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Alphabet Stock Investors Just Got Great News From a Wall Street Analyst. It's Bad News for Nvidia
Alphabet is ramping up efforts to compete with Nvidia in the market for AI accelerators.
How Turkey Hacked the Hair Transplant Industry
‘What a joke’: Github Copilot’s new token-based billing spurs consternation among devs
Models & Gen AI
If ChatGPT disappeared tomorrow, what AI tool would you switch to?
Just a fun question. If you suddenly couldn't use ChatGPT anymore, which AI tool would become your daily driver and why? Interested to see what people here are actually using besides the obvious options. submitted by /u/ritik_bhai [link] [comments]
Use any model and any provider with the official OpenAI Codex Desktop App, without modifying its code, and continue to use the official models in parallel?
All in the title. The official OpenAI Codex Desktop App only accepts models that are from OpenAI and from a curated list. But there is a trick, you can make it think you are using the official OpenAI servers and models by just impersonating the model name and using another one. There is three things you need to do 1) The first one is to modify the officiel config of the codex desktop app. This is none intrusive and doesn’t break the app in anyways, and can also be reverted. You can do that by going in the settings and clicking the « open config.toml » link. Then you need to modify the official config by pointing it to your own server where your models are living. model = "gpt-5.3-codex" model_provider = "multivibe" model_reasoning_effort = "xhigh" personality = "pragmatic" sandbox_mode = "danger-full-access" approval_policy = "never" [model_providers.multivibe] name = "Multivibe" base_url = "http://127.0.0.1:1455" wire_api = "responses" env_key = "MULTIVIBE_API_KEY" 2)
Speed difference between Windows 11 and Linux with llama.cpp: a myth when using medium and large MoE models
As the title says, there is no speed difference between Linux and Windows when using llama.cpp. I myself kept two operating systems on my computer for a long time because of this misconception. But when I got tired of constantly switching, I decided to check how much performance I’d lose if I moved to Windows. First, a brief overview of the PC used in these tests: - CPU: Core Ultra 7 265KF under water cooling, with a slight overclock to 5.6/4.7 GHz core frequencies - Motherboard: Asus Z890 with three PCIe slots, two of them PCIe 4.0 x4 - RAM: Kingston Beast DDR5 192 GB (4×48 GB) at 6400 MHz, with slightly reduced voltage and relaxed timings to keep temperatures down - GPUs: Nvidia GeForce RTX 5080 16 GB + RTX 5060 Ti 16 GB + RTX 5060 Ti 16 GB, all undervolted with a slight memory overclock - PSU: 1200 W 80 Plus Gold — 1000 W would have been enough, but I went with headroom from the start Operating systems used: Ubuntu 26.04 with KDE and GNOME — I also ran one test with Xfce — an
PolyRange: Contamination-resistant offensive-AI benchmark for web targets (that ain't a benchmark, THAT's a benchmark)
Author here. The short version of why I built this: Cyber-AI evaluation is converging on the same diagnosis from multiple labs. Anthropic's Claude Mythos system card this year: their cyber ranges "lack many features often present in real-world environments such as defensive tooling," and CTF-style benchmarks are saturated to the point Anthropic is questioning whether to continue reporting them. UK AISI's most recent multi-step cyber paper (Folkerts et al.): "No active defenders. Our ranges are static." OpenAI's Trustworthy Third-Party Evaluations playbook: "Evaluators should prefer private or newly constructed tasks where possible." Carlini at DeepMind, last year on Latent Space: stop relying on standardised public benchmarks; construct private custom ones. The diagnosis is converging. The methodology piece is what was missing. PolyRange operationalises the diagnosis. Every deploy is freshly LLM-generated by the researcher's choice of generator model — so OpenAI's "newly constructed
I built mlx-Chronos — a community benchmark leaderboard for local LLM engines on Apple Silicon (oMLX, Rapid-MLX, mlx-lm, Ollama) [P]
Hey! I'm a CS student and I got tired of not being able to compare MLX inference engines properly — every benchmark out there is either made by the engine's own developers, runs on an M3 Ultra nobody has, or just shows tok/s with zero context. So I built mlx-Chronos — a small open source CLI tool that runs a standardized benchmark protocol on your Mac and lets you submit your results to a shared community leaderboard. What it measures: Cold and cached TTFT (Time to First Token), with a proper methodology — unique prompts per trial, cache priming, no interleaved phases Throughput (tok/s), with mean/stddev/min/max across repeated trials Engine process RSS and system RAM peak, sampled continuously during inference Thermal state and hardware info Supported engines: oMLX, Rapid-MLX, mlx-lm, Ollama (MLX backend) The leaderboard is basically empty right now since I only have an M2 8GB. Would love results from M3 Max, M4, M4 Ultra, or anything with more RAM — that's where things ge
The Biggest Tell That Something Was Written by AI
Article URL: https://www.theatlantic.com/technology/2026/05/how-to-tell-ai-writing/687345/ Comments URL: https://news.ycombinator.com/item?id=48338514 Points: 3 # Comments: 1
GitHub Copilot charges GPT 5.5 with a 57x multiplier per request from June first
Article URL: https://docs.github.com/en/copilot/reference/copilot-billing/request-based-billing-legacy/model-multipliers-for-annual-plans Comments URL: https://news.ycombinator.com/item?id=48339069 Points: 3 # Comments: 0
I put Google’s 24/7 AI assistant Gemini Spark to work, and it’s actually pretty useful
Gemini Spark helps automate everyday tasks, from inbox summaries to local event planning, but it’s unclear why Google made it a separate product.
Amazon’s AGI in the Real World: Why Visa Just Spent Millions on AI Agents for the Future of Commerce
Amazon isn’t just building AI—it’s building AI that moves furniture, delivers groceries, and negotiates with suppliers. And Visa believes this will dominate global commerce. Here’s why. The Deep-Dive: Amazon’s new Prime skunkworks division is deploying “physical-world agents” (yes, real robots with intent). These aren’t drones; they’re AI systems integrated with IoT devices, logistics networks, and even brick-and-mortar stores. Visa’s $1B investment in Replit (via their acquisition) isn’t just about code—it’s about enabling seamless payment integration with these agents. •What’s happening: Agents can now autonomously manage supply chains. Imagine an AI warehouse manager that reroutes shipments in real-time based on demand spikes. •Visa’s angle: They see agentic commerce as the next wave. Agents will handle end-to-end transactions, from a smart kitchen ordering out-of-stock items to a hotel concierge book your entire trip. •Reddit trend tie-in: A r/Artificial post this week showed
mudler/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-APEX-MTP-GGUF just released !
Description of the module: I host 30+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory) — enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled — APEX-MTP GGUF APEX (Adaptive Precision for EXpert Models) quantizations of lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled, with the MTP (multi-token prediction) head bundled for in-the-box self-speculative decoding. What's different from the plain APEX repo? These GGUFs bundle the model's MTP (multi-token prediction) head alongside the trunk in a single file, courtesy of llama.cpp PR #22673. With a recent llama.cpp (>= commit 255582687) you can enable self-speculative decoding using just this one file — no separate draft model needed: llama-ser
Made a program using LocalLLM based on llama.cpp for fellow Book Lovers!
TL;DR: I built an Ebook reader embedded with a compact translation model. Hi! I know this post has a promotional nature, but it contains a concept that I believe readers who love books will appreciate, so please take a look. While talking to an AI developer from an English-speaking country living in the Middle East, I complained that the books I wanted to read weren't translated into Korean. When I suggested that we no longer need to carry English-Korean dictionaries like in the past and that AI could handle the translation, he agreed it was a great idea. That’s when I started development. He also strongly recommended that I promote this on the r/LocalLLaMA subreddit, saying that the community is tech-savvy and would have a lot of insights to offer. (Yes, I actually visit r/LocalLLaMA often myself. Using an LLM without security concerns is everyone's dream. I haven't achieved it yet due to financial constraints, but based on my experience renting GPUs, I believe a 70B model would sat
Robot foundation models keep hiding behind fine-tuning numbers. Wall-OSS-0.5 is trying a different approach
Most robot foundation model demos are hard to interpret because the impressive number usually comes after task-specific fine tuning. Wall-OSS-0.5, a new open-source VLA release from X Square Robot, is interesting because the report tries to measure what the pretrained checkpoint can do before that extra adaptation step. The setup is a 4B vision-language-action model built around a 3B VLM backbone plus action-generation components. According to the report, the pretrained checkpoint was evaluated on a 17-task real-robot suite without task-specific fine tuning. Four tasks crossed 80 task progress: block sorting, fruit sorting, ring stacking, and a held-out deformable task, rope tightening. The part that seems more important than the raw score is the framing. In language models, nobody would accept only a fine-tuned downstream score as evidence that pretraining worked. With robots, that has been much harder because the evaluation is physical, slow, embodiment-dependent, and expensive. A
Deepeseek inside claude code -Easist way
For those who cant afford claude models and wanna use claude code, deepseek v4 pro is closest best and cheapest option. How to use deepseek API inside claude code (easist way ever): We will use AI to replace AI. Just feed your existing claude code this prompt "Yo Claude, you’re expensive af 💀 Do everything needed to fully switch Claude Code to DeepSeek API automatically. Set up the complete settings.json config, API integration, model selection, base URL, env variables, testing, debugging, and optimization for low cost + strong coding performance. Use this DeepSeek API key: "sh......................" Make it fully working, minimal, and production ready." Thats it! Thank me later! submitted by /u/Agreeable-Pen-9763 [link] [comments]
Is this even real ?
I randomly came across this and honestly I can’t tell if it’s real or one of those AI demos that looks impressive but doesn’t actually work. From what I understand, it’s claiming you can fine-tune models, do image training, test them in a playground, and deploy them as an API from a phone. That sounds a little too convenient, which is why I’m skeptical. I haven’t tried it myself yet, but I’m curious if anyone here has. submitted by /u/Raman606surrey [link] [comments]
AI news from 200+ sources
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If You're Building Generational Wealth in 2026, This AI Stock Deserves a Spot in Your Portfolio
This small data engineering company might be an overlooked gem in the AI boom.
NVIDIA (NVDA): The Best Debt-Free S&P 500 Stock to Buy Now
NVIDIA Corporation (NASDAQ:NVDA) is one of the 10 Best Debt-Free S&P 500 Stocks to Buy Now. On May 27, 2026, NVIDIA Corporation (NASDAQ:NVDA) CEO Jensen Huang said the company plans to invest about $150B per year in Taiwan, Reuters’ Wen-Yee Lee reported. Huang described Taiwan as the “epicentre” of the AI revolution and a long-term […]
Google Cloud Strikes Major AI Deal With European Buyout Giant EQT
EQT has struck a deal with Alphabet Inc.‘s Google Cloud aimed at speeding up artificial intelligence projects across more than 300 companies held in the buyout firm's portfolio. The arrangement gives these businesses access to Google Cloud's AI and security...
1 Thing Investors Should Know About Meta's New Subscription Strategy
Meta is rolling out subscription tiers to its AI chatbot.
Snowflake CEO says monster quarter shows why software firms need new pricing models to thrive in AI age
Sridhar Ramaswamy predicts that companies reliant on seat-based income will scramble to justify their premiums as employees use AI to accomplish an immense amount of work.
New AI model finds a cheaper path to healthier eating
Breakfast cereal bowls, deli sandwiches, pizza dinners, soups, yogurt plates. Most people do not eat from a blank slate, they eat from habit. That is part of what makes nutrition advice so hard to follow. It is also part of what a new artificial intelligence system tried to solve. submitted by /u/Brighter-Side-News [link] [comments]
SoftBank pledges €75B to build Europe's biggest AI facility in France
Article URL: https://www.ft.com/content/1022f9bd-5b6d-44a5-9303-c8b05b8c6463 Comments URL: https://news.ycombinator.com/item?id=48339542 Points: 1 # Comments: 1
Meta is reportedly developing an AI pendant
Meta seems to be making big bets on AI-powered hardware.
Hermes.... All In
After understanding how much I don't know and how much I have to learn I am going to place my first bet on the AI casino table , and for now it's Hermes. I have also decided to go the locally hosted route and would be very grateful if successful user of Hermes share with me there physical stack (p. S I ha never touched i. Os) and any additional setup do's and dont's specifically surrounding using Hermes! TIA! submitted by /u/TasteCertain4323 [link] [comments]
Google’s AI mode is threatening me… i was just trying to look up a family guy clip…
submitted by /u/Early_Mail9268 [link] [comments]
Terence Tao argues AI could bring division of labor to math for the first time in history
Mathematician Terence Tao describes how AI could reshape math research by enabling division of labor for the first time. Until now, researchers had to master every step themselves, from framing problems to verifying results. Tao sees "industrial mathematics" emerging: large AI-supported teams instead of lone geniuses, with humans staying indispensable for "inspired guesses." The article Terence Tao argues AI could bring division of labor to math for the first time in history appeared first on The Decoder.
![[AINews] Founders and Forward Deployed Engineers](https://zmstgxtziqmvvwzllahg.supabase.co/storage/v1/object/public/article-images/latent-space/f3a79a00-96c4-4e6c-8c6c-6d0d2bf99078.jpg)
[AINews] Founders and Forward Deployed Engineers
a quiet day lets us highlight the new AIE WF focuses
The Week’s 10 Biggest Funding Rounds: Anthropic Dominates In An Otherwise Slower Week For Megarounds
Venture funding has always been a world of haves and have nots. And these days, the haves are having more than ever. Case in point this week was generative AI giant Anthropic's $65 billion Series H funding, followed by a billion-dollar funding for an AI software developer.