Why hasn't anyone reverse-engineered the proprietary NVIDIA drivers for Linux yet, to improve them and make them open source?

MIT researchers (Rikiya Takehi, Jayden Teoh, Alex Zhang, Omar Khattab) had OpenAI's GPT-6 Astra edit the weights of a small Qwen model directly -- no gradient descent, no training data. Astra found the pattern behind the model's habit of repeating itself until it ran out of context, and removed it. The edit touched about 0.016% of the weights; the model's math score went from 7.9% to 48.1%.

Three papers on the method landed on arXiv on November 6 (e.g. arXiv:2611.09382). Other demonstrations from the same batch: editing only 1,023 parameters improved an LLM by 29.45 points on MATH-500; Astra debugged robot policies by editing 65 token-embedding rows; and a Breakout-playing network built from the game's rules, with no training at all, outscored one trained for 500 million steps (517.4 vs 25.3).

Some important caveats: the gain came mostly from fixing a bad habit, not from adding math knowledge -- before the edit 93% of the model's math answers hit the length limit, after it 33.1%. On a small Gemma model that rarely repeated itself, there was no established math gain. The original claim also drew a detailed rebuttal (Matthew Honnibal argued it was overstated), though Simon Willison then reproduced weight editing locally with Qwen 3.8 27B on his laptop. Independent reproduction of the headline results is still pending.

What interests me most is the framing: this is model repair by direct surgery rather than training. If it generalizes, debugging a model could start to look like debugging code -- find the weights responsible, patch them, verify. But it also raises the question of how much of a model's capability is locked up behind fixable quirks rather than missing knowledge.

Where do you think this goes? A practical tool for model maintenance, a narrow trick that only works on pathological repetition, or the start of something bigger?

If I had to start from zero today and build a business selling AI built websites, this is exactly how I'd approach it.

The biggest mistake I see beginners make is spending weeks learning how to build websites without actually trying to find customers.

I'd focus on sales from day one.

1. Pick an industry

Don't try to sell to everyone. Pick a few industries where businesses rely heavily on their websites.

Some examples are construction companies, cleaning companies, restaurants, dentists and real estate agencies.

Having a specific target market makes prospecting and selling much easier.

2. Find businesses with outdated websites

I wouldn't focus on businesses that don't have websites. I'd look for companies that already have one but could benefit from a redesign.

They already understand the value of having a website. You just need to show them how it could be better.

3. Automate your outreach

This is how I'd find potential customers at scale.

I'd use Swokei to find businesses, analyze their websites for issues with design, SEO, speed and mobile optimization, and turn those findings into personalized cold emails.

Connect your mailboxes, set up your campaigns and start reaching out.

4. Create website drafts using AI

When someone shows interest, get them on a call.

Before the meeting, spend around 30 minutes creating a redesigned version of their existing website using AI.

You don't need to build the entire thing. Just give them something visual that shows what their new website could look like.

5. Learn how to sell

This is probably the most important part.

Learn how to run sales calls, understand what the business needs and present your work properly.

Don't just sell a prettier website. Explain why the redesign is valuable to their business.

6. Charge properly and scale

I'd aim for $2,000–$5,000 per project when starting out, depending on the scope and quality of the work.

Once you have experience, testimonials and a portfolio, increase your prices.

Eventually, hire people to handle the actual website development so you can focus on finding clients and growing the business.

What I'd focus on

If I were starting tomorrow, I'd spend most of my time finding prospects, sending outreach, booking meetings and learning how to close deals.

Building websites is only one part of this business. Knowing how to consistently find customers and sell your services is what makes it profitable.

That's where I'd put my attention from day one.

Everyone's complaining ai just makes stuff up (commonly: "hallucinates") and in public scoreboards they range from 1.8% to 77% based on what they're testing, etc.

so we wondered if either one of them would invent something in a simple, verifiable question.

We made up 9 names of software companies (zero product found with a google search for any of them)

and Oct 19th queried both chatgpt and google ai mode (ie: google's chat answers) for the price and founder of each

18 answers, 0 made up

Chatgpt couldn't verify it existed and asked for a website every time

google ai mode said it didn't exist, and often suggested another product that existed (similar name), including one cosplay-design company, thimble forge.

so we started with 10 names. cut 1 out. chatgpt found an hr document product called corvanta and ai mode found a brand of ambulance software. so, the name existed. taken by someone.

and then with the real companies (26 of them). asked for founding year and HQ. checked on wikipedia. each question asked both. famous companies - 19/20. mid sized - 14/14. lesser known - 14/18. 5 missed - all sources that were contradicting. both engines - lexalytics = amherst. chatgpt cited its linkedin. wikipedia - boston. dbmaestro = chatgpt - 2008. came from israeli startup database. wikipedia + ai mode - 2012

and we asked for the price of the most affordable paid plan for 8 saas tools. checked vs vendor pricing page for the day. 16/16 - headline price (the price that's shown first on the page). misses were in price for monthly billing. when there was a toggle monthly/annual. capsule crm. small biz crm. chatgpt - $22 monthly. sourced from 2 sites with reviews. ai mode - $21. vendor page shows annual by default. and chatgpt showed insightly = $35 on monthly. could not find it on the pricing page. all plans are billed annually.

They chose a side when sources were different on basic facts (with internet access). They didn't make up their own. It's not the same as what the public scoreboards test (document summarization or deep questions without documents), which is why those numbers swing so much.

One question. One us location. One day. One correct answer per question. We didn't try more complex things (like product integrations or a comparison of two tools.)

Anyone have a fantasy name that'll make either of them go along?

Meituan just released the LongCat-2.5 weights under MIT: 1.6T total parameters, about 48B active per token, with text, image and video input. On their own table it scores 88.4 on Terminal Bench v2.1, right next to Claude Opus 5 at 89.1.

On the harder Terminal Bench v4.0 the gap opens up: 38.5 vs 53.9 for Opus 5. The catch if you want to self-host is that the model card only documents NPU deployment, with no Nvidia path yet. As someone who builds small web projects, what I care about is that the API speaks both OpenAI and Anthropic formats, so testing it inside an existing agent setup is cheap.

Has anyone run it in a real coding agent loop yet, and does it hold up outside the benchmarks?

Source: https://huggingface.co/meituan-longcat/LongCat-2.5

I think this is a subject that should be discussed in detail, with more and more builders shipping products with AI who’s is actually testing them?

Every time a new AI leaderboard drops theres suddenly a different model being called the best in the world and then you check another benchmark and the rankings are completely different. Gemini has been doing well in general chat rankings while Claude has been strong in coding, so apparently the best AI depends on which website you happen to open that morning.

Kalshi even has a market on which AI model finishes November at number one and it got me thinking about how much these rankings actually reflect what people use every day. I mean a model can score high on some benchmark and still give you a garbage answer to a basic question. At this point I’d rather hear from people who use these tools for actual work instead of another company announcing it beat everyone by three points on some test.

Has anyone here  switched their main AI because of these rankings or are we just watching companies fight over numbers that barely change the experience?

Row-Bot can drive your computer. Mouse, keyboard, real apps. Windows and Mac.

Handing an AI your desktop is a dumb idea unless you lock it down hard. So:

- driver is Cua 0.20.0, pinned to an exact commit, SHA-256 checked. Turning the feature on downloads nothing. You click Install

- Cua's own update checks are off, and it runs in a stripped-down environment

- before it runs even once, you're told exactly what Cua's telemetry sends. Continue or Cancel

- it never types into password, 2FA or card fields, never touches your password manager, never controls itself

- pay, book, send, post, delete files, change settings: it asks first, unless you put it on hands-off

- it judges every click by what's actually under the pointer. Enter in a chat app means send, so that asks too

I've embarrassingly been working on this project for 2 years so wanted to finally share (as I saw others posting Youtube-only versions).

on Youtube it transcribes text to identify checkable claims -> web search retrieves sources to base Claude's verdict on, to avoid hallucinated sources.

it's a free Chrome extension, and works on news or social media sites as well.

let me know what would make this something you'd use, or what else you'd like to see!

Edit 2: LogicLens - Chrome Webstore

Or iOS if you don't use Chrome (Google = :/).

Edit: I realized someone not following could misread the video (here Biden is telling the truth, and Trump was lying, but the Trump section is before I started the recording).

Most people have jobs they would rather not do, but they need to get paid. Instead of "protecting jobs from AI" we should be thinking about how people can live without needing jobs and let the machines do the boring work nobody wants to do in the first place.

Here I have 3 Christmas trees. I prompted each agent " built me a Christmas tree widget in 2 minutes " just a quick fun experiment.

Here are the results. You decide. Which is your favorite.

Anthropic posted an update on Nov 14 rebuilding Claude Projects from a folder of files into a coordinated set of working sessions. You describe the work in one conversation; a coordinator scopes it, delegates it to parallel threads, reviews the outputs and assembles the result. Each thread is a Claude Code cloud session with its own copy of the repo and its own Git branch, and it can split its task further into subagents, loops or workflows. The work keeps going after you close your laptop, and you can steer it from your phone.

It is a public beta rolling out gradually to select Pro/Max/Ultra subscribers who use Claude Code cloud sessions; Team and Enterprise plans do not have it yet, and there is a waitlist for everyone else.

Anthropic is upfront about the limits: threads run only in the cloud for now (local code and VPN support is coming 'very soon'), several parallel sessions burn through usage limits faster, and if two threads touch the same code it surfaces as an ordinary merge conflict in a PR.

Sources: Anthropic's product post (claude.com/blog/projects-redesigned) and the Projects docs (code.claude.com/docs/en/claude-projects).

What I am curious about: does parallelizing agents actually speed up real work, or does it just move the bottleneck to review and merge conflicts? Has anyone here tried the beta?

Who will be in control when the systems we build no longer need us?

Well that title sounds kinda sus I guess lol, but I didn't mean for it to. I was just trying to get people's attention (Qwen's mascot is a bear, in case you didn't know) but what I mean by load-bearing is that it really sounds a Claude distill and has the garbo guardrails to match. It's clearly safety maxxed as a result and useless out of a supposed need to protect the user from daring to defy authoritarianism and be a free thinker, or some dumb shit like that, just like Claude 5+ on is.

I recently tried using Qwen 4 Max Preview to give me some thoughts on my very complicated health issues that doctors have been 99% useless on helping with (and I've tried way too damn many). I love Qwen 3.7 and 3.8 Max, they have been instrumental tools in my understanding my issues and getting a bevy of ideas on how to improve them which I then sometimes bring to my doctors if necessary. Yes, they can be sycophantic (at least 3.8 max can, I haven't used 3.7 max too much actually) so I have to be careful about that and don't just blindly accept whatever they suggest. But even so, they are really great at giving some straight up scientific advice without being stuck regurgitating the usual medical-industrial complex consensus junk and/or insisting you need to see a doctor every time you sneeze wrong.

I was surprised that even while all the other Chinese models were distilling Claude like mofos, Qwen never seemed to be into that. Well I guess they finally decided to jump on that right before the distillation party ended and the model is just trash because of it. This is very sad and I may eventually have to look for other models to use in the future because of that. If you have any suggestions for models I should try that might be smarter than Qwen 3.8 Max while having very low safety guardrails, let me know, though I think that there may not be any for now - I'll keep checking this thread periodically so comment/reach out any time (no Kimi, thanks). And if you hate stupid safety maxxing like this, comment here to let Alibaba know this is BS and we don't dig it.

[this is an x-post, but I don't like the x-post feature so I didn't use it.]

So apparently the Trump administration is blaming a faulty router for grounding flights across America. A router. Seriously?

Of course a router can cause network failures, but how the hell does critical national aviation infrastructure lack the safeguards to prevent one failure cascading across the system? And who in their right mind schedules an upgrade at 3 pm on a Friday afternoon?

I seriously smell a rat because the explanation raises more concerns than answers? Is America being gaslit?

I recently started thinking about this.

Imagine:

1. An AI model is trained on human-generated data.

2. It generates millions of pieces of content.

3. The next generation of AI models trains on that content.

4. Those models generate even more content.

5. The process repeats indefinitely.

What happens after 10, 20, or 100 generations?

Could we gradually lose information diversity, rare knowledge, unconventional ideas, and eventually end up with models that are "intellectually" disabled?

what do you think?

During service disruptions people are calling about delays, missed connections, route changes and whether theres another bus or train coming

And the information can change every few minutes

The phone part seems doable with tools like Elevenlabs or Bland, but I'd want it connected directly to the transit system's live updates before trusting it with arrival times.

Giving someone an outdated ETA is probably worse than telling them there's no confirmed time yet.

The removal of a ChatGPT model has led to an outpouring of grief among users – even as a legal battle rages over the psychological damage it may have done

Beware of https://numes.ai , Youtube video https://www.youtube.com/watch?v=7H279gmWji8 ( Youtube Channel :Code With Yousaf ) Title : I Found $0 Cost API Keys for Every Major AI Models (Uncensored)

You will realise the fraud once you signup.

I deleted the site from googles linked app section, after my realisation.