标签：#artificial

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 Most of the "guides" out there are just static, out-of-date tables or dense walls of text.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]

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]

Float32 was the true enemy – not backpropagation, not the architecture. BIN16 replaces every floating-point operation with a single boolean operation: popcount16(XNOR16(a,b)). The result: 82 % MNIST at H=512 with zero floats, zero gradients, zero AdamW and zero learning rate tuning. The training converges immediately in epoch 1 – without warm-up, without decay, without hyperparameter search. Both layers use identical XNOR+popcount operations – training and inference run directly in off-the-shelf DRAM with only 5 transistors per cell. This is the only neural architecture where the same hardware performs both training and inference without modification. The remaining 18 % to 100 % is the bit-mass limit – no training deficit. The groundbreaking insight came when we stopped fighting against float and embraced pure boolean computation. Every complexity – AdamW, backprop, LR schedules, BLAS – dissolved as soon as we removed floating-point numbers from the architecture. Three groundbreaking insights changed everything. Float was the true enemy: backpropagation, AdamW or momentum were never the problem. Float32 introduced numerical noise and instability. Bitwise centroids converge instantly: a running bitwise majority vote per class reaches final accuracy in a single epoch. Random projection is entirely sufficient: W0 does not need to be trained – a random boolean projection provides adequate separation. The entire training consists of only four steps and 220 lines of C – without learning rate, without GPU, without any conventional optimization. This architecture opens the door to a future in which neural networks compute directly in memory. No more expensive GPUs, no endless hyperparameter tuning marathons. Instead, pure, efficient logic that is ready for use immediately and everywhere. Imagine: AI systems that train and infer in off-the-shelf DRAM – energy-efficient, lightning-fast and accessible to everyone. BIN16 is the first step into this new era. Identical operations

We ran into something that didn't seem like a problem until it was. Each agent had access to the tools it needed and everything worked fine in isolation. The issues started once agents were running in parallel. Two parts of the system would try to use the same tool or hit the same resource at the same time. Results became inconsistent and it wasn't obvious why. Limiting access helped in some cases but slowed things down elsewhere. Too much access caused race conditions. Too little caused steps to stall waiting for something to free up. Most of the coordination logic ended up sitting outside the agents themselves. Every new agent added more decisions around what it should be allowed to access and when. There isn't a shared way to manage tool access across a multi agent system. How are you handling this when multiple agents are running at the same time? submitted by /u/Logical-Bite-4221 [link] [留言]

submitted by /u/NISMO1968 [link] [留言]

The global health care sector is under increasing strain. Decades of chronic underinvestment and constraints in recruitment have coincided with a surge in demand for services for aging populations. Gaps in provision are already taking a toll, with fragmented access to care and high rates of stress and burnout among staff. And it’s getting worse.…

Anthropic filed a confidential S-1 with the SEC this week, moving toward a public listing that will put disclosure obligations and investor return expectations directly in tension with its safety-first positioning. The IPO filing lands as GitHub Copilot ends flat-rate billing and switches to metered consumption, meaning teams with heavy usage face immediate cost spikes with no grace period to audit seat activity. OpenAI's frontier models and Codex are now available directly on AWS , which changes vendor-lock assumptions for inference pipelines and removes the proxy layers some teams were routing around. These two moves together suggest the "get developers hooked, then price for real" phase is now active across the stack. The security picture is worse. A researcher documented a Meta AI social-engineering exploit that handed attackers access to high-profile Instagram accounts by manipulating the agent through its account-management tool calls. No sophisticated jailbreak required. Any agent with write permissions to external accounts is now a confirmed social-engineering surface, and the Meta incident is the clearest public proof of that so far. Separately, malicious npm packages reached Red Hat Cloud Services repositories and were downloaded at scale, which means JS dependency audits for cloud-native stacks need an immediate re-run against known-bad versions, not a scheduled one. On the hardware side, Intel's Crescent Island GPU ships with up to 480GB VRAM , which revises local inference capacity planning for large MoE models in ways that weren't on most teams' roadmaps six months ago. Alphabet announced an $80 billion equity raise for AI infrastructure , which will tighten GPU allocation queues and data center procurement timelines across all cloud providers regardless of whether you're an Alphabet customer. The pattern across all of this: monetization is accelerating faster than the trust infrastructure required to support the attack surface already in production. A

If you work a white collar job, you’re probably scared of AI replacing you. AI started at the desk — data entry, customer service, software. Now its stepping onto the factory floor: Amazon robots moving inventory, Figure bots handling BMW parts, Tesla building Optimus for repetitive labor, and warehouses being automated. But at the end of the day, AI is a technology. We cannot stop it any more than we could stop electricity or the assembly line. The problem is not that machines are becoming powerful. The problem is the economic machine around it. Let’s face it: Capitalism doesn’t have the ability to support this kind of technology. Capitalism was built for a world of scarcity, where human labor was necessary and wages gave people access to goods. But as AI advances exponentially, it can produce more with fewer workers, while capitalism still distributes wealth through jobs it is actively eliminating. The result is abundance trapped behind an archaic wage system. I believe that we NEED to get governments and major tech companies to start seriously planning for a universal basic income funded by AI-driven productivity. As automation replaces more human labor over the coming decades, UBI will become essential to prevent mass instability and ensure that the wealth created by AI supports society as a whole, not just the companies that own it. We already know the wealth gap is too wide. If we don’t start addressing AI-driven inequality now, that divide will grow exponentially as more labor is automated and more wealth concentrates at the top. Without a plan to distribute the gains from AI, we risk mass instability and eventual economic collapse. Capitalism built the machine that could end scarcity, but not the system that could distribute its output. It’s time that we, as a global society, start thinking about phasing out that old machine. submitted by /u/SuddenEducation442 [link] [留言]

Donald Trump killed an executive order to regulate AI. Now, administration officials and AI executives are trying to figure out if there’s anything left to piece back together.

submitted by /u/lilubba [link] [留言]

Hello this might be weird but I am doing a study on society's view on AI as a school project. Therefore I am asking all kinds of communities and trying to get a very wide audience. This is clearly an AI sentric sub so hopefully his is relavent? I would be very happy if any of you would like to be a part of it! submitted by /u/Timely_Special_5011 [link] [留言]