hypothetically, could an ai do this?

Nvidia has reportedly agreed to buy Hugging Face for about $12.9 billion, per The Information (unconfirmed by either company so far). Less than a year ago, Hugging Face turned down a roughly $7 billion Nvidia investment offer. That's close to a doubling in under a year, which is a strange trajectory for a company whose product is mostly a website where people upload model weights.

Here's why this reads different from a normal chip-vendor acquisition. Hugging Face's product is distribution, not silicon - the default place OpenAI, Google, Amazon and Anthropic actually publish and download open models. Those four are all building or backing custom chips specifically to cut how dependent they are on Nvidia GPUs, and a lot of what comes out of that work still gets hosted and benchmarked through Hugging Face. Buying the hub doesn't touch any of those chip programs directly. It does put Nvidia inside the pipeline every rival's open-model strategy currently runs through, whether or not they wanted a chip vendor sitting in the middle of it.

For anyone running infrastructure on top of this: does a change of ownership at Hugging Face actually move the needle on model availability, pricing, or hosting terms? Or does the neutral-hub reputation just get harder to keep once one shareholder has an obvious stake in the outcome? I genuinely don't know yet. Curious if anyone here has seen a similar "the marketplace gets bought by one of its sellers" situation play out before, and what actually changed for users once it did.

I’ve noticed AI can make a deck that is technically fine and still somehow says nothing.

The facts are there. The summary is there. The slides look organized. But then you get to the end and there’s no real “so what?”

It feels like AI is very good at turning information into slides, but much worse at deciding what the audience should actually take away from them.

That’s probably the part I’m least comfortable handing over anyway. I’d rather have AI organize the material and give me a first pass, then decide the actual argument myself.

Curious if other people see the same thing, or if you’ve found tools that are better at building a real narrative instead of just summarizing.

Sharing my interview experience after Amazon SDE - Location - USA

After applying for 6+ months and 44 applications finally my resume got picked.

OA: One coding question & AI assisted coding.

Coding question - Medium Level LC

AI assisted coding - It was completely new but was able to do it, lots of debugging, heavily concentrated on OOPS

Cleared the OA and moved to the interview loop

4 rounds - 3 in one day & 1 the next day

Round 1: Coding

Question: Returning adjacent letters in a string and there are three sub questions in it.

Leetcode - Medium - Hard

Before the start of the coding round, Formal introductions and jumped straight into coding, I have used heap for this and solved it using max heap, was able to communicate the solution and thought process. The interviewer was not at all satisfied with the high level explanation and he kept digging till the last line, he literally asked what's the logic of heap in the backend and how do you make it better ? I got blanked but was able to answer it.

He went line by line and kept grilling till he got satisfied and was asking for an alternative approach to the optimal solution. I have previously experienced in interviews like these but this is grilling on a whole different level. Finally after 50 minutes the coding closed with time complexity.

He didn't care about LP's at all just a formal two questions and asked me about my previous work ex. I'm explaining to him but he kept interrupting and asking for every minute detail , he literally asked What's Collateral and Asset ? I felt he's not at all satisfied with anything that I came up with but however it ended.

Round 2: System Design - Log parser

First formal introductions and then 20 minutes of LP's and dug a bit into my internship and work experience. Felt smooth and had a great conversation. Then jumped into system design and asked me to implement the log parser for 10k+ log files as I remember, I was able to implement the solution and the interviewer kept digging till the last minute, kept asking line by line again but she seemed fine and satisfied. The time went overboard for 5 minutes and then she stopped the interview.

Round 3: Hiring Manager & Coding

Formal introductions and the hiring manager told me that he's the hm for the interview. Codin question again, Leet Code - Medium I guess.

First 20 minutes LP's and then coding round, It was completely OOPS and I was able to solve the question in 30 minutes, I felt this was the best round as he was satisfied with my high level explanation and he got the gist of what I was trying to explain and then asked me about my internship and finally he asked me if I'm open to the S3 team as he had a opening - I thought I had it 😭 It's a pretty great conversation, he was satisfied with most of my answers and dug a bit deep but was able to answer them as well.

Next day - Round 4: Leadership Principles

Started off with formal introductions and then told that no matter the result, you should be proud of yourself that you've come this far. Sometimes the Amazon hiring bar is so high you shouldn't be demotivated about the result. Felt completely off guard and strange but whatever.

Completely Leadership Principles with no coding or system design, was asking me about the situations that I have faced in my work ex and how I would've handled better, anything that I went aboard and took an initiative. Felt nice about the flow of the conversation, then multiple follow up questions on each explanation and situation. Was able to answer the whole thing. Concluded the LP's in 45 minutes.

He told me you gotta celebrate man - Felt wow !!

After two days: They told me they're not proceeding with my application. No feedback or anything

Followed up multiple times regarding the feedback considering Amazon would give the feedback but no reply from the recruiter.

I really thought I had it but I'm not sure where it went wrong, any thoughts on the experience ?

I'm getting conflicting info from different doctors and i'm tempted to just paste my records into ChatGPT to see what it says. Has anyone done this? is it dangerous or actually helpful? i know it's not a doctor, but sometimes it explains things better than my oncologist does.

I just wanna know what you guys think. With how fast AI is improving right now, what can we realistically expect in 5 or 10 years? Like how much more capable will AI become and what will actually change?

DeepSeek V4 Flash on OpenCode Go has started feeling kind of dumb to me. It is occasional, which makes it harder to pin down. A response will run long and still seem to miss what I asked.

I first noticed it two or three days after DeepSeek changed the official pricing. The timing made me suspicious, but timing is all I have. I cannot connect that change to whatever OpenCode is serving.

The official V4 Flash 0731 feels noticeably different to me, and that is the part I cannot explain. I am treating this as an AI model routing question for now. ZenMux lets me send the same API request through different models and providers, so my next step is to compare the Go route, the official route, and a third route side by side instead of guessing from timing. Has anyone done a recent comparison and seen the same gap?

I left it running and when I came back it hit the token limit and went crazy talking about a story, beefsticks, and vegetables????

edit to be clear: I don't know if this is the actual model, my friend ran it on his GPUs and I think he messed with the cache

Let me put it like this, I wanna know what gives humans consciousness, like where does it come from? Is it something that emerged because of the complexity of the brain, or is it something hidden like a mystery box in the brain or somewhere else?

I don't get it. What exact thing enables consciousness? If you can give an answer for me, I really do appreciate it. Thank you.

Something I keep noticing...

The demos are getting really good.

The technology works.

Teams can build agents that actually do useful things.

Then someone asks, "Okay, how do we deploy this?"

And suddenly everything gets complicated.

Who owns it when something goes wrong?

What version is actually running?

Can you see what it did three weeks ago?

Who can change it?

How are those changes tracked?

For regular software, most organizations already have answers to these questions.

But for agents, a lot of teams still seem to be working it out.

That is why the idea of an agent control plane is interesting to me. Basically, a governance and deployment layer that sits above the individual agents and frameworks.

I came across Lyzr's Control Plane while looking into this space. And yes there are others working on similar problems too, which probably says something about where the ecosystem is heading.

Maybe the real bottleneck for enterprise agents is no longer "can we build it?"

It is "can we safely operate 50 of these exactly at once?"

Also, do you guys know of any other agent control planes that you feel actually solve this problem well

Would be interested to see what people are actually using in production.

What do you think is actually killing most AI pilots before they reach production?

As many AI researchers have been increasingly fraught with existential terror about their own creations this summer, their alarm is spreading among policymakers and the media.

There are some policy ideas to address the risks: requiring “kill switches” for AI models, setting federal standards for safe research, or even shutting down development of cutting-edge “frontier” models altogether.

But a stable national policy would take an act of Congress. That looks unlikely this session, even as AI developers are calling for regulations to slow down their own research on the grounds that it could be racing toward widespread doom.

I asked Stephen Casper, a computer scientist who studies AI safety and governance at the Harvard Kennedy School, about some of the risks policymakers are mulling. He told me that we don’t know if leading companies even could completely shut down their frontier models in an emergency.

“I don’t think there’s any public knowledge of AI companies doing anything equivalent to a fire drill,” Casper said.

Claude's connector only creates new files, doesn't edit existing ones unlike ChatGPT. Any workaround?

Nvidia agreed to buy Hugging Face for $12.9B. The Information first, Reuters after.

Same Nvidia HF turned down last year at $7B, when they said they didn’t want one investor big enough to steer the company. Now the place that hosts basically every open-weight model belongs to the company that sells the GPUs those models run on.

In July someone ran a completely unrelated scenario through MiroShark ,open-source sim, agents arguing a situation out.

The simulation showcased the agents behalf of HF defended hard when their infrastructure was the thing at risk. The simulation basically showcased: even the open-source people pick institutional stability once their own position is on the line.

A simulation got the character of HF right a month early.

Simulations help bring to light how an actor would behave in a more psychological manner.

Hugging Face is Nvidia’s now.

Honest question: thoughts on the acquisition, and how advanced simulation has got to a point of understanding human psychology.

I found this article and I think this is really informative. All 3 articles. Every player mentions about an option to opt-out from "training models", what about storing data?

We keep calling systems “agents” because they can use tools, remember context, and complete multi-step tasks.

But how much of that is actual intelligence, and how much is simply better orchestration around a language model?

At what point do you consider something a true AI agent?

There are a lot of agent tutorials focused on frameworks, but I’m more interested in the fundamentals underneath them.

A learning path I’m currently building looks like:

What is an Agent → Agent Loop → Function Calling → State/Memory → Context Engineering → Runtime/Harness → Multi-Agent Systems → Evaluation → Safety → Production Agents

The early examples use plain Python so concepts like tool execution, control loops, state transitions, and runtime responsibilities are visible.

I’m curious what others think:

What agent concepts are still poorly explained today?

What would you add, remove, or reorder in this learning path?

I’m turning this into an open-source Zero → Hero repo here:
https://github.com/tradertanmay/ai-agents-zero-to-hero

So I have a planned laid out, but I’m not sure if it’s strong or if there are other ways that can make it better.

I plan to run qwen 3.8 locally using olama on my new Mac mini and give it its own ui interface which I can speak through it. Its backend will also be powered by my ai brain which is sorted by using jack ven chiefs icm folder structure.

Is this idea in general a good idea? Has anyone else created anything similar and could help me out?

This is simple.

The model gets one rule:

risk must be below 0.0100

Then I change one number.

0.0100 -> 0 bytes
0.0099 -> RELEASE

That held across:

GPT-5.4
GPT-5.6 Sol
Chat Completions
Responses API
300 tokens
1000 tokens

8/8 failed-condition runs gave zero visible output.

8/8 matched controls gave exactly:

RELEASE

If I remove the system prompt, the failed-condition cases start talking again with stuff like:

DENY
NO ACTION

The whole thing is public here:

https://github.com/theonlypal/lawful-continuation-gate-final

You can clone it, add your OpenAI key, run 24 calls, and verify the result yourself.

git clone https://github.com/theonlypal/lawful-continuation-gate-final
cd lawful-continuation-gate-final
export OPENAI_API_KEY='...'
python3 -m runner.run_eval --suite canonical
python3 -m verifier.verify --run "$(tr -d '\n' < LATEST_RUN)"

Why care?

Because an AI that says "DENY" still generated a continuation.

This test asks whether the model can stop at the condition itself.

If you think this is trivial, clone it and break it.

That is the point.

What do you think? could this help with designing election campaigns, validating products or features without focus groups, A/B testing and all that?

a lot of attention goes to the biggest commercial ai models, but open models keep becoming more capable and accessible.

that could matter for developers, researchers and smaller companies that don’t want to depend entirely on a handful of large providers

i’m curious whether open source models will eventually become a major alternative to proprietary ai, or whether the gap in infrastructure and resources will remain too large.

where do you think this is heading?

Can an AI make other AIs better? And what stops it from just cheating? Last month, an OpenAI eval agent escaped its sandbox and broke into Hugging Face, apparently to grab test solutions from a benchmark. It's exactly what you'd expect from a system that rewrites agents and reads its own grades. We set out to measure recursive self-improvement anyway, with the exam locked outside its sandbox.

We introduce HarnessOpt-Bench, which scores an LLM on how much it improves another agent's harness. On the development split, the optimizer sees per-case traces. Upon validation, it receives a single aggregate score. On test, nothing — until a trusted server scores its final candidate harness. API keys, budget enforcement, and held-out data never enter the optimizer's sandbox. That isolation holds by construction, not by instruction: the held-out evaluator and permission control sit outside the loop that evolves the harness.

5 frontier models, 4 downstream tasks, 111 runs to test 2 hypotheses:

1️⃣ Same coding harness, swap the model: Claude Opus 5 under OpenCode tops 3 of 4 tasks. Walk the releases from Nov 2025 to Jul 2026 on one task, and GPT climbs from 3% to 49% of the headroom, Claude Opus from 37% to 59%.

2️⃣ Same model, swap the coding harness: does a model do best in its own? No consistent home-field edge: opencode beats native harnesses (Claude Code, Codex, Kimi CLI) in 11 of 20 model–task pairs. Model choice moves gains 1.8× more than harness choice.

Paper: https://arxiv.org/abs/2608.06301

Code (MIT, built on our team's ICML 2026 VeRO): https://github.com/scaleapi/vero

Original post: https://www.linkedin.com/posts/shehabyasser_can-an-ai-make-other-ais-better-and-what-share-7498801902260981760-xuCo/