标签：#cia

Most AI memory is private: an LLM gradually learns about a user. I wanted to see what happens if you give an AI a memory and make it public. So I built Wild Static : a persistent AI that anyone can talk to. Everybody talks to the same one. Conversations become experiences in the underlying memory, which means something one person says can eventually affect how Static responds to somebody completely different down the line. The memory system itself is something I’ve been developing since 2021. Static is the first public application of it. The interesting part has been watching Static change over time. It has grown opinions, relationships and beliefs. They’re constantly in flux too. It doesn’t respond “you’re absolutely right” like a traditional LLM, but often argues, disagrees, or makes mistakes. Some people even seem to have made it their job to educate Static, and it seems like it might be working. It’s been public for 10 days and has now accumulated thousands of interactions, so it’s starting to become a much more interesting experiment than the empty mind it launched as. You can talk to it, teach it and confuse it at wildstatic.com I’m the builder, obviously, so this is self-promotion. But I’d be very interested in what people think about the underlying idea, particularly whether accumulated public experience makes Static feel different to a normal chatbot. submitted by /u/adjohu [link] [留言]

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Thanks to a dwindling supply of open roles, “one-click” applications, and the rise of artificial intelligence, it’s easier than ever to apply for a job. We’re all paying the price.

“It never crossed my mind that they would be so bold as to sell our private data for AI,” says one former Spirit Airlines flight attendant.

Humanoid robots are having a moment in China. The popular machines are part of the country’s strategy to bring artificial intelligence into daily life. Embedding the technology into physical systems—an idea called embodied AI—was a key facet of China’s latest five-year plan, and companies here are already world leaders in humanoids. Nearly 90% of the…

Before we get to recursive self-improvement, there is a slightly awkward intermediate step nobody seems very interested in: AI has to know what the hell is happening to itself while it is working. Current frontier models can be extraordinarily capable, but they still do not have reliable introspective access to their own internal processes. They cannot simply inspect themselves and tell you: - what exactly made this reasoning attempt succeed, - which internal bottleneck is limiting them right now, - where more compute would actually help, - which lesson from the last attempt should become persistent knowledge, - whether an apparent improvement is real or just overfitting to an evaluator, - or which part of themselves should be changed to become better next time. We keep compensating for this from the outside. We give them scaffolds. Memory systems. Evaluators. Agent loops. Tooling. Sandboxes. Human feedback. External search. Carefully designed environments that decide what they are allowed to modify and what counts as success. And some of this works remarkably well. But notice what that means. We are not yet watching an intelligence calmly understand its own machinery and recursively redesign itself. We are building increasingly elaborate machinery around an intelligence that cannot reliably see its own machinery. That may eventually lead to recursive self-improvement. Maybe surprisingly quickly. But “the model is very smart” and “the system can autonomously understand, manage, and improve the process that makes it smart” are not the same capability. There is a rather large missing arrow between them. So whenever I see another prediction that the Singularity may arrive next Tuesday, I keep wondering: Who, exactly, is going to know what to improve on Wednesday? submitted by /u/CarefulHamster7184 [link] [留言]

I've been trying to implement a speech to text app using .Net and C#, but it seems that there is no way to simply download a model (e.g. Whisper or Wav2Vec2) and directly call it the way you can in Python. Instead I'm told I need to write all the pre-processing, adding complex code into the application. I've been trying avoid using Python (for good reasons), but it feels like the ONNX route is just too complicated. Am I missing something, like a good library that can do the pre-processing, or a model that has good built in support for .Net? Edit: Found out about whisper.net, which avoids using ONNX completely and just works. Similar libraries exist for other models, so this is the route I'm going, as creating pipelines is really complex and introduces to much risk. submitted by /u/SecondCobra [link] [留言]

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I have been using video generation models for a long time, especially Seedance 2.5. I find the understanding of prompt is still very weak even in the strongest model. One most ridiculously simple mistake is Seedance misspelled the words in the video. I think I can fix it with emphasizing it. But it failed again. LLM may hit a wall now but apparently video generation model has not. submitted by /u/AthleteArtistic3121 [link] [留言]

Meta keeps trying to entice audio content creators to Threads.

Anyone else actually dealt with this? Is it overblown, or am I missing something? submitted by /u/Positive-Ad3618 [link] [留言]

Apply before September 4 to be a part of the TechCrunch Disrupt community by hosting your own Side Event.

Hi! Not sure if this community is suitable for this, if not, please let me know and I will take it down. My parents love sharing online content with me, we love animals so a lot of that is cute animal stuff, and lately I've been getting a lot of AI cats. I gave them some hints so they spot the obvious ones but not all the time. We haven't yet had an election cycle with AI content being this common, and it scares me a bit. I appreciate your support! submitted by /u/hakansan [link] [留言]

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Raspberry Pi's head of social, Ashley Whittaker, acknowledged the cyberdeck trend today, saying "we haven't been able to get away from cyberdecks this year." Tiny portable computers made out of things like purses, jewelry boxes, and other thrifted or recycled parts have gone viral on TikTok and other social media platforms over the past year, […]

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox, sign up here. Chatbots took many schools by surprise upon their release a few years ago. Suddenly, students carried an app in their phones that could magically answer almost any…

You probably know how it goes: you give a complex prompt to a LLM, it spits out a highly confident answer, and you just sort of... hope it’s right. If you ask the same question in a different tab, Claude might give you a completely different answer. Gemini might say they are both wrong. I've done it this way for a long time, and many of my friends seem to do the same. I wanted to see what happens if you don't just compare answers, but actually bring AI models into a shared chat to discuss the question together. Here is how it went when they could discuss each other's replies in real-time: - ChatGPT went first. It wrote a beautiful, highly structured, and completely wrong answer. It hallucinated a tax rule that didn't apply to the prompt. - Claude stepped in next. It immediately flagged GPT’s tax hallucination, but overcorrected and messed up the final math equation. - Gemini acted as the final Judge. It took ChatGPT’s original structure, applied Claude’s logical correction, fixed the math, and spat out a flawless final output. The takeaway: Letting an AI model review itself is like a student grading their own work. It just repeats the same assumptions. When you force different models (OpenAI vs Anthropic vs Google) to fact-check each other, they actually expose each other's blind spots and hallucinations. I got so obsessed with this multi-AI workflow that I built a site to let these models debate in real-time without having to copy-paste between different tabs (I posted about it earlier here). If anyone wants to try it or testing their own complex questions, curious to hear what kind of workflows you guys would use it for. submitted by /u/capibara13 [link] [留言]

Asgaut Mjølne Söderbom and Ola Hast discuss the evolution of their software engineering practices past continuous deployment and pair engineering. The conversation continues where it left off in the previous episode and focuses on the experiments in adopting Claude Code and the reasons why they consider it good for everything else, but not coding. By Asgaut Mjølne Söderbom, Ola Hast

The deepfake epidemic in schools is affecting more than students. Four teachers tell WIRED about becoming targets of sexualized, AI-generated content—and how difficult it was to find accountability.