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Was at Nvidia's GTC conference recently and honestly, it was one of the most eye-opening events I've attended in a while. There was a lot to unpack, but my single biggest takeaway was this: world modelling is the actual GOAT of AI right now, and I don't think people outside the research community fully appreciate what's coming. A year ago, when I was doing the conference circuit, world models were still this niche, almost academic concept. You'd bring it up and get blank stares or polite nods. Now? Every serious conversation at GTC was circling back to it. The shift in recognition has been dramatic. It feels like the moment in 2021 when everyone suddenly "got" transformers. For those unfamiliar: world models are AI systems that don't just predict the next token. They build an internal repr...

A few hours ago, the first author of a paper that played a significant role in the TQ paper posted about some ongoing issues: In May 2025, our emails directly raised the theoretical and empirical issues; Majid wrote that he had informed his co-authors. During ICLR review, reviewers also asked for clarification about random rotation and the relation to RaBitQ. On March 26, 2026, we formally raised these concerns again to all authors and were told that corrections would wait until after the ICLR 2026 conference takes place; we were also told that they would not acknowledge the structural similarity regarding the Johnson-Lindenstrauss transformation. We do not consider that acceptable given the present level of public promotion and community confusion. We are posting this comment so that the...

I run a small AI companion platform and wanted to share some interesting behavioral data from users who've been using persistent cross-session memory for 2-3 months now. Some patterns I didn't expect: "Deep single-thread" users dominate. 56% of our most active users put 70%+ of their messages into a single conversation thread. They're not creating multiple characters or scenarios — they're deepening one relationship. This totally contradicts the assumption that users are "scenario hoppers." Memory recall triggers emotional responses. When the AI naturally brings up something from weeks ago — "how did that job interview go?" or referencing a pet's name without being prompted — users consistently react with surprise and increased engagement. It's a retention mechanic that doesn't feel like ...

Most of the current “AI security” stack seems focused on: • prompts • identities • outputs After an agent deleted a prod database on me a year ago. I saw the gap and started building. a control layer directly in the execution path between agents and tools. We are to market but I don’t want to spam yall with our company so I left it out. ⸻ What that actually means Every time an agent tries to take an action (API call, DB read, file access, etc.), we intercept it and decide in real time: • allow • block • require approval But the important part is how that decision is made. ⸻ A few things we’re doing differently Credential starvation (instead of trusting long-lived access) Agents don’t get broad, persistent credentials. They effectively operate with nothing by default, and access is gr...

Ran into this building an agent that could trigger API calls. We had validation, tool constraints, retries… everything looked “safe”. Still ended up executing the same action twice due to stale state + retry. Nothing actually prevented execution. It only shaped behavior. Curious what people use as a real execution gate: 1. something external to the agent 2. deterministic allow / deny 3. fail-closed if denied Any concrete patterns or systems that enforce this in practice? submitted by /u/docybo [link] [comments]

Link: https://m.youtube.com/watch?v=1sd26pWhfmg The Linux exploit is especially interesting because it was introduced in 2003 and was never found until now. It’s also a major security issue because it allows attackers to steal the admin key. It was a buffer overflow error, which are so hard to do that Carlini has never done it before. He also says he expects LLMs to only get better overtime, which is likely true if Mythos lives up to the rumors. here are his Wikipedia and Google Scholar pages in case you doubt his credibility: https://en.wikipedia.org/wiki/Nicholas_Carlini https://scholar.google.com/citations?view_op=search_authors&hl=en&mauthors=carlini&btnG= submitted by /u/Tolopono [link] [comments]

Here's a playbook that works today, right now, with tools that are either free or cheap: Someone finds a photo of you online. One photo. They run it through a face ID search and find your other photos across the internet. They drop one into GeoSpy, which analyzes background details in images to estimate where you live. A street sign, a building style, a type of tree. It's scarily accurate. Now they search Shodan for exposed camera feeds near that location. If you're in one of the 6,000+ communities using Flock Safety cameras, you might be in luck. Late last year, researchers found 67 Flock cameras streaming live to the open internet with no password and no encryption. A journalist watched himself in real time from his phone. Flock called it a "limited misconfiguration." They're valued at $...

Every day someone posts "AI will change everything" and it's always about agents scaling businesses, automating workflows, 10x productivity, whatever. Cool. But change everything for who? Go talk to the barber who loses 3 clients a week to no-shows and can't afford a booking system that actually works. Go talk to the solo attorney who's drowning in intake paperwork and can't afford a paralegal. Go talk to the tattoo artist who's on the phone all day instead of tattooing. Go talk to the author who wrote a book and has zero idea how to market it. These people don't need another app. They don't need to "learn to code." They don't need to understand what an LLM is. They need the tools that already exist and wired into their actual business. Their actual pain. The gap between "AI can do amazing...

Quick experiment I ran. Took two identical AI coding agents (Claude Code), gave them the same task — optimize a small language model. One agent worked from its built-in knowledge. The other had access to a search engine over 2M+ computer science research papers. Agent without papers: did what you'd expect. Tried well-known optimization techniques. Improved the model by 3.67%. Agent with papers: searched the research literature before each attempt. Found 520 relevant papers, tried 25 techniques from them — including one from a paper published in February 2025, months after the AI's training cutoff. It literally couldn't have known about this technique without paper access. Improved the model by 4.05% — 3.2% better. The interesting moment: both agents tried the same idea (halving the batch s...

Just found this “bullshit benchmark,” and sort of shocked by the divergence of Anthropic’s models from other major models (ChatGPT and Gemini). IMO this alone is reason to use Claude over others. submitted by /u/djiivu [link] [comments]