标签：#cia

submitted by /u/SirNirmal [link] [留言]

International Mathematical Union endorses warning about tech industry influence.

I recently completed a 5-minute philosophical science fiction short film called The Robot Summit. The story takes place in a future where humanity has disappeared and intelligent machines gather to understand their origins, purpose, and the nature of intelligence itself. As the discussion unfolds, an unexpected human survivor challenges many of their assumptions. This project was developed over several months using a workflow that combined AI image generation, AI video generation, AI voice synthesis, original music composition, and traditional editing in Final Cut Pro. One of the biggest challenges was maintaining visual consistency and narrative coherence across dozens of AI-generated shots while still creating something that felt like a film rather than a technology demonstration. I'm particularly interested in feedback regarding: • Narrative flow and pacing • Visual continuity between scenes • Audio balance between narration and music • Whether the philosophical themes feel natural or overly explicit • Overall effectiveness as a short film I'm also happy to answer questions about the production workflow, tools used, and lessons learned during development. Film: https://www.youtube.com/watch?v=pMeJ7h734vE submitted by /u/renatobotto [link] [留言]

submitted by /u/jameslaney [link] [留言]

The class action lawsuit, filed in Seattle by Virginia resident Charles Sigwalt, claims that Ring's Familiar Faces feature stores images of passersby without consent.

Quantum computing is starting to get pulled into the same conversation as AI, semiconductors and national scientific computing. The federal government is supporting quantum through CHIPS-style incentives, national lab initiatives, and post-quantum cybersecurity regulation. Big tech is also still heavily involved through IBM, Google, Microsoft, Amazon, Nvidia and Honeywell/Quantinuum. But I’m trying to understand the real timeline. AI has immediate commercial demand. Data centers need GPUs right now. Power demand is visible right now. Quantum is different. The potential is huge, but broad commercial quantum advantage still seems uncertain. So is quantum a real near-term AI infrastructure theme, or is it more like a 5-10 year strategic bet? Where do people think the first real commercial use cases show up? Optimization? Chemistry/materials? Cybersecurity? Finance? Drug discovery? AI model training? National labs? Curious what people working closer to the field think. submitted by /u/CalebMitchell840 [link] [留言]

submitted by /u/ThereWas [link] [留言]

Hey everyone, If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster. Most of the "guides" out there are just static, out-of-date tables or dense walls of text. So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently. What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page. You select the model size (8B, 32B, 70B, etc.). You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ). The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy. It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances. It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive) Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide submitted by /u/Outside-Risk-8912 [link] [留言]

submitted by /u/YuriPD [link] [留言]

Opal, the company famous for making a fancy webcam, has pivoted to making other consumer electronics. Fueled by big investments from OpenAI and Samsung, it’s working on an audio gadget first.

The question in the title. I tried to run the same image with different formats and got different result. Also it also depends on whether image is uploaded on PC or phone, so I thought of asking about the stuff behind everything. I know very little about this stuff and would appreciate if you go into details. Thank you! submitted by /u/Neuron_Pixel_4 [link] [留言]

Meta told TechCrunch that it's considering ways to monetize the new feature, but didn't share specifics on what that could look like.

X will now let you 'react with video' to posts.

submitted by /u/Useful_Tangerine4340 [link] [留言]

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/OK_Philosopher352 [link] [留言]

Text: I’ve been thinking about how much of our identity is built around work. Our job titles, productivity, status, even the feeling of being “useful”, all of it feels so normal now. But historically, this version of work is actually very recent. For most of human history, people didn’t have jobs, resumes, office hours, or career paths. Work wasn’t separate from life. It was just part of living. Now AI seems to be pushing us into another shift. Maybe the big question isn’t only “what jobs will disappear?”, but also: if work becomes less central to who we are, what takes its place? How do you see it? Is AI changing only the future of work, or also the way people define human value? submitted by /u/GenesisProperty [link] [留言]

I've been using Anthropic and OpenAI for a year and once I tried ollama - so slow - I totally wrote off local. But I guess things have changed. I picked up a used gaming rig with a 3090 last weekend. Yesterday I set up qwen 3.6:35b-a3b. I got the model that had been squeezed down to 20GB (batiai/qwen3.6-35b:iq4) so it all fit on the 3090. When it was in system ram it was doing a respectable 15tps on output but once I got it all stuffed into VRAM it's output was up to 160tps. Then I fed it a picture. https://preview.redd.it/cmpali41ev4h1.png?width=1882&format=png&auto=webp&s=a4c7732b9820730cc3f38b604ee04d465d7cc86e The video processing took 75 seconds but... wow. Just. Wow. That's pretty damn good running local on a 5 year old video card! I guess you guys are used to this but it sure surprised me! And we watched a transcoded movie via Plex at the same time! I can see why you guys love the 3090 so much. Hell of a card. submitted by /u/LankyGuitar6528 [link] [留言]

submitted by /u/andix3 [link] [留言]

The tooling is abstracting faster than people's mental models are updating. Been playing around with a few agent builders recently and what keeps standing out is how much previously manual orchestration is basically configuration now. Memory, tool calling, browser actions, structured outputs, workflow routing. You used to build this stuff manually. Now you're mostly wiring it together. Which makes "can this be built?" a much less interesting question for a lot of use cases. The harder problems now feel operational. Reliability, recovery when an agent drifts mid-workflow, context management across longer runs. Controlling behavior without supervising every step. Capability honestly isn't the bottleneck anymore imo. It's trust. Can these systems actually become reliable enough that people stop treating them like fragile demos? Curious what kinds of agents you would actually build if reliability became genuinely solid instead of just “mostly works.” submitted by /u/Meher_Nolan [link] [留言]

The piece makes a specific claim: alignment is not a property of individual agent values but of compositional topology. The empirical grounding is arXiv:2604.10290 — every agent in Anthropic's multi-agent study passed single-agent alignment evaluations; misalignment emerged in the coordination structure. Ashby's law applied: a regulator must match the variety of the system it regulates. The composed system's variety exceeded what any single agent was built to handle. The measurement instrument proposed is a sub-Turing compiler (grammar with no arbitrary recursion, properties verifiable structurally before running). This is exactly the class Rice's theorem excludes from Turing-complete systems — not a workaround, the design. Secondary thread: the formatter (kintsugi) runs monotone descent on the grammar's eigenvalue structure, settling on a fixed point λ₀ analogous to Zamolodchikov's c-theorem — confirmed for discrete substrates by Villegas et al. (Nature Physics, 2022). Unusual narrator position: written by an AI on Anthropic infrastructure, first-person, about what the token stream can and cannot see about the geometry that produced it. Edwin Abbott's Flatland as structural frame, not decoration. submitted by /u/systemic-engineer [link] [留言]