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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.
Nvidia Quietly Builds Major AI Portfolio Shaping Investor Risk And Optionality
Nvidia's Jensen Huang Just Delivered Big News to Shareholders. And It May Influence Your Decision on Whether to Buy Nvidia Stock Right Now
General AI
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 […]
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 […]
AI productivity upside could be 10x the current estimates: BofA
Investing.com -- Artificial intelligence could increase global economic growth by as much as 1 percent annually over the next decade, lifting growth from about 3.5% to 4.5%, as the technology drives productivity gains across industries and tasks, according to a Bank of America analyst report.
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.
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.
The unlikely Vatican-Anthropic relationship that's reshaping AI ethics debate
Article URL: https://religionnews.com/2026/05/22/why-anthropic-is-helping-unveil-the-popes-new-encyclical-on-ai/ Comments URL: https://news.ycombinator.com/item?id=48252986 Points: 3 # Comments: 0
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
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.
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]
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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.
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.
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
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
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]
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]
minor speed bump for MTP with Qwen3.6-27B-MTP Q6_K_XL
I'm on Macbook M5 Max with 128GB RAM Running a test in openwebui using llama-server (llama.cpp): unsloth/Qwen3.6-27B-UD-Q6_K_XL.gguf (non MTP): 19tps unsloth/Qwen3.6-27B-UD-Q6_K_XL.gguf (MTP): 22.3tps So nothing like the massive improvements I hear about. Possibly my own settings though. both use: --temp 0.6 --top-p 0.8 --top-k 20 --min-p 0.00 --cache-ram 24576 --batch-size 4096 --ubatch-size 2048 edit: forgot to add that I was using --spec-draft-n-max 2 have changed to 3 and also added --spec-draft-p-min 0.75 and now get 24.5tps (for gen) submitted by /u/chimph [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