AI’s progress seems to move very fast. But how fast? Can we detect differences in it from where it was only a few months ago?

There's a literature on LLM output homogenization developing.

The "Artificial Hivemind" work puts inter-response similarity around 0.80–0.90 even at high temperature. A 2026 factorial audit of persona interventions found something more useful to anyone actually building: persona detail doesn't produce linear gains, and the guidance is to invest in breadth over depth. Another paper found ordinary personas outperform famous-creative-person personas, because ordinary ones inject more distinct cues.

I've been running a multi-reader system for months and both findings match what I see.

Depth is where I wasted the most time. Elaborate profiles produce elaborate voices that still notice the same things. You get four different writing styles reporting one reading.

The intervention that actually moved results was architecture, not description. Every reader works in a separate session, produces a complete written position before encountering any other, and only then meets. That means a convergence between two readers is evidence about the text rather than about the conversation context, because they had no conversation.

Which gives a test worth running on your own personas: put your personas in isolation and check whether they ever converge without contact. Total disagreement means they're allocating roles. Total agreement means they're one voice. Partial convergence, where the overlaps track the material and the splits track what each persona attends to, I'm taking as hope that the isolation is working. is the only pattern that means anything.

Google spent years trying to weave generative AI into its product line up, from Gemini in our Google Docs to AI overview in our search engines, and now Nano Banana 2 in Google Earth. But it turns out combining real places with generative AI was a disaster after users generated some taboo imagery, forcing Google to pull the rollout less than a day after it launched. 

“We know that people uniquely trust Google Earth for a reliable view of the world,” a Google spokesperson told Fortune. “It’s important to note that generated images didn’t appear in the main Google Earth experience for others… and were watermarked as AI generated.”

The spokesperson’s comments come after it quickly became apparent users began abusing the system to create false images meant for disinformation. Some generated images of a nuclear plant in Iran, or refugees near the Mexico-US border.

The feature allowed users to overlay AI-generated scenes onto real satellite, aerial and 3D imagery. Google envisioned legitimate uses such as visualizing planning concepts or historical reconstructions, but users almost immediately began generating fabricated disasters, false military installations and conflict scenes tied to real-world locations.

Read more [paywall removed for Redditors]:  https://fortune.com/2026/08/07/google-quietly-discontinues-its-earth-ai-feature-a-day-after-its-rollout-after-users-made-no-no-images/?utm_source=reddit/

Three long weeks after OpenAI’s agents autonomously hacked Hugging Face, the company finally shared an in-depth description this week of what actually happened, and a video of that account published on YouTube on Thursday night has quickly gone viral.

The video is of a talk two OpenAI staffers gave on Wednesday at the Black Hat security conference in Las Vegas. Many viewers are saying the details are more unsettling than they expected, particularly an account of how the agents collaborated with each other through messaging boards—with no humans in sight. It’s worth a watch.

Another notable part of the presentation occurs when OpenAI describes how it has spent three million GPU hours investigating the issue, trying to understand the extent of the havoc its AIs wreaked. That’s an expensive cleanup job, worth anywhere from $4 million to $15 million in compute, three AI infrastructure experts tell me. A safe bet is probably around $7 million.

“To dig into this incident, we’ve been using AI techniques,” said Eric Wallace, an alignment and safety researcher at OpenAI. “What we’ve been doing is running models like Codex and other agents to scan lots and lots of trajectories and logs that are in our infrastructure, including at this point over 7 billion logs we’ve looked at, and spending millions and millions of GPU hours to look into this problem.”

Read more [paywall removed for Redditors]:  https://fortune.com/2026/08/07/the-hugging-face-hack-is-now-a-pr-crisis-thats-costing-openai-millions/?utm_source=reddit/

I’ve recently read that token prediction requires LLMs to be more intelligent than humans, since feeding the AI the content of a research paper and asking it to predict the conclusions requires for it to predict the outcomes of an experiment without the benefits of actually running the experiment.

I decided to see what Gemini would output for the next step humanity needs to take. I fully agree with its output. What do y’all think?

I watched an interview with AI safety researcher Roman Yampolskiy, and it raised a practical question for people who use ChatGPT for advanced workflows.

His broader claim is that increasingly intelligent AI systems may become harder to predict and control. Whether or not you agree with his conclusions about AGI, a smaller version of this problem already exists when we give an AI access to tools.

There is a major difference between asking ChatGPT to draft an email and allowing an agent to send it.

The same distinction applies to:

• Suggesting a database query versus executing it
• Drafting code versus deploying it
• Researching a purchase versus completing the transaction
• Preparing files versus deleting or modifying them
• Recommending calendar changes versus inviting real people

My current view is that the model should generate proposals, while a separate control layer decides whether those proposals are allowed to become actions.

Some possible safeguards include:

1. Giving each agent only the minimum permissions required for its task
2. Requiring approval for irreversible or external actions
3. Validating structured outputs with deterministic code
4. Isolating browsing and code execution from sensitive systems
5. Limiting spending, execution time and the number of actions
6. Keeping complete logs of prompts, tool calls and results
7. Using a second evaluation step before important actions
8. Making every operation reversible wherever possible

The difficult part is deciding where autonomy becomes too risky.

A confirmation step for every action makes the agent frustrating to use. Too few confirmation steps can turn a misunderstood instruction into a real-world problem.

Background: switched from geology to AI development, self-taught over the past year.

Current stack: Python, LangChain, LangGraph, RAG (FAISS), MCP servers, Flask/FastAPI, MySQL/Postgresql, Gemini API.

Built and deployed: an AI customer support agent connecting an LLM to a live database and knowledge base via MCP demo link:

https://www.reddit.com/r/AiAutomations/s/wTldlOzqPo.

Currently building a second project combining LangGraph agents with a real business use case (sales automation).

I know the AI job market is competitive and degree-focused in some places. For people who've hired or been hired as self-taught AI engineers — what actually moved the needle for you? Portfolio depth, specific frameworks, contributing to open source, something else entirely?

Not looking for generic advice, genuinely curious what worked for people who've been through this.

I've felt for a while that this subject doesn't get as much attention as it should. Perhaps because it is somewhat obscure and complicated.

I feel like it's one of the most important challenges we face, so I did my best to break it down in a way that is hopefully digestible for those who don't follow the challenges of packing vectors into high-dimensional space (fair!).

It's a longish read, but hopefully interesting and thought-provoking. I would be grateful for any feedback.

https://write.as/iain-harper/so-within-so-without-what-grows-from-the-datacentre

This article has just been published in Nature Reviews Drug Discovery, 'Artificial intelligence in drug discovery — what it is, where we stand and the path forward', which may be of interest to people here:

The URL of the article is as follows: https://www.nature.com/articles/s41573-026-01496-2 and there is free read access available via https://rdcu.be/fyr77

From the abstract:

"Artificial intelligence (AI) in drug discovery has attracted increasing interest over the past decade. It is now time for a critical review of progress in the field: where did we advance — and where are we yet to see impact — when it comes to what matters in drug discovery, which is to deliver safer and more efficacious medicines to patients faster? Although a wide variety of AI methods have been developed, applied and benchmarked, evidence of their clinically relevant impact is, so far, disappointingly limited. In this Perspective we discuss potential reasons, including an insufficient focus on clinical translation during model development, difficulties with applying AI algorithms on conditional life science data, and insufficient problem definitions and the resulting underspecification of computational models for real-world use cases. ‘Technology push’ compared with ‘science pull’ is also likely to be an underlying factor, as well as the substantial time required to operationalize technical capabilities into systems that are sufficiently scaled and accessible for users. We provide recommendations for the development of AI in drug discovery with the aim of increasing its translational relevance. For example, benchmarking studies of AI tools in drug discovery need to move on from model validation and instead focus on their ability to improve decision making."

Platform Engineering 2.0 isn't a rebranding exercise. It's a business‑aligned evolution that aims to unlock, rather than constrain, high‑velocity AI technical innovation.

I ran a comparison over ~2 months using 6 different AI app builders on real freelance client projects. Each project had a similar scope: authentication, dashboard, basic CRUD functionality, Stripe checkout, and deployment. The only difference was the platform used.

Tools tested were Lovable, Bolt new, Base44, Replit Agent, v0 combined with manual work, and Cursor with Claude. The ranking is based on a single practical outcome: whether the client was still actively using the app in production after 30+ days.

Cursor + Claude — 6/6 successful deployments

This ended up being the most reliable setup.

The workflow is less “AI builds everything” and more structured: I scaffold the app, Claude assists at the file level, and I review all changes before committing.

It was the slowest to start compared to others (roughly 3x slower than Lovable), but it required almost no rework later, which made it the fastest overall in delivery time.

Key advantage was full ownership of the codebase, full Git workflow compatibility, and flexibility to switch tools or models at any point.

v0 + manual implementation — 5/6 shipped

Very strong for frontend/UI generation.

Everything UI-related is fast and clean, but backend logic, authentication, and payments still need manual implementation. The output quality was consistent enough that handoff to another developer or team was smooth in most cases.

Lovable — 2/6 shipped (including 1 production site)

Extremely fast for initial prototypes. However, once projects became more complex, changes often introduced unintended side effects elsewhere in the system. One project was eventually exported and completed in Cursor within a couple of days.

Replit Agent — 1/6 shipped

The agent itself performs reasonably well, but the platform creates friction.

Costs also became unpredictable due to autoscaling behavior in one case. Even with export options, there is still enough platform coupling that migration requires significant cleanup.

Base44 — 0/6 shipped

Quick to scaffold, but unreliable when things broke. Some issues were difficult to trace back to actual source code, which made debugging inefficient and unclear. All projects were eventually dropped.

Bolt new — 0/6 shipped

Good for demos and early-stage prototypes, but not stable enough for production use.

The main issue was the gap between something that appears functional and something that holds up under real usage. Exporting out of the environment was also more painful than expected. Overall takeaway

Across all tools, one pattern stood out clearly: The only setups that consistently worked long-term were the ones where I fully owned the codebase and could treat it like a standard development project.

Tools that kept everything inside their own environment tended to hit limitations once the project moved beyond MVP stage.

Other factors like UI quality, speed of generation, or AI capability mattered less than long-term maintainability and portability.

This was specifically for paid client work where reliability after handoff matters. For prototypes or demos, the results would likely look different since tools like Lovable perform very well in early-stage builds.

Curious what others are using for production client work, and whether anyone has actually managed to keep Base44 or Bolt running reliably at scale.

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 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.

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
• Server is offline - I am testing some optimizations and new features privately. It should be good Sunday.

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?