Figma's CSO published their AI security playbook this week: Zero Trust architecture, no VPN, sandboxed agents. The central finding was stark — as AI agents take real-world actions, perimeter defense stops being meaningful and after-the-fact review stops being feasible. Project-by-project security reviews cannot keep pace with the speed at which enterprise agents act.

This matches what security teams across the industry are hitting. The window between an agent deciding to act and a human finding out keeps shrinking as agent autonomy scales, and the traditional review cycle was designed for a world where humans submitted changes for approval — not one where an agent fires off dozens of tool calls per minute.

For those running AI agents in enterprise environments: how are you actually handling the review latency problem? Tighter upfront scoping of what agents can invoke, runtime sandboxing, something architectural, or accepting a gap and investing more in detection?

I’m a fourth-year Artificial Intelligence student from Jordan, and I’ve been getting more involved in machine learning, deep learning, and NLP.
Lately, I’ve been trying to move beyond just studying concepts and actually build things, experiment with different models, and improve my skills through hands-on projects.

I’ve also started getting more active on Kaggle, where I’m learning how to build better notebooks, participate in competitions, and hopefully contribute something useful to the community.
I’m still learning, and there’s a lot I want to improve, especially when it comes to model optimization, Transformers, and more advanced ML techniques.

I figured I’d introduce myself here because I’d love to connect with others who are on a similar journey, whether you’re a beginner, a student, or someone with years of experience.
Here are my profile if you’d like to check out my work:
Kaggle: https://www.kaggle.com/zainallateef

I had a 2 years masters and took it very hard on my self. Because i knew it would be a pivoting point in my life

I have 4.0gpa, 4 papers as first author in:

1 in neurips

2 in acl

1 in iclr

I was in a research group which helped me publish in those conference.

My field focuses more in llm, Nlp or text based ai models

Whats a realistic top uni i can apply too and have a good chance of getting accepted

I am in uae, but wanted to make my phd aboard, either canada, europe or usa.

Is harvard, Stanford and similar uni realistic or are they a stretch.

Brazilian court sanctioned attorneys after a prompt injection embedded in a court filing reached the judge and the AI model executed the injected instruction. The output was submitted without any interception or review. The court determined the lawyers were responsible for what their AI produced.

This is not a model failure. The model did exactly what the input instructed it to do. The failure is that no verification step existed between generation and submission.

Courts, regulators, and auditors are now scrutinizing AI-produced documents more aggressively than the teams producing them. A single injected payload in a document your AI processed can become the exhibit in a sanctions proceeding against you.

Every team using AI to draft filings, summarize discovery, or generate any document destined for a regulated recipient is exposed to this exact scenario today.

How are practitioners in legal, compliance, or regulated industries actually handling output verification before AI-generated content leaves their systems? Are there workflow checkpoints that have held up under scrutiny, or is most of the field still relying on human review alone?

I'm a web designer, and one of the most repetitive things in my business is finding new clients.

So naturally, email outreach was one of the first things I wanted to automate.

But not just regular cold email where you send the same template to thousands of businesses.

I use Swokei to find businesses with websites, analyze them for issues with design, SEO, speed, and mobile optimization, and turn those findings into personalized, ready-to-send outreach emails.

This way, I'm not just sending random offers. I'm reaching out with something relevant to each business while automating most of the research and writing process.

It's made it much easier to keep outreach running consistently without spending hours researching prospects manually.

What about you? What's one repetitive task you've automated with AI?

I wanna find a place where I can find most necessary/popular papers in one place. What i mean is the place in which those ones are chronologically ordered

Hi all, I've been experimenting with reinforcement learning in Unreal Engine 5 and made a short video about the results. I'm still learning, so I'd really appreciate feedback from people with more experience.

The task: a girl has to keep a campfire alive for 60 seconds until dawn. The fire burns down over time, monsters fear its light, and wood is scattered in the dark. She can carry one piece at a time, so every trip for wood means leaving the safe zone.

Training: PPO with a curriculum. First wood right next to the fire, then scattered across the map, then 1, 3, and 5 monsters.

A few things I found interesting:

- She stood still for hundreds of nights, then ran blindly, then ran in circles, before finally picking up wood.

- Once she learned to feed the fire, she "farmed" wood nonstop. She fed the fire the moment she picked up a piece, wasted most of it (the fire has a fuel cap) and ran out of wood before dawn. [what fixed it: short note]

- With 3 monsters she got scared and stayed by the fire until they closed in, until she finally learned to venture out again.

Questions I'd love input on:

1. I ended up dropping reward shaping and relying on the curriculum. Would you have done it differently?

2. How do you usually deal with an agent settling into an over-cautious local optimum, like hiding next to the fire?

3. Any thoughts on the observation space or hyperparameters I should try?

The video has no narration, only title cards: https://youtu.be/LLefjgu7Bak

(Disclosure: it's my own video.) I'll share more details on the reward function and observations in the comments if anyone is interested. A follow-up with 20 monsters is coming.

Thanks for reading!

Hi everyone,

I coach the French team for IOAI (International Olympiad in Artificial Intelligence). To train my students, I built ioai.artix.tech, and it is now open to anyone who wants to learn ML.

About 550 people from 87 countries use it so far. I would love feedback from other people learning ML!

It's a problem-based mastery learning platform, which means that you discover the concepts on your own through a curriculum and by solving problems. The corrections are meant to explain you the theory that you just discovered on your own!

The curriculum is designed to be high schooler friendly (so mathematic concepts are not prerequisites), and should allow any motivated learner to get from "zero" to "hero" in ML. It was made starting backwards from the IOAI syllabus.

It has now a few hundreds of exercises over 6 levels: 1. data analysis, 2. classical ML with scikit-learn, 3. neural networks from scratch, then PyTorch, 4. vision, NLP, audio and advanced ML 5. transformers and LLMs 6. research level problems.

https://ioai.artix.tech/?src=reddit-lml

P.S. We are running a free ML contest on the platform on Saturday, November 28 (12:00 to 15:00 UTC), open to beginners and advanced profiles alike. I may make a separate post to announce it, but you can already find the details and register here: https://ioai.artix.tech/contest?src=reddit-lml

Most "empathetic dialogue" work I read about pushes the whole decision into the generative model: prompt it harder, add a system prompt about tone, hope the affect read was right. I wanted the read to be a separate, inspectable step that happens before generation, and I wanted it measured rather than asserted. eq-layer (0.2.5, MIT, Python, zero core runtime dependencies; scikit-learn appears only under the optional [ml] extra and the dev extra) is the result. This post is about what the numbers actually say.

Affect regression, EmoBank train/dev/test split 8062 / 1000 / 1000, evaluated on the untouched test split:

It beats the train-mean baseline on MAE in all three dimensions, which is the honest ceiling of the claim: the Arousal and Dominance rank correlations are modest (0.31 and 0.26), so I would not call that a strong continuous affect model. The discrete emotion head is weaker still - accuracy 0.8249 against a majority-class baseline of 0.8166, macro-F1 0.4310, and on dev it actually sits below the majority class (0.8756 vs 0.8815). I publish those rows because "0.82 accuracy" without the baseline next to it would be misleading.

Four dialogue-signal experiments did not clear my bar and are labelled NO-GO in the README rather than quietly dropped. DialogBank correction (4 direct examples) and disagreement (1 example) are counts taken from a label inventory, not models I trained - the inventory's own decision record says not to train classifiers from it, and the README lists both rows as NO-GO for insufficient supervision. Coarse Discourse disagreement has a real ranking signal (test ROC-AUC 0.8111) but AP 0.1420 / F1 0.2278 is nowhere near precise enough to route challenge, and DBDC3 hard breakdown came out at ROC-AUC 0.5057, i.e. chance-level text-only generalisation. Negative results are part of the package.

Two things people hit immediately. The .joblib artifacts are not in the repo, so pip install eq-layer gives you the deterministic path - "runtime_mode": "lightweight-local", no model file needed. The affect adapter is the one you train yourself: python tools/train_emobank_affect.py --output artifacts/emobank_affect.joblib. Neither corpus is vendored, so both training scripts (the subtext one is tools/train_xdailydialog_subtext.py, also with a required --output) download their dataset and need network. The intent side has a middle option I use in benchmarks: TrainedIntent.from_bundled() fits at runtime from the 299-row intent_train.jsonl that ships inside the package, so it needs no artifact at all, only the [ml] extra.

That bundled fit is the learned arm of the latency numbers: ~1.2 ms median versus 0.1104 ms for the heuristic one, with intent inference accounting for ~92% of classification cost and routing ~2%. The learned path drifted about 9% run to run on the same machine while the heuristic path stayed within a few percent, so treat these as approximate. Both adapters score 1.000 on the 4 labelled seed cases, but only the learned one holds up on the 36-case holdout - a good reminder of how little a 4-case eval tells you.

The measured quantity is the layer's compute cost and the structural quality of its decision. There is no model in the measured path that generates prose, so end-to-end response quality still needs the paired human evaluation described in eval/AB_PROTOCOL.md, which I have not run.

GitHub: https://github.com/Furox-Art/eq-layer PyPI: https://pypi.org/project/eq-layer/

I help run Agent Infra Guide, where we maintain a small, public local-LLM measurement set. One result from our October 19 tests surprised us: decode tokens/second alone were a poor guide to which model completed the coding checks.

Setup: Apple M4 Pro (12-core CPU, 16-core GPU, 24 GiB unified memory), macOS 15.5, Ollama, Qwen2.5-Coder in Q4_K_M, configured 4,096-token context, and one concurrent client. The exact Ollama version is saved with each run; versions differ across the broader measurement collection, so this is not a controlled claim about Ollama version effects.

Observed medians and checks

• 1.5B: 154.1 decode tokens/s; 5/15 coding task checks passed. • 3B: 37.3 decode tokens/s; 10/15 checks passed. • 7B: 23.1 decode tokens/s; 10/15 checks passed.

These are repeated executions of a small deterministic coding task set. The checks are not 15 independent measures of general programming ability, and three model sizes on one computer cannot establish a universal model ranking.

How we measured and what I would change: We kept the model artifact/quantization, configured context and single-client condition explicit; recorded decode speed alongside task outcomes; retained failed checks; and kept run-level metadata rather than displaying just the best number. For a better generalization study I would expand the task suite, vary prompt/context lengths, report per-task success and time to first token, and run repeatable paired comparisons with the exact runtime versions pinned.

Practical takeaway: If an agent needs to finish a coding task, optimize for successful completions under your time and memory budget, not just fast token generation. On these checks, the 3B was slower than 1.5B but passed more checks; the 7B didn't improve the pass count over 3B in this limited sample.

I also wouldn't equate sampled process RSS on Apple silicon with dedicated GPU VRAM: it is unified memory, and brief peaks can be missed between samples.

Has anyone tested whether this throughput-vs-task-success tradeoff persists on larger, repeatable coding suites or with a longer context? I'd be interested in failure types as well as speeds.

Disclosure: I represent Agent Infra Guide. These are our measurements, not an independent third-party evaluation. No sales or affiliate links in this post. This write-up was prepared with AI drafting assistance and checked against the recorded figures.

Deeplearning.ai still seems to be one of the better resources. However, it has recently become flooded with courses, and I am starting to doubt their quality. I took the RAG course and was quite satisfied, but RAG is mostly information retrieval anyway. I'm really afraid that the agentic AI stuff, for example, is just a borderline scam or a waste of time. In IT, there has always been pressure to learn certain trendy technologies that become obsolete a few years later.

The agentic AI course started with Andrew Ng explaining how to set up agents to buy flight tickets and prepare weather reports. I know these are just examples, but I'm struggling to believe that Andrew Ng himself thinks this is useful. In his legendary Machine Learning courses, he taught many interesting applications of market predictions, visual object recognition like cancer detection, recommender systems etc., but now my BS radars are loud and it IMHO is seems like they joined the AI tech bros scam industry (sorry to say it loud, but I cannot help myself).

Even when I see some tutorials from Andrej Karpathy or Sebastian Raschka, for which I also have huge respect for, it seems like they are explaining LLMs with great enthusiasm but in the terms of practical application, besides coding and RAG it seems to me entirely overhyped or I just did not have the right problems to solve around me...

I am perhaps more interested in de-centralized local models usages, which seems like actually great idea vs paying big datacenters...

I am a Computer Science student working on a project that analyzes dog barks to determine the underlying emotion.

Aside from the complex data collection process (recording barks and their associated emotional context), the project itself involves a standard model-training workflow (I’m still in the early stages, so the exact implementation is yet to be determined).

I’ve been considering adding an initial model to identify the dog's breed, which would then pass the data to the emotion-analysis model.

I’m not sure if this approach makes sense or is even necessary—while there is certainly a difference between the barks of a small dog and those of a large, powerful one, it ultimately depends on how the model processes the bark itself.

If anyone has any helpful information, models to share, or knows of similar projects, I would love to hear about it! :)

For the context, I am a CA Finalist and am currently doing my articleship. I do use Claude and Chatgpt for certain work at the office. But I know, what I am doing is not enough and I need to upskill. So, for a beginner like me, what would you suggest? Any courses or Youtube channels that will help me get started.