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NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

NVIDIA AI

OpenAl Announced vs. Current Operational Compute

submitted by /u/Business_Garden_7771 [link] [comments]

Anthropic Announced vs current compute capacity (Sources Below)

source list: Google Cloud TPU deal — up to 1M TPUs, “well over 1 GW” expected online in 2026 https://www.anthropic.com/news/expanding-our-use-of-google-cloud-tpus-and-services https://www.googlecloudpresscorner.com/2025-10-23-Anthropic-to-Expand-Use-of-Google-Cloud-TPUs-and-Services (Anthropic) Fluidstack / Anthropic $50B U.S. AI infrastructure — Texas + New York, sites coming online through 2026 https://www.anthropic.com/news/anthropic-invests-50-billion-in-american-ai-infrastructure https://www.fluidstack.io/about-us/blog/fluidstack-selected-by-anthropic-to-deliver-custom-data-centers-in-the-us (Anthropic) Microsoft + NVIDIA deal — $30B Azure compute commitment + up to 1 GW additional capacity https://blogs.microsoft.com/blog/2025/11/18/microsoft-nvidia-and-anthropic-announce-strategic-partnerships/ https://blogs.nvidia.com/blog/microsoft-nvidia-anthropic-announce-partnership/ (The Official Microsoft Blog) Google + Broadcom next-gen TPU deal — multiple GW starting 2027; Broadcom SEC filing says ~3.5 GW https://www.anthropic.com/news/google-broadcom-partnership-compute https://investors.broadcom.com/static-files/c906d370-921b-4bc2-bb7b-57877dfcf1ae (Anthropic) Amazon / AWS deal — up to 5 GW, nearly 1 GW by end-2026 https://www.anthropic.com/news/anthropic-amazon-compute (Anthropic) AWS Project Rainier — operational now, nearly half a million Trainium2 chips; Claude expected on 1M+ Trainium2 chips https://www.aboutamazon.com/news/aws/aws-project-rainier-ai-trainium-chips-compute-cluster (Amazon News) SpaceX / Colossus 1 — all Colossus 1 compute, >300 MW, 220k+ NVIDIA GPUs within the month https://www.anthropic.com/news/higher-limits-spacex https://x.ai/news/anthropic-compute-partnership (Anthropic) Independent reporting for SpaceX deal https://www.reuters.com/business/retail-consumer/anthropic-unveils-dreaming-feature-help-its-ai-agents-self-improve-2026-05-06/ (Reuters) submitted by /u/Business_Garden_7771 [link] [comments]

Claude Code has 240+ models via NVIDIA NIM gateway

TIL Claude Code has 240+ models via NVIDIA NIM gateway — Nemotron-3 120B for agentic coding is surprisingly good So I was messing around with /model in Claude Code today and noticed something most people probably don't know about — after the standard Claude models (Opus, Sonnet, Haiku), there's a whole NVIDIA NIM gateway section with +239 additional models you can switch to mid-session. Some of the models I spotted: nvidia/nemotron-3-super-120b-a12b (with and without thinking mode) 01-ai/yi-large abacusai/dracarys-llama-3.1-70b-instruct ...and hundreds more I've been running the Nemotron thinking variant for multi-file refactoring and it's genuinely solid. It reasons through changes before touching your code — exactly what you want for agentic tasks. Latency is higher than Claude obviously, but if you're burning through Opus credits on long sessions this is worth experimenting with. How to try it: Open any Claude Code session Run /model Scroll past the four standard Claude options — NIM models appear below Hit d to set one as your session default, or pass --model at launch Anyone else been routing Claude Code through NIM? Curious what models people have had luck with — especially for Python or Rust codegen. submitted by /u/shadowBladeO4 [link] [comments]

Use Case: How I chain ChatGPT+Agents+Codex workloads

Context: I run interaction forensics and how people, communities, narratives, institutions and companies impact AI. Please note, all operations are human+AI. Summary: I have used digital forensic tools/OSINT in the past such as Maltego and wwanted a tool I could integrate with AI. So I built my own Airgapped. This tool is the first iteration and will later be used to assist in high-risk controlled environments such as child protection agencies. This is the current architecture and workflow. https://preview.redd.it/26w74lxfgz1h1.png?width=1935&format=png&auto=webp&s=4a064b2f5e84e230913f9e7758de2b29a1f41ac8 Tools Used and function: * Codex+Manus: Assistance in building the tool and incorporating logic. Bulk transfers of older method to current database. Data was collected by me and sorted into our database structure. * Agents: Amending and adding bulk data to database. * GPT+Manus: Verification and updates of data. The final output: Interface: https://preview.redd.it/t2x6v9l0iz1h1.png?width=1776&format=png&auto=webp&s=c1be628542af6420eb4efee9f7ec62c2d40146f9 Inferences and patterns identified when AI (LLM+AGENTS) review data. https://preview.redd.it/nkdio3z5iz1h1.png?width=832&format=png&auto=webp&s=01d0f0bc45e1968d0c692d712932f03e35969924 I add my own as well. Along with collaboration with AI to validate my understanding. Evidence based Artifacts: All knowledge is sourced and tagged https://preview.redd.it/fwcmjn28jz1h1.png?width=1253&format=png&auto=webp&s=861dcf33480d6e22919cf563a362c1c33c044734 These tie into a pattern identification graph so I can identify what may or may not be related. https://preview.redd.it/pegwypialz1h1.png?width=1424&format=png&auto=webp&s=d4b50e756354dc021fc106f5e91da3015ae0bd74 Would love any feedback for improvements. Please remember, the next iteration is for child protection where I intend to airgap a localised LLM with training corpora. The main idea is to MINIMISE users from having to review images and identify patterns/locations to expedite rescue. I want to add, this is also entirely self funded. I run a separate business to ensure I have funds for this and potential future hardware/licensing. submitted by /u/ValehartProject [link] [comments]

The Smartest Money on Earth Sold $8B in Microsoft and Cut Nvidia 93% in Q1

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

We compiled 42 of the Generative & Agentic AI interview questions (and how to actually answer them).

Hey Everyone, The AI engineering job market has shifted massively in the last 6 months. Interviewers are no longer just asking "how does a transformer work?" or "how do you write a good prompt?" They want to know if you can architect production-grade multi-agent systems, prevent RAG hallucinations, and manage state across LLM calls. I’ve been building a visual learning sandbox for multi-agent workflows (agentswarms.fyi), and today I just launched a completely free AI Interview Prep Module inside it. I compiled 42 top interview questions specifically for GenAI and Agentic AI roles. But instead of just giving a generic answer, the module breaks down the "Standout Answer" and teaches you the mental model of how to answer it like a senior architect. Here are two examples from the list: Question 1: When would you use a Multi-Agent Swarm instead of a single LLM with multiple tools? ❌ The average answer: "When the task is too complex, multiple agents are better than one." ✅ The standout answer: "You use a swarm to prevent context dilution and enforce the Principle of Least Privilege. If you give one 'God Agent' 15 tools and a 4k-word system prompt, its reliability drops and hallucination risk spikes. By routing to specialized sub-agents with narrow instructions (e.g., separating the 'Data Extraction Agent' from the 'Customer Chat Agent'), you isolate failure points and allow for parallel execution." Question 2: How do you handle hallucinations in a financial RAG pipeline? ❌ The average answer: "I would lower the temperature to 0 and give it a better system prompt." ✅ The standout answer: "I would decouple data extraction from text generation. I'd use a deterministic node or a strict JSON-enforced agent to only extract the hard numbers from the retrieved context. Then, I would pass that structured data to a separate Synthesis Agent. Finally, I'd implement an 'LLM-as-a-judge' evaluation loop before returning the final output to the user." What's in the full list? The 42 questions cover: RAG Architecture & Vector Databases Agentic Routing (ReAct vs. Planner-Executor) Evaluation metrics for non-deterministic outputs Security (Prompt injection prevention in multi-agent loops) You can read through all 42 questions, answers, and the "how to answer" breakdowns right in the dashboard here: https://agentswarms.fyi/interview-questions For those of you who have interviewed for AI Engineering roles recently, what is the hardest system design question you've been asked? I'd love to add it to the list. submitted by /u/Outside-Risk-8912 [link] [comments]

Anthropic just published a pretty alarming 2028 AI scenario paper and it's not about AGI safety in the usual sense

Anthropic dropped a new research paper today outlining two possible futures for global AI leadership by 2028, and it reads more like a geopolitical briefing than a typical AI safety paper. The core argument: The US currently has a meaningful lead over China in frontier AI, primarily because of compute (chips). American and allied companies (NVIDIA, TSMC, ASML, etc.) built technology China simply can't replicate yet. Export controls have made that gap real. But China's labs have stayed surprisingly close through two workarounds: Chip smuggling + overseas data center access - PRC labs are apparently training on export-controlled US chips they shouldn't have. A Supermicro co-founder was recently charged for diverting $2.5B worth of servers to China. Distillation attacks - creating thousands of fake accounts on US AI platforms, harvesting model outputs at scale, and using that to train their own models. Essentially free-riding on billions in US R&D. The two scenarios for 2028: Scenario 1 (good): US closes the loopholes, enforces export controls properly, the compute gap widens to 11x, and US models stay 12-24 months ahead. Democracies set the norms for how AI is governed globally. Scenario 2 (bad): US doesn't act, China reaches near-parity, floods global markets with cheaper models, and the CCP ends up shaping global AI norms, including potentially exporting AI-enabled surveillance tools to other authoritarian governments. What makes this interesting beyond the politics: Their new model, Mythos Preview (released to select partners in April), apparently let Firefox fix more security bugs in one month than in all of 2025. That's the kind of capability jump they're warning China shouldn't be the first to achieve, specifically around autonomous vulnerability discovery. The framing worth discussing: Anthropic is explicitly calling distillation attacks "industrial espionage" and pushing for legislation to criminalize them. This positions them as political actors, not just AI researchers. Whether that's appropriate for an AI lab is a conversation worth having. What do you think - is the compute gap as decisive as they claim, or is algorithmic innovation enough to close it? submitted by /u/Direct-Attention8597 [link] [comments]

Replaced my $15/mo Wispr Flow subscription with a free local macOS app I built using Claude Code

I spend most of my day writing prompts to Claude. Read a study recently that said people speak ~3x faster than they type, which lands differently when "writing" is basically your whole workflow. Looked at Wispr Flow – it's genuinely great, but $15/month forever for something I'd mostly use to dictate to Claude felt wrong. So I spent two weeks of evenings building my own with Claude Code. How Claude helped I'd never shipped a Tauri / macOS app before this. Claude Code did the bulk of the actual code: The menu bar app structure, global hotkey capture, and paste-anywhere flow UI and onboarding Integrating the local model runtimes (Parakeet / Whisper for transcription, Gemma 4 for polishing) The model download / storage logic so the app ships without bundling gigabytes of weights A lot of debugging I would not have had the patience for on my own I made the product and design calls; Claude wrote the vast majority of the code. Two weeks of evenings, usually an hour or two at a time. What it does Menu bar app for macOS. Hold a hotkey, talk, release – text is copied to your clipboard. Works in any app: Claude.ai, Cursor, Slack, browser, IDE, whatever. Two open-source models doing the work: Parakeet (NVIDIA) / Whisper for transcription Gemma 4 (Google) / Apple Intelligence for polishing the raw transcript into something readable Everything runs locally. No cloud calls, no API keys, no telemetry, no account. Fully offline after download. Free for personal use, no signup. Download: https://vox.rizenhq.com/ Caveats macOS only. Apple Silicon required (M-series chip). Windows build is next. It's two weeks old. Bugs I haven't found yet exist. ~90% of Wispr Flow's quality, not 100%. Enough for me to use every day. What it's saving me 40–60 minutes a day, mostly on prompts. Dictating to Claude feels noticeably more natural than typing to it. The ask Feedback, especially from people who talk to Claude a lot: Where does it break? Bug reports > compliments. What did you use it with? What feature would make you switch from Wispr Flow (or start using voice-to-text at all)? Tech notes No separate model download – onboarding handles it Gemma 4 options: E2B, E4B, 26B. E2B runs on phones; 26B is overkill for most machines. I use E4B – great quality, fast. RAM (Parakeet + Gemma 4 E4B): ~200mb idle, ~300mb while speaking, brief spike to 4–6GB during transcription/polish, then back to 200mb CPU: ~0% idle, ~20% peak during use EDIT BTW, I develop it during my live streams from 8:30 am to 10:30 am ET everyday here. I show the code and decisions I make live on the stream. If you want to ask questions / push for some features / push to make it open source / etc. - join the stream, push for it in the chat and I'll consider it! Also, seeing the number of feedback, and feature requests in the comments I've decided to create a discord server to make sure that nothing will be lost and everything will be addressed. You can join here. submitted by /u/EfficientLetter3654 [link] [comments]

openai/gpt-5.5-pro API In=$30.00 Out=$180.00

Is this an openrouter bug? https://preview.redd.it/sz826138ul0h1.png?width=879&format=png&auto=webp&s=066f38f4a6d5a8eeee142e7a8a356d8bc511c6f1 submitted by /u/ArtdesignImagination [link] [comments]

5 enterprise AI agent swarms (Lemonade, CrowdStrike, Siemens) reverse-engineered into runnable browser templates.

Hey everyone, There is a massive disconnect right now between what indie devs are building with AI (mostly simple customer support chatbots) and what enterprise companies are actually deploying in production (complex, multi-agent swarms). I wanted to bridge this gap, so I spent the last few weeks analyzing case studies from massive tech companies to understand their multi-agent routing logic. Then, I recreated their architectures as runnable visual node-graphs inside agentswarms.fyi (an in-browser agent sandbox I’ve been building). If you want to see how the big players orchestrate agents without having to write 1,000 lines of Python, I just published 5 new industry templates you can run in your browser right now: 1. 🛡️ Insurance: Auto-Claims FNOL Triage Swarm Inspired by: Lemonade’s AI Jim, Tractable AI (Tokio Marine), and Zurich GenAI Claims. The Architecture: A multimodal swarm where a Vision Agent assesses uploaded images of car damage, a Policy Agent cross-references the user's coverage database, and a Fraud-Detection Agent flags inconsistencies before routing to a human adjuster. 2. ⚙️ Manufacturing: Quality / Root-Cause Analysis Swarm Inspired by: Siemens Industrial Copilot, BMW iFactory, Foxconn-NVIDIA Omniverse. The Architecture: A sensor-data ingest node triggers a diagnostic swarm. One agent pulls historical maintenance logs via RAG, while a SQL Agent queries the parts database to identify failure patterns on the assembly line. 3. 🔒 Cybersecurity: SOC Alert Triage & Response Inspired by: Microsoft Security Copilot, CrowdStrike Charlotte AI, Google Sec-Gemini. The Architecture: The ultimate high-speed parallel routing swarm. When an anomaly is detected, specialized sub-agents simultaneously investigate IP reputation, analyze the malicious payload, and draft an incident response ticket for the human SOC analyst to approve. 4. 📚 Education: Adaptive Socratic Tutor & Auto-Grader Inspired by: Khan Academy Khanmigo, Duolingo Max, Carnegie Learning LiveHint. The Architecture: A strict "No-Direct-Answers" routing loop. The Student Agent interacts with the user, but its output is constantly evaluated by a hidden "Pedagogy Agent" that ensures the AI is guiding the student to the answer via Socratic questioning rather than just giving away the solution. 5. 📦 Retail/E-commerce: Returns & Reverse-Logistics Swarm Inspired by: Walmart Sparky, Mercado Libre, Shopify Sidekick. The Architecture: A logistics orchestration loop that analyzes a customer return request, checks inventory levels in real-time, determines if the item should be restocked or liquidated (based on shipping costs vs. item value), and autonomously issues the refund. How to play with them: You don't need to spin up Docker containers or wrangle API keys to test these architectures. You can load any of these 5 templates directly into the visual canvas, see how the data flows between the specialized nodes, and try to break the routing logic yourself. Link: https://agentswarms.fyi/templates submitted by /u/Outside-Risk-8912 [link] [comments]

Anthropic just partnered with SpaceX and doubled Claude Code rate limits effective today

Anthropic just partnered with SpaceX and doubled Claude Code rate limits effective today Big news dropped this morning. Anthropic signed a deal to use all compute capacity at SpaceX's Colossus 1 data center. That's 300+ megawatts and over 220,000 NVIDIA GPUs coming online within the month. But the part that actually matters to developers right now: What changed today: - Claude Code 5-hour rate limits are doubled (Pro, Max, Team, Enterprise) - Peak hours limit reduction on Claude Code is removed for Pro and Max - API rate limits for Claude Opus models raised considerably This is on top of their existing compute deals 5 GW with Amazon, 5 GW with Google/Broadcom, $30B of Azure capacity with Microsoft and NVIDIA, and $50B in infrastructure with Fluidstack. They also mentioned interest in developing orbital AI compute with SpaceX. Which is a sentence I did not expect to read in 2026. For those of us building with Claude Code daily, the doubled limits + no more peak hour throttling is the headline. Rate limits have been the most frustrating bottleneck when you're deep in a long coding session. Anyone else noticing a difference already? submitted by /u/Direct-Attention8597 [link] [comments]

Anthropic Just Secured a Reserve.

Anthropic announced a major partnership with SpaceX to utilize all compute capacity at the Colossus 1 data center. This agreement provides Anthropic with over 300 megawatts of additional capacity comprising more than 220,000 NVIDIA GPUs within the month. submitted by /u/DragonflyOk7139 [link] [comments]

New Compute Partnership with Anthropic and xAI

https://x.ai/news/anthropic-compute-partnership submitted by /u/WhyLifeIs4 [link] [comments]

If OpenAI was a UCL club

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

Anthropic: AI will fully replace software engineering by 2027. Also Anthropic: Currently hiring for 122 SWE openings.

I’m not playing a gotcha game here. AI is undeniably changing software engineering and I can’t think of a better AI use case than coding. But is AI replacing software engineering end-to-end? I’m not so sure. Anthropic’s own hiring trend tells a very different story than the AI replacement messaging Dario Amodei has been running. In fact, Anthropic’s software openings have seen a steady increase (184%) since Jan 2025. We’re shipping more software than ever. You’d think that means more engineers, not fewer. The industry signals point in that direction, too: - Amazon planning to hire 11,000 SWE interns in 2026 - NVIDIA claiming compute costs more than employees - SaaS reliability metrics down across the board (see GitHub) - AI coding tool pricing models currently unsustainable - Companies reporting no wide-scale AI productivity gains Software jobs are down big time since the 0-interest rate era and the recent “AI transformation” layoffs are real. It’s tough for engineers right now. My inkling is that’s a temporary setback, though. AI is here to stay. But so are software engineers. - Joel Griffiths submitted by /u/ImaginaryRea1ity [link] [comments]