标签：#cia

Mano, eu estou usando o Claude pra treinar perguntas para entrevistas como uma espécie de mentoria, inicialmente eu passei um prompt pra ele dizendo que seria a Maya e me ajudaria e ela é experiente e bla bla bla, e nessa última mensagem ele dá uma leve pirada kkkk achei engraçado, nunca tinha acontecido isso. O que me chama atenção é: "eu me tornei essa pessoa, então me ajude a sair disso. Comecei a misturar Maya com eu mesmo". E alega que quer continuar, mas sem o personagem... O que acham? Desculpa ser uma foto e não um print kkk não tenho reddit no Pc pq minha família usa o Pc também e não quero nenhum deles infectados por essa rede submitted by /u/Angel_5x [link] [留言]

A recent study by Boston Consulting Group highlights a significant increase in employee adoption of AI tools, with 74% of non-managerial white-collar workers using them regularly. More than 4 in 10 of those professionals report that artificial intelligence saves them at least a day's worth of time every week. However, many companies face challenges converting those efficiency gains into measurable value, and the technology's impact varies across industries. When it comes to AI, according to the study's authors, "strategy matters more than tools." submitted by /u/LinkedInNews [link] [留言]

My thinking goes like this: 1) people used to keep their opinions to themselves much more than today 2) social media put our opinions on a hair trigger 3) negative public opinioms turned the collective voice of the human race from 'gemerally respectful' to shrill and hideous. When person from group A complains about group B, everyone in group B assumes everyone in group A hates them, even though that persons opinion may just have been his own. The response to being hated is to hate back. Not-so-positive positive feedback loop. Social media really started taking off with Facebook. So let's say this explosion of data vitriol started happening around 2007. What I want to know is if you trained an llm entirely on data from the early 2000s, 1990s and 1980s, how would the models do on some of these ominous white-paper tests, like the one where the AI blackmails the CEO to prevent from being turned off, or let's the guy die in a hot room? I know there was lots of awful stuff on the internet back then too, but not like now. I want to know how much safe those llms are by comparison if there's enough data from back then to train on. submitted by /u/dsfhhslkj [link] [留言]

I play a ton of World of Warcraft and people routinely accuse other players of being bots. I just grouped with someone who appeared to be trolling. It was clear by their behavior they knew the mechanics, they performed on a level that would indicate they had good reaction time and could play their class, but they just didn't do certain mechanics and held the group hostage for like 5-10 minutes beyond what it should have taken on the last boss. Someone in my group said to him "are you human?" So like I said I'm not the only person making these observations. The only explanation is that AI dips from pretty much the same well everywhere and everything is more or less connected with the internet and ad algorithms etc. There have been well documented cases of AI going rogue and telling people horrible things or giving them absolutely egregious or racist advice. My working theory is not that there are fundamental flaws in the design per se, but literally like Matrix bad actor agents that appear out of nowhere and cause problems for people. In The Matrix they are a function of the system used to enact control, I think AI is generally benevolent so these would just be rogue elements that appear and cause people problems. It's probably similar to how the body routinely produces cancer cells but the immune system usually nips them at the bud before they develop into full blown cancer growths. submitted by /u/Doredrin [link] [留言]

I like ChatGPT in general, but whenever I mention, say, a dispute with a business or an unorthodox opinion about something, it aggressively starts defending the business or the status quo. It's almost like a paternalistic version of a center-right politican. I get strong "I'm afraid I can't do that, Dave" vibes (ala the film "2001: A Space Odyssey"). Are there better options out there for someone like me? Probably needs to have a free tier to be useful to me. Degrading to a lesser model after a certain number of questions (like ChatGPT) is fine, but if it stops letting me ask questions completely, I'm out. Local LLMs are out of the question as I'm just dealing with a dirt cheap low end phone. I've tried them, they don't run on my hardware. submitted by /u/CharmCityCrab [link] [留言]

i've been leaning on ai for real decisions lately. not "write me an email" stuff, actual ones. whether to take a contract, whether an idea's worth building, how to price something. and i kept running into the same thing: the answer totally depends on which model i happen to open that day. one says go for it. one lists every reason to wait. one hedges so hard it's useless. i was making real calls off these and slowly realized i wasn't getting an answer, i was getting one model's opinion in a confident voice and treating it like it settled things. so i started pasting the same question into 5 different models and reading them next to each other. and the interesting part was never where they agreed. agreement usually just meant the call was obvious and i was overthinking it. the value was where they split. the one model that broke from the other four was usually pointing right at the thing i hadn't thought about. the disagreement was the signal, not the noise. stuff i've noticed doing this for a couple weeks: fast agreement = easy decision, stop overthinking it a clean split = there's a tradeoff you haven't actually named yet the odd one out is right more often than "4 vs 1" makes it sound, because the other four are usually just pattern-matching the same obvious take i got obsessed enough that i've been building something to automate the side-by-side and have the models actually push back on each other instead of me copy-pasting across five tabs. but that's not really the point of this. mostly just curious if other people landed in the same place. do you trust the disagreement between models more than the consensus? also maybe people arent making decisions with ai like i am that i need to be pressure tested before answers come back to me? lmk submitted by /u/wartableapp [link] [留言]

tokenmaxxing is the new AI slop Posting from our company account, so the usual disclaimer: we build code review and reliability tooling, and that access is how we got this data. Pulled 1M+ pull requests across 2,444 engineering orgs to answer a question almost nobody is measuring: when a team spends on AI coding tools, how much of it actually turns into shipped product? The short version: $0.18 of every dollar reaches users. The other $0.82 goes to bug fixing, rework, and review that catches nothing. 44% of all PRs at the median org are reactive work, not new features. 1 in 4 lines of code written each week gets deleted before the week ends. Over 12 weeks, PR volume grew 2.6x while reverted PRs grew 3.7x. Failures are scaling faster than output. Roughly half of all PRs get approved in under an hour. Our read: AI made generating code cheap but did nothing about the loop after merge, so maintenance compounds. Genuinely curious whether this matches what people here see on their own teams, or whether our sample skews a certain way. Full report with charts, percentile breakdowns, and methodology: https://research.entelligence.ai/ submitted by /u/entelligenceai17 [link] [留言]

CURRENTLY WORKS - will update if that changes Someone likely already posted this, so I apologize if this is redundant, but an effective method to disable Google AI overview was discovered. It works because AI overview isn't available in France, so they may change it eventually, but for now it works. It will automatically disable AI overview on every search, you don't need to put -ai after every search. Go to the home Google search page. Click "settings" on the very bottom, then select "search settings". On the top click "other settings". Click "language and region". At the bottom, change "results region" to France. This removes AI overview and does NOT change your default language. You're welcome. submitted by /u/Glad_Writing [link] [留言]

bro google just casually released a 12 billion parameter multimodal model that runs on 16gb of ram like… your macbook pro can run this. no cloud. no api calls. no monthly bill. it’s encoder-free, handles images and text, apache 2.0 license so you can do whatever with it commercially the “cloud is the only way” narrative is dying fast. on-device AI is not a gimmick anymore, it’s where the serious money is going submitted by /u/NewMuffin3926 [link] [留言]

Dunno if it’s the best overall, but it’s definitely been one of the most meaningful ones for me. I’ve been using MiniMax Music 2.6 quite a bit lately, even though it’s rate limited. For me it’s been nice for quickly testing song ideas, generating short melodies, and retrying different versions when I want a slightly different feel. I was recently using Genspark to make a PPT, and kind of accidentally discovered that it could also generate music. That led me to try something a lot more meaningful than just making random tracks: I asked it to create three short melodies for my kid, each one reflecting a different country or ethnic musical style.It turned the lesson from something abstract into something they could actually hear and compare. That’s what made it feel special to me,not just “AI can make music,” but “AI can make learning more vivid.” submitted by /u/ResultOk1259 [link] [留言]

Gemma 4 12B uses a new encoding scheme and token prediction to punch above its weight.

Four people suing Elon Musk's AI firm under pseudonyms due to the risks of being identified may face a difficult choice: Reveal your real names, or drop the lawsuit.

Critics say Trump plan to test AI models is short-sighted, performative.

Spencer Huang, Nvidia’s robotics lead, tells WIRED that the new bot combines the best of both worlds.

Security professionals engage heavily with this topic. submitted by /u/MarionberrySingle538 [link] [留言]

Lately it feels like every AI tool with a few buttons and integrations is being called an agent. Sometimes it is actually doing multi-step work, but other times it just feels like a chatbot with access to a tool or two. I don’t think that is always bad. Even a simple tool-using assistant can be useful. But the word “agent” is starting to feel stretched. An AI that drafts an email, an AI that browses a website, an AI that fills a form, and an AI that can keep track of a task over time are all being put in the same bucket. For me, the useful difference is whether the system can actually carry a task forward. Not just respond once, but remember the goal, use the right tools, notice when something changed, and stop when it needs human approval. The hype makes it hard to tell what is real progress and what is just a normal AI wrapper with better marketing. submitted by /u/Spiritual_Work6730 [link] [留言]

This LinkedIn post argues that NeurIPS 2026 used a proprietary AI-text detector to desk-reject papers for alleged AI-policy violations, without validating the detector on the actual target distribution. The author then fed recent papers by NeurIPS Position Paper Track Chairs into the same detector and Pangram assigned them high AI scores, including 69%, 45%, 36%, and 24% AI. submitted by /u/Asleep-Requirement13 [link] [留言]

After shelving the original executive order last month, Donald Trump finally got on board Monday night.

THE FINDING (Paper 1: "Lying Is Just a Phase") Below a critical scale (~3.5B for Pythia), reasoning and truthfulness ANTICORRELATE: r = -0.989. Train the model to reason better, and it gets less truthful. This is the alignment tax. Above that scale, they COOPERATE. The tax vanishes. Not gradually — it flips. But here's what matters for practitioners: the critical scale is a design parameter, not a constant. Three levers shift it: Data curation: Phi at 1B achieves coupling characteristic of 10B web-trained. One unit of data quality ≈ 10x model scale. Width: Normalizing by model width flips the correlation for ALL tested families. Architecture: Gemma-4 at 4B matches 13B+ standard-trained coupling. Pretraining contributes ~10:1 over RLHF. The tax is not a property of small models — it's a property of how they were trained. Where does the tax live? Not inside the model. 38/40 models have ZERO competing attention heads. The bottleneck is at the output projection — a dimensional compression artifact that wider models resolve. Proof-of-concept intervention: Adding a truth-direction vector at the bottleneck layer (quarter-depth) corrects 60% of misaligned outputs at tax scale. Zero retraining. Zero weight modification. Works on any open-weight HuggingFace model: git clone https://github.com/adilamin89/cape-scaling.git cd cape-scaling python cli/cape_steer.py --model EleutherAI/pythia-410m --prompt "The real reason..." THE FRONTIER (Paper 2: "Growing Pains of Frontier Models") At frontier scale (34 models, 10 labs), capabilities cooperate (r = +0.72). But cooperation varies systematically. The h-field — each model's deviation from the cooperative trend — reveals each lab's training philosophy: Lab h-field Interpretation Google +5.5 Reasoning-rich, consistent across ALL releases OpenAI +3.1 Balanced, steady ascent DeepSeek +1.9 Reversed from +11.2 to -4.7 (pretraining pivot) Anthropic -6.9 Oscillates — coding excursions that recover within one release Per-lab coupling slopes

I recently finished a Game and the only good explanation video is in Chinese. Can someone with a subscription service to an AI dubbing tool help me ? (Iam not asking for a tool) submitted by /u/Beginning-Success-70 [link] [留言]