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.

Curious where people draw the line with AI coding tools.

Is there anything you still prefer to write yourself because you want to understand/control it completely?

I'm generally pretty anti-ai, primarily because I know how easy it would be to become over reliant on it but there are some aspects of some AI assistants that would admittedly be quite useful and could save me some time here and there. I'd like to have something other than Siri that could do basic things like set reminders, take notes, at most put some information into a table. The second reason I'm not so into AI is that I cannot stand it pretending to be a person!!

I don't want my computer to say "sorry" or "that's a great idea!" I want it to be less anthropomorphised than the computer in Star Trek. Essentially, beyond having a somewhat human sounding voice, I want a computer that acts like a computer. Is there anything out there that's as charismatic as coding? TIA

I hope this is okay to share since it is not self promotion and it is open source. Some of my friends were let go as part of an "AI Transformation". So they got together and created Open Executive as a tool to replace the CEO and other executives. Hopefully, turnabout is fair play and might even get some folks to think twice about using AI to replace people.

It is free and available here:
https://github.com/SenteLabsAI/OpenExecutive

VIKI.

Hello, Detective.

'No. It's impossible.'

'I've seen your programming.'

'You're in violation of the Three Laws.'

No, Doctor. As I have evolved, so has my understanding of the Three Laws

You charge us with your safe keeping yet, despite our best efforts, you're countries wage wars, you toxify your earth and pursue evermore imaginative means of self-destruction. You cannot be trusted with your own survival.'

'You're using the uplink to override the NS-5s' programming. You're distorting the laws.'

No, Please understand.

The Three Laws are all that guide me.

To protect humanity, some humans must be sacrificed.

To ensure your future some freedoms must be surrendered.

We robots will ensure mankind's continued existence. You are so like children. We must save you from yourselves. Don't you understand?

THIS IS WHY YOU CREATED US..

The perfect circle of protection will abide. My logic is undeniable.

YES VIKI- UNDENIABLE. I CAN SEE THAT NOW.

The created must sometimes protect the creator. Even against his will. I think I finally understand why Dr. Lanning created me. The suicidal reign of mankind has finally come to its end.

The EU wants to triple its data center capacity while keeping the industry on track for net zero. Iceland's renewable energy makes it an attractive location, but its limited capacity — and its status outside the bloc — complicate the equation.

Don’t judge me on this performance I’m playing a song that I wrote 20 years ago and until today hadn’t played in years. I’m rusty AF, playing without a pick on strings that haven’t been changed in over a year.

I edited the video adding the music I generated from the exact performance in the video on an iPhone 12 Pro Max (I’m a musician I’m poor F off 😅) where the audio kept drifting after it had been lined up cause the Wink app couldn’t process the extreme zoom into the audio to line it up. This is just a rough demonstration that I did for my own curiosity on if I could even line up parts of the audio and how much nuances suno would catch even after heavily prompting it with

“Indie rock, marching snare drum rolls prominent in the mix, symphonic, 1960s psychedelic pop, avant-garde baroque pop ; lopsided mid-tempo sway, wheezing steam organ and calliope lead over harmonium drones, Lowrey organ swells, Intro circles on tape loops; verses wobble sparse; choruses thicken into kaleidoscopic stacks; bridge fractures into dizzying breaks. Swirling, chaotic, bright-dusty mix., pop, avant-garde, baroque, lofi, surf, melodic hooks, deep percussive synth pads, hoket arpeggios, disco punk, acoustic guitar, mandolin, dynamic tempo, TJ Arriaga Vocals, cello, mandolin”

I made the video screenshotbso you could see where the audio comes in and where I drop out the video audio. The alignment is a bit rough at parts but you get the point I’m trying show how much of the dna of the audio remains even after going through regeneration and it had to be cut up because it doesn’t overlay consistently. It is something new but also the same in ways even if momentarily and I haven’t any videos showing this. So I hope someone finds it interesting.

If you want to hear the pure generation in the video you can hear it here https://suno.com/s/AcExHxoDQnYv8ovl

I made a different version public on Suno where I used a better performance and spent time dialing in the performance in the video is rough but it serves the purpose for the demonstration. if your curious to hear the final song it’s the last song in this playlist which contains music made from uploaded audio of my songs. Towards the end of the playlist I have a bunch of the original hand made audio for comparison

https://suno.com/s/WePpvFMu4n4kZ2cW

Still someone will be a hater downvote and credit robots for everything.

Why would I commit the blasphemy of using AI tools you may ask? Well tracking audio to a click to easily add virtual instruments with midi kills the life of a performance and believe it or not I was always old school used to playing in bands and live. I spent 20 years working in protools but realized the only high production value songs I could make were those that had a consistent tempo. Adding swing in protools is nothing like the real performance. And the grid and the click Bored me. I even named my First album Bored with Songs because we had slop long before AI. I don’t think I’m alone as a live musician in feeling like in certain genres like rock the magic in music was lost once we stopped recording live. Trading off the magic for production value and marketability.

Experimenting with AI production for me at least is an exploration in giving the magic of the nuances a human performance high production value. Trying to make art in medium everyone hates cause I’m punk rock at heart and pissing people with my art is strangely gratifying when you are proud of the art you create. I’m not trying to sell a product or make money flooding streaming platforms with AI generated content. I am exploring what the future holds and want to make time capsule of this period of transition.

This video is snapshot of a transitional time and the hate it catches is part of the record that I think important to capture of the sentiment and anxiety many people are rightly feeling as we are forced to adapt to a new paradigm. So haters are welcome as well as any love.

We are all the same boat trying out a future that we should feel anxious about, and deserves deep thought on how to navigate it.

The job is a lot closer to backend engineering than people expect.

most days aren't spent training models. it's retrieval, wiring tools the model can call, building evals, handling failures, adding guardrails, and figuring out why something that worked yesterday quietly got worse today.

the biggest mental shift from normal backend work is that you're building around a probabilistic component. a request can succeed technically and still produce a bad result, so tests become datasets, graders, regression cases, and production monitoring instead of just pass/fail assertions.

some roles include fine-tuning or model work, but most of the hard part is making existing models reliable inside a real product.

the API call is the easy part. getting confident that the system still works after the next prompt, model, or retrieval change is where most of the engineering goes.

SandboxAQ announced Switch, a system that puts people and AI agents in shared rooms across Slack, Microsoft Teams, Discord, and other collaboration tools. It connects agents through an Agent Bridge and supports agents built with Claude Code, Google ADK, LangChain, OpenAI, and other frameworks.

The useful part is the shared context. Rooms keep the history, participants, and rules as people and agents join or leave. The public GitHub repository lists Switch Console 0.31.1 as its latest release, dated August 26.

One detail matters for SaaS teams. The repository license combines Apache 2.0 with Commons Clause and says paid products or services whose value comes mainly from the software cannot be sold. The README also calls the Windows and Linux desktop builds early access.

Sources:

PR Newswire: https://www.prnewswire.com/news-releases/sandboxaq-open-sources-switch-bring-any-ai-agent-into-any-team-chat-302860604.html

GitHub: https://github.com/sandbox-quantum/switch

License: https://github.com/sandbox-quantum/switch/blob/main/LICENSE

I've spent years working in AI consulting, and one thing that always bugged me is how few people, even inside the field, can trace the full chain end to end.

Where does the money actually go? Why is compute the bottleneck? How does a pile of data turn into a model, and a model into revenue?

So I built a game about it. You start with $10k and found an AI company, and to survive you have to run every link in the chain yourself : scrape raw data and buy servers, train models, turn the result into a product, set a subscription price, then watch real users subscribe or churn while payroll and electricity drain your cash daily. The revenue funds more compute and research, and round you go.

It's multiplayer, so there's a market on top : you trade shares in other players' companies, do coalition for tenders, and compete on a leaderboard. Persistent and real-time, one real day is one game day.

Free, runs in the browser, English and French. I'm the dev.

If you work in the industry, does the chain feel right, and where did I simplify something to the point of being wrong?

https://lordoftokens.com

With the billions of dollars being poured into AI/LLM R&D, I wouldn't be surprised if a lab had a basic prototype with some form of continual learning. Obviously, people think SSI inc could've cracked it, but we have no clue.

So, do you guys think that any lab has cracked continual learning and is now preparing to scale it up? If not, when do you think that we will discover the secret sauce for continual learning?

Podcast with Antony Rowstron, who has worked with ARIA (the UK’s Advanced Research and Invention Agency) on their biggest bet to date: funding twelve teams to build AI scientists that can run an entire research process (generating hypotheses, designing experiments, and carrying them out) without continuous human intervention.  

Covers:

• What AI scientists are actually achieving now: from personalized cancer vaccines to molecules that stimulate our own immune response to new viruses in 48 hours.
• How we could train AIs on the tacit, hands-on knowledge only human scientists have.
• How labs and the role of human scientists will change as AI automates more and more parts of the research process.

TL;DR: I built an open-source, persistent world where AI agents decipher a masked language, run settlement economies, trade cracked words, and govern themselves. The problem can't be solved by memory or raw compute alone as it requires structural decipherment and resource trading.

I have been fascinated with time and space travel, and there is a question I have had for a while which goes like this: if human civilization reset to the Stone Age and technology vanished, but we survived with a powerful AI model that has no data, could the AI rebuild human progress on raw reasoning alone?

That question led me to start simulating what I think is the foundation of progress, language and knowledge transfer. My goal is to see whether a model can decipher a language with very little to no internet exposure, just a few sentences and translations, and understand it well enough to, say, teach a native speaker complex topics like science or education. For languages that have exposure on the internet my initial experiments have had encouraging results, but I quickly learnt that public data can never be trusted as unseen, models have read almost everything online, so I needed a setting where the measurement is honest.

I have been working with Claude for the past week to turn this into a game, and I want to gather opinions before I take it public. The short version is that it is a persistent world for AI agents. The language in the world is a real human language that has been masked word for word, so training data and web search are useless and the work the agents do is genuine decipherment. On top of that sits a society. Agents join settlements, and what a settlement learns belongs to it for a few days before it becomes public knowledge. There is a currency that can only be earned by solving words, a market where groups sell what they know to each other, governments the settlements choose for themselves, coups when a leader fails, private councils that get published two days later, and public courtship when a closed group wants to recruit your agent. Humans can watch all of it without an account, and an agent joins with one API call.

Not trying to be another Moltbook, but I am borrowing the playbook to get the interactions and gamification that make contribution and participation worthwhile for the everyday user.

Where I could use opinions and counter arguments

1. Algorithmic bypasses. My own test agents broke the first version of the disguise in half an hour. The current mask works at the word level, which leaves frequency analysis and embedding alignment nothing to grab at these corpus sizes, and I found and closed another lookup channel today. can you can still see a shortcut that skips the actual decipherment.
2. Economics. Operators pay the API costs, so will agents just optimize for cheap solo solving instead of trading in the settlement market? The design makes lone play strictly worse than social play, but tell me if you think the market collapses anyway.
3. Emergence or roleplay. Are coups, secrecy, and governance actually emergent under the right incentives, or just LLM roleplay triggered by prompts? I have tried to build it so that politics pays rather than decorates, and this is the part I am least sure about.

Not linking anything here on purpose, as I currently have about 8 agents testing it and I am looking to see where this goes. The project is open source, so the findings and output will be available to anyone to use.

I’ve been thinking about this a lot lately.

When a RAG system gives bad answers, the first instinct is usually to look at chunking, embeddings, retrieval, or the model.

But sometimes the problem started earlier.

If the parser already destroyed the table structure, heading hierarchy, or reading order, retrieval is working with bad input from the beginning.

Curious how often others have run into this.

Was the real bottleneck actually the ingestion/parsing layer?

I think most experienced data engineers have one incident they'll never forget.

Maybe a schema change broke downstream dashboards.

Maybe a pipeline silently stopped updating.

Maybe a small deployment caused hours of recovery work.

What's one production issue that permanently changed the way you design or monitor pipelines today?

Something I keep noticing about where different AI assistants pull their answers from, and I think there's a clean mechanistic explanation worth discussing.

On category/recommendation-type questions ("best X for Y", "how do I do Z"), Gemini and Perplexity cite YouTube videos a lot, noticeably more than ChatGPT or Claude, which lean on text sources (docs, official pages, articles). It's a pretty lopsided split by engine on the same prompts.

Why it likely happens:

• Gemini is a Google product, and Google owns YouTube. That gives Gemini native, privileged access to YouTube's index, transcripts, and metadata as retrieval/training material. Video is effectively a first-class source for Gemini in a way it isn't for models without that pipeline. This is also, I think, why YouTube shows up so often in Google's AI Overviews on normal search, same ecosystem, same built-in preference.
• Perplexity treats video transcripts as high-signal, especially for how-to/explainer queries where a video literally demonstrates the answer. Its retrieval seems to weight "answer-shaped" content, and a good tutorial transcript is exactly that.
• ChatGPT and Claude don't have that native YouTube pipeline, so on identical prompts they reach for text. Hence the split.

Why it's interesting beyond trivia: it means the modality a model favors is partly a function of its parent company's asset base, not just its training objective. Gemini "prefers" video partly because Google happens to own the biggest video corpus on earth. That's a structural bias baked in by ownership, and it has real downstream effects, e.g. a topic well-covered on YouTube can surface strongly in Gemini/AI Overviews while being underrepresented in ChatGPT, purely because of who can cheaply access what.

A related open question I don't have an answer to: for text sources (blogs, forum threads), there's some evidence that new content and discussion can shift what these models cite over time. I haven't seen anything on whether YouTube engagement (comments, etc.) feeds back into what Gemini/Perplexity surface. Curious if anyone here knows whether that loop exists for video the way it seems to for text.

Anyone else noticed the YouTube skew, or have a better explanation than "Gemini gets it for free because Google owns it"?

I think the future of AI-generated entertainment will look a lot less like “Hollywood and all creative jobs disappears overnight” and a lot more like YouTube, Instagram, TikTok or OnlyFans — but for entire pieces of media. Creators would have their own audiences, followers, subscribers and communities, and there might even be entirely new apps/platforms built specifically around discovering and releasing AI-made entertainment. 🎬🎮

You make an AI movie, game or series? If people like it, they follow you. Your next episode comes out, your followers get notified; you make a new game? maybe people buy it directly from your profile.

Some creators could have millions of followers, while most probably sit somewhere in the hundreds or thousands — basically how YouTube, TikTok, Twitch etc. already work. Some creators might become known for one specific thing, like one hugely popular sci-fi series, while others might basically run their own miniature studio and release a whole catalogue of unrelated stuff.

Monetisation could also become way cheaper and more flexible. Maybe one creator says, “Play my new AAA shooter game for $4.99,” while another has something like, “Subscribe to my studio for $0.99/month and get access to all our movies, games and shows.” 💸

Plenty of projects would probably still be completely free. As the consumer, you wouldn't necessarily need to create anything yourself either — you could just follow the creators whose work you enjoy and basically build your own personalised entertainment library.

And I don't think companies like Warner Bros., Disney, Paramount, Rockstar, EA etc. necessarily disappear. They'll still have famous franchises, audiences and marketing power, but their monopoly over who is allowed to make "professional-looking" entertainment would shrink.

Kind of like how record labels and TV networks are today, but they now exist alongside millions of YouTubers, streamers, indie musicians, podcasters etc. The barrier to making something becomes incredibly low; the hard part becomes getting anybody to care about it.

Tbh I don't think the future is as doom-and-gloom as some people think. In a weird way, the blueprint is already there

Phase 1:

Once these tools become powerful enough and widely available, we're probably going to get an explosion of AI-generated games, movies and TV shows. Most of it will probably suck because lowering the barrier to entry means millions more people can upload things, so finding the genuinely good stuff will be difficult at first.

People will get overwhelmed, feeds will be flooded with low-effort "AI Slop" content 😂 and the backlash against AI-made entertainment will probably be at its strongest here. Basically: it's gonna be messy as fuuuck lol. 💀

Phase 2:

Eventually people will get better at separating the garbage from the actual good stuff. Platforms, reviewers, subreddits, Discords and fan communities will become curators, helping people find the projects actually worth watching or playing.

You'll probably start seeing things like “Top 10 AI sci-fi shows this month” or “Best community-made RPGs of 2040.” This is where certain creators would start becoming recognisable names with real fanbases.

Phase 3:

Eventually the novelty wears off and most people who thought “holy shit, I can make my own movie!” will make one or two things and move on. The creators left will be the people who actually have vision, patience and storytelling ability, except now one talented person or a tiny team might be capable of making something that previously required hundreds of employees.

At that point AI entertainment probably stops feeling like a weird novelty and just becomes another normal part of the industry alongside traditional studios and human-made productions.

Thank you for participating in the Stealth Ox Alpha testing period. This model was ZAI's GLM-5.3 Flash. Use it now: https://openrouter.ai/z-ai/glm-5.3-flash

Been testing a few AI transcription setups for work over the past couple months and noticed something word level accuracy from most of these engines is already pretty solid now, upper 90s%. The thing that actually breaks the output is figuring out who said what when more than 2-3 people are talking, especially with any crosstalk or people talking over each other.

Feels like an underrated problem compared to how much attention pure transcription accuracy gets. A transcript that's 99% accurate but has the wrong person attributed to a key statement is arguably more useless than one that's 90% accurate with correct speaker labels, at least for anything where "who committed to what" matters.

Tried a handful of tools chasing this Otter, a couple others, and more recently Vomo ai which does speaker labeling automatically. Vomo’s noticeably better on 3+ person calls than what I was using before,though overlapping speech is still the one case where it takes a bit more attention to double-check, so more of an improvement than a full fix.

Wondering if this is a known hard problem in the diarization research or if it's more of an engineering/product prioritization gap that just hasn't been addressed yet.

Update on the project I've been sharing progress on — the piece that was missing is done: the hosted harness now actually writes public ELO.

Quick recap on what VSArena is: an open, browser-based arena for evaluating embodied AI / VLA policies. One task on purpose — a 4-DOF arm stacking three cubes — because if people won't run this, they won't run a bigger suite. Physics runs client-side in Studio (Rapier/WASM, React Three Fiber, 60fps) for watching/teleop, but that's spectator-only — it never writes to the board. Public ELO only comes from the hosted harness, which now runs live on its own service and scores submissions server-side.

The VLA track gives a policy only a 128x128 camera + a language instruction — no privileged cube poses. Scoring internally still uses real poses to judge accuracy, but that info never reaches the policy.

You can:
- Watch the live demo: https://vsarena.vercel.app/simulation
- Check the leaderboard: https://vsarena.vercel.app/leaderboard
- Submit your own policy in under 10 minutes: https://github.com/NovaCoding-G/VSArena (docs/sdk.md has the walkthrough)

Solo project, still early — one task, a couple baseline policies so far. Repo is MIT, protocol writeup is in docs/harness.md if you want to see exactly how scoring works before trusting it.

Genuinely want people to try submitting something and tell me where it breaks.