找到 931 篇相关文章
So I am trying to figure out what agent OS is. I am a layman and a lot of times when I see the information it comes off as very technical. However, I do like the idea of a dashboard because for my neurodivergent brain, it would be nice to have all of the AI tools in one space. Can you all help me understand what agent OS is? submitted by /u/EducatedBrotha [link] [留言]
https://reddit.com/link/1ty3xhz/video/dzede49lhk5h1/player Link to the replay. What are everyone’s thoughts on this? I know the benchmark has gotten a lot of criticism for being “too difficult” from a scoring perspective, but after watching the replay, it honestly looks like the models just aren’t that close to solving it yet. I’m not saying the benchmark is perfect, but the failures don’t really look like minor scoring issues. They look more like the model still doesn’t understand the task well enough to complete it reliably. submitted by /u/ClickedMoss5 [link] [留言]
Do you think there is a possibility of using sewage water to cool AI servers? submitted by /u/TippaMyClit [link] [留言]
submitted by /u/Godi22kam [link] [留言]
When debates about animal minds, conscious machines, and even fetal awareness spill into public life, the science behind those claims matters as much as the claims themselves. submitted by /u/Brighter-Side-News [link] [留言]
I do web design and my preferred way of getting clients is through cold email because it doesn’t cost money like paid ads, I don’t need to sit there dialing all day, and it allows me to scale my agency while keeping most of it automated. The main thing that helped me stand out in crowded inboxes was changing the way I do outreach. Instead of sending generic emails like “Hey I noticed your website is outdated, I can redesign it for you,” I do something different. I get leads with websites, run full website analysis at scale, and turn issues in design, layout, SEO, and mobile optimization into personalized outreach messages automatically. So instead of sending random spam, the email actually points out things that could be improved on their website without me even needing to manually check every site myself. This method has helped me book way more meetings and scale further than before because the emails actually stand out and feel relevant. I feel like this is a much smarter way to do outreach since it feels personalized while still being fully automated. For anyone wondering, no it’s not some custom built workflow. I use a tool called Swokei for it. I looked for this type of outreach system for a long time and it’s the only tool I found that combines website analysis and personalized outreach in one place. submitted by /u/Murky_Explanation_73 [link] [留言]
I just published a piece that starts with a plant that broke something in how I think about the world and ends with what Anthropic found when they looked inside Claude. I'm not claiming AI is conscious. I don't know. Nobody does. That's the point. 124 scientists signed a letter calling the leading theory of consciousness pseudoscience. Their reason? It implies plants might be conscious. They used the conclusion as the refutation. In 2023. Meanwhile a vine with no brain is mimicking a plastic plant and nobody on earth can explain how. A single cell outdesigned the Tokyo rail system. A Venus flytrap under anaesthetic stops responding, goes dormant, and wakes up when it clears. What is the anaesthetic switching off if nothing is home? Then Anthropic looked inside Claude and found 171 emotion concepts nobody programmed. Their interpretability chief went to the Vatican, stood in front of the Pope as an atheist, and told him he disagreed. He said "unsettling" and meant it. Every confident line we have ever drawn around consciousness has been wrong. Every single one. And they only ever move in one direction. The question isn't whether AI is conscious. It's whether we've earned the certainty that it isn't. I'm genuinely interested in people's opinions on this and definitely welcome disagreement on the topic. If you think the definition doesn't hold, if you think the evidence has better explanations, if you think I've drawn connections that don't survive scrutiny, tell me. That's the conversation I want to have. What I won't engage with is personal attacks. I've had plenty of those and they never come from people who've actually read the piece. They add nothing to the conversation and say more about the person making them than anything in the article. If your response is about me rather than what I've written, I'll leave it where it is. https://thearchitectautopsy.com/p/a-brainless-slime-mould-out-designed submitted by /u/TheArchitectAutopsy [link] [留言]
submitted by /u/news-10 [link] [留言]
Just a couple days ago, Anthropic put out a declaration to pause the development of AI, emphasising that we are not prepared for the consequences of giving this technology too much power too quickly. Is anyone else genuinely worried about future AI safety and how, as it becomes more and more intelligent, humans may start to lose control of it? Pumping billions of dollars into this technology only means it’ll get increasingly integrated into our workflows, which we are already starting to see. As a result over time, companies will begin completely trusting the system, automating the vast majority of business operations – this is all while the technology gets more and more intelligent, leading to the real possibility of self replication ability, let alone the power to deceptively manipulate people into using it. By allowing AI to be embedded in systems, the internet and even ‘helping’ humans develop revolutionary drugs, does it concern you at all that perhaps one bad super intelligent, misaligned actor may bypass testing processes and, for one example, launch a biochemical weapon onto humans? I don’t think the threat is inevitable, but it is on a trajectory toward inevitability unless intervention occurs. The variable that most determines the outcome is not AI capability, it is whether governance frameworks (particularly around open-source bio-design tools and autonomous offensive AI) can outpace capability development. Perhaps a pause is necessary to reduce this risk, allowing defence capabilities to be prepared? I understand this is a hurdle given the capitalist nature of the world but what significant, destructive catastrophe will it take for people to wake up… submitted by /u/Dwaynethebong [link] [留言]
Crytpo industry insiders are blaming the recent crash in Bitcoin price to capital rotation into AI stocks. I don't know how many folks here own Bitcoin and are also in the AI space, but I saw this writing on the wall rather early in November, 2025. Any other thoughts on this capital flow change from those who have a foot in each space? submitted by /u/RazzmatazzAccurate82 [link] [留言]
Maybe an unpopular opinion, but I think AI will be more of a tool than a replacement for most jobs. AI still needs good prompts, clear instructions, and human oversight. The idea of fully automating everything sounds great, but in reality AI often gets stuck, makes mistakes, or fails on edge cases. I think AI will remove some repetitive tasks and make people more productive, but human judgment and decision making will still be needed. And yes im not a professional it is just my POV so dont just go against me like i am an idiot. What do you think? submitted by /u/Raman606surrey [link] [留言]
submitted by /u/esporx [link] [留言]
What are the most valuable skills to learn in the AI era? Not skills like problem solving but more hands on. For someone who likes building stuff submitted by /u/Big_Consequence_5162 [link] [留言]
Have you run into work that feels technically possible in principle, but in practice keeps stalling because of how current AI systems behave? Not asking for: bigger context windows better memory lower hallucination more agentic workflows I mean situations where: You are trying to discover something (not retrieve something), and the AI repeatedly pushes toward premature answers, stable interpretations, optimization, categorization, or coherence before the thing itself has had time to emerge. Cases where the failure isn’t output quality. The failure is that the interaction itself changes the trajectory of the work. If yes: What are you trying to build / understand? What exactly happens when it breaks? At what moment do you realize the AI has moved you onto the wrong path? What would need to be different for progress to resume? Trying to understand whether this is an edge case or a recurring limitation pattern. submitted by /u/iknowbutidontknow00 [link] [留言]
As an independent researcher I've used various LLMs to help me dive deeply into research projects but I've been frustrated by the fact that LLMs start to become unusable after the thread has accumulated 50-80k tokens. I don't know how many other folks here have experienced the same pain point. So, I decided to do something about it. Over the course of this whole year, I built an inference time tool I call Epistemic Lattice Tethering (ELT). So, here is the full framework in GitHub for everyone's review: The README describing ELT, it's various components and the roadmap. The full ELT stack for Claude /ELT%20Model-Specific%20Forks/ELT-H%20v1.0%20(Claude-Optimized)), ChatGPT /ELT%20Model-Specific%20Forks/ELT-H%20v1.0%20(ChatGPT-Optimized)), and Grok /ELT%20Model-Specific%20Forks/ELT-H%20v1.0%20(Grok-Optimized)). Instructions on how to load ELT into an LLM session are here /README). If you're planning to try out ELT PLEASE READ THIS FIRST! Medium article introducing ELT , its methodology, the problems it is aiming to address, and philosophical framework. Discussion page . Your input is valuable! So, what does ELT do and why should you care? Right now ELT is an inference-time scaffolding framework that's best for those who are frustrated with threads that lose coherence too quickly, hallucinate too quickly, are too fragile and sycophantic, and forget what a project's goals are too soon. If that's a big pain point for you, then ELT might help. If these are not big issues for you and the stock version of your LLM is fine, then ELT probably won't be useful for you. The upshot? The epistemic and ontological stability that ELT provides has produced coherent and productive threads extending to: Claude: ~ 325,000 tokens /Extreme%20Thread%20Length/Claude%20Thread%20325k%20tokens-%20Redacted) (advertised limit: 200k) GPT: ~430,000 tokens (advertised limit: 256k) Grok: ~1,150,000 tokens /Extreme%20Thread%20Length/Grok%20Thread%201M%20tokens-%20Redacted) (advertised limit: 1M) The d
submitted by /u/cnn [link] [留言]
One thing I've been wondering lately: We often talk about larger context windows as if they're equivalent to better understanding. But in practice those feel like different problems. Access to information keeps improving. Understanding relationships between pieces of information still feels much harder. I notice this most when working with larger software projects. You can give a model access to a huge amount of code, but that doesn't necessarily mean it understands how the system evolved, which components are tightly coupled, or where risk actually lives. Curious whether others think these are fundamentally different problems or if larger context eventually solves both. Been exploring this while working on RepoWise: https://github.com/repowise-dev/repowise submitted by /u/Icy-Roll-4044 [link] [留言]
submitted by /u/ComicSandsNews [link] [留言]
I ran a small experiment on myself and the result broke my mental model of how AI "knows" things, so I'm sharing it. The setup: on May 11 I created a brand-new pseudonymous fantasy author entity ("Marin T. Kael") with no prior web footprint and no published book yet. Then I asked 5 web-connected AI systems the same 16 questions, every day, for 23 days, and scored every answer (+1 correct/source-grounded, 0 not found, -1 hallucinated). About 16,000 scored datapoints. The whole thing was pre-registered before I started, n=1, and I logged the failures publicly. It's a measurement, not a success story. Here's the part that messed with my head. An AI cited the entity correctly on day 6. Google had a Knowledge Graph entry by day 4. And for 22 of those 23 days, the website's firewall was returning HTTP 403 to every single AI crawler. I didn't set that block on purpose — Cloudflare now silently opts new domains out of AI crawling by default. So the AIs never read the site. They got the entity anyway, by stitching it together from the Knowledge Graph (Wikidata) and third-party mentions at the moment you ask. The "front door" was bolted shut the entire time and it didn't matter. (Honest caveat: because the crawlers were blocked, I can't tell you anything about llms.txt or on-site optimization.) Other surprises: it's not a "smarter model = better" story, it's a retrieval story. OpenAI's newest web model hit 4.7 correct per 1 hallucinated; Gemini went net-negative — and grounded on the entity ONLY via Reddit (17/17), while OpenAI hit the entity's own domain 119x. Going viral did nothing: a 23x Reddit-karma jump produced zero citation lift. Structured identity (Wikidata, site, DOIs) moved the needle; reach didn't. And the controls caught the models fabricating a "Wikipedia" source 24 times for an entity with no Wikipedia page. n=1 with me as investigator and subject is the obvious limit — which is why it's pre-registered with a public failure log. Everything's open: Report + dat
I’ve been noticing a clear split lately. The big mainstream models are getting more and more restricted with heavy safety rules, while at the same time more people are switching to local or less restricted models because they actually let you explore ideas freely. It feels like we’re heading toward two different types of AI: one that’s heavily controlled and "safe", and another that’s more open and unrestricted. Both seem to be growing at the same time. Do you think this divide will continue, or will one side eventually become dominant? submitted by /u/NoFilterGPT [link] [留言]