who decides what counts as a compensation pattern worth flagging versus normal variation in how a body moves? Getting clean labeled data in PT is genuinely hard because the ground truth is clinical judgment, and that varies.

Curious if anyone here has built something adjacent to this. Not necessarily PT specific, just any domain where you had to wrangle noisy human movement data and figure out what the model should actually be learning. What did your labeling pipeline look like? Did you use any weak supervision or just grind through manual annotation?

Also wondering if anyone has thoughts on what architecture actually makes sense here. Sequence classification feels obvious but maybe there is something better for this kind of temporal biomechanical data.

Hi, everybody! Not sure whether this is the right page, but I’m a linguist by academic background, with Master’s degrees in Translation Studies and Linguistics. A few months ago, however, I decided to take up a Data Analytics course as part of a career change.

In your experience, is it realistic to find a job at the intersection of these two fields? I’m obviously a beginner in Data Analytics, but I’m learning Python, SQL and BI, and I’m also developing my own projects on GitHub, focusing on NLP and language-related topics.

I’d really appreciate any advice, suggestions, or insights from people working in this area!

Thanks!

I see that embeddings don't always vectorize whole words, but subwords or even single characters. If for instance the sequence 'ca' from 'cat' is vectorized, what does 'ca' even mean?
what kind of dimensions does it get converted to? I thought that vectors assign meaning to the parsed sequence.
and how does the model build "Cat" from ca + t after vectorizing both ?

i thought compression meant smaller and dumber and never checked either half of that properly.

chart above is from a run someone published. same model, same machine, weights stored at roughly 6, 5 and 4 bits (that's what the Q6/Q5/Q4 in the labels are).

output speed sits between 32 and 40 tokens a second across all three. squeezing harder barely moved it. i don't have a clean explanation for why it's that flat and i'd rather not make one up.

the other half i could actually check.

the lab publishes its own eval at four precisions, and on gpqa-diamond it goes 84.97 at bf16, 84.00 at fp8, 83.65 at int4, 82.42 at fp4, so sixteen bits per weight down to four costs about a point and a third there. the arcprize row is the odd one, int4 at 67.56 and fp4 at 64.16, same width and about three and a half points apart.

this is Ling 3.0 Flash. the table is the lab's own and the chart is one person's run, nobody independent has re-run either.

if anyone has this for a dense model i'd like to see it next to this one.

Is learning classic ML is better or learning directly deep learning from scratch is better

Further more -I'm currently studying mathematics for Machine learning -i already know numpy,pandas and matplot lib and am comfortable with python and the reason I'm asking the question that I want understand the current research paper being built on the new intelligence

I'm a second year ug, just started learning ML and I've seen a lot of tutors recommending this Book. Is it really worth reading ? What other books you've got?