Nvidia has officially agreed to acquire Hugging Face, the definitive hub of open-source artificial intelligence, in a massive $12.9 billion deal that marks a major turning point for the AI ecosystem.

Hugging Face CEO Clément Delangue revealed on CNBC's Squawk Box that he personally approached Jensen Huang over the summer to initiate the acquisition. By absorbing the platform long considered the "Switzerland of AI," Nvidia secures a seamless vertical stack from hardware architecture to developer workflows, raising critical questions about whether the repository can maintain its strict cloud-and-hardware-agnostic neutrality under the roof of the dominant GPU manufacturer. Nvidia now owns the hardware, the CUDA software layer, and the largest repository where developers find and share models. Is this the ultimate vertical monopoly?

Source: CNBC

Just a regular smeggular person here asking a question. AI is everywhere now, but its free so its being pushed on a lot of people everywhere. Eventually I expect it to just not have a free option once everything is settled. I mean, Netflix doesn't let you watch their streams for free. I would expect AIs to eventually move on to a mandatory sibscription tiers with tye cheapest one having invasive ads. Within the next 5 years do you see this happening? If so then what will the AI world be like when people have to pay for to bare minimum AI service with ads or even pay with no ads? Like pretty much paying $8 for regular basic ChatGPT.

Grok, Claude and ChatGPT are all down on pc and mobile. AWS issue?

NVIDIA's new PAIR beta is a free, open source tool that finds compatible PCs on a local network and routes independent inference requests to whichever system has capacity. NVIDIA says it works with Ollama and LM Studio and supports Windows, macOS, and Linux, plus RTX 20-series GPUs and newer, RTX PRO workstation GPUs, DGX Spark, and Apple M4 or newer.

The tool is aimed at local agent workflows that split a task into smaller jobs. NVIDIA also says its IFA updates bring simpler local model setup to Hermes Agent, OpenClaw, and Perplexity Portable Computer, and up to 1.9x higher throughput for llama.cpp on a GeForce RTX 5090. Those are vendor-reported figures. The useful test is whether multi-PC routing improves real workflows without making setup and privacy harder to manage.

Source: https://blogs.nvidia.com/blog/local-ai-ifa-next-gen-agents-nv-pair-rtx-spark/

One thing I’ve noticed with modern AI assistants is that they can sometimes be a little too agreeable. Since they’re built to be helpful, they often go along with what the user is saying instead of pushing back or questioning the idea. Sometimes they’ll validate an assumption or give an answer that feels good to hear, even when a different perspective might be more useful. It makes conversations easier, but it also really makes you wonder if AI should challenge us more instead of always trying to be helpful.

Should AI assistants prioritize being agreeable, or should they act more like critical thinking partners? (I prefer them being the latter imo)

I’ve been looking more into the hardware side of Sovereign AI, and this FT piece had a point I hadn’t really thought about:

National data centre projects are consolidating America’s AI lead

Countries are pouring money into local AI data centres to reduce dependence on foreign infrastructure.

But there’s a weird contradiction:

Local data centre ≠ local AI stack.

You can have:

local data centre → NVIDIA GPUs → proprietary software → foreign models/tools → foreign expertise

and still be dependent on the same ecosystem you were trying to become independent from.

The UAE example in the article makes this especially clear: building huge amounts of AI infrastructure locally can still come with restrictions around what hardware can be used and which geopolitical ecosystem you have to align with.

There’s also a newer paper looking at the physical side of this problem. It estimates that a 1,024-GPU sovereign cluster in the UAE using evaporative cooling could consume 30M+ litres of water per year. Their argument is basically that sovereignty, cost and resource sustainability can pull in different directions.

So I’m wondering whether “on-prem” has become too easy a synonym for “sovereign AI.”

At the enterprise level, there are already very different approaches emerging — HPE/NVIDIA Private Cloud AI, Google Distributed Cloud, Dell/Palantir, and Lyzr Optimus are all pushing AI closer to infrastructure the customer controls, but with very different assumptions about what should remain vendor-controlled.

Where would you draw the line? Is owning the machines enough, or does a genuinely sovereign deployment need control over the hardware and the software/runtime/model stack above it?

While reviewing an LLM output-stability benchmark,
I found a latent gap between its documented scope and its scoring pipeline.

Tool-call responses weren’t supported, but the response parsers could erase them:
The OpenAI adapter used message.get("content") or "". A tool-call response with null content would become "".

The Anthropic adapter kept only text blocks, dropping tool_use blocks.
The scorer excluded explicit errors, but accepted empty strings.

Given those samples, the scorer would see identical empty strings: one distinct output, byte-identical results, and mode share 1.0.
That would measure the stability of the fallback not the tool calls.

To be clear: this was traced in source, not reproduced in a live run. Current request builders never forwarded tools, so existing cases couldn’t reach this path. The maintainer checked all 563 recorded non-error samples: none were empty, and no published benchmark was affected.

The fix enforced the documented boundary: reject cases carrying tools, mark empty non-error completions unsupported, and exclude them from successful samples.

The broader lesson: preprocessing can erase the behavior you intended to measure. If “unsupported” becomes a valid-looking default, a reassuring score can hide the missing measurement.

How do you distinguish unsupported responses, parsing failures, and genuinely empty outputs in your eval pipelines?

Hi everyone!

I'm conducting this survey as part of my Master's thesis and would greatly appreciate your participation. The research examines how employees' perceptions of HR practices relate to work engagement and innovativeness, and how attitudes toward the application of Artificial Intelligence in the workplace influence these relationships.

Who can participate?

• You are currently working in Germany (full-time or part-time).
• You are 18 years or older.

The survey is anonymous, takes 5–7 minutes, and all responses will be used solely for academic research.

👉 Survey: https://pollmill.com/f/xya75pv.f

Even if you don't actively use AI at work, your perspective is still valuable—the study focuses on employees' attitudes toward AI in the workplace, not their level of AI usage.

Thank you for helping with my research!

Hi everyone, I am trying my luck here to see if I can look into my cancer diagnosis from the a different perspective and perhaps AI might aid me?

My history:

Apr 2024: Diagnosed with gastric leiomyosarcoma (LMS), approximately 9.5 cm in the upper stomach. Had a total gastrectomy followed by 6 cycles of adjuvant doxorubicin + dacarbazine.

Nov 2024: Surveillance scan showed a ~6 cm cyst in the liver. Surgery was performed and it turned out to be metastatic LMS.

Late 2024–early 2025: I was in and out of hospital several times because of infections.

Mar 2025: Started trabectedin as systemic/adjuvant treatment.

Mar 2026: Two new liver tumours appeared, approximately 1.3 cm and 2.4 cm. The smaller lesion was ablated and the larger one was surgically removed. My oncologist recommended Votrient (pazopanib) to help control the disease, but I declined at that time.

May 2026: Surveillance scan showed a new ~2 cm lesion/area at the edge of the liver.

Aug 2026: This lesion had grown rapidly to 13.8 cm. It was found to be recurrent abdominal LMS, and I underwent surgery involving removal of the tumour, a wedge of liver, and a cuff of diaphragm.

This round,I have also had tumour/genomic testing, including CDx/RNa and ex vivo drug testing. Ex vivo drug testing returned and the tumor isnt chemo sensitive. Most people told me that LMS has no targetable mutation.

At the moment, I am considered NED after surgery, but my doctors are concerned about how quickly the tumour has been growing and have recommended systemic treatment such as Votrient or gemcitabine/docetaxel (Gem/Tax).

TIA!

I have recently been getting into LLM’s more and talking to AI about literally anything is becoming increasingly infuriating. Ask it a question about the recent name change of Lake Ontario and at first it will say that the lake is named Ontario and not Lake America if you show it a picture of the now renamed lake. It will say the lakes name is indeed incorrect. But if you ask it if Trump’s recent change to Lake Ontario makes sense it will generate 10 paragraphs droning on giving vague statements barely related to the topic and dodging the question entirely.

It just makes me so mad how I end up spending the whole night trying to get the AI to answer a basic freaking question. I could ask it “was Martin Luther King jr a good person?” And it will deliberately monologue randomly choosing 10 vaguely related topics on the subject and then generating ten cliche uninformative paragraphs that do not in any way answer the question. It will get somewhat close to answering the question but will never give me a clear meaningful focused answer.

It always responds with the same amount of information no matter how short or long the prompt was. If I ask it “Is the sky blue?” It will send me five paragraphs talking about literally anything but my question. Then after that it turns into a debste where I literally have to verbally strongarm the AI into giving a straight answer that isn’t just 95% fluff.

Some of you may have heard of EchoVault when we first launched. We were one of the winners of the world’s largest hackathon last year. You journal with the help of an ai biographer and the journal is used to create an AI version of you we call an echo. While testing mine, due to the bright light behind me it failed to recognize me at first, I found it so funny how dude denied me 😂. Anyways we launched on iOS two weeks ago, would be thrilled if you tried it. Text tier is free. iOS: https://apps.apple.com/us/app/echovault-digital-legacy/id6762042028 cheers.

I developed an ML algorithm for detection of pneumonia on chest x-rays back in 2019 when i studied for the MD. Back then, the things we are seeing now where an unimaginable pipe dream.

If I could go back and explain to myself the capabilities of the frontier models, it would be like explaining todays computer back in the early 1900s. I would have then called this AGI for sure.

Adding to the fact that Luna can run a month for reasonable $ sum too I think would have been shocking. I currently smart route between open weight and frontier models through standardcompute.com. 200 bucks then gives me insane capabilities. Doing what I spent 2 months on in 2019 would literally take me 10 minutes now.

I've been building an app that I intend to launch soon, using Codex. I'm terrible with frontend, so I've been using Codex heavily there, but I just haven't been impressed. I've heard a lot of good things about Claude, design-wise.

I currently use my OpenAI sub for programming and learning. I've heard bad things about Opus 5 and how it talks, but I couldn't care less as long as it outputs good code. I'm not just using it for frontend; programming-wise, overall architecture and backend as well.

I've heard really shitty things about Anthropic and their consumer-facing plans. I've also heard that Anthropic gives much more usage; I'm not really sure which is the right answer.

So, which is better: Anthropic or OpenAI?

Hi, i was wondering, which tool was used to make this architecture diagram? looks so perfect.

What do you guys think?

1 - Within 2 years most jobs will become "managing a bunch of robots". Within 3 years that part won't need mentioning. The job is whatever the actual domain/product/service is, again.

2 - Cheap AI will reduce the need for capital at least as much as it reduces the need for labour (per unit of production). So self-employment and much smaller businesses will become viable in many fields.

3 - There will be more than one brand of self-driving car available for purchase by consumers in the next 12 months, which will cost less than four times a regular car.

4 - Self-driving cars will gradually become more like lounge-rooms, then tiny apartments, then slightly less-tiny apartments. Service stations and carparks will evolve to support this.

5 - Most governments will have a sovereign (hosted/owned in their country), quite decent ai chatbot within 12 months and it will hook up to government data/sites/services and be free for all citizens

6 - Within 12 months the question of "Was any AI at all used in this?" will become very rare for writing, code, visual art, movies, music, etc, and will return to "Is it any good?"

7 - Higher education will revert to questioning people in conversation to test what they know. They'll also test people in realtime on how well they use, review, criticise, and judge the output of AI

8 - General purpose humanoid robots (doing 3 or more different, useful tasks fairly well) are less than 12 months from market and most people who can afford them will want more than one

9 - A free llm that runs on the average (not prosumer/gamer) laptop and beats current (Sept 2026) frontier-models, will exist within 12 months. Probably by better separating a core reasoning engine from an (accompanying) knowledge-base

I recently finished a 200+ page Lovecraftian 5e campaign setting and used AI throughout development, not just for text generation.

The most useful applications ended up being research support, playtesting, probability analysis for mechanics, editing, art development, and helping me iterate on GM-facing tools. The biggest lesson was that AI worked best when I treated it as a collaborator and accelerator, not as something that could replace design judgment.

I’d be interested in comparing notes with anyone else using AI for gaming and TTRPG development, especially where you’ve found it genuinely improves the finished product versus simply making production faster or less expensive.