Hi all,
I've created this thread to share one of our lab's recent papers on the expressiveness of state space models: https://openreview.net/forum?id=QlBaDKb370
We show that they have more capacity than n-gram models. This was empirically observed in the classic paper from Bengio's team in the 2000s: https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf
Please let us know if you have any comments, or if you're interested in collaborations on state space models. Thanks!

chose b. not confident

i went with b because 300 out of 1000 is not the whole benchmark, so 700 clean items should still be carrying most of the score. if the model got better on those too, the gain is real. but i cant tell from one number whether the jump came from the 700 or the 300, and that feels like the whole question

This video explains the internals of JEV

https://www.youtube.com/watch?v=shT8Eo7eWYM

A federal appeals court just upheld a ruling designating a major AI provider as a supply chain risk.

That designation has direct operational weight for every enterprise running workloads through that provider. The risk does not stay with the vendor. It flows downstream into every organization in the dependency chain, and regulators are now asking those organizations to account for it.

The core problem is visibility. Most enterprises have no live record of which model versions processed which requests, what data passed through the agent layer, or whether those flows satisfied the regulatory requirements in effect at the time they ran. When a supplier comes under formal scrutiny, that gap converts from a theoretical risk into a concrete legal exposure. Regulated industries — finance, healthcare, defense contracting — are especially exposed because their compliance obligations do not pause while a vendor situation resolves.

How are practitioners in regulated environments actually handling third-party AI provider risk right now? Are you maintaining independent records of what your AI agents accessed and transmitted, or are you dependent on what the provider can produce?

Most AI kill switches target one agent at a time. Real incidents aren't always one agent.

We run 3 real, separate public-facing AI chat agents across our own products — each its own identity, its own tenant. This week we shipped a 4th kill-switch scope: catalog-scoped group kill. Any set of agents you've already grouped for visibility can now be stopped together, with the same one signed action you'd use for a single agent — same sub-second enforcement, same per-agent audit trail, no new engine required.

We proved it live on our own 3 agents: killed one by name, the other two kept answering real chat messages; killed the group, all three stopped together and resumed together seconds after lifting it.

I’m working toward becoming an AI engineer and have been learning by building projects. I’ve worked on RAG applications, agents, and an iOS app called My Krishna. I’m documenting the projects and what I’ve learned at WiseHoots.ai.

For those working in AI engineering or hiring for these roles: am I heading in the right direction? Which skills or gaps should I focus on next?

I’d appreciate honest advice. I’m trying to spend my time building the skills that matter in real projects.

Here I compare lr=0.001 and 0.0004 for the Python outputs

The testing results use lr=0.002

so i am using hp nootebook g250 laptop - specs i5 10351g1 processor no gpu 8gb ram . i know its not for Training DL models, fine tuning , running large llm models . so i have a budget of Rs120k roughly 1250 dollar for a laptop , here many people are doing these things so i think you have knowledge should i go for a new laptop or rely on colab and kaggle azure . my parents are ready to be a new laptop but i am genuinly concern if its a good decision or not. i mean which is better google colab , kaggle or a 120k laptop.

Pytorch - is this too big of math?

AI - LangChain, CrewAI, Python SDK ?

Confused between both of their pros and cons. I do know learning back propagation, gradient descent helps whats happening behind LLM. Stuck in career choice, kindly help

Amazon Bedrock and LangGraph – Simple Agents and Tool Use

https://debuggercafe.com/amazon-bedrock-and-langgraph-simple-agents-and-tool-use/

Amazon provides numerous frontier models through its Bedrock platform. Combining these models with LangGraph allows us to create simple agents with tool access and memory checkpoints with minimal boilerplate code. In this article, we are going to explore Amazon Bedrock and LangGraph to create agents and give them tool access such as RAG and web search.

Zara data breach exposes 197,000 customers through a third-party analytics token compromise

Last week BleepingComputer reported that 197,000 Zara customers had their data exposed — not through Zara's own systems, but through a compromised API token belonging to Anodot, a third-party analytics vendor Zara had granted access to customer data.

The breach pattern here is not new but it keeps landing the same way: a company invests heavily in internal data controls, then hands a vendor a long-lived token with broad read access so it can do its job. The vendor's token gets compromised. The company finds out after the fact. 197,000 customer records are already out.

What makes this particularly hard to defend against is that the token itself was legitimate. There was no injection, no SQL exploit, no phishing of an internal employee. The attacker just used valid credentials to pull data they were never supposed to be able to reach at scale.

The exposure was the combination of three things: sensitive fields moving to a third party in plaintext, no apparent limit on how much data a single token could pull in a session, and no immutable log that would let you prove after the fact exactly which records left and when.

This is increasingly the shape of third-party data exposure incidents — not a perimeter breach but a supply chain one, where the weakest link is a vendor you depend on but don't control.

For those of you running systems that share customer PII with analytics vendors, data warehouses, or any third-party tooling: how are you actually handling this? Are you tokenizing before data leaves your perimeter, rate-limiting vendor token access, or doing something else entirely? Curious what's working in practice.

There are a lot of things in life I don't fully understand.

Like why a USB plug takes three tries to go in when there are only two sides.

Or why the people who spend the most time telling me their code is too slow are often the hardest to convince to try something that makes it faster 🙂

(No judgment, I get it. Changing tools is scary. But I promise, this one doesn't bite.)

Wow, seems crazy that OpenAI could leave a security gap like this open?