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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


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...

Both of these tech titans are riding the artificial intelligence wave, but one of them seems like a better long-term investment than the other.

The tech giant just landed another AI infrastructure win.

Someone’s offering an unusual deal for a 13-acre property in Mill Valley, just north of South Francisco.
![[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)
Paid reads and analysis for "Factories Can No Longer Outrun AI"
![[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)
The prodigal Tiger returns... but is no longer the benchmarks leader.

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

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.

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.
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]
submitted by /u/simrobwest [link] [comments]

The U.S. president said that Iran plans to make an offer aimed at satisfying U.S. demands, but added that he did not yet know what the offer entailed.
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 FreeWhen 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

Meta Platforms is rolling out computer tracking software to employees as part of its Model Capability Initiative to train AI agents on white collar tasks. The system records keystrokes, mouse movements, and screen activity with no opt out, raising internal concerns about privacy and digital surveillance. This internal AI data collection effort aims to support more capable automation tools for office work, potentially affecting Meta's culture and regulatory risk profile. For investors...
Article URL: https://squishplugin.dev/ Comments URL: https://news.ycombinator.com/item?id=47915017 Points: 1 # Comments: 0

Article URL: https://chatgpt.com/share/69ee4690-60ac-83ea-b28c-f4ce6284a75a Comments URL: https://news.ycombinator.com/item?id=47911892 Points: 4 # Comments: 0

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.

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.

DeepSeek unveiled the V4 Flash and V4 Pro series on Friday, touting top-tier performance in coding benchmarks and big advancements in reasoning and agentic tasks.
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
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]
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]

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 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 […]