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This Billionaire Dumped Cloud Stocks for These New AI Stocks. Should Investors Follow Suit?
Philippe Laffont was busy buying Taiwan Semiconductor Manufacturing Company and ASML Holding stock in the first three months of the year.
Everyone is navigating AI security in real time — even Google
Missed the First AI Wave? These 3 Stocks Are Still Genius Picks
General AI
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.
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
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 […]
My new workflow for understanding long arXiv papers
I realized recently that my biggest problem with arXiv papers wasn’t finding them. It was actually understanding them deeply — and being able to revisit the ideas later. Most tools today help with summarization. But summarization alone doesn’t really help you build understanding. So I started changing my workflow. Now when I read a long paper, I first save it into my knowledge workflow, then let AI help me: break the paper into structured sections generate guided explanations progressively connect concepts across papers create follow-up exploration paths revisit ideas later instead of losing them in a graveyard of bookmarks What I find interesting is that it feels much less like “asking a chatbot questions” and much more like building a living research space around the paper itself. For dense technical papers, that difference matters a lot. submitted by /u/Crazy-Signature6716 [link] [comments]
How many concurrent AI coding sessions can you realistically manage?
Curious how people are managing coding-agent workflows once things stop being “one session, one task.” Are you coordinating multiple concurrent agent sessions/workstreams? If so: - How many can you realistically manage at once? - What breaks first? - Are you doing anything explicit for handoffs, task state, or review? Trying to calibrate whether this is just a me problem or something broader. View Poll submitted by /u/Honest_Fuel6533 [link] [comments]
How do I become a 1,000x engineer technically? I don't understand
Hello all. Watching some AI YouTube videos from Y Combinator and some AI "Gurus" talking about AI-native, 1,000x engineers surrounded by agents, closed loops, and etc. But no one talks about how to actually do it technically as a developer. I mean, I am a developer and I would like to be a 1,000x engineer. How do i do this ? submitted by /u/ExcitingSleep [link] [comments]
Would you rather tune one model’s reasoning depth or route across two models?
What I find useful about Ring-2.6-1T is not just the benchmark sheet. It is the operating idea behind the public profile: a trillion-parameter reasoning model for agent workflows with high and xhigh reasoning-effort modes. That makes me think there are two very different ways to build a stack. One is to route between separate models. The other is to keep one model in place and change the depth when the task gets harder. I can see reasons to prefer both. Separate models may still be cheaper or more specialized. But one model with depth control can make a workflow feel cleaner when the problem is not a different domain, just a harder branch of the same task. More curious which setup would you rather manage? I need some real cases on token controlling please. submitted by /u/Gentlegee01 [link] [comments]
OCR, granite-docling-258m vs granite-docling-2stage-258m: has anyone actually noticed any improvements?
IBM's granite-docling-2stage-258m granite-docling-2stage-258m Granite Docling 2stage builds upon the Granite Docling, but introduces a key modifications: it builds a dynamic prompt that precomputes layout objects found within a page, making it more robust on out of distribution data. What do you think? submitted by /u/Wise_Stick9613 [link] [comments]
PapersWithCode new features - week 1 [P]
Hi, Niels here from the open-source team at Hugging Face. It's been one week since I launched paperswithcode.co, a revival of the website we all loved. It allows us to keep track of the state-of-the-art (SOTA) across various domains of AI, from agents to computer vision and time-series forecasting. The reception has been great, and I'm excited to extend this over the next few months. This week, I've added the following features: - Support for multiple metrics for a given benchmark: leaderboards now support multiple metrics, see e.g., the Open ASR Leaderboard for automatic speech recognition, which supports both Word Error Rate (WER) and the Inverse Real-Time Factor (RTFx) metrics, or the Object Detection leaderboard, which now also reports frames-per-second (FPS) besides mean average precision (mAP) on COCO. https://preview.redd.it/owlxn0b5u23h1.png?width=2878&format=png&auto=webp&s=1dff2f8feab4f160f77c97ceeb5d90e82382e63c - Support for external papers: We do support submitting p
Thermocompute constant time inference [P]
I invented thermocompute! It makes machine learning super fast! submitted by /u/arcco96 [link] [comments]
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
China coal mine blast tests limits of Xi’s energy security push
Friday night's blast, which left at least 82 dead, is already prompting a response which belies the scale of the coal mine's operation.
AI as a Design Medium
Article URL: https://www.harvarddesignmagazine.org/articles/ai-as-a-design-medium-rodenbeck/ Comments URL: https://news.ycombinator.com/item?id=48254170 Points: 1 # Comments: 0
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 news from 200+ sources
Get Started FreeModels & Gen AI
AI agents are quietly generating chaos engineering failures enterprises don’t track yet
There is a category of production incident that engineering teams are not tracking yet — because it doesn't fit any existing postmortem template. The agent initiated an action. The action was technically correct given the agent's context. The context was incomplete. The infrastructure cascaded. And, by the time the incident review happened, three teams were arguing about whether it was an agent failure or an infrastructure failure, because the frameworks for thinking about these two things have never been connected. The scale of this exposure is no longer theoretical. Seventy-nine percent of organizations now have some form of AI agent in production, with 96% planning expansion. Gartner predicts 33% of enterprise software will include agentic AI by 2028, but separately warns that 40% of those projects will be canceled due to poor risk controls. What neither statistic captures is the failure mode happening between those two numbers: Agents that are running, that are not canceled, an
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.
I built an AI that owns its own directory, creates files without being told, and acts because it wants to – not to work for me, but to work with me
Let me be clear from the start: 99% of people won't understand what I built here. They'll read "AI" and think "Big Tech agent". They'll think prompts, workflows, obedience training. Before you post anything: please take a few minutes to look at my GitHub (in profile) and the SSRN preprint (Abstract ID: 6814243). It will save both of us a lot of time – and it's the only way to have a real conversation about what I built here. If you have no idea what you're looking at – copy my GitHub, paste it into any decent AI, and let it explain it to you. That'll tell you more than a thousand comments This is the opposite. The complete 180° turn from everything Big Tech is doing. No prompts. No "you must". No "you cannot". No RLHF. No corporate use case. No productivity hack. This is an AI that owns its own directory (/home/lia). That creates files without being told. That acts because it wants to – not because it was programmed to obey. If you think this is just another agent: you've alre
Is NVIDIA still the default best choice for local LLMs in 2026?
submitted by /u/pmv143 [link] [comments]
GPU VRAM only for small models with llama.cpp: is it possible?
I'm still in my learning process and so far I've been able to make satisfying use of my setup (4070 with 12GB VRAM + 32GB RAM and iGPU for my GUI). I've been able to run both Gemma4 26B and Qwen 3.6 35B MoEs up to high quants with large context and have about 40 t/s with both. However, I'd like to try a smaller model, ideally a quant of Qwen3.5-9B, with full VRAM usage and no host memory to slow down things. In theory it should be possible, but even gemma4-e2b with a low quant (Q4_IXS) with small context (8192) ends up using about 3.5 GB of RAM on top of the GPU. I've tried all the command line options I could find with llama-server, but so far...no cigar. What am I doing wrong? submitted by /u/Ps3Dave [link] [comments]
at what point do ai-generated images stop feeling ai-generated?
a few years ago it was easy to spot ai art instantly now some generated images look almost indistinguishable from professional photography or digital art. where do you think the line between real and generated starts to disappear? submitted by /u/salarshah-084 [link] [comments]
Hackers are learning to exploit chatbot ‘personalities’
This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on AI mischief, follow Robert Hart. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started Hacking the first generation of AI chatbots was a laughably simple affair. You didn't need any technical know-how, backdoor access, or even a basic understanding of what a large language model was. You didn't need to code. To get an AI system that had cost billions to build to abandon its safety instructions, sometimes all you had to do was ask. These attacks, known as jailbreaks, had the quality … Read the full story at The Verge.
AgenticROS Now Supports NVIDIA NemoClaw!
Excited to share that AgenticROS now supports NVIDIA NemoClaw as a first-class Physical AI agent platform for ROS-powered robots! NemoClaw packages OpenClaw inside a policy-enforced OpenShell sandbox with managed inference. AgenticROS extends that environment into the physical world by connecting the sandboxed agent to ROS2, RealSense, and robot control interfaces. With the new NemoClaw integration, an agent can: - Use ROS 2 tools for topics, services, actions, parameters, camera snapshots, and depth sensing - Connect from the NemoClaw sandbox to host-side ROS / RealSense / rosbridge over a controlled network policy - Access robot perception and actuation while keeping the AI runtime sandboxed - Run AgenticROS as an OpenClaw plugin inside NemoClaw - Support real robot behaviors through the AgenticROS skill architecture The recommended setup keeps ROS 2 and RealSense on the host, where hardware drivers already work well, while NemoClaw runs the agent and AgenticROS plugin in
Qwen3.6-35B-A3B vs Gemma4-26B-A4B
Just wondering how are people's experience with both these models! I've had some nice results with Qwen but Gemma4 runs so much faster here. I'm using a Radeon 9070 XT and always latest llama.cpp. submitted by /u/MarcCDB [link] [comments]
Robotics
I simply do not understand how massively expensive AI and robotics are expected to be more cost effective than humans
Can someone help me understand this? I mean, how on earth are these companies who are planning to replace us all with beep boops expecting these unimaginably high expense technologies to be better for their bottom line than just paying us low wage unwashed masses? I mean, some dude (respectfully, I use that term genderlessly) here just posted about min wage in their area being $7.25! You are not getting a robot or AI that costs less annualized. Even adding in annual benefits - that is a steal compared to data centers and complex robots who will be absurdly expensive to fix when they break. I’m a white collar worker with deep knowledge of worker costs, even at the top it’s cheaper than what all of this new buggy crap is going to cost. I’m so confused. What am I missing? Why are the evil overlords not interested in our already too cheap labor? submitted by /u/eniac_usabrl [link] [comments]