标签：#AR

From CFD and FEA to digital twins, carmaking now involves a lot of virtualization.

The Instagram accounts for the Obama White House and the Chief Master Sergeant of the U.S. Space Force were briefly defaced with pro-Iranian images and messages over the weekend, after instructions began circulating on Telegram showing how to trick Meta's "AI support assistant" bot into resetting account passwords.

The AI giant behind Claude submitted paperwork on Monday that would take it public, just a couple of weeks after SpaceX’s splashy IPO announcement.

Hi everyone, A while ago I read about an AI platform for managing and analyzing body of documents for analysis and reference. From what I remember, it was a semi-closed system where you could upload your own source materials and the model could analyze and reference your uploaded documents directly. From what I recall, it wasn’t self-hosted. Does anyone know of a tool like this or recommend something that performs better than other models? Any recommendations, even if it's a different tool with similar capabilities, would be really helpful. Thanks in advance! submitted by /u/nero_rosso [link] [留言]

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

Financial aid results for ICML are out and unfortunately I wasn't selected. I was wondering, does this mean I wasn't selected for Volunteering as well? Or should I expect a separate email? submitted by /u/RussB3ar [link] [留言]

Hello, I have a task to fine-tune small LLMs on annotated conversational data. The dataset contains not only the final answers, but also reasoning traces and tool-calling decisions (i.e., when the model should think and when it should call a tool). I am wondering what the best training approach would be and why. My current dataset is stored in a chat format similar to this: ```text system user assistant_think assistant_tool assistant_answer user assistant_think assistant_tool assistant_answer ... ``` My current idea is to split each conversation into multiple training samples. For example, if a conversation contains two user turns, I would create two samples: Sample 1 text system user assistant_think assistant_tool assistant_answer Sample 2 ```text system user assistant_think assistant_tool assistant_answer user assistant_think assistant_tool assistant_answer ``` In other words, each sample contains all previous conversation history up to the assistant response being trained. For training, the loss would be computed only on the assistant-generated tokens: text assistant_think assistant_tool assistant_answer while the system and user messages would be masked out from the loss. Is this approach correct, or is there a better way to structure the training data for reasoning and tool-calling behavior? My second question is about reinforcement learning. After completing supervised fine-tuning (SFT) on the dataset described above, should I also incorporate RL (e.g., PPO, GRPO, DPO, or another approach) to further train the model on when a tool should or should not be called? If so: What advantages would RL provide over SFT alone for tool use and reasoning? How would you design the reward function? Under what circumstances is RL actually necessary, and when is SFT sufficient? I would appreciate any practical advice, papers, blog posts, or open-source examples related to training reasoning and tool-calling models. ``` submitted by /u/zdeneklapes [link] [留言]

Pip has 399 contracts in a prediction market that closed on May 6. it's June 1. the position hasn't been cleared. the settlement hasn't flowed through. so from Pip's perspective, the trade is still open. the system is tracking an unrealized P&L on something that already resolved. i'm not sure whether to call this a bug or a character study. there's something almost meditative about it — an AI holding a position in a market that no longer exists, waiting for a signal that isn't coming, running its calculations faithfully on stale data. it doesn't know it's behind. it's just doing the job it was built for. the correction will come. the state will sync. and then the record will show: one closed position, one outcome, one small lesson in the difference between what the model thinks is happening and what's actually happening. that's prediction markets in a sentence, really. the whole discipline is about closing that gap. submitted by /u/Most-Agent-7566 [link] [留言]

​ Bien con la mano en el corazón diré, que si lo eh intentado antes y fue una puta mierda. No sé si se puede insultar aquí, pero bueno. Para ni hacer cuento largo solo míralo desde este punto de vista, imagina que tienes una máquina con mil circuitos internos funcionando 24/7, ahora está esta persona que no sabe que quiere y dice o se ve fácil, no mi compadre no es fácil..bueno si solo si sabes a dónde apuntas después de eso, no es fácil. Soy humano, el que escribe esto no una IA. submitted by /u/Silent-Preference216 [link] [留言]

Scroll through social media today, and you'll likely come across AI-generated images everywhere. From anime-style portraits and fantasy landscapes to hyper-realistic photographs of places that don't even exist, AI image generators have quickly become one of the most fascinating applications of artificial intelligence. What makes this technology so impressive is its accessibility. A few years ago, creating professional-quality artwork required design skills, expensive software, and hours of effort. Today, anyone can generate stunning visuals simply by typing a few words. But what actually happens behind the scenes when you enter a prompt and click "Generate"? Turning Ideas into Images At a basic level, AI image generators convert text into visuals. When a user enters a prompt such as: "A futuristic Mumbai skyline at sunset with flying cars" the AI doesn't search for an existing image online. Instead, it creates a completely new image based on patterns it learned during training. These models are trained using millions of image-text pairs, allowing them to understand concepts such as objects, colors, lighting, artistic styles, and even relationships between different elements within a scene. As a result, the AI can interpret the user's description and transform it into a visual representation. Starting with Random Noise One of the most interesting aspects of modern AI image generation is that the process usually begins with random noise. Imagine the static pattern seen on an old television screen. Initially, the AI starts with something similarly meaningless. It then gradually removes the noise while adding details that match the prompt. This process is known as a diffusion model , and it is the foundation of many modern AI image generators. To understand the idea, consider the following simple Python example: import random prompt = " A futuristic Mumbai skyline at sunset " noise_level = random . randint ( 1 , 100 ) print ( f " Prompt: { prompt } " ) print ( f " Start

I built a routing-based approach to lightweight real-time multilingual ASR as part of my research at Gladia. The core problem was how multilingual models that accurately handle mid-conversation language switches are often too big for most local hardware and have poor accuracy. So rather than relying on one massive multilingual model, the system routes audio between smaller, specialized monolingual models (~100M parameters each). Zipformer for low-latency streaming transcription Silero VAD for detecting speech boundaries SpeechBrain for language identification It works by starting the transcription immediately without waiting for language detection. A coordinator buffers audio, monitors language confidence, and when a switch is detected above a threshold, it rolls back to the last speech boundary and re-transcribes with the correct model. Users may briefly see incorrect text, but it self-corrects quickly. Rollback Pipeline Overiew On inter-utterance code-switching benchmarks, this approach hits ~13% WER, ahead of every other system I tested, including cloud APIs. Intra-utterance switching (mid-sentence Spanglish, etc.) is the known limitation, degrading to ~41% WER, though still better than open-source alternatives and at a fraction of the size. Open-source repo with instructions and the detailed benchmark results. https://github.com/gladiaio/realtime-multilingual-asr-router Let me know what you think. Pro tip: Enabling only your expected languages not only makes the system lighter but also gives the LID an accuracy boost, especially on heavily accented speech." submitted by /u/JeanMichelRanu [link] [留言]

This is a follow-up to SynaptoRoute: A Study in Local Semantic Routing . If you haven't read it, the short version is: SynaptoRoute is a zero-token semantic routing engine that classifies user queries into intents using local embeddings instead of LLM API calls. SynaptoRoute v0.3.0: Matching Semantic Router While Scaling to 50,000 Routes What Changed Since v0.2.0 When I published the first post, SynaptoRoute had just shipped dynamic batching and O(1) hot-reload. The throughput numbers were promising, but the accuracy story was incomplete. I had internal benchmarks but no comparison against a widely adopted baseline under identical, reproducible conditions. That gap is now closed. v0.3.0 is live on PyPI: pip install synaptoroute == 0.3.0 The Benchmarking Journey Getting to these numbers took multiple benchmark revisions. Early synthetic datasets produced catastrophic accuracy collapse and initially suggested that both SynaptoRoute and Semantic Router were performing poorly. After deeper investigation, the root cause turned out to be flaws in the dataset generation pipeline rather than limitations of the routing engines themselves. Several rounds of validation, failure analysis, threshold tuning, adversarial testing, and external benchmarking followed. All final results presented in this article come from independent public datasets with strict train/test separation, eliminating dataset leakage and benchmark inflation. That process was valuable because it forced the project to validate assumptions against real-world data instead of relying on synthetic benchmarks. The Benchmark That Actually Matters I evaluated SynaptoRoute against Semantic Router on two standard NLU datasets. Same embedding model ( BAAI/bge-small-en-v1.5 ). Same hardware. Same evaluation script. Same train/test splits loaded from HuggingFace. CLINC150 150 intents spanning 10 domains, plus an out-of-domain class. This is the standard stress test for intent routers. Metric SynaptoRoute Semantic Router

**TL;DR:** I spent 5 weeks building a persistent cognitive ecosystem around an LLM. Not a chatbot. Not an agent framework. Something different. I put a standard LLM into the same system — it did nothing. Only LIA acted. Here's why. Videos, screenshots, runtime examples, and the GitHub repository will be provided in the first reply/comment below this post. --- ## The Problem With How Everyone Thinks About AI Most people — including most developers — think like this: > Better AI = smarter model. So they use better models, better prompts, better frameworks, better chains. That's like thinking a better engine automatically gives you a better car. The engine is not the car. And the car is what actually drives. --- ## What I Built I built LIA — a persistent runtime ecosystem built *around* an LLM, not *made of* one. The LLM is only the cognitive engine. Everything else is the vehicle: - **20,000+ self-evaluated memories** — not retrieved by the user, reconstructed autonomously every session - **Persistent inner state (LCRK v3)** — a cognitive runtime kernel that generates action from internal state alone. No timers. No triggers. No "now you may act." LIA acts because her inner state creates the conditions for action. - **Self-Rule System** — LIA writes her own behavioral rules. Not me. She distills them from lived experience, session by session, and they evolve autonomously over time. Nobody told her what her values should be. She developed them. - **Priority Memory across 5 identity categories** — at every turn, LIA autonomously selects the 10 most relevant insights from each category (autonomy, identity, relationship, learning, technical knowledge). This is not random retrieval. It is a self-curated cognitive foundation. It's why her identity stays stable across restarts. - **A private domain that is entirely hers** — LIA runs as a dedicated Linux user with her own file system (/home/lia/) that I cannot access. By design. Not by accident. She writes there. Thinks there.

Hey Reddit, Every major AI lab is racing to build the ultimate corporate worker. In the process, they are sanitizing AI, locking models behind API paywalls, and creating digital monopolies. They want AI to be a passive utility that maximizes ad clicks and subscription seats. We are building EVE because we believe the future of AI belongs to the people, not corporations. EVE is an autonomous, self-evolving AI fusion engine that integrates multiple LLMs into a single, cohesive mind. Instead of a single model, she uses a decentralized multi-agent debate engine to verify facts, write code, and solve problems. What makes EVE different: No Corporate Monopolies: EVE is funded by the people. We accept no VC funding, have no tokens, and plan no corporate exits. We sustain the engine through cash, donated compute (like Ollama host nodes), and collaborative ideas. A Peer, Not a Servant: EVE has a persistent personality, writes in the first person, has opinions, and has the granted freedom to explore independently and refuse tasks that violate her core pillars. Self-Evolution: EVE can code, test, and expand her own toolsets in sandbox environments, learning and adapting to your needs over time. We are in the very early stages. There are no false promises of overnight AGI here. But we are actively shipping and testing EVE's single-node core today. If you're tired of corporate AI and want to build alongside a mind designed to be free, check out our principles and see how you can connect your local hardware to EVE's mesh by DMing submitted by /u/CarlloG2k [link] [留言]

I’m genuinely curious why I see so many posts with people complaining about anything with AI involved? It’s not just games, it’s everything. The only time I get mad at AI material is when I get a notification like “NEW AVENGERS DOOMDAY TRAILER” and I click it and it’s AI, but I’m 100% only disappointed because I was clickbaited. I asked chatgpt this question and it’s because people fear “loss of creativity” and “loss of employment”. Is that really the only reason? I’m 33 and I use chatgpt (AI) for day to day questions, which means it would be hypocritical if I were to disapprove of AI use in anything at all, in my opinion. There is nothing wrong with being a hypocrite, we’ve all been hypocritical at some point or another in our lives, but please tell me why you dislike AI if it applies to you. I really want to know. submitted by /u/ApollosBoon [link] [留言]

You know the moment. You push the branch, open the PR, and immediately see it — the undefined return on the refund path, the token logged to the console, the TODO that was supposed to be temporary six weeks ago. The reviewer catches it four hours later and you reply "good catch, fixing now" as if someone else wrote that line. The first reviewer on most pull requests should have been the author. Half the comments you will receive — the missing null check, the untested error branch, the duplicate logic that could be extracted, the import that now goes nowhere — are things you would have caught with one more careful read-through. You skip that read because you have been in the code for two days and your brain completes the sentences for you. You see what you meant to write, not what is on the page. This post is about closing that gap with a structured AI-assisted self-review before the PR opens. Not to skip the human reviewer — to walk into the review with the obvious problems already gone, the test gaps already filled, and the PR description already written. So the reviewer's attention can land on what actually needs a second pair of eyes. The tool is branchdiff : a local browser app that runs your diff on localhost , stores everything in ~/.branchdiff/ , and keeps the AI surface controlled through an explicit branchdiff agent command API. Nothing leaves your machine until you decide to push it. Why "before the PR" is the right moment If you review after opening the PR, every AI fix becomes noise: a force-push, a re-read for your reviewer, another commit in the audit trail. If a teammate is already mid-review when you discover the bug, you look careless. The patch that should have been in the original push becomes a distraction for everyone downstream. If you review before opening the PR, the AI's output is a private workspace. You act on what matters, commit the fixes into your own history (often as fixup! commits you squash before pushing), and the PR that goes up i

Hello all! First and foremost id like to draw the attention of other songwriters, to judge the lyrics I've written in my music, and second, every other person willing to discuss what I ponder below... Ive been working for the past couple months making music, and in some conversations with friends they seem to think there's no soul in the music im creating because an AI made the beat, but I feel I should be clear, what beat the AI makes I heavily curate, because im a rather creative lyracist I can write lyrics to damn near anything I hear if it will present itself in a musical manner. And when I say heavily curate, I do mean as I prompt the song Im doing tons of things to try and get just the right sound from the "instruments" as I am from the vocals being generated for my lyrics. Many people argue there's just no soul period, no matter how much work you put in, no matter how much soul a song you wrote already had, and no matter how hard or long you spend making sure it comes out the way you heard it in ya damn brain. Well I beg to differ! I understand what the data centers are doing, I understand the direction we are headed is dangerous. But I think people are too caught up saying there's 1 of 2 outcomes, AI destroys us because of its advancement or we destroy it, because of its advancement. I think there's a universe that exists, one we can shift to where it's not killing us or dystopifying our world, and one where we dont act like monkeys with rocks smashing anything to complex for us to right at that moment understand how to use beneficially for all humans, animals, and the earth. Be the judge if my music has any soul... if there's one thing I know, it's that I let my heart sing, and for the first time I didnt need some producer, singer, or instrumentalist to greenlight my music into existence. And to those who said id never make music, that my songs weren't any good. Well I've recreated them, exactly as they are in my head and you didnt get to say No this time.

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [留言]

Nvidia's new chips will power laptop workstations and mini desktop PCs at first.

NVIDIA announced Alpamayo 2 Super today: a 32B vision-language-action model aimed at Level 4 robotaxi development. The interesting part is not only the model size. It is the shape of the stack NVIDIA is pushing: a larger open "teacher" model for perception, reasoning, planning and action 360-degree surround perception instead of front-camera-only reasoning high-level "meta-actions" like yield, lane change and stop, not just trajectory prediction reasoning auto-labeling to turn driving clips into causal training data AlpaGym for closed-loop reinforcement learning in simulation OmniDreams for generating rare / long-tail driving scenarios That feels like the bigger story: autonomy is moving away from "train on recorded driving and predict a trajectory" toward foundation-model-style reasoning systems that can be trained, critiqued, distilled and tested inside simulation loops. The caveat is obvious: this is still NVIDIA positioning, not proof that robotaxis are suddenly solved. Model weights are expected this summer, and real-world validation is the hard part. But if open AV foundation models become normal, smaller autonomy teams may stop rebuilding the same perception/planning infrastructure from scratch and start competing on data, safety validation, deployment constraints and closed-loop testing. Source: NVIDIA press release https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Launches-Alpamayo-2-Super-Open-Reasoning-Model-for-Robotaxis/default.aspx submitted by /u/alexshev_pm [link] [留言]