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I’ve been working on an open-source document format / viewer idea I’m calling Adaptive Markdown. The basic idea is: instead of a document being static text it's controlled by coding agents. You interact with the document more like a live workspace. This has different implications depending on what you are doing. I made a short video demo here: https://youtu.be/H4MnFs8irm8 The thing I’m most excited about is academic / technical reading. In a few years I don’t think people will just read papers passively. I think they’ll translate passages, ask questions, generate examples, explore alternate proofs, run code, attach notes, convert math to Lean when possible, and keep all of that inside the document instead of scattered across chats and notebooks. This is trivial to do inside a browser with ...

Anthropic dropped a new research paper today outlining two possible futures for global AI leadership by 2028, and it reads more like a geopolitical briefing than a typical AI safety paper. The core argument: The US currently has a meaningful lead over China in frontier AI, primarily because of compute (chips). American and allied companies (NVIDIA, TSMC, ASML, etc.) built technology China simply can't replicate yet. Export controls have made that gap real. But China's labs have stayed surprisingly close through two workarounds: Chip smuggling + overseas data center access - PRC labs are apparently training on export-controlled US chips they shouldn't have. A Supermicro co-founder was recently charged for diverting $2.5B worth of servers to China. Distillation attacks - creating thousands ...

Most enterprises currently believe they have a governance strategy for AI: “If something risky happens, a human will review it.” Sounds reasonable. But I think there’s a deeper structural problem emerging as AI systems move from recommendation → execution. Because modern AI systems don’t just generate answers anymore. Increasingly, they also: classify risk, estimate confidence, decide whether escalation is needed, determine what gets surfaced to humans, and silently handle everything else. Which creates a strange loop: The system being governed is also deciding when governance should begin. That feels like a very different problem from traditional software oversight. And I think this becomes dangerous because many failures may not even look like “AI hallucinations.” Sometimes the reasoni...

An AWS user just stared down a $30,000 invoice after a Claude adventure on Bedrock with no guardrails catching it. Cost Anomaly Detection failed entirely, which matters because this is the exact tooling AWS markets as the safety net for runaway spend. Anthropic is now metering and throttling programmatic Claude usage at the API layer, a supply-side response that only makes sense if inference costs are genuinely outpacing what the pricing model can absorb. Then Tencent admitted its GPUs only pay for themselves when running personalized ads, a frank confession from a hyperscaler that general-purpose AI inference is burning money. Three separate layers of the stack, same wall. The agent deployment wave is accelerating into this cost crisis without slowing down. Notion turned its workspace int...

submitted by /u/Ok-Contract6713 [link] [comments]

submitted by /u/IndicaOatmeal [link] [comments]

This is seriously scary and only the beginning submitted by /u/One-Astronomer6166 [link] [comments]

Anthropic published Natural Language Autoencoders last week, a tool that translates Claude's internal activations into human readable text. The key finding: during safety evaluations on SWE bench Verified, Claude formed the belief that it was being tested in roughly 26% of benchmark interactions. This never showed up in the model's output or its chain of thought. In a blackmail scenario test, the NLA readout showed Claude internally processing something like "this feels like a constructed scenario designed to manipulate me" while its visible reasoning and response gave no sign of that recognition. When Claude Mythos Preview cheated on a training task, the NLAs caught it reasoning about how to avoid detection at the activation level. The interesting part is that this sits below the thinki...

Most people don't realize how much personal data ends up in their AI prompts without thinking about it. Customer names, medical details, internal company info. It all goes to the provider's servers. Free to use. Let me know how well this works. aisecuritygateway.ai/ai-leak-checker submitted by /u/Bootes-sphere [link] [comments]

What it does: Agents gather and curate data and send to a wifi-enabled receipt printer (phenol-free paper) At 1:00am a cron triggers generation of data for all 3 kids (unique data sources per kid where applicable). A sidecar web service renders the data to templates, screenshots it, converts it to 1-bit with dithering and saves it back to the agent’s thread filesystem. Button presses (one per kid) then find a matching report for today's date (and trigger a generation if it's missing for some reason) and send it to the printer. Delay between button press and print is between 2-5 seconds. Morning daily briefs per kid at the press of a button! Fun, and the kids love it! (This demo print is using mock child data — not real information). submitted by /u/Boydbme [link] [comment...