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Nvidia Quietly Builds Major AI Portfolio Shaping Investor Risk And Optionality
Nvidia (NasdaqGS:NVDA) has built one of Silicon Valley's largest private AI investment portfolios under CEO Jensen Huang. The company nearly doubled its private company holdings over the past year, according to its latest quarterly disclosure. Nvidia deployed record capital into emerging AI and technology ventures, including nearly US$18b into private ventures in a single quarter. As of 26 April, Nvidia reported more than US$42b in private company holdings linked to the broader AI...
Nvidia's Jensen Huang Just Delivered Big News to Shareholders. And It May Influence Your Decision on Whether to Buy Nvidia Stock Right Now
I was one of the internet’s first influencers. AI just killed the whole category — and created something better
General AI
DoubleVerify (DV) Launches AI-Powered Pre-Screen Content Controls on Meta (META) Threads Feed
DoubleVerify Holdings Inc. (NYSE:DV) is one of the cheap AI stocks to buy according to analysts. On May 18, DoubleVerify launched AI-powered pre-screen content controls on Meta’s Threads feed to enhance brand protection for advertisers. This capability allows brands to avoid content they deem unsuitable before impressions are transacted, building upon DV’s existing post-bid brand […]
This Artificial Intelligence (AI) Stock Will Beat Nvidia, AMD, Broadcom, and Intel to Become the Biggest Winner in AI Inference
Arm Holdings' business model makes the stock a solid investment for those looking to capitalize on the growing demand for AI inference.
AMD (AMD) Announces $10B Investment in Taiwan AI Infrastructure
Advanced Micro Devices Inc. (NASDAQ:AMD) is one of the best multibagger stocks to buy in 2026. On May 21, Advanced Micro Devices announced a strategic investment of over $10 billion across the Taiwan ecosystem to scale advanced packaging manufacturing and expand partnerships for next-generation AI infrastructure. The initiative focuses on delivering high-performance, energy-efficient solutions to […]
Scan any codebase in 3s, then verify what your AI builds
Article URL: https://github.com/anatomia-dev/anatomia Comments URL: https://news.ycombinator.com/item?id=48253446 Points: 5 # Comments: 0
AI didn't kill your junior pipeline. You did
Article URL: https://andrewmurphy.io/blog/ai-didnt-kill-your-junior-pipeline-you-did Comments URL: https://news.ycombinator.com/item?id=48253237 Points: 8 # Comments: 2
Ferrari is using IBM’s AI to create F1 superfans
IBM and Scuderia Ferrari HP take TechCrunch inside how they are redefining the fan experience.
Elon Musk has given up on solar power (on Earth)
Elon Muks's xAI has gone all in on natural gas, while SpaceX is obsessed with orbital data centers. What happened to the "solar-electric economy" he promised?
SAAS - 24/7 AI Receptionist Pricing?
How do AI agencies charge per month for an AI solution that answers calls 24/7, schedules appointments, follows up, re-engages previous customer after 6 months, and sends review request to every customer that completes a treatment submitted by /u/FitAd831 [link] [comments]
What’s your current / best AI voice agents stack in 2026?
I’m curious what people are actually using right now for AI voice agents in production. Not just “best in demos” — but the stack that works well for real calls, real latency, interruptions, handoffs, CRM sync, and overall reliability. I checked LuMay Voice Agent and got <500ms latency, which felt pretty solid in testing. For me, the biggest factors are: latency interruption handling call quality workflow automation CRM integration fallback/recovery when the agent gets stuck I’ve seen different setups around Vapi, Retell, Twilio, and custom stacks, but I’d love to hear what’s working best for you right now. What’s your current stack, and what’s the one thing it does better than the others? submitted by /u/Legitimate_Sell6215 [link] [comments]
Shannon Got AI This Far. Kolmogorov Shows Where It Stops
Article URL: https://medium.com/@vishalmisra/shannon-got-ai-this-far-kolmogorov-shows-where-it-stops-c81825f89ca0 Comments URL: https://news.ycombinator.com/item?id=48253580 Points: 3 # Comments: 0
From Vibe Coding to AI-Assisted Engineering: Lessons from Real Projects
Article URL: https://medium.com/@eritonsilva/from-vibe-coding-to-ai-assisted-engineering-lessons-from-real-projects-c403b85eaad1 Comments URL: https://news.ycombinator.com/item?id=48253987 Points: 3 # Comments: 0
Joe Rogan accidentally exposed AI in four words [video][12 mins]
Article URL: https://www.youtube.com/watch?v=waFl4uBfXRA Comments URL: https://news.ycombinator.com/item?id=48253414 Points: 3 # Comments: 0
Takaichi considering new quota for growth investment
The plan would cover 17 growth areas, including artificial intelligence and semiconductors, to support long-term corporate investments.
The Week’s 10 Biggest Funding Rounds: Massive Deals For Medical Devices, Futuristic AI Gadgets And Frontier Labs Lead
Along with the usual heavy dose of AI, this week’s list also includes large deals for aerospace and defense, fintech, and retail technology.
Probabilities are not the right concept
Introduction This sequence is an attempt to sketch a unified framework for several interconnected questions: Where do Bayesian priors come from? What even are probabilities? How should we deal with infinite ethics? What's going on with anthropics? I hope to lay out both some of the existing answers and my own preferred synthesis.[1] I understand that many people have already thought about these questions, and I have only read portions of the existing literature. I think most of what I will write here, even in the section about my preferred synthesis, is not novel. People whose writing I'm building on include Wei Dai, Paul Christiano, Joe Carlsmith, Scott Garrabrant and Richard Ngo. I've also listened to some people like Lukas Finnveden, Vivek Hebbar and Ryan Greenblatt talk about related topics, which was also influential on me.[2] However, most of the prior work is scattered across many, often very confusingly written blog posts, and I can't easily tell where I first came across vario
AI is being used to resurrect the voices of dead pilots
People used AI on a spectrogram image of cockpit recordings to reconstruct them, forcing the NTSB to temporarily block access to its docket system.
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Google’s new anything-to-anything AI model is wild
Just a stuffed deer having the time of his life. | Image: Gemini / The Verge Last year I deepfaked my kid's stuffed animal to make it look like his plush deer was on vacation. It was an experiment to see if I could re-create the events depicted in a Gemini ad Google was running, and I never showed the videos of Buddy the deer on his adventures to my four-year-old. But it was a revealing exercise that made me think a lot about the difference between some harmless fun with generative AI and full-on slop. Maybe that Venn diagram is a perfect circle! Maybe not. But what I know for sure is that the tools to make realistic videos are surprisingly good, requiring surprisingly little effort and know-how. And that trend is c … Read the full story at The Verge.
OpenAI launches a ChatGPT Powerpoint plugin and warns it might accidentally delete your content
OpenAI brings ChatGPT directly into PowerPoint. A new beta plugin creates presentations from notes, documents, or images and edits existing slides. The add-in is available worldwide across all tiers. OpenAI recommends saving important decks before using it. The article OpenAI launches a ChatGPT Powerpoint plugin and warns it might accidentally delete your content appeared first on The Decoder.
Looking for “wow factor” AI Agent / automation ideas in Strategic Sourcing (Fortune 50 Company)
Hey everyone, looking for some ideas / inspiration from this community. I work at a large Fortune 50 company in the healthcare space , and my role is in Strategic Sourcing, where I focus on negotiating contracts with suppliers and improving commercial terms. One of my personal objectives this year is to automate or build AI Agent ~10–20% of my work, so I’ve been actively exploring different ways to apply AI and automation in a meaningful way. Right now I: Use Microsoft 365 Copilot (GPT-5 chat model) for day-to-day support (summaries, drafting, thinking partner, etc.) Have access to some additional tools, but options are somewhat limited due to company security / restrictions I’m already familiar with the basics (identifying repeatable tasks, starting small, simple automation), but I’m trying to go beyond that and find ideas that actually create a bit of a “wow factor” , something that noticeably changes how the work gets done, not just improves efficiency by 5%. Some areas I’m t
Is there any reason for an uncensored model if you have no interest in roleplaying?
My rag I've been building is much in response to having a LLM that I feel more confident in knowing where the knowledge base is coming from especially after the Open AI deal with the Pentagon. So, when I saw "uncensored" heretic models, I thought that was the main usage of those models and thought I would need them. But in doing various tests, it seems there's random problems that come up with them that don't come up in regular versions. And then even when I do run into something like qwen3.6 acting like it's giving me a more state approved answer for a no-no topic, I've found that if I just put a prompt ahead of it to not give me any propaganda, it basically "jailbreaks" the answer. But, if the model isn't trained on the info anyways, then there's not really a benefit to it. Are uncensored models just for people wanting...the special roleplaying? Before I write them off. Genuinely curious, not judging how people use them. submitted by /u/vick2djax [link] [comments]
Vision-capable LLMs vs. OCR for long-document (including charts, images, tables, etc.) QA
I benchmarked vision-capable LLMs (the "just attach the PDF and let the model read it" pattern) against OCR-based pipelines on 30 long, image-heavy PDFs from MMLongBench-Doc (https://github.com/mayubo2333/MMLongBench-Doc). There were 171 questions in total, using Claude Sonnet 4.5 as the LLM. Post-retry results: Approach Accuracy $/query LlamaCloud premium + full-context 59.6% $0.1885 Azure premium + full-context 58.5% $0.2051 Azure basic + full-context 54.4% $0.1062 Agentic RAG 53.2% $0.0827 Native PDF (vision LLM) 52.0% $0.2552 LlamaCloud basic + full-context 50.9% $0.1049 Native PDF came 5th of 6 on accuracy and was the most expensive arm at $0.2552 per query. Two findings: Vision underperformed on chart-heavy and table-heavy pages, the territory that the "vision LLMs make OCR obsolete" claim most often points to. Premium OCR with layout extraction held up better there. The native-PDF arm had a 7% intrinsic failure rate (related to PDF file size) that
Expedia ML Scientist II interview experience anyone ? [D]
I have an Initial Technical Screen interview (45 Mins) coming up for ML Scientist II: Agentic AI role, and wanted to know what to expect. Would really appreciate any info. Haven't found much information on this interview experience. Thanks! submitted by /u/Leather_Letterhead96 [link] [comments]
Vision-capable LLMs vs. OCR for long-document (including charts, images, tables, etc.) QA [D]
I benchmarked vision-capable LLMs (the "just attach the PDF and let the model read it" pattern) against OCR-based pipelines on 30 long, image-heavy PDFs from MMLongBench-Doc (https://github.com/mayubo2333/MMLongBench-Doc). There were 171 questions in total, using Claude Sonnet 4.5 as the LLM. Post-retry results: Approach Accuracy $/query LlamaCloud premium + full-context 59.6% $0.1885 Azure premium + full-context 58.5% $0.2051 Azure basic + full-context 54.4% $0.1062 Agentic RAG 53.2% $0.0827 Native PDF (vision LLM) 52.0% $0.2552 LlamaCloud basic + full-context 50.9% $0.1049 Native PDF came 5th of 6 on accuracy and was the most expensive arm at $0.2552 per query. Two findings: Vision underperformed on chart-heavy and table-heavy pages, the territory that the "vision LLMs make OCR obsolete" claim most often points to. Premium OCR with layout extraction held up better there. The native-PDF arm had a 7% intrinsic failure rate (related to PDF file size) that
Vision-capable LLMs vs. OCR for long-document (including charts, images, tables, etc.) QA
I benchmarked vision-capable LLMs (the "just attach the PDF and let the model read it" pattern) against OCR-based pipelines on 30 long, image-heavy PDFs from MMLongBench-Doc (https://github.com/mayubo2333/MMLongBench-Doc). There were 171 questions in total, using Claude Sonnet 4.5 as the LLM. Post-retry results: Approach Accuracy $/query LlamaCloud premium + full-context 59.6% $0.1885 Azure premium + full-context 58.5% $0.2051 Azure basic + full-context 54.4% $0.1062 Agentic RAG 53.2% $0.0827 Native PDF (vision LLM) 52.0% $0.2552 LlamaCloud basic + full-context 50.9% $0.1049 Native PDF came 5th of 6 on accuracy and was the most expensive arm at $0.2552 per query. Two findings: Vision underperformed on chart-heavy and table-heavy pages, the territory that the "vision LLMs make OCR obsolete" claim most often points to. Premium OCR with layout extraction held up better there. The native-PDF arm had a 7% intrinsic failure rate (related to PDF file size) that
Your AI agents need a terminal, not just a vector database
When agentic workflows fail, developers often assume the problem lies in the underlying model’s reasoning abilities. In reality, the limited information provided by the retrieval interface is often the primary limiting factor. Researchers at multiple universities propose a technique called direct corpus interaction (DCI) that lets agents bypass embedding models entirely, searching raw corpora directly using standard command-line tools. The limits of classic retrieval In classic retrieval systems such as RAG, documents are chunked, converted into vector representations (or embeddings), and indexed offline in a vector database. When an AI system processes a query, a retriever filters the entire database to return a ranked "top-k" list of document snippets that match the query. All evidence must pass through this scoring mechanism before any downstream reasoning occurs. But modern agentic applications demand much more. "Dense retrieval is very useful for broad semantic recall, but when an
Robotics
Kawasaki launches Silicon Valley hub to accelerate deployment of physical AI
Kawasaki Heavy Industries has launched a new physical AI development center in Silicon Valley as part of a broader push to accelerate collaboration between Japanese and American companies in artificial intelligence, semiconductors, and robotics. Called the Kawasaki Physical AI Center San Jose, the facility will focus on developing real-world applications for physical AI systems through […]
Business & Industry
California governor signs first US executive order to protect workers from AI job loss
California's governor just signed the first executive order by a US governor aimed at protecting workers from AI-driven job loss. The article California governor signs first US executive order to protect workers from AI job loss appeared first on The Decoder.