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I tried Amazon’s Bee wearable and am both intrigued and slightly creeped out
Like other AI wearables, Amazon's Bee offers an odd combination of convenience and privacy anxiety.
Hackers are learning to exploit chatbot ‘personalities’
Nvidia Quietly Builds Major AI Portfolio Shaping Investor Risk And Optionality
General 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
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
AI didn't just commoditize content — it made credibility the scarcest resource on the internet. What comes next will reward experts, not entertainers.
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 […]
Did SpaceX’s $28.5 Trillion Bombshell Reveal Elon Musk’s Genius — or Peak Galaxy Brain?
The stock market has spent the past year rewarding companies tied to artificial intelligence, data centers, and energy infrastructure. Yet all of those themes may soon take a back seat to a single IPO. On June 12, SpaceX is expected to begin trading publicly in what many investors believe could become the largest IPO in ... Did SpaceX’s $28.5 Trillion Bombshell Reveal Elon Musk’s Genius — or Peak Galaxy Brain?
Salesforce Touts AI Promise over Reality in SaaSpocalypse Fight
Article URL: https://www.bloomberg.com/news/articles/2026-05-22/salesforce-touts-ai-promise-over-reality-in-saaspocalypse-fight Comments URL: https://news.ycombinator.com/item?id=48257522 Points: 2 # Comments: 1
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 […]
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
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?
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.
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]
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.
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
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
Built a production RAG chatbot with custom MCP servers as the action layer, sharing what I learned
I've been building agentic tooling at work and wanted to share one pattern that worked. Instead of a chatbot that only retrieves and answers, I wired custom MCP servers in as the action layer, so staff trigger live workflows (create records, pull reports, start processes) from natural language. A few takeaways: Separating retrieval (RAG over docs) from actions (MCP tools) made the system far easier to debug Most of the real work was edge cases in how the model decides when to act vs answer Clear tool descriptions mattered more than prompt tuning Happy to go deeper in comments. I'm a full-stack engineer, in SF May 26 to June 10 looking for my next role in AI/agents, so if your team works on this, feel free to reach out. submitted by /u/ViPeR9503 [link] [comments]
AI agents generate code fast, but they still don’t understand blast radius
I’m building a VS Code extension called Ripple because I kept seeing the same problem with AI coding agents: They can generate code fast, but they often don’t know what a change will affect. A file can look small. A utility can look safe. A hook or config file can look simple. Then the AI edits it, and suddenly other parts of the project break because the agent didn’t know the blast radius. So Ripple tries to give AI agents local codebase context before they edit. It scans a JS/TS project locally and generates: - what imports a file - what depends on it - risky/shared file signals - agent workflow guidance - focus files for safer edits - local architectural history It does not upload code. No account. No telemetry. It runs locally inside VS Code. I tested it on a local clone of an open-source TypeScript repo. Manual search showed direct text matches, but Ripple surfaced a wider file-level impact path. I’m not claiming this solves everything. It’s not a replacement fo
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]