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Nvidia's Jensen Huang Just Delivered Big News to Shareholders. And It May Influence Your Decision on Whether to Buy Nvidia Stock Right Now
During Nvidia's earnings report, Huang talked about the next chapter of the AI story.
I was one of the internet’s first influencers. AI just killed the whole category — and created something better
Hackers are learning to exploit chatbot ‘personalities’
General AI
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...
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.
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.
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 […]
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]
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
Show HN: Directionally bad – a newsletter about risks of AI centralization
Hello, I'm sultan, a Research Analyst working to analyze and explore the world of decentralized AI. I have launched a new newsletter for everyone curious about where DeAI is going, and what's up with the centralized AI labs. Would be glad to hear your feedback! Comments URL: https://news.ycombinator.com/item?id=48246326 Points: 1 # Comments: 0
Sci-Hub has created a new AI chatbot. Is it any good?
Article URL: https://cen.acs.org/policy/publishing/Sci-Hub-created-new-AI/104/web/2026/04 Comments URL: https://news.ycombinator.com/item?id=48243836 Points: 4 # 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.
Japan may add farming and anime courses to technical colleges
In response to many local governments aiming to establish new "kōsen" technical colleges, the ministry hopes to develop talent in a wide range of areas.
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
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
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?
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.
How VCs and founders use inflated ‘ARR’ to crown AI startups
Some AI startups are stretching traditional revenue metrics when talking about progress publicly. And their investors are fully aware.
Stellantis expands Qualcomm partnership for AI-powered vehicle platforms
Stellantis and Qualcomm Technologies have announced an expansion of their multi-year technology collaboration to power next-generation Stellantis vehicles with Qualcomm Technologies’ Snapdragon Digital Chassis system-on-chips (SoCs). The expanded collaboration integrates Snapdragon Digital Chassis solutions with STLA Brain, Stellantis’ electronic and software platform, enhancing cockpit, connectivity and advanced driver assistance system (ADAS) performance. The scalable technology […]
AI news from 200+ sources
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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.
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
Bateschess – Chess Analytics Feeding Stockfish Evals into LLM's
Article URL: https://bateschess.com Comments URL: https://news.ycombinator.com/item?id=48253002 Points: 2 # Comments: 0
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 […]