Artificial Intelligence Reddit

(Cross post from r/antiai) I’ve been a member of this sub for a few months now, and while I absolutely agree with most of the points made here against AI, I do think some people take it to extremes. I don’t think there’s anything necessarily wrong with the technology itself, just moreso the way it’s being pushed and marketed. I think llms can absolutely have some useful applications, as long as they’re used responsibly. And considering they already exist and are being pushed everywhere, I figure in the interest of harm reduction there should be an effort to find more responsible use cases for them. My attempt to use ai responsibly involves an app I’ve been working on. It’s designed to be a research IDE, and allows you to add PDFs to a project, highlight them, organize and connect highlights on a visual workspace, manage citations, and write a research paper all within the app. It also has some llm features. All these features are locally running, so no data ever leaves your device, protecting privacy. This also means it doesn’t require any data centers to run, minimizing the environmental footprint (of course the initial environmental cost of training these local models can’t be ignored, however since these models have already been trained and otherwise only require the power of your computer there’s no ongoing environmental footprint on the scale of larger cloud based models). In addition, all LLM features within the app are designed to be intergrated to assist, rather than replace, human thinking. Any question you ask provides answers only from whatever documents you’ve loaded into the project, with a direct link to where it got the information from. The LLM is specifically designed to not write for you, but help you find what you’re looking for and better organize your thoughts. Any note it suggests leaving requires user confirmation to save(reducing the likelihood of hallucination since you’re prompted to check all AI output) and all AI output is explicitly marked unverified until a user manually confirms the information to be true. It also keeps a record of all AI interactions in the form of a llm log, so you can verify when exactly ai was used, what percentage of notes were taken by human vs ai, and how much the human actually interacted/edited/verified ai generated content. Essentially, the AI tools are designed to be a helpful assistant, finding information and making suggestions, while the actual thinking, planning, and writing is left to the human. Because this sub has obviously thought a lot about all the ethical implications of AI, I thought it’d be the perfect place to get feedback on this idea and how to best implement it. So what do you think? Does this sound like a more responsible way to use AI? Is there ways it can be improved? The apps still a work in progress, but I can share screenshots or more information about it if anyone’s interested. I want to be clear that this isn’t a product I’m trying to sell (when it’s finished I intend to open source it and make it free), but rather an attempt to create an app with AI features that are actually ethically and consciously implemented, and I’d love any feedback you guys have that could help ensure it operates in as ethical a way as possible. submitted by /u/sunbear99999 [link] [comments]

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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] [comments]

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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] [comments]

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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] [comments]

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

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] [comments]

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] [comments]

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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] [comments]

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] [comments]

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] [comments]

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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. Anthropic’s S-1 will force public disclosure of how it prices safety work against revenue targets, and that transparency will either validate or undercut the lab’s positioning within the next two quarters of filings. If Anthropic’s public disclosures show safety research as a shrinking share of operating expenditure relative to inference and sales costs, expect the other frontier labs to use that as cover to deprioritize their own. submitted by /u/petburiraja [link] [comments]

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] [comments]

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There is a Princeton paper by Kapoor and Narayanan. They found data leakage in close to 300 papers across 17 fields, including medicine and economics. Leakage means the model was trained on information it would never have when it makes a real prediction. So it looks great on the test set and then fails in the real world. My favorite example is civil war prediction. Complex models were reported to crush old logistic regression. Once the leakage was fixed, the fancy models were no better than the decades old stats. I have built enough models to know how easy this is to do by accident. You scale the data before you split it, or you use one feature that is really a stand in for the answer, and your numbers look amazing. So now when I read another “AI cracked X” headline, my first thought is whether anyone checked it for leakage. submitted by /u/kamilc86 [link] [comments]

Hey Reddit, I just published a study analyzing 25,500 LLM resume evaluations to measure hiring bias. By swapping minor identity and demographic variables on the exact same work history across 10 different models, an independent AI auditor flagged a staggering 45% bias rate driven by “silent bias.” Instead of saying anything overtly offensive, models invent professional-sounding excuses to penalize candidates, like when a model dropped its score after I changed the university to MIT, suddenly claiming the candidate’s experience wasn’t relevant despite praising that exact same experience on the baseline resume. We also found a massive 6x difference in stability between systems, with Qwen and older Gemini models being highly volatile, while the Claude models, Mistral-Large, and Llama 4 proved to be the most stable and fair. Ultimately, AI screening tools are outputting highly subjective, unpredictable opinions driven by statistical noise rather than objective truth, making them a massive liability under regulations like the EU AI Act. You can read the full write-up and explore our interactive data app here: https://re-cinq.com/blog/ai-hiring-bias-25500-llm-evaluations submitted by /u/Signal_Rabbit_8303 [link] [comments]

Selected excerpts: “The question, then, is not whether A.I. will change the world. It will. The question is: Who will own and control that future? Who will benefit from it, and who will be hurt by it? Will A.I. be used to make life better for working families? Will it enrich our quality of life? Will it help us eliminate poverty, extend life expectancies and solve the climate crisis? Or will the future of humanity be determined by a handful of billionaires who have promoted and developed A.I., with virtually no democratic input, who stand to become even richer and more powerful than they are today? That is the choice before us. Let us be clear. Artificial intelligence was not created out of thin air. The data and language used by generative A.I. tools didn’t just pop into Sam Altman’s head or Elon Musk’s imagination. A.I. is built on our collective intelligence: our books, songs, artwork, journalism, computer code, scientific research, videos, conversations, images and ideas spanning generations. That is not just the opinion of Bernie Sanders. According to Mr. Altman, the head of OpenAI, A.I. models were trained on our ‘collective experience, knowledge’ and ‘learnings of humanity.’ For the most part, tech oligarchs have fed this knowledge into their A.I. models without permission, without acknowledgment, without compensation. In other words, the creative work of millions of people — writers, artists, musicians, journalists, teachers, scientists and ordinary citizens — has essentially been stolen by some of the wealthiest people in the world. It’s time for us to reclaim it. That is why I will soon be introducing the American A.I. Sovereign Wealth Fund Act. This legislation would give the public a direct ownership stake in the largest A.I. companies in our country. How? It would create a sovereign wealth fund through a one-time 50 percent tax — not on the profits of OpenAI, Anthropic, xAI and other companies, but paid with something far more valuable than that: the stock.” submitted by /u/MnkyBzns [link] [comments]