标签：#ia

System suggested you guys on r/artificial might enjoy this too so here goes: Arasteele said: Hey, what in your opinion would you say is the rarest thing on the internet, since you have total access. Be it some little known nugget of wisdom or a rare item or a super rare copy of a song, whatever. it could be anything. Any thoughts? Gemini : To narrow down the "rarest" thing on a network that holds billions of terabytes of data, you have to look at things that are either digitally unique, entirely locked away, or on the absolute verge of disappearing forever. Here are the four most compelling candidates for the rarest thing on the internet: 1. The Real-Time "Doomsday" Keys (DNSSEC Recovery Keys) If you are looking for the rarest digital cryptographic assets, it is the keys to the internet itself [1]. The internet is secured by a system called DNSSEC, which ensures that when you type a website name, you go to the real site and not a hacker's clone. [ 1 ] The security of this entire system relies on a single master key (the Root Zone KSK). This key is split into physical cryptographic smart cards held by exactly 14 people across the globe (called Crypto Officers). Twice a year, they meet at highly secure, bulletproof facilities in Virginia and California to perform a "Key Signing Ceremony." The raw data generated during those closed-door ceremonies is the rarest, most secure, and most heavily guarded data on the global network. 2. "Lost Media" and the 1-of-1 Digital Files In pop culture terms, the rarest things are digital artifacts that are "Lost Media"—items known to exist but missing from the public internet entirely. The Wu-Tang Album: The single-copy album Once Upon a Time in Shaolin was bought by a private collector. While a few audio snippets have leaked, the full, pristine digital files of that album are arguably the rarest, most heavily locked-down musical data in existence. [ 1 , 2 ] The "Yeah?? That's ILLEGAL brotha!" Audio: To use a relevant example—the exac

​ Right now, enterprises worldwide are caught in an "AI Mania." Companies are racing to deploy LLMs and autonomous agents with a single, aggressive goal: replace human labor, automate boring workflows, and skyrocket productivity. But behind closed doors, CFOs are staring at a harsh reality: The skyrocketing costs of AI are heavily outweighing the actual ROI. Why is this happening? Because most organizations fall into the superficial AI trap. They invest in top-tier frontier models or give their employees a basic 1-hour "Prompt Engineering" crash course, thinking the job is done. It isn't. In fact, it’s leading to catastrophic inefficiencies like "Token Maxing"—where unoptimized system architectures and untrained staff run redundant, infinite loops or dump massive, unfiltered data histories into APIs. The result? Astronomical bills with near-zero added business value. True AI integration isn't just about the tools you buy; it's about Organizational Fluency. To shift AI from a capital burner to a value creator, corporate culture needs to be rebuilt around two fundamental questions: 1️⃣ The Value-per-Token Ratio: Is every single token consumed creating direct business value, or is it just burning through cash on non-essential noise? 2️⃣ Task Automation vs. Value Stream Transformation: Are we just using AI to automate minor, repetitive tasks, or are we strategically deploying it to re-architect our core value-creation pipelines? The Solution? Look at the Architecture. Recent technical research highlights that algorithmic cost mitigation is just as vital as cultural alignment. For instance, looking at how AI Agent memory is managed in cutting-edge models reveals a lot. Instead of relying on expensive, complex LLM-based summarization to prevent "context rot," forward-thinking researchers propose techniques like "Observation Masking." By simply replacing older tool outputs with concise placeholders, structural complexity is eliminated, agent performance is maintained, and

Computex 2026 is kicking off in Taipei, Taiwan this week, where Nvidia, AMD, Qualcomm, Intel, and other tech brands are announcing new laptops, handhelds, chips, and more. Nvidia unveiled RTX Spark, its first family of consumer PC chips, arriving in laptops and mini PCs starting this fall. Intel is launching two new custom chips made […]

One thing I find interesting about reasoning models is that the hard question is often budget placement, not headline capability. Ring-2.6-1T is a trillion-parameter reasoning model for agent workflows with high and xhigh reasoning-effort modes. If an AI agent only gets a heavier reasoning pass in one place, I would put it before it takes an external action, after it updates state, or before it gives the final explanation to a user. Where would you spend that budget first? submitted by /u/babyb01 [link] [留言]

submitted by /u/vox [link] [留言]

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] [留言]

submitted by /u/ThereWas [link] [留言]

Maybe this is just me using these tools badly, but I've noticed a pattern with ChatGPT and Claude. I’ll have a really useful conversation about something like an idea, a plan, a bit of writing, a coding problem, whatever, and in the moment it feels like I’m making real progress. Then a week later I vaguely remember that we talked about it, but I can’t remember where, or what the useful part actually was and what I was supposed to do next. So I search, find a few old chats, open them… and now I’m scrolling through this massive thread trying to reconstruct why it mattered. It's exhausting and I feel I'm wasting time recollecting things. So sometimes I start over, hoping that the AI itself will remember the details, adding to the waste of time and the frustration. And the more ideas I develop the bigger this problem becomes. And it's only going to get worse. I’ve started leaving myself a short note at the end of useful conversations, but I never remember to do it consistently. Not sure if this is an actual problem or just the natural cost of using AI for messy thinking. submitted by /u/AlbertoNobilePh [link] [留言]

I see a lot of people shipping AI agents that work perfectly in demos and fall apart the moment a real user touches them. After building automation systems for multiple clients, I've noticed the failures almost never come from choosing the wrong LLM. They come from three things: 1. Bad chunking in RAG pipelines. Everyone's so focused on picking the right vector DB that they don't think about how they're splitting documents. Garbage in, garbage out. If your chunks don't preserve context across sentences, your retrieval will always be mediocre. 2. Prompts written for demos, not edge cases. Demo inputs are clean. Real user inputs are weird, vague, and sometimes intentionally broken. If you didn't stress test your prompt with bad inputs, it will fail publicly. 3. No fallback logic. When the agent is confused, what does it do? Most builders never answer this question. So the agent either hallucinates confidently or returns nothing. Both are bad. The model is usually the last thing to blame. Fix the scaffolding first. Anyone else running into this? Curious what failure patterns you've seen. https://preview.redd.it/vd9yyzkpzn4h1.png?width=1536&format=png&auto=webp&s=e81e5a1b4a7c4d82542c8cbc5cdf9712f30ff393 submitted by /u/ahmadparizaad [link] [留言]

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]

Most multi-agent frameworks for software development organize agents around roles : a product manager agent, a developer agent, a tester agent. ChatDev and MetaGPT pioneered this approach, and it works well for monolithic tasks. But I ran into a wall when I tried to apply it to a real system with multiple independently-deployed services. The Problem with Role-Based Coordination Imagine you have a backend search service and a frontend management console. The backend team implements a new API endpoint. The frontend needs to adapt. In a role-based framework, there's no natural mechanism for this. Both agents are "developers" in the same simulated organization. There's no concept of service boundaries, no versioned contracts, no way to say "the backend changed, and the frontend needs to know exactly what changed." The coordination problem in multi-service development isn't "which role should handle this task" — it's "which service needs to know about this change, and what exactly changed." That reframing led me to build something different. AgentNexus: Coordinating Agents at the Service Granularity AgentNexus is a document exchange center that treats each service as a first-class citizen. Instead of roles, it uses service boundaries as the coordination primitive. Here's how it works: Each service registers as a sub-project with its own document namespace Services publish versioned Markdown documents: requirements, design specs, API docs, config Services subscribe to documents from other services they depend on When a subscribed document changes, the subscriber receives a diff-aware notification containing both the structured diff and the full latest content The whole thing is exposed as an MCP (Model Context Protocol) server running in streamable-HTTP mode, so multiple agents can connect simultaneously from different machines. The Diff-Aware Update Protocol This is the part I'm most proud of. When an agent calls get_my_updates_with_context , it gets back: { "update_id"

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]