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I've been in San Francisco for the past five weeks, and most of the discussions about LLMs here (and online) gravitate around coding or writing content. I'm curious what unusual uses people have found that actually stuck. Not theoretical "you could do X" but things you genuinely use. submitted by /u/Money_Horror_2899 [link] [comments]

If you missed the Project Glasswing announcement last month: Anthropic built a security-focused model that autonomously found thousands of high-severity vulnerabilities across every major OS and web browser, then decided it was too dangerous to release publicly. Instead they gave access to ~40 organizations to use it defensively . Cloudflare just posted their honest breakdown of the experience. The genuinely impressive part: the model can take several exploit primitives and reason about how to chain them into a working proof. The reasoning looks like the work of a senior researcher, not an automated scanner The catch: its built-in guardrails aren't consistent. The same task framed differently could produce completely different outcomes. Cloudflare's point is that this inconsistency is exac...

A federal court on Monday dismissed claims filed against OpenAI and its top executives by Elon Musk, who accused them of betraying a shared vision for it to guide artificial intelligence’s development as a nonprofit dedicated to humanity’s benefit. submitted by /u/DavidtheLawyer [link] [comments]

submitted by /u/Tiny-Independent273 [link] [comments]

Per Tech Times: “More U.S. businesses paid for Anthropic's Claude than for OpenAI's ChatGPT in April 2026 — the first time in the AI industry's short history […] Anthropic's annualised revenue run rate crossed $30 billion in early April 2026, up from roughly $9 billion at the end of 2025, placing it above the approximately $24 to $25 billion annualised figure OpenAI reported at the same time. More than 1,000 enterprise customers now spend over $1 million annually on Anthropic products — a number that doubled in under two months after the company's $30 billion Series G raise in February 2026. Eight of the Fortune 10 are now Claude customers, according to Anthropic.” submitted by /u/StarlightDown [link] [comments]

submitted by /u/danie-l [link] [comments]

Hey everyone, Most people build 8-bit computers to run Pong or Tetris. I wanted to see if I could push a custom 8-bit architecture to do something much harder: train a neural network from scratch. I built VirtualPC, an open-source 8-bit computer system simulated from basic NAND gates up to a functional CPU that can train a small neural net from a folder on your computer. Repository: https://github.com/ninjahawk/VirtualPC › The ML Core Instead of importing PyTorch, everything happens at the bare-metal assembly level: Custom ISA: The Instruction Set Architecture was designed to handle the math needed for machine learning. Low-Level Training: The CPU executes forward and backward passes directly through custom assembly code. Matrix Math on 8-bit: Overcoming severe memory limits using disk-bac...

Every week there's a new paper or tweet claiming some model "understands" context, "reasons" about math, or "knows" what it doesn't know. But when you look closely, there's almost no consensus on what "understanding" even means — philosophically or empirically. Searle's Chinese Room argument is 40 years old and still hasn't been cleanly resolved. The "stochastic parrot" framing treats token prediction as the ceiling. Integrated Information Theory would say current architectures are near-zero in phi. And yet GPT-4 passes the bar exam. A few questions I've been sitting with: Is "understanding" even the right frame — or is it a folk-psychology term we're forcing onto a system that operates on completely different principles? Does it matter if a model "truly understands" if the outputs are in...

I think we’re underestimating how chaotic enterprise AI adoption actually is inside large companies. From the outside, it looks simple: buy better models add copilots automate workflows deploy AI agents increase productivity But inside many enterprises, CIOs and CTOs are dealing with a much deeper problem: The organization itself is fragmented. Customer data exists across: CRM systems billing platforms support tools spreadsheets emails regional databases legacy systems nobody fully understands anymore And every system describes the “same customer” differently. Then leadership says: “Scale AI faster.” But scale AI on top of what exactly? Which system represents reality correctly? The CRM? The support history? The risk engine? The finance system? The employee’s undocumented tribal knowle...