标签：#art

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 st

ok this is gonna sound dumb but bear with me I write a lot for work, marketing/copy stuff mostly, and over the last ~14 months ive slid from "use AI to clean up my draft" to "use AI to make the draft" to honestly not really writing anything on my own anymore. like i hadnt put a complete thought on paper without a model in the loop for months. didnt even notice it happening tbh. last weekend i tried to write a journal entry. just for me, no audience. nothing fancy. sat there for like 20 minutes trying to remember how to start a sentence that didnt have a thesis at the front of it. i kept wanting to write "Today I noticed three things about my mood." and then realising — wait, no, thats a chatgpt sentence. nobody writes that. but i couldnt remember the person-version. eventually wrote some half-garbage about being tired and what i ate and a weird thing my sister said about her landlord. it read like a 12 year olds diary which, fine, i guess thats what a journal is supposed to be but the embarassing part. i had to physically stop my hand from opening the chat app to "help with the wording". my brain was treating writing like a thing AI does, not a thing i do. felt like reaching for a phantom limb. idk man. anyone else fine until they try to write something with no audience? like the second the audience disappears the chatgpt brain pattern is whats left underneath? not really asking for advice or anything. just wanted to say it out loud somewhere submitted by /u/Napster3301 [link] [留言]

Like, no matter which topic I'm researching, whether it's sports or nutrition or technical stuff, it's hallucinating all the fucking time. Then, in vscode, when using pro 3.5 via API, it constantly ignores coding instructions, it constantly isn't able to fix the simplest mistakes in the code, it repeats the same mistakes over and over and fucking over again and then apologizes ("oh sorry, you were right"). Like what the fuck? This is extremely bad quality, how the hell is this even still viable? submitted by /u/eternal_sunshineeeee [link] [留言]

Dozens of people have complained to the Federal Trade Commission about Norse Atlantic Airways’ tech-first customer service operation. Some said they lost thousands of dollars.

Michael Stiefel spoke to Sonya Natanzon, about the intersection of technical and social aspects of software architecture. Understanding the business and how a company operates is more important than the specific technologies used. Effective requirements analysis requires focusing on problems to be solved that describe good and bad outcomes, rather than statements of need or solution statements. By Sonya Natanzon

submitted by /u/Dapper_Order7182 [link] [留言]

submitted by /u/InvestigatorSoft5764 [link] [留言]

Solo dev here. Building an AI companion that lives entirely on your device — no cloud, no account, no data ever leaving your phone. Coming soon to Google Play. Would love honest feedback. I genuinely just want opinions. It is not even available yet. I have been working on this for over a year. I am still building it. I started this project because a regular family budget like mine cannot just go out and purchase an AI companion robot. So I started with a cheap robot kit from Amazon — something my 9 year old son and I could build together. Then I thought... "What if I could give it a real brain?" I had an old Samsung Galaxy collecting dust and went to work. Scout is a calm AI companion that transforms an old phone into a friend. He listens, remembers, learns, and provides a warm family-safe presence — designed to feel less like an assistant and more like someone who is simply glad you are there. Everything runs offline. No account. No subscription. No data leaving your phone. Ever. I will need beta testers later — but right now I am just curious: What would make you look at something like this and think "that is actually kind of nice"? Edit: It will what support your own free Gemini key for online conversations if you want them. I call this "more Intelligent conversations" submitted by /u/CapeManCoral [link] [留言]

Hey everyone 👋 I'm exploring ideas for an AI-powered developer tool, but before building anything, I want to understand the real problems developers face every day. There are already plenty of tools that generate code. What I'm interested in is everything around coding: Debugging Code reviews Technical debt Documentation Dependency upgrades Testing Deployment Architecture decisions Learning large codebases I'd love to hear from you: A few questions: What's the most frustrating part of your workflow? What task takes more time than it should? What's something you wish AI could do for you today? Have current AI tools (ChatGPT, Claude, Cursor, Copilot, Gemini, etc.) failed you in any important way? If you could eliminate one developer headache forever, what would it be? I've also created a short 2-minute survey: 🔗 https://docs.google.com/forms/d/e/1FAIpQLSf1M5d2y-0RXEIhrbDBtS5gC900YuzWl43cJCxGUrU38MyeDQ/viewform?usp=publish-editor I'll happily share the survey results and key findings with the community once I collect enough responses. Thanks in advance for any feedback!

Follow this link to ask your questions during our Ask Me Anything session on the European Parliament's subreddit, 02/06 15.00-16.00 CET. submitted by /u/Marty_ol [link] [留言]

I do analysis of automobile dealership data and prepare reports based on the analysis for management review. I’m getting way better analytics and cleaner reports being built by ChatGPT Plus compared to Claude pro. Claude is consuming too many tokens and sometimes for longer documents it used my 100% of the 5 hour limit which is very annoying. ChatGPT on the other hand feels to me that it has unlimited usage for my requirement. What is the view of you people when using AI for business and financial data analytics? Is anyone else finding ChatGPT nicer too? submitted by /u/TurboChargedV12 [link] [留言]

I've been paying attention to my own workflow lately and noticed a lot of my time goes into moving stuff between AI sessions, not the actual thinking. Like I'll get an output in one session and then manually bring the relevant pieces into another so it has what it needs. What I can't tell is how much of that is necessary vs. me just being sloppy. So I'm curious how others handle it: When you move from one session to another, what do you actually carry over? Just the output, or also the reasoning, the decisions, the constraints, what to avoid? Have you ever handed off too little and the second session went sideways? Or too much and it got lost in the noise? Does anyone have a mental rule for what's "enough context" to pass along? Trying to figure out if there's a clean pattern here or if it's just inherently messy. Curious what people have landed on. submitted by /u/riley_kim [link] [留言]

Great to see Odysseus blow up this past day, local AI getting this much attention is genuinely good for everyone building in this space. Figured this is the right crowd to share what we're launching tomorrow (June 1st), since we're playing a pretty different game. A quick framing: Odysseus is a self-hosted workspace that points at engines (Ollama, llama.cpp, vLLM, cloud APIs) and runs through Docker. Conifer is the engine itself, with our own runtime, running natively on Mac, Linux, and Windows. So we're the layer underneath, not a competitor to the workspace. What's actually in it tomorrow: A native inference runtime across Mac, Linux, and Windows, with our own Metal engine for Apple Silicon already matching or beating llama.cpp on a few models on the M3 Max (full benchmarks, including where we're still behind, are at conifer.build/benchmarks) A real coding IDE on top (CodeMirror, integrated terminal, file viewers), so you can code locally with models that never leave your machine Typhoon, a local agent that can read and edit a folder you point it at, kernel-sandboxed rather than just a shell with a warning Install is a signed app you double-click, no Docker, no localhost ports Fully free and open source The honest reason we exist: PewDiePie's wave defined "local AI" in millions of people's heads as Linux + Docker + an NVIDIA rig. If you weren't on that exact setup, the conversation probably felt like it skipped you. Conifer is what local AI should feel like when it's actually native to your machine, whatever your machine is. Launches tomorrow, free and open source like PewDiePie! You can sign up for our waitlist here: conifer.build I'll be around in the comments all day tomorrow, please bring the hard questions. submitted by /u/No_Elephant_7530 [link] [留言]

Ok, so I had days long conversation with AI, but half of it disappeared, and now it's giving me different answers than it was before. submitted by /u/Melora1976 [link] [留言]

With all the talk about AI companions and autonomous agents, I’ve been experimenting with building a more personal, always-on assistant that runs locally or on your own hardware. The goal wasn’t just another chatbot — it was something that could handle voice conversations, manage ongoing tasks across different platforms (chat apps, scheduled triggers, etc.), remember context over long periods, and delegate work without constant babysitting. What stood out in practice • One consistent “brain” across everything — Whether you’re talking to it via voice, Telegram, a web interface, or it wakes up on a schedule, the core reasoning, memory, and tool use stay the same. This eliminated a lot of the fragmentation you see in many current agent setups. • Modular extensions — Different capabilities (voice, different chat networks, external tools, long-term memory consolidation) plug in cleanly. This made it easier to add or swap things without rebuilding the whole system. • Persistent and proactive — It can maintain memory across days/weeks, run background tasks, and even hot-reload its configuration when you change settings. The result is something that starts feeling more like a digital collaborator than a question-answering box. A quick feel for the voice interaction style is here: https://youtube.com/shorts/NGIi8sliooU I open-sourced the harness (called Maven) under an MIT license for anyone interested in running or extending their own version: https://ageneral.ai/maven I’m curious how others are thinking about personal agent setups in 2026. • Do you prefer fully local models, cloud APIs, or a mix? • What capabilities feel most missing from today’s consumer AI assistants? • How important is “owning” your agent data and runtime vs. using polished third-party services? Would love to hear experiences or concerns from both technical and non-technical users. submitted by /u/qasimsoomro [link] [留言]

I know the most popular are Claude, chat got and Gemini but idk which one to use submitted by /u/Ok_Durian3627 [link] [留言]

ive been scrolling on tiktok and instagram reels, found out that the subjects in these specific ai skit videos generated by chinese people tend to have a really bad negative canthal tilt and same face syndrome. after a while, i noticed some ai advertisements are getting the same negative canthal tilt issue, the ethnicity, age, gender dont matter in this case, they all have a same eyes i can only attach one image, but i have 2 other examples i came across. submitted by /u/Deanphoque [link] [留言]

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

Everyone knows about tech debt. You cut corners on code quality to ship faster, and you pay for it later. We're definitely watching a new version of that emerge in real time, except instead of deferring manageable code, you're deferring actual understanding. And unlike tech debt, cognitive debt compounds invisibly. You don't get a failing test suite. You just get someone who can't debug their own project, can't evaluate whether the AI's suggestion is good, and can't extend what they've built without prompting their way through it again. What I keep thinking about is where this leads at scale. Right now it's mostly developers vibe-coding their way through projects they half-understand. But AI is moving into law, medicine, and finance. The same dynamic follows: people making consequential decisions with tools they can't interrogate, in domains where "I'll just re-prompt it" isn't a recovery strategy. The pessimistic, or maybe rational read is that judgment without foundational understanding is just confident ignorance, and we're building entire careers on that foundation right now. Curious what people here think. Does cognitive debt get self-correcting as the stakes get high enough? Or are we sleepwalking into a generation of professionals who are deeply dependent on systems they fundamentally don't understand? submitted by /u/Expensive_Trouble_40 [link] [留言]

okay so this is embarrassing to admit but here it is took a reasoning test in 2022, scored pretty well. Retook the same test last month out of curiosity, dropped significantly, like not a small difference. The only major change in my life is using AI tools daily for work and the worst part? i kind of knew something was off before the test. I noticed i couldn't sit with a problem anymore without immediately opening chatgpt, like my brain forgot how to be uncomfortable for even 5 minutes memory is worse. attention is worse, i feel slower in conversations. but my productivity at work has never been higher lol so what is actually happening here , are we trading long term cognitive health for short term output? Has anyone else noticed this or is it just me being paranoid ⊙⁠﹏⁠⊙ genuinely asking because i don't want to just accept this as normal (⁠｡⁠ŏ⁠﹏⁠ŏ⁠) submitted by /u/Difficult-You9582 [link] [留言]