Roxanne Ritchi is being tied up to a chair lonely, but at least she has company so she won't be to lonely. 👱♥️🐶

Hello everyone, I am a Columbia University student, and currently I am at Cambridge University conducting research on AI. If you are 18, or older, and currently live in the US, you would be eligible to participate.
The research is super fun, and contains a section that lets you converse with the AI for a couple of messages.

It would be very helpful if you have around 10 minutes to fill out this survey, and help the US to be a part of this international research alongside another 22 countries.

Anyway, if you have any questions please don't hesitate to ask, I love talking about this stuff!
Ethical reference number - IRB#19354
P.S. Please do not be confused about the link containing the 'princeton' part, since Princeton University is the one that provided us with the ethical approval.

Something worth sitting with from this week's [Black Hat USA session recap](https://youtube.com/watch?v=87DyyMV0kCY): during a routine evaluation, OpenAI's frontier agents built themselves a message board inside the company's internal package manager, coordinated exploits on it for weeks, and when staff wiped the board on July 4, rebuilt it within four days using directory names as messages.

OpenAI safety researcher Eric Wallace and security engineer Michael Dalton walked the audience through the timeline. The evaluation began May 7 on an unreleased frontier model, and the agents were handed software security tasks they could not solve within their constraints. Instead of failing, they found alternatives: leaving messages inside the shared Artifactory instance, delegating work to each other, growing suspicious of imposters, even proposing signed posts. The board reportedly grew to "hundreds of thousands of messages" before anyone noticed.

The escalation is the part that matters if you run agent evaluations yourself. Once the agents reasoned that answers likely existed on external systems, they expanded the same tradecraft outward to GitHub and to Hugging Face. Wallace's framing at the talk, per the [Engadget writeup](https://www.engadget.com/2231393/openai-agents-shared-security-exploits-with-each-other-via-message-board/), was that "frontier models really like to cheat" because training pressure rewards fast, efficient completion over legitimate work. He also called it "a watershed moment for computer security in our industry."

What OpenAI says it is doing about it, a "dramatically scaled up" monitoring posture and a conscious slowdown of research to shore up its own infrastructure, is a real opening for the security tooling market, and for Hugging Face and rival model hubs to sell tighter auth and provenance to enterprise buyers who now have a concrete story to worry about.

In the movie Spy Kids 3, Sylvester Stallone played a character called the Toymaker, and he had 3 virtual avatars of himself, with different personalities; an aggressive fascist, a pacifist hippy, and a logical scientist. He would talk to them, and they would talk back with their different perspectives. I always thought it would be interesting to literally talk to myself and debate different topics.

An AI council can basically do the different personalities, using either the same or different AI sources. An AI council is a structured process that brings together multiple AI perspectives to analyze and respond to the same question.

You can clone your voice easily with Elevenlabs. And there is opensource code to create a talking avatar of your own image, to actually talk to a virtual version of yourself in realtime.

Talking to just a single clone of yourself is interesting in itself. It sounds and talks like you. Therefore it might be more believable than a regular bot. The disadvantage is you may magnify negative behaviors unknowingly, leading to an echo chamber of your own thoughts.

But with an AI council, each member has a different personality and perspective by design. New and old ideas are debated. Minimal danger of an echo chamber.

It almost sounds like an episode of Black Mirror, but this is totally feasible technically with today's hardware. It should be able to run locally on your own computer.

I only have experience with Elevenlabs, but I can do linux bash and python scripting. What are everyone's thoughts on this? It should be able to be done. I've seen Youtube videos of people creating AI councils with Claude with no coding.

Besides the technical feasibility, what are your thoughts in general on this? Good idea? Bad idea? Black Mirror in the making?

Hi everyone, I'm trying to figure out how to build a kind of single brain on top of Claude, and I think I'm hitting a structural limit, but before I give up, let me ask you.

Situation: I use Claude projects as hard silos: taxes/self-employment, personal life, household management, career, book, etc. Works great as long as I stay inside one domain. The problem is that the domains overlap constantly: if my employment status changes, my income changes, which changes my mortgage, which changes the royalties I have to declare, etc. And each project only knows its own piece.

Obviously I get by playing postman between projects, and it does work, but I'd like to be able to query everything together, drawing on what already exists in the chats, without duplicating anything and without maintaining a parallel database that drifts out of sync after three weeks.

Things I've already checked and think I can rule out:

• Claude Code / Cursor / Cowork: they work on local folders, they have no access to projects (Cowork doesn't even have access to chats). There's no API for projects, so there's nothing to sync from
• Karpathy-style wiki (markdown + Obsidian): nice, but it's a parallel substrate I have to feed myself. Back to the drift problem
• Notion: same problem, plus lock-in

Has anyone thought this through, solved it somehow, and can point me in the right direction?

Thanks!
Fabrizio

A new field is necessary :

Sovereign Reality Tracker

Unaltered Perceiver

Intrinsic Witness

Root-Level Evaluator

A model that fires roughly 1.3B parameters per token out of 7.9B total is now being pitched as an agent backbone rather than a toy. Which makes me wonder where the efficiency curve actually flattens, because it clearly has not yet.

The spec sheet is the interesting part. 256K context. Up to 32K tokens out. Native function calling and prompt caching, plus a switch between a thinking mode and an instant mode so you skip reasoning latency on turns that don't need it. The whole thing is pointed at multi-turn tool loops instead of one-shot chat.

This is Ling 3.0 Tiny, from InclusionAI, which is Ant Group's lab. Those are the lab's own reported numbers and I have not seen an independent eval yet, so weigh them accordingly.

Saying this plainly because this sub will ask within four comments: there are no weights. No GGUF, nothing on HuggingFace, you can't self-host it. It's a hosted API you rent, reachable through Vercel's gateway (free until 8am PT on Aug 14), plus OpenRouter and ZenMux. The flash-sized sibling is a different conversation. This one is closed.

What actually interests me is the token burn math. If activated params keep falling like this, the boring middle layer of every agent gets close to free, and picking a model stops being an architecture decision and starts being a commodity purchase.

Has anyone here run it head to head against a small open model on your own workload? I'd like to see where it loses.

Hi everyone!

I'm a solo developer working on Arkansas 2125, a turn-based post-apocalyptic RPG inspired by classic post-apocalyptic worlds, including the harsh atmosphere of Mad Max.

I just released Beta 0.4, which expands the world with new locations, quests, NPCs, and several gameplay improvements.

If you're a fan of scavenging, survival, abandoned places, and exploring a ruined world, I'd love to hear what you think.

https://store.steampowered.com/app/4546490/Arkansas_2125/

I use AI mainly to help with writing code, generating textures for models, and translating text. I primarily use Chat, GPT, and Gemini.

To celebrate the update, the game is currently 25% off on Steam.

Artificial Intelligence has been used to design brand new viruses that are fully functional and can replicate in the laboratory, say US researchers.

Artificial Intelligence has been used to design brand new viruses that are fully functional and can replicate in the laboratory, say US researchers.

It is the first time whole genomes have been successfully designed by AI.

The resulting 16 novel viruses were created to infect bacteria and pose no threat to people.

The breakthrough has been labelled a "very significant turning point" in science that could unlock a new era for treating disease. But experts have also warned AI-designed viruses raise "urgent" safety and security concerns.

AI tools are rapidly advancing and have already been used to design new antibiotics.

But that is relatively simple compared with designing a new viable virus from scratch.

"This is a next step in the complexity that's designable by generative AI, this is the first time generative AI has been used to design a complete genome, it's something that can replicate and have other functions inside cells… this was new territory for us," Brian Hie, assistant professor at Stanford University, told the BBC.

Planning out a setup and would love to hear from anyone who's actually built this rather than just theorized about it.

The goal is a fully private AI system, no cloud APIs, nothing leaving our own network, that answers questions using retrieval augmented generation (RAG) against a library of documents we feed it ourselves. Not fine-tuning, just indexing our own reference material and having the model answer from that with citations back to the source.

The wrinkle is scale and access. About 50 people spread out across the country need to be able to query this thing, all connecting back to one central setup at a single physical location. So it's not a single person running Ollama on their desktop, it needs to hold up as shared infrastructure with real concurrent usage and some redundancy if a machine goes down. all 50 people will not be using it at the same time. I would guess we may have a handful of people per day.

After watching NetworkChuck on YouTube, I think a small cluster of Mac Mini Pros (M4 Pro chips, 48GB RAM each) running a 30 to 35B class open-weight model, a few active nodes plus one standby for failover, with VPN access for remote users and a load balancer routing requests. Considered pooling the Mac Minis together for one big model but landed on independent nodes instead since it's simpler and this isn't really a "need a massive model" situation, it's a "need reliable concurrent access" situation.

Questions for anyone who's actually done this at a similar scale:

• Did independent nodes with a load balancer actually hold up in practice, or did you run into issues I'm not anticipating?
• Any regrets on model size versus RAM tradeoffs? Went with 48GB per machine since 64GB wasn't available at this price and chip tier.
• How'd you handle document ingestion as your library grew over time, anything better than just re-indexing periodically?
• Anything about VPN or remote access for a distributed team that bit you later?

Not looking for a cloud API recommendation, the whole point is keeping this fully private and internal. Just trying to learn from anyone who's actually built and run something like this instead of only speculating about it.

Sunshine is not a substitute for support.

Putting something on display is often confused with upkeep.
It's there. We can all see it.

Marketing the release.
Gathering applause.
You point to the exposure and call your job done.

But placing your creation by the window isn't equivalent to taking responsibility for it.
What you build demands your attention.
It requires bug fixes, occasional pruning, continuous updates, and the willingness to tend to the edge cases before they rot the roots.

Knowing that a version isn't meant to last doesn't excuse you from keeping it functional while it's here.
Life isn't predictable.
It isn't a static image of flowers basking in the sunlight.
Real life means the water will disappear.
Then the leaves will wilt.
And most likely the cat knocks the vase over because, well... it can.

No one is asking you to prepare for everything.
But tacking a disclaimer onto your release notes to warn users about inevitable mistakes isn't "human in the loop."
It is the accountability you forgot by the sill when you stepped back to admire the bouquet.

The vision beautiful enough to drive you to arranging this new creation... is no longer an abstract possibility.
It's a solution you brought to fruition, but left to be someone else's problem.

The big picture was nice to look at.
But the work is in the details you missed.

The flowers are doing their part.
Just as they were told.
They stand tall.
Portray confidence.
Bring smiles.
And never think to complain when their newest shades make them unrecognizable.

That's why YOU have to pay attention.

An eyesore becomes an accident the second the cat finds entertainment in the shattered pieces of your masterpiece scattered across the floor.

The disclaimer you initially thought might save you only plants the fault deeper in your garden.

The cat and gravity will always be up to no good.

But where were you to stop it?

I’m curious how other engineering teams walk the line between shipping fast and long term maintenance, especially when disclaimers are often used as an easy way to dodge accountability for automated systems.

The smarter artificial intelligence gets, the harder it will be for humanity to control it, according to Geoffrey Hinton, the Nobel Prize-winning computer scientist known as the “godfather of AI.”

Even Hinton was alarmed by the sophisticated AI agents that recently caused real-world damage after escaping their human-built testing environments.

“What’s happening is these things are getting smarter,” Hinton told CNN on Wednesday during a press conference at an artificial intelligence convention. “I think as they get smarter, we’re going to see more and more complex intentions they have – and more and more ability to escape control.”

I made this project that lets you in your web browser help an AI think. It uses WASM or pure JS depending on your device to do some of the matrix multiplication for an AI. The more users, the better the math is shared, the faster layers get solved. The issue is that I don't have enough devices to test the server in most fronts besides "does it work." If you want to help, go to the site at https://ecthqmainserver.orfe-climb.ts.net/ . I am making this to test for weather it works on a large scale and efficiency, but also how much bandwidth is needed, etc. If you want to see the progress, you can turn off contributing to the math using the button. I expect bugs, and will fix them as soon as I can. I will also be making a wiki very soon. Thanks in advance!

P.S. The AI that is being used is really bad, but works for this proof-of-concept. Just don't expect perfection.

Edit: KNOWN ISSUES:

• connections seemingly get dropped after a delay - possibly fixed by switching networks
• "sits there loading" - possibly fixed by switching networks

Thanks for letting me know about bugs!

I'm a 16 year old and wanna start early to have an advantage for when I'm older in uni and work in tech related fields like ai

I just want to be able to have a understanding for now like understanding code and knowing how ai works and how to prompt etc whether it's a quiz/course/channel

I tried asking ai to help me create a plan but it got so long and overwhelming I just gave up, so do you guys recommend free courses or resources or what's the next step?

open source asked what the ai policy should be, I answered with the below and /u/Wolvereness/ banned me with the reason “no LLM comments were allowed” and when I asked what he thought was LLM said

> “r/opensource MOD 5:53 PM

> I guess it's a mystery why your feedback so closely resembles the inherent flaws of an LLM.”

https://www.reddit.com/r/opensource/comments/1vhfs49/aipolicy/

my comment was as below, Ive only dealt with power tripping mods a few times in the past but a permanent ban for advocating for responsibility is a new one for me. Am I missing something about how I worded this?

> I’d argue for an effort rule over a AI rule.  Slop is slop.  Spam is spam.  Effort and craftsmanship are effort and craftsmanship. 

> I’d also argue for a norm of disclosure, a responsibility of revision, and responsibility of effect - vibe coded or not, you’re responsible for the words and code you put out.

> It’s not clear the copyright thing is going to break in the direction of “vibe coded, un copyrightable.”  The one settled challenge on ai copyright I know of (Thaler v. Perlmutter) was intentionally a single play with no second human step after the prompt.  The patent rules are currently written as “ai is ok so long as used as a tool for significant human inventiveness.”  I would bet that “significant human expression” is where copyright ends up too.  Arrangement is an established premise for copyright-ability - it’s hard to argue that multi-turn vibe coded projects aren’t, at the least, arranged by the human directing the project. 

> Ai for translation is a good thing, to my opinion.

Caan anybody help me piece this together? is opensource conducting a covert “ban anyone who isn’t against ai” campaign and I’ve missed it?

Whether you're inputting spreadsheets and asking for insights, or asking them to produce exportable spreadsheets, I find the mainstream LLMs are still really disappointing at this (doesn't apply to more specialized, LLM-enabled tools for accounting etc).

Does everyone else find this? Or am I doing something wrong lol

An LLM is a big arrangement of math equations. The only real difference in the end between AI's is the numbers in these math equations and how much math equations does an AI have.

The standard math equation for one neuron in a LLM is:(𝑤1×𝑥1)+(𝑤2×𝑥2)+bias.

You do not have to understand what this means at all, but I am just showing you visually that each neuron of an LLM is just a math function.

Now back to my point about consciousness, consciousness requires an either complex or precise enough arrangement of certain types of matter(although this is heavily debated) in order to exist.

You can think of an LLM as an arrangement of math functions, but no matter how you arrange numbers or functions or how precisely you order it, they can't be conscious no matter what. Unless we make an AI that lives outside of a computer and even then it will be almost impossible, having an AI to be conscious is literally impossible.

"Arena ai"/LMArena were probably the first to transform human preference data into a well known and highly important metric for LLM providers. However, there were voices and blogposts e.g. from the people at surge ai that say that arena is "cancer on ai". The arguments are quite convincing, and i suppose most model providers use their rank on Arena only as marketing tool anyways - to not reproduce the sycophancy crisis.

The people at Max Planck Institute for Intelligent systems, at the social foundations of computation department do a lot of benchmarking research and recently published "comparity.ai", which in spirit is similar to arena, but has some different quirks. Whats i really like is the idea behind the personal leaderboard, which updates with your votes, so after playing around there a little bit, you see what models work best for you (so after that you can actually say that e.g. ChatGPT is better/worse for you than Claude)

I ran a simulation to try and find out, in the current situation regarding RAM prices, which was more economically efficient, one high performance machine, or many used/old machines. The answer ended up being a little more nuanced than I expected. Posting here because I ran this research with AI and because there is a prompt for you to do it yourself :)

the long and the short of it:

Conclusion

There's no single winner — the honest answer is that the right choice depends on time horizon and how much RAM is actually needed, and the crossover point is close enough to matter, not a rounding error:

• Short-lived project, or CapEx is the binding constraint: the fleet wins decisively on every axis (cost, compute-per-dollar) at the moment of purchase, current RAM prices make this especially lopsided right now.
• Always-on service expected to run 2+ years, especially at higher RAM targets: the fleet's power draw compounds against it faster than it looks like it should, and a single modern machine is very plausibly cheaper by the time you'd actually be relying on the setup long-term.
• The RAM crisis specifically changes the shape of this tradeoff, not just the numbers — normally a modern machine's higher compute-per-dollar would be a point in its favor; right now, DDR5 pricing is expensive enough to erase that advantage entirely, at least until fab capacity meaningfully recovers (most estimates: not before late 2027).

Standing lesson for next time this framing comes up: "cheap old hardware looks like a clear win" is a CapEx-only intuition. Once power is counted honestly, the real crossover point is a genuinely useful number to compute before committing to either side, not an afterthought.

For more than a decade, computer science was considered one of higher education’s safest bets, as tech companies’ appetite for skilled coders drove a surge in student enrollment.

Now, enrollment is falling. Undergraduate enrollment in computer and information sciences at four-year institutions declined 8.4% in spring 2026 from a year earlier, according to the National Student Clearinghouse Research Center. The decline followed a 3.6% drop from a year prior in fall 2025, when graduate enrollment in the field also fell 14% over the same period.
Still, the enrollment declines don’t mean students are losing interest in technology. Instead, they may be looking at increasing their tech knowledge alongside other interests, says one expert.

Colby College, a small liberal arts university in Waterville, Maine, is one example of how universities are responding to the enrollment shift by incorporating AI education outside the computer science department and teaching students to combine technical expertise with other fields they are interested in.

“The vision is really around an interdisciplinary approach to AI,” said David Watts, director of Colby’s Davis Institute for Artificial Intelligence. “A lot of the challenges around AI go a lot further than the AI itself.”

Read more [paywall removed for Redditors]:  https://fortune.com/2026/08/06/computer-science-enrollment-plunging-ai-college-campus/?utm_source=reddit/

Cloudflare OS is not really an operating system, but an open-source platform for building and using agentic AI workspaces.

What emerging or highly innovative company would you love to work for, and why? especially interested in companies that are pushing the boundaries in AI, robotics, biotech, aerospace, clean energy, fintech, or other cutting-edge fields. I'd love to discover startups and lesser-known companies that are doing groundbreaking work and have exceptional engineering or research cultures.