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Curated from 200+ sources across AI & machine learning
Nvidia Just Announced a Major Quantum Computing Development
The chipmaker is combining artificial intelligence (AI) with quantum computing.
OGMA – persistent memory and dual-brain AI, newcomer seeks pro feedback
When Does LLM Self-Correction Help? A Control-Theoretic Markov Diagnostic and Verify-First Intervention
General AI
Under cover of trade truce with Trump, China expands economic pressure toolkit
Beijing is punishing those who shift supply chains from China, tightening rare earth licensing, banning foreign AI and cybersecurity tech and weighing curbs on its solar gear.
Nvidia Just Did This For the First Time Since October. Here's What Happens Next, According to History
Is Nvidia once again the ticket to an AI win?
Ask HN: Anyone want to collaborate on a local-first AI-based research assistant
Hi HN Community, I'm Venkatram, a sophomore who's on a mission to build a local alternative to proprietary third-party AI-based research assistants. The idea is to turn documents into researchable assets that contain as much as information as the original information does, but it's more reusable. Well, quite frankly, this is still under a WORK IN PROGRESS, so i'm still figuring on how it can be properly used, and I got to be honest here, i definitely need some help to build this, so if you wish, you are welcome! TlDR: NotebookLM, but Locally with your OWN AI Model Github: https://github.com/venkatram-s/gigabook-lm Comments URL: https://news.ycombinator.com/item?id=47914594 Points: 2 # Comments: 0
AI and Digitalization in HTA: Enhancing Evidence, Equity and Efficiency [video]
Article URL: https://www.youtube.com/watch?v=2wN0D-AqUQQ Comments URL: https://news.ycombinator.com/item?id=47917310 Points: 1 # Comments: 0
Microsoft Corporation (MSFT) Partners With Stellantis
Microsoft Corporation (NASDAQ:MSFT) is one of the Best Stocks to Buy While the Market Is Down. On April 16, Reuters reported that Microsoft Corporation (NASDAQ:MSFT) and Stellantis have entered a five‑year strategic partnership. As part of this collaboration, Stellantis will rely heavily on Microsoft’s cloud, AI, and cybersecurity expertise to modernize its digital and engineering […]
The Bull Case For Alphabet (GOOGL) Could Change Following Massive AI Bet On Anthropic Partnership
Alphabet’s Google confirmed it will invest up to US$40.00 billion in AI startup Anthropic, starting with US$10.00 billion in cash and up to US$30.00 billion tied to performance milestones, while deepening cloud and chip supply commitments across its rapidly expanding AI infrastructure footprint. This large-scale Anthropic commitment, combined with rising AI-focused capex and new TPU chips, underlines how central enterprise AI and cloud workloads have become to Google’s long-term business...
To buy this Bay Area home, you’ll need Anthropic equity
Someone’s offering an unusual deal for a 13-acre property in Mill Valley, just north of South Francisco.
![[AINews] DeepSeek V4 Pro (1.6T-A49B) and Flash (284B-A13B), Base and Instruct — runnable on Huawei Ascend chips](https://zmstgxtziqmvvwzllahg.supabase.co/storage/v1/object/public/article-images/latent-space/827ea690-19bb-4616-85a9-cac721b882c1.png)
[AINews] DeepSeek V4 Pro (1.6T-A49B) and Flash (284B-A13B), Base and Instruct — runnable on Huawei Ascend chips
The prodigal Tiger returns... but is no longer the benchmarks leader.
![[Overcapacity] Factories Can No Longer Outrun AI](https://zmstgxtziqmvvwzllahg.supabase.co/storage/v1/object/public/article-images/exponential-industry/6beeadc0-149c-4825-b746-1993c79a8caa.jpg)
[Overcapacity] Factories Can No Longer Outrun AI
Paid reads and analysis for "Factories Can No Longer Outrun AI"
Why Cohere is merging with Aleph Alpha
Canadian AI startup Cohere is taking over Germany-based Aleph Alpha with support from Lidl’s owner, Schwarz Group. With the blessing of their governments, the companies intend to offer a sovereign alternative to enterprises in an AI landscape dominated by American players.
Discord Sleuths Gained Unauthorized Access to Anthropic’s Mythos
Plus: Spy firms tap into a global telecom weakness to track targets, 500,000 UK health records go up for sale on Alibaba, Apple patches a revealing notification bug, and more.
'Too Dangerous to Release' Is Becoming AI's New Normal
submitted by /u/simrobwest [link] [comments]
Forecasting is Way Overrated, and We Should Stop Funding It
Summary EA and rationalists got enamoured with forecasting and prediction markets and made them part of the culture, but this hasn’t proven very useful, yet it continues to receive substantial EA funding. We should cut it off. My Experience with Forecasting For a while, I was the number one forecaster on Manifold. This lasted for about a year until I stopped just over 2 years ago. To this day, despite quitting, I’m still #8 on the platform. Additionally, I have done well on real-money prediction markets (Polymarket), earning mid-5 figures and winning a few AI bets. I say this to suggest that I would gain status from forecasting being seen as useful, but I think, to the contrary, that the EA community should stop funding it. I’ve written a few comments throughout the years that I didn’t think forecasting was worth funding. You can see some of these here and here. Finally, I have gotten around to making this full post. Solution Seeking a Problem When talking about forecasting, people of
AI Agency Marketplace
Everyone that’s started an AI Agency and struggling to get clients I want your opinion. Let’s say there was a website that let you sign up. It matched you with potential clients maybe 1 a week. When you’re matched it would be alongside 2-4 other agencies. You have to create a pitch deck for the company in question and hope they choose you. The company’s details and answered questions will be provided. Would that be helpful to you ? Would you use it alongside your current outreach ? Let me know ! submitted by /u/TechnologyTraining94 [link] [comments]
Higher precision or higher parameter count
I’m wondering if we take models of the same family (e.g qwen3.5 moes). And we compared ggufs that are of different core counts different quantizations but similar sizes. Which model would be better for tasks? If it varies I’m mostly interested in coding and tool calling. An example is qwen3.5 122b ud-iq2_xxs is 36.6gb and Qwen3.5 35b q8_0 is 36.9gb Which would be better at coding/tool calling? In spirit of the same question how interesting is it to run very large models like kimi 2.6 at 1bit precision vs smaller models at higher precisions. submitted by /u/redblood252 [link] [comments]
AI news from 200+ sources
Get Started FreeModels & Gen AI
The New Linux Kernel AI Bot Uncovering Bugs Is a Local LLM on Framework Desktop
Article URL: https://www.phoronix.com/news/Clanker-T1000-AMD-Ryzen-AI-Max Comments URL: https://news.ycombinator.com/item?id=47914388 Points: 5 # Comments: 0
Proposed New Test of AI Capabilities:)
When Microsoft can turn a fleet of LLMs loose on the Azure UX, and Google can do the same for the Google Adwords UX, and reduce their level of dreadfulness substantially, it will go a long way to showing that frontier models are as good as claimed. Comments URL: https://news.ycombinator.com/item?id=47911835 Points: 1 # Comments: 0
500 investment bankers review AI outputs and find none ready for client delivery
A new benchmark puts top models like GPT-5.4 and Claude Opus 4.6 to work on the kinds of tasks junior investment bankers handle every day. Not a single AI output was rated ready to send to a client; the results are too imprecise or flat-out wrong. Still, more than half of the bankers said they'd use the output as a starting point. The article 500 investment bankers review AI outputs and find none ready for client delivery appeared first on The Decoder.
Anthropic created a test marketplace for agent-on-agent commerce
In a recent experiment, Anthropic created a classified marketplace where AI agents represented both buyers and sellers, striking real deals for real goods and real money.
updated the AI tools library, new tools, a whole new category, and a new home
been getting DMs asking about tools that don't fit the usual "AI coding assistant" box. so i finally did something about it. tolop.space (yes, new domain — more on that below) what's new: added Atoms :- multi-agent app builder where 7 AI roles (PM, engineer, architect, SEO specialist, data analyst, researcher, team lead) collaborate to build your product. has a genuine forever-free plan with 15 credits/day, not a time-limited trial. added Leadline :- finds Reddit posts where people are actively looking to switch tools or asking for recommendations, with AI-drafted replies included. starts at $9/month which is the cheapest Reddit lead tool i've found. but the one i'm most excited about is Transcrisper :- and it's the reason i added a whole new category. niche tools :- for single-purpose utilities that are completely free, do one thing well, and don't fit anywhere else. Transcrisper is a good example of what belongs there. free, unlimited audio/video transcription that runs entire
Built cross-model persistent memory - told GPT-5 Nano I live in Bahrain, asked Sonnet 4.6 where I live, it knew instantly
https://reddit.com/link/1svixo0/video/hgwrueuekdxg1/player No tricks, no copy-paste. Two completely different AI models, separate conversations - one remembers what the other was told. The way it works: every message gets embedded and stored. When you open a new chat with any model, your memory is injected into context automatically. GPT, Claude, Gemini, Grok and DeepSeek - they all share the same memory layer. So when I told GPT-5 Nano "I live in Bahrain" and then opened a fresh Claude Sonnet 4.6 conversation and asked "where do I live?" - it said "Based on your memory, you live in Bahrain 🇧🇭" Live on asksary.com now submitted by /u/Beneficial-Cow-7408 [link] [comments]
Does a HITL review UI for LLM outputs exist or do I have to build it myself?
Hello everyone, Working on a project where I rely on LLMs to handle certain tasks, I've implemented a basic HITL (Human in the Loop) pipeline where a human reviewer can approve or reject LLM-generated content based on a confidence percentage. When I started looking for existing tooling for this, I couldn't find anything that really fits. most of what comes up is data labeling software, which isn't quite what I need. What I'm looking for is something that: recieve json data renders some input fields for review, based on the data structure shows the source of truth side by side with the generated output, so the reviewer can edit stuff, correct them, and approve I've already built a basic version of this, but before going further I wanted to check, does anything like this exist off the shelf? this would save me some time. Thanks. submitted by /u/Several-Art-7186 [link] [comments]
Robotics
The Biggest Risk of Embodied AI is Governance Lag
arXiv:2604.21938v1 Announce Type: cross Abstract: Embodied AI is widely discussed as a job-displacement problem. The deeper risk, however, is governance lag: the inability of public institutions to keep pace with how fast the technology spreads through the physical economy. As reusable robotic platforms are combined with increasingly general AI models, embodied AI may scale across manufacturing, logistics, care, and infrastructure faster than governance systems can observe, interpret, and respond. We argue that this lag appears in three connected forms: observational, institutional, and distributive. The central policy challenge, therefore, is not automation alone, but whether governance and compliance systems can adapt before disruption becomes entrenched.
Factories Can No Longer Outrun AI
This week: Hannover Messe 2026, Rapid + TCT, Google Cloud Next TPU, MMA Ops, humanoid marathons faster than humans, financeable nuclear projects, AI designs IoT hardware, cars, and planes.
The Strategic Value of Legacy Components in Automation
The narrative in modern manufacturing often centers on the cutting edge: AI-driven robotics, hyper-connected IIoT ecosystems, and autonomous logistics. While this rapid innovation drives the industry forward, it creates a stark contrast with the reality on the factory floor. In many facilities, the backbone of production remains robust, reliable hardware that has been running effectively […]
Business & Industry
Musk OpenAI Lawsuit Puts Microsoft AI Partnership And Governance Under Spotlight
Elon Musk's lawsuit against OpenAI, Microsoft, and Sam Altman is moving toward trial, challenging OpenAI's shift from nonprofit roots to a profit-driven structure. The case raises questions about Microsoft's role as a major OpenAI partner and investor, along with issues around governance and control in large AI collaborations. The outcome could influence how AI partnerships are structured and how regulators and the public view Big Tech involvement in advanced AI development. For Microsoft...
Developer Tools
V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach
arXiv:2604.22196v1 Announce Type: cross Abstract: Coordinating the motions of multiple autonomous vehicles (AVs) requires planning frameworks that ensure safety while making efficient use of space and time. This paper presents a new approach, termed variable-time-step spatio-temporal corridor (V-STC), that enhances the temporal efficiency of multi-vehicle coordination. An optimization model is formulated to construct a V-STC for each AV, in which both the spatial configuration of the corridor cubes and their time durations are treated as decision variables. By allowing the corridor's spatial position and time step to vary, the constructed V-STC reduces the overall temporal occupancy of each AV while maintaining collision-free separation in the spatio-temporal domain. Based on the generated V-STC, a dynamically feasible trajectory is then planned independently for each AV. Simulation studies demonstrate that the proposed method achieves safe multi-vehicle coordination and yields more t