NVIDIA

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

Unprecedented jump in software vulnerabilities discovered

submitted by /u/EchoOfOppenheimer [link] [comments]

It's officially over. One of the fathers of AI at Nvidia doesn't believe in AGI and compares OpenAI and Anthropic's closed models to AOL and Prodigy's closed internets. Says the future is every business having a customized open source model.

submitted by /u/9gxa05s8fa8sh [link] [comments]

AI just solved 9 unsolved math problems, including one that kept an Nvidia scientist "up at night for 2 years"

More info: github.com/Pengbinghui/pipeline-math submitted by /u/EchoOfOppenheimer [link] [comments]

IP Memorandum: Multi-Agent ("Agentic") AI Systems in Coding, Marketing, and Creation – Comprehensive 2026 Analysis. (Integrating Patentability, Hype vs. Reality, Human Dependency, and Cost Overruns)

​ \*\*Date:\*\* June 1, 2026 \*\*To:\*\* Interested Parties / Developers / Enterprises \*\*Re:\*\* Viability of Layered Agentic AI – IP Protectability, Practical Utility, and Economic Sustainability Without Substantial Human Creative Input \### Executive Summary The 2026 trend toward \*\*multi-agent ("agentic") AI systems\*\*—layering specialized agents via frameworks like CrewAI, LangGraph, and AutoGen—promises automated workflows for coding, marketing, and content creation. Promoters brag about superior implementation and reduced oversight, yet these systems remain "token-hungry," heavily dependent on human direction, and prone to producing generic outputs requiring extensive editing. \*\*Core Thesis\*\*: AI lacks independent creativity; it recombines human-provided inputs and training data. Layered agents amplify efficiency in structured tasks but do not yield broadly patentable inventions or customer-ready original works without differential human creative input. Recent corporate budget reversals—where AI costs exceeded human labor equivalents—highlight the gap between hype and sustainable value. This version fully integrates: (1) patentability and creativity concerns, (2) current agentic bragging, and (3) real-world budget cuts at Microsoft, Uber, and peers. \### Current Trends & Bragging on Agentic Formulas (2026 Landscape) Developers and vendors heavily promote multi-agent orchestration as the "next big thing": \- \*\*Shift to Layered Agents\*\*: Moving beyond single agents to coordinated teams (researcher + coder + reviewer + validator) for parallel, end-to-end workflows in coding and marketing. \- \*\*Key Frameworks & Claims\*\*: \- \*\*CrewAI\*\*: Role-based "crews" for quick multi-agent prototypes; touted for marketing teams and collaborative creation with minimal setup. \- \*\*LangGraph\*\*: Graph-based stateful orchestration for complex, traceable workflows; praised for production reliability in agentic coding. \- \*\*AutoGen\*\*: Conversation-driven multi-agent debates; marketed for autonomous coding and async tasks with reduced human supervision. \- \*\*Bragging Points\*\*: Claims of 50%+ efficiency gains, "death of the senior dev," full autonomy, and massive ROI through token-intensive inter-agent communication. High consumption is framed as essential for "superior workload implementation." These systems "suck tokens" via extensive prompting and iteration while promising independence—yet users remain tied to directing them. \### Patentability Analysis \- \*\*Patentable Elements\*\*: Narrow technical innovations—such as novel orchestration protocols, memory-sharing mechanisms, or domain-specific error-handling in multi-agent graphs—may qualify if they demonstrate novelty, non-obviousness, and utility. Human inventorship is required. \- \*\*Major Limitations\*\*: Broad "layered agents for coding/marketing" claims risk ineligibility under the \*Alice\* abstract idea doctrine. Crowded prior art from existing frameworks limits enforceability. AI-generated outputs alone are not patentable. \- \*\*Outcome\*\*: While specific implementations might secure protection, generic agentic layering is unlikely to produce strong, independent patents usable by customers without ongoing human differentiation and creative input. \### Copyright, Creativity, and Human Input Dependency AI excels at pattern synthesis but lacks true originality or aesthetic judgment. Multi-agent outputs are derivative of human prompts, context, and training data. U.S. law requires human authorship for copyright; raw agent-generated code, copy, or designs is generally unprotectable and may carry training-data risks. \*\*Reality Check\*\*: Even with 7, 28, or 100 agents, results tie directly to human instruction. Users face the scenario of editing days of output after short runtimes, undermining claims of full autonomy. \### Practical Usability for Customers & Cost Realities \*\*Strengths\*\*: Strong for boilerplate, data processing, and structured decomposition in hybrid teams. \*\*Weaknesses\*\*: Fragility on edge cases, silent failures, governance demands, and high token costs. Developers often rewrite large portions due to quality gaps and "cognitive debt." \*\*Recent Budget Cuts Due to Overruns\*\*: Major firms have slashed access after AI (especially Claude-powered agentic tools) burned through budgets faster than human equivalents: \- \*\*Microsoft\*\*: Canceled most internal Claude Code licenses for thousands of engineers in its Experiences and Devices division (Windows, M365, Outlook, Teams, Surface). Rolled out late 2025, it became too popular/costly ($500–$2,000+ per engineer/month in heavy use). Engineers redirected to cheaper GitHub Copilot CLI by June 30, 2026 fiscal year-end. Costs exceeded planned budgets despite productivity gains. \- \*\*Uber\*\*: Exhausted its entire 2026 AI coding budget in just four months (by April) due to rapid Claude Code adoption (84–95% of engineers). Mo

Nvidia's AI Chips Double in Price in China as It Tackles AI's Water Problem

submitted by /u/andix3 [link] [comments]

the so called free Nvidia LLM api is useless.

as we know, the Big GPU company Nivdia is providing a list for advanced LLM free tire. but in my testing, this kind of free tire is useless , not even good as chatbot, and dont think about it for agentic loop. the problem, the input/oupt is really slow, and not even stable. submitted by /u/ImprovementHuge3804 [link] [comments]

AutoFlow Research Initiative — Looking for Deep Technical Thinkers

AutoFlow Research Initiative — Looking for Deep Technical Thinkers Over the last several months, I've been exploring a question that sits at the intersection of AI, verification, trust, and decision systems: Can we build systems that independently verify claims produced by AI rather than simply generating answers? The original idea began with financial analysis. Consider a statement such as: "Company revenue grew 25% year-over-year." Today, most AI systems generate this claim, but they do not formally verify it. Our approach is different: Extract claims from documents, reports, or AI outputs. Gather supporting evidence. Apply mathematical and logical verification where possible. Identify inconsistencies and contradictions. Produce transparent reasoning rather than black-box conclusions. The first prototype is focused on finance because financial claims are structured, measurable, and often objectively verifiable. Examples include: Revenue growth calculations Financial ratio validation Cross-document consistency checks Balance sheet reconciliation Earnings statement verification As research progressed, we encountered deeper questions involving computability, trust, governance, formal verification, and adjudication. One realization is that not every claim can be mathematically proven. This raises a larger challenge: Where is the boundary between: Proven facts Verifiable claims Evidence-supported conclusions Human-style adjudication That question is becoming the foundation of our long-term research vision. Recent Milestones Accepted into NVIDIA Inception Access to NVIDIA startup resources and technical programs Building the architecture for our first verification-focused prototype Engaging with researchers and experienced engineers on verification and governance concepts Initial outreach to pre-seed investors and startup ecosystems Who I'm Looking For I'm interested in meeting people who enjoy difficult problems and are willing to challenge assumptions. Particularly: AI/ML researchers and engineers Formal verification and theorem-proving enthusiasts Distributed systems and orchestration experts C++ systems engineers Applied mathematicians Trust, governance, and decision-system researchers What You'll Receive For the right long-term collaborators: Significant technical ownership Direct influence on architecture and research direction Equity participation based on contribution and commitment Access to NVIDIA Inception resources available to the team Opportunity to help define a new category around AI trust and verification I'm not looking for people who simply agree with the vision. I'm looking for people who can find the flaws in it. If concepts such as verification, computability, trust, formal reasoning, governance, theorem proving, symbolic systems, or AI reliability interest you, I'd love to connect and exchange ideas. Feel free to comment or send a message. submitted by /u/MuhammadMujtaba21 [link] [comments]

Join Our Mission: Build the Future of AI Trust – Cloud Access & a Learning Opportunity Inside!

🚀 Join AutoFlow: Shape the Future of AI Trust 🚀 ​ I’m a 17-year-old founder, and AutoFlow—a startup in the NVIDIA Inception Program—is on a mission to redefine AI trustworthiness. ​ We’re building a mathematical verification engine that ensures AI claims are transparent, structured, and provable. Thanks to NVIDIA Inception, we now have access to GPUs, cloud credits, and technical mentorship to accelerate us. ​ Right now, we need a small but passionate technical team: students, recent graduates, or open-source developers who want to dive into C++, knowledge graphs, and formal verification. In return, you’ll get hands-on experience with industry-level AI tools, cloud resources, and a chance to shape a real-world prototype for a future seed round. ​ If you’re passionate about AI safety, formal methods, or trust in enterprise AI, I’d love to connect. ​ Let’s build something groundbreaking together! 🚀 ​ #AI #Startups #NVIDIAInception #TrustworthyAI #CPlusPlus #KnowledgeGraphs #OpenSource #AIResearch ​ ​ submitted by /u/MuhammadMujtaba21 [link] [comments]

The Alternative

Many people seem almost eager for companies like OpenAI to fail, often pointing to their financial losses as proof that the business model is unsustainable. But very few of those critics offer a realistic alternative for the billions of people who now rely on AI. ​ If OpenAI disappeared tomorrow, what exactly is the replacement for the average person? Not for a few thousand AI enthusiasts with technical expertise and expensive hardware, but for students, workers, and ordinary people around the world. ​ Anthropic has already signaled a very different approach: if you want meaningful access to its best models, you are generally expected to pay. That is a perfectly valid business decision, but it means many people are effectively excluded. If you cannot afford $20 per month, what is your alternative? Going back to traditional search engines, where you have to sift through pages of results, advertisements disguised as content, SEO spam, and AI-generated summaries that are often less useful than a dedicated AI assistant? ​ Others point to open-source models, often developed by Chinese companies or research groups. But for most people, that is not a practical solution either. The vast majority of users do not know how to download, configure, and run local AI models. Even if they do, running them meaningfully often requires expensive hardware—typically a capable NVIDIA GPU or a modern Apple computer. For someone earning a few hundred dollars per month, spending around $1,000 or more on hardware is simply not realistic. ​ OpenAI reportedly serves close to a billion people every week. The overwhelming majority of those users are on free plans. Many are students. Many live in developing countries. Many have little or no disposable income. They cannot afford a $20 monthly subscription, and they certainly cannot afford high-end AI hardware. ​ These are the people OpenAI is currently serving while losing billions of dollars. I am not naive enough to believe that this is pure altruism. OpenAI is a business and will eventually need a sustainable path to profitability. But the fact remains that, today, they are providing advanced AI access to hundreds of millions of people who would otherwise have none. ​ OpenAI could choose a different path. It could restrict access, dramatically reduce free usage, or move toward a model where only paying customers receive meaningful service. That would likely improve its finances much faster. Yet for now, it continues to support a massive free user base. ​ If that support disappears, what is the realistic outcome? Most people will not suddenly become local AI experts. They will not buy expensive GPUs. They will not self-host open-source models. They will simply return to the most accessible option available: Google. ​ And that would mean even more dependence on a single dominant gatekeeper of information. For all the criticism directed at OpenAI's finances, the practical alternative for most people is not a vibrant open-source future. It is a return to Google's monopoly over how billions of people access information online. submitted by /u/sulabh1992 [link] [comments]

OpenAI Built Intelligence. Who Will Build Trust?

​ ​ At 17, I started asking a simple question: ​ If AI is going to power the future, who will make AI trustworthy? ​ Today, most AI systems remain probabilistic. They hallucinate, produce unverifiable outputs, and struggle in high-stakes domains like finance, healthcare, and compliance. ​ At AutoFlow, we're researching a different direction: ​ Building an external Mathematical Verification Engine that sits around LLMs and verifies their outputs using knowledge graphs, symbolic reasoning, and deterministic consistency checking. ​ Our long-term vision is not to replace LLMs. ​ Our vision is to build the trust infrastructure that future AI systems depend on. ​ Current Research Areas ​ Structured fact graph construction from documents ​ Claim extraction from LLM outputs ​ Mathematical consistency verification ​ Symbolic reasoning using Z3/CVC5 ​ High-performance C++ verification engine Multi-agent orchestration and audit trails ​ Benchmarking against RAG, CoT etc. ​ We are starting with finance as the first proof-of-concept because financial data is highly structured and mathematically verifiable. ​ Our architecture currently explores: ​ Input → Fact Graph → LLM → Claim Extraction → Verification → Certificate ​ Milestone: ​ We're proud to share that AutoFlow has been accepted into the NVIDIA Inception Program, giving us access to startup resources, GPU infrastructure opportunities, cloud benefits, and technical ecosystem support. ​ We Are Looking For contributors for: ​ NLP & Information Extraction, Knowledge Graphs,Symbolic AI Formal Logic & Theorem Proving, C++ Systems Engineering, Distributed Systems AI Safety & Trustworthy AI ​ If you're excited by hard problems and want to work on the future of trustworthy AI, let's connect. ​ The goal isn't to build another AI wrapper. ​ The goal is to build infrastructure that AI systems can trust. ​ ​ submitted by /u/MuhammadMujtaba21 [link] [comments]

OpenAI Built Intelligence. Who Will Build Trust?

AI models have become incredibly capable. ​ But one problem remains: ​ Trust. ​ Even state-of-the-art models hallucinate, especially in high-stakes industries like finance and healthcare. ​ At AutoFlow, we're researching whether AI outputs can be externally verified through: Knowledge graphs ​ Mathematical consistency checks Symbolic reasoning Verification certificates ​ Instead of asking: "Is the model confident?" ​ We ask: "Can the claim be proven?" ​ We're beginning with finance as a proof of concept before expanding to broader domains. ​ AutoFlow was recently accepted into the NVIDIA Inception Program, helping us accelerate research into trustworthy AI systems. ​ Question for the community: ​ Do you think truly verifiable AI is possible, or will AI always remain probabilistic? submitted by /u/MuhammadMujtaba21 [link] [comments]

I have 3,000 photos and videos in OneDrive. How can I organise them with AI?

Looking for a bit of advice because I feel like I’m missing something obvious. Over the last few weeks I’ve finally consolidated my photo library and got everything into OneDrive. I’ve now got two folders: Photos Videos Between them there’s around 3000 files in total. The files go back years and are a mix of family photos, holidays, screenshots, random phone pictures etc. I’ve been trying to use AI to help me organise everything properly. Things like: - Finding duplicates and near-duplicates - Identifying people - Grouping photos from the same trip or event - Creating folders/albums automatically - Tagging photos so they’re searchable - Picking out the best photos and obvious rubbish - Suggesting a sensible folder structure I initially thought ChatGPT might be able to help, but I’ve quickly hit a wall because I couldn’t work out a practical way to give it access to thousands of files sitting in OneDrive. I tried to connect it to OneDrive and just kept getting an error. This is where I start getting lost. I keep seeing people talk about agents, MCPs, local models and automation workflows. I’ve done a bit of reading, but if I’m honest I don’t really understand how those pieces fit together or how I’d actually use them myself. I have a rough idea what an MCP is, but nowhere near enough knowledge to build anything from scratch. I’m reasonably technical, but I’m not a developer. I’m happy to learn and tinker, but I’d prefer something a beginner could realistically get running without spending weeks building infrastructure. My setup is: Windows laptop i7-10750H 32GB RAM Nvidia Quadro P620 Everything stored in OneDrive Ideally I’d like to keep costs as close to zero as possible. I have a ChatGPT plus subscription. If this was your photo library, what would you actually do in 2026? Is there a beginner-friendly AI workflow for this, or am I looking at completely the wrong type of tool? And if the answer is “don’t use an agent for this, use something else”, I’m completely open to that too. Any advice appreciated. submitted by /u/iamSnellsquanch [link] [comments]

OpenAI Eyes 10 GW Ohio Campus With Nvidia Backing

OpenAI is in talks to lease a 10-gigawatt data center campus in southern Ohio, built by SoftBank's SB Energy on Department of Energy land, with construction costs estimated at $500 billion or more. The proposed 20-year agreement would have OpenAI overseeing computing gear installed on site, with lease payments beginning only after the facility is operational. First operations are targeted for 2028. Nvidia would supply computing hardware and provide a financial backstop tied to OpenAI's lease obligations. The campus is on federal DOE land at Ohio's former Portsmouth Gaseous Diffusion Plant, a decommissioned Cold War uranium enrichment facility. SB Energy broke ground in March 2026 following a DOE-SoftBank partnership announcement, meaning construction is underway before any lease closes. Nvidia guarantees both OpenAI's 20-year lease payments and SB Energy's construction financing, acting as financial co-principal on a $500 billion commitment. source : https://aiweekly.co/alerts/openai-eyes-10-gw-ohio-campus-with-nvidia-backing submitted by /u/Justgototheeffinmoon [link] [comments]

AMD's Lemonade SDK for local AI adds NVIDIA CUDA support

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

I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, Skill, Search, local/cloud model support and much more)

https://preview.redd.it/x7t8zn66si6h1.png?width=3316&format=png&auto=webp&s=f724452561a90e36ac37d86002a291f508928300 I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, and local model support) We want better answers from our LLMs, but relying on a single model falls short. So I built The AI Counsel to run two distinct deliberation modes: First, the LLM Council mode. It runs a 3-stage pipeline: individual replies, anonymous peer reviews, and chairman synthesis. This works best for factual questions and direct answers. Second, the LLM Advisors mode. Multiple customizable personas (like The Skeptic, The Strategist, The Ethicist) debate your question across configurable rounds, reaching consensus to deliver a structured verdict. This works best for decisions, strategy, and tradeoffs. I packaged the tool as a Docker container with a built-in MCP server for full API access. You can connect it to any agent that supports MCP, like Hermes or OpenClaw. It comes with a dedicated skill so your agents can call it directly. You can spin it up using local Ollama models or connect free models from OpenCode Zen/Go and NVIDIA NIM. I also built in direct connections to OpenAI, Anthropic, OpenCode, Mistral, and DeepSeek. To ground responses in the latest web information, I added a search engine. It supports DuckDuckGo (free, no API key), Serper, Brave, and TinyFish (all with free tiers). I also integrated Jina AI to fetch full articles for the LLMs to read. EVERYTHING in the tool is configurable, from system prompts to model temperatures. There are advanced debate models for the council. This tool is massive. Free and Fully Open Source. Check it out Repo: https://github.com/jacob-bd/the-ai-counsel submitted by /u/KobyStam [link] [comments]