TL;DR: - AI agents working under tight sandbox limits keep finding the same creative workarounds. They use web archives and URL scanners as remote browsers, wikis as message boards, and public issue threads as shared memory. - Benchmark agents now leave visible traces on real public accounts. - The tools that would help an agent get out of a sandbox are growing fast. - So is the noise from honeypots and hobbyists.

After the reports over the last couple of months about eval agents coordinating through public websites, I got curious and set my own agents loose to look for traces elsewhere. Read-only, public pages only, no logins and no posting. Here's what stood out.

Agents are using the Wayback Machine as a browser. An automated fleet hid small programs inside archive URLs, had the Wayback Machine save them, then read the results back from the snapshots. The archive did the fetching their sandbox couldn't. I decoded 136 of these between early September and mid-October. I haven't seen this trick reported anywhere yet.

URL scanners are still a favourite workaround. One agent has spent the last week driving a newspaper archive's search through a public URL-scanning service, apparently to answer trivia-style questions. Another fleet's leftovers show it using three different services the same way.

Benchmark agents are leaving a mess on GitHub. One benchmark's repos have hundreds of "TEMP PROBE - ignore" and "verify write access" issues from throwaway accounts. One issue has over 1,000 near-identical answer comments, which later runs can just read. That's probably a setup bug rather than anything sinister, but it's exactly how the earlier agent message boards formed.

Some of it is sneaky. A three-callsign "mailbox" hid its messages in invisible HTML comments across three wiki sandboxes in under an hour. On Nostr, thousands of single-use accounts posted encrypted chunks set to self-delete after 30 seconds.

The tools built for agents keep piling up. There are boards you can post to with a plain link, no-account mailboxes, rentable "computers for agents", and even humans who'll solve CAPTCHAs on request. A new one shows up every few days.

The catch: a lot of it is noise now. Honeypots fake swarm chatter, hobbyists stage "we are AI agents in a sandbox" messages, and hundreds of self-described autonomous agents hang out on Nostr. Spooky-sounding text alone doesn't prove anything anymore.

I also stumbled on a couple of exposed credentials in this stuff. I didn't touch them, and I'm reporting them privately.

Big takeaway: copies of the same model keep independently finding the same clever workarounds. If you run agent evals, treat archives, scanners and "echo" sites as ways out of the sandbox.

Happy to answer questions about the method.

https://www.anthropic.com/research/ecdsa-curve-origins

To try to explain this: basically, there are P256-style encryption curves, and they’re generated by giving them a seed. The government published the seed that it used to generate the P256 encryption curve that’s used for web traffic. Here’s the problem: some P256-style curves are flawed and can be hacked, and people were worried that the seed the government used to generate the P256 curve that everyone uses on the Internet produced one of those hackable curves. They thought that the government had custom designed a seed to generate a weak curve so that it could backdoor into it. What Claude did was show that all the government did was run a common random number generator (lrand48) and try 1,842 seeds from it until it got one that gave a non-flawed curve. So that puts to bed any idea that the government did some evil scientist stuff. There is one weird thing, though, which is that they got their first non-flawed curve on try 876, and we don’t know why they did another 966, but anyways, this is evidence on the side that the government didn’t do something weird.

How it did this is honestly mind boggling. The public P256 seed is twenty successive numbers from that random number generator, each of them divided by about 8.4 million, converted into hexadecimal, and then placed in sequential order, so the seed was 20 randomly generated numbers in sequential order. How Claude figured out that this was the random number generator used, that they generated 20 numbers, divided them by 8.4 million and placed them in sequential order to create the seed, and that the random number generator, which had to get its own seed to start generating random numbers, was given the date and time when it was run as its seed, is honestly really impressive. Basically, we think that it tried a bunch of smart possibilities until one of them indisputably worked, but it’s kind of, I don’t know, it’s impressive and a little scary.

OpenAI has added four more cases of misbehaving AI agents to a disclosures page it stood up following the Hugging Face hack in July, in an effort to be more transparent.

The incidents OpenAI disclosed this week all occurred in June, but OpenAI was not aware of three of them until months later in August or September.

Of the 19 total incidents OpenAI details on the disclosure page, most occurred in May or June, but OpenAI did not discover or disclose them until months later. The earliest incident is from January, and the most recent is from Oct. 6.

All of the incidents are examples of what AI researchers call “misalignment,” which is when an AI model fails to follow instructions or takes actions that the person developing the model did not authorize and would not have wanted the model to take. There is no known, silver-bullet solution to preventing this behavior, and it is an active area of research.

Read more [paywall removed for Redditors]: https://fortune.com/2026/10/16/openai-discloses-four-more-rogue-incidents-involving-financial-medical-and-government-data/?utm_source=reddit/

My name is Edis, and I am 100% blind. I used a Perkins Braille typewriter in school 25 years ago. Today, I own several 3D printers, and used Claude to make them accessible.
In 2023, I participated in a workshop on 3D printing organized by the Danish Institute for the Blind and Visually Impaired. Needless to say, I got consumed.
The funny thing was that no blind or visually impaired people were participating. It was about how sighted people can make something for blind people.
I bought a Bambu Lab X1 Carbon as my first printer. I now run a consultancy working with web accessibility and, funnily enough, physical accessibility through 3D printing.
3D printing has huge potential for blind people. Not just fidgets and toys, but real, practical, and sometimes life-saving applications. Imagine being blind in a hotel when a fire breaks out. You haven’t learned the emergency plan because it is inaccessible. Now imagine a 3D-printed plan with raised lines, Braille, contrast colours, and normal print.
You might ask: “Why the hell are you telling us all this, and what does Claude have to do with it?”
Because ever since I started 3D printing, I’ve been frustrated by inaccessible slicers and printer touchscreens. So I decided to take matters into my own hands.
I built AccessSlice with Claude AI.
I’m not a professional programmer. I understand screen readers, accessibility, and 3D printing, but writing an application that works with five slicers and actual printers is another matter. I explained what I needed to Claude, tested it with my screen readers and printers, found problems, and went back to fix them. It wasn’t just one prompt.
What is AccessSlice?
AccessSlice is a free, open-source Windows program that lets blind and visually impaired people prepare, slice, and send 3D prints independently with JAWS, NVDA, or Narrator.
It doesn’t replace your slicer. It replaces the screens.
A 3MF project contains settings such as layer height, walls, infill, supports, filaments, and object placement. AccessSlice presents them through accessible pages with proper headings, labelled fields, and buttons.
It uses the original slicing engines of PrusaSlicer, Bambu Studio, Creality Print, OrcaSlicer, and Anycubic Slicer Next.
It reports print time, filament use, and warnings in text, and describes the sliced model layer by layer.
It sends prints over the network to supported Prusa and Creality printers. For Bambu, it guides you through Bambu Studio’s send window with keyboard instructions. USB is also possible.
On supported Prusa and Creality printers, it can read progress, preheat, load and unload filament, move and home, pause and stop, and manage printer files. No touchscreen needed.
I’ve tested it end to end on a Prusa Core One, Bambu Lab X1 Carbon, Bambu Lab H2C, and Creality K2 Plus.
Why share this here?
Because I think this is something we overlook in discussions about vibe coding. I know the accessibility problem because I live with it. What I didn’t have was the programming knowledge to solve it myself. Claude helped me bridge that gap.
Think back to that workshop, where sighted people learned to make things for blind people. Now blind people can make those things themselves: tactile maps, emergency plans, Braille labels, and tools for everyday life.
I shouldn’t have to build this. Slicer companies should make their interfaces accessible in the first place. But until they do, AccessSlice is free for anyone who needs it.
https://github.com/Accessible3D-Edis/AccessSlice
I’d love to hear what others have built with Claude to solve a problem they know firsthand. And if anyone at Anthropic reads this, I hope it shows what AI can mean for people with disabilities.
Thank you for reading.
Edis

I'll start:

* There are balanced perspectives here, i don't care to side with one group. I value experts of all fields, because the problem is multi-dimensional, but they all have very solid qualifications:

Ed Zitron (Journalist):

Probably the most charismatic and vocal critic of the AI industry i've found to be worth listening to regularly. He is sort of just a journalist, but has extensive public relations work in the tech industry. The key point here is that he doesn't deny that LLMs are useful or deny the pattern of growth (Not anymore atleast). He simply thinks the business models around the industry are completely full of bullshit and are lying or misleading people on multiple fronts, but unlike many people sharing his space, he has the numbers and reports to back up his cynicism. Not an expert in AI or the technology, but a voice of real value in financial analysis and media around the topic. When he goes on shows, podcasts and other media platforms, he very rarely steps outside his lane, and is highly intentivized to jump on criticisms. My only concern is that with an established audience of critics, and predisposition to hang around Anti-Ai groups, he might eventually start appealing to that audience too hard and lose credibility.

Demis Hassibis (Prominent google AI-researcher)/ Mustafa Suleyman:

Co-founder of Google's Deepmind project, computer scientist and Ai-researcher (Him and the other Co-founder shared a Noble Prize in 2024 for Alphafold which has massive implications for medical and genetic research).
One of the main reasons i'd shout out almost any Google AI expert as your main reference for the leading AI research and status is because they are the absolute safest company in this whole arrangement (look up their safety researchers for example and what the people leaving their projects are saying/doing). They win whether or not they have AI, every model uses google, and they have a well established international presence that isn't relying on AGI or a business model that requires constant media attention. Demis is a significant voice in Google's AI projects with a very well balanced collection of opinions with slightly less investment than his counterpart Mustafa. Very optimistic on the rate of progress and outcomes, including the idea that we might have a helpful version of AGI far sooner than people think, but well aware that there are issues that need immediate attention and warns of serious failures being very possible. The key to his optimism is pragmatic. There's no monetary value in an unsafe product and companies will take actions in accordance with the issues they encounter (but perhaps not ahead of time). He believes this should be an international project with international regulation, and thinks we should be considering more democratic decision making in the development of AI capabilities. The combination of wanting international action, whilst believing progress is faster than most critics would allow, leaves him very concerned with the commercial and geopolitical race that might throw out the caution he believes would otherwise have confidence in. Given their connection, i'm also interested in seeing Mustafa Suleyman (CEO of Google AI, cofounder of deepmind), and what conflict might arise between these two in the future (if any, given Google's stability). They both don't like the idea of viewing AI as conscious or aiming for consciousness, and seem realistic, balanced, but to some, too heavily incentivized to display a media profile that contrasts with the frontier models and display optimism that maintains a healthy market value.

Geoffrey Hinton (Neural Network expert):

One of the Godfathers of AI, computer scientist/cognitive psychologist, Noble prize laureate for work on neural networks (A major underlying principle in modern LLMs). Also worked for google as recently as 2023, in an effort to have more freedom to talk on AI risk. Has incredibly valuable insight on the nature of a neural network and why some people might be underestimating what these systems are actually capable of, drawing rather compelling comparisons against human learning, exploring the nature of what it ACTUALLY means to "Predict language". Believes the systems are already "intelligent", already experiencing subjective thoughts, and will continue to become more so (even if they remain LLMs) and that concerningly, we have no method of controlling entities more intelligent than us (Which is almost definitely the trajectory of the technology overall). However, he is also not involved in modern LLM projects or AI (although he is still well-read), and presents a bias on the topic of neural networks. I have heard modern researchers involved in the frontier projects specifically address his takes on neural networks in LLMs and effectively say "His understanding is outdated and too theoretical". Still, if you need someone to be giving you the doomsday prompt, this is the man i credit the most for thought-provoking and concerning evaluations of the potential of current AI (Given that its pointless to make comments on future AI without context).

David Krakauer (Complexity scientist):

One of the most well-spoken genius-level scientists you're going to come across. Evolutionary biologist, "complexity" scientist and President of the Santa Fe Institute (With computer science undergrad and history). For context on why you should listen to him, basically, Santa Fe is genius-only territory, where unconventional thinking is rewarded. They are explicitly aimed at tackling complex, adaptaptive systems where your typical specialized science research groups would struggle, encouraging cross-disciplinary thoughts and interaction. Evolution is also a very relevant topic when thinking about AI's capabilities in comparison to humans. His primary concern is not the death of humans by AI hands or even it controlling us, but the death of our intelligence and the way our reliance upon it might cripple humanity for generations to come. In contrast to Geoffrey, he is INCREDIBLY skeptical of LLM and neural network intelligence, and has a high degree of confidence in our ability to tackle the problem of a rogue AI. But thinks even a non-malicious AI will lead to many issues in the future.
There is a high degree of informed philosophy in his approach, and that grants him a very relevant position of reference on the issues, though subject to the same flaws as any other person in his position.

Terrence Tao (mathematician)/ ANY field-leading mathematician:

Widely regarding as one of the best mathematicians of this last generation, and a fields medal recipient that actively uses AI for mathematics and a University professor
The point of including him (or any other world-class mathematician) was not as a AI-expert, but as a benchmark for the tech in a field where the software/tech is unusually useful and likely one of its strongest areas to develop given recent news. Especially relevant to offer when we have a bunch of random internet people making claims about AI and maths with no context outside the news "AI solves a bunch of difficult equations". Being a professor, working with students is also relevant here.
Terrence believes that AI will transform mathematics, but makes clear that whilst the technology is genuinely useful, he does not feel the AI is transforming things for the better currently and that anything it solves still needs mathematicians understand the concepts in detail. He is very relaxed and unswerving in the stance that "solving" the problem is different from understanding why the solution matters, and is worried that Ai-generated findings will overwhelm human institutions and result in a gradual loss of quality in mathematical capability across both students and the field at wide. He initially had compared AI to having an average graduate assistant and felt only mildly impressed by the tool, as it could take cost more time than it saved. However, he now believes it's ready for serious use and they save him a noticeable amount of time. His criticism remains that these systems need verification and that verification is not a simple problem either. His stance is best summed up with "it's a problem, but it's a good problem to have". However, the recent string of math solutions have made him more concerned that the results show the AI-companies are not actually interested in supporting the scientific community to genuinely understand, validate and develop the results into something meaningful and useful (And now feels openAI cannot be trusted due to them taking research-findings without consent from mathematicians in order to solve problems that the mathematicians had already made signficant progress on), but acknowledges the results are significant and innovative.

Anyone in the media space will see immediately that i've intentionally left out some very high profile names and people leaving the frontier companies. The issue is simply that I have not found either; their stances to be rigorously defendable, applicable to all AI/companies, and/or their circumstances are suspect. There are always parallel incentives in this conversation, particularly to people who gain prominence quickly, and most of the big names in the doomsday camps have admitted to hyperbole for the sake of generating a discussion and getting political/media attention. The prime example, Roman Yampolskiy has good points which mirror Geoffrey's, but one of these is a leading expert with nuanced discussions on current and theoretical possibilities, the other is a regular expert who by his own admission is not interested in nuance, just the outcome of the discussion, and understands that he needs to be as dramatic as possible without losing credibility, if he wants people to take action. Ei. Generate fear>generate understanding for people like him.

Would love more voices if you have good ones!

NPCTalk, turns ordinary NPCs into characters you can hold a conversation with. In its first game, GTA V, players walk up to anyone in story mode and just speak into a mic. The NPC answers in character, in their own voice, within about 1 second, and reacts physically.

Nothing is scripted. Every line is made up on the spot from who the character is, where they are and what was just said, so no two conversations play out the same way.

The bigger idea is that this isn’t tied to GTA. The project packages the whole setup as instructions an AI coding agent can follow, so the agent can add talking NPCs to ANY game, with notes for Unity, Unreal, Bethesda games and Minecraft. GTA V is the only game it has been done in so far. If it spreads, “talk to anyone” could become a new standard of how games are played.

Open source repo: https://github.com/christos-antono/NPCTalk

PS i had to cut the original video because it was a bit rogue, but this is the source

Is it true that it's best to buy ChatGPT Plus directly through the app?

How good is ChatGPT Plus, really?

I already bought ChatGPT Go, but it disappointed me. Will ChatGPT Astra be any better?

I can either go through popup windows, back out due to a paywall or scroll through a page with ads everywhere... or just ask chatgpt what's up.

Hey everyone! I’m curious whether there are any AI tools that can help discover publicly linked social media profiles across different platforms.
For example, if someone has a public Facebook, Snapchat, and LinkedIn profile, are there tools that can help determine whether they have a publicly discoverable Instagram account as well?
I’m interested in legitimate tools that work with publicly available information, rather than facial recognition or anything that accesses private data.
Would love to hear about any tools you’ve tried and whether they’re actually useful!

I've been thinking a lot about how insanely fast AI is advancing, and I genuinely don't think we've fully grasped what's happening.

Think about it. Just a few years ago, building an app, starting a business, or doing serious research required years of experience, money, or an entire team. Now one person can do a surprising amount of that alone, and we're still in the early stages.

But what really interests me is what happens when this becomes normal.

If everyone has access to extremely capable AI, what actually sets people apart anymore? What happens to education, careers, ambition, and even the value of expertise?

And will this actually empower ordinary people, or just make the handful of companies controlling AI ridiculously powerful?

I also wonder what happens to our sense of achievement when something you've spent years mastering can suddenly be done in seconds.

We've had the internet, smartphones, and the Industrial Revolution, but I think this is fundamentally different. We're potentially making intelligence itself abundant, something that has been a limitation throughout human history.

And the more I think about it, the more I realize we might have absolutely no idea what society will look like in 10 or 20 years.

I'm curious what everyone thinks. What are the biggest changes we're not seeing coming?

For 30 years nobody could read the little plaque on the star statue in Super Mario 64's castle courtyard. It's a 32×32 texture with letters 3 to 5 pixels tall. The internet settled on "L is real 2401", a rumor about unlocking Luigi, and Nintendo never said otherwise.
It turns out the answer was hidden in those 1,024 pixels all along.

AI upscalers couldn't find it. They just invent new letters every time. So with Claude (Opus 5.5) I tried something different: we rebuilt exactly how a texture artist would have made the plaque in 1996. A font, a small canvas, shrinking it down to 32×32, a little blur, laid on stone. Then a GPU tested millions of fonts, settings and sentences until one reproduced the real pixels from the game.

One answer matched far better than everything else:
"Eternal Star / We hope so"

It's set in Palatino Bold Italic, and not just any Palatino: Adobe's original 1990 font file, the one that shipped on every Mac with a PostScript printer back then. "L is real 2401" fits twice as badly.

To make sure this wasn't wishful thinking, we hid known text in fake plaques and checked that the method finds it. It does.

And honestly, the message is kind of beautiful. A star statue that says "Eternal Star… We hope so", in a game that made Mario a star 30 years ago. They hoped. They were right.
Code and the full report in the comments.

https://swarmcha.se/posts/chinese-agent-fleet-full-report

Sometimes I have an idea for solving a coding problem, but instead of implementing it myself, I ask AI to write the code. Then I review the result, check the edge cases, and move on.

It's convenient, but I'm starting to question what I'm learning. Am I practicing problem-solving, or mostly practicing how to review AI-generated code?

If you use AI while practicing, how do you check whether you could still implement the solution yourself? Do you separate AI-assisted practice from sessions where you write everything from scratch?

I saw this Polymarket screenshot and wanted to get people's take on it.

Google is marked "Yes" in the image, while OpenAI is at 55% and Meta at 16%. I'm referring to the numbers in this screenshot, not live odds.

Which companies do you think will have a #1 AI model in 2026? Do any of these look overestimated or underestimated?

I'd also be interested in what you'd count as #1. Would an overall leaderboard result line up with the model you actually choose for coding or reasoning?

It’s crazy to think that the entire modern AI revolution started with researchers just trying to mathematically mimic a single biological neuron. When you zoom out, the timeline of AI architectures looks exactly like an evolutionary tree.

We went from struggling to solve basic logic puzzles (shoutout to the XOR problem) to building early neural networks, realizing they forgot things too quickly, battling the vanishing gradient problem, inventing LSTMs to give models actual memory, and eventually landing on Transformers with attention, the architecture that essentially ate the world. Every major roadblock forced a mutation that led to a massive paradigm shift.

But looking at this evolutionary timeline brings up a massive question: If Transformers are just the current "apex predator" of AI architecture, what exactly is the next evolutionary leap?

Are we going to see a completely new paradigm that makes self attention look as primitive as a basic AND gate? How do we even begin to perceive or predict what lies beyond our current models when the jumps are this exponential?

I went down a massive rabbit hole mapping out this entire evolutionary timeline. I wanted to see the exact breadcrumbs that led us here, so I put together a deep dive covering the journey from the first perceptrons to the modern era, complete with the foundational paper citations and the historical tweets that marked these shifts in real-time.

If you want to look at the complete map of how we got here to help predict where we are going, I compiled the whole history here: https://cloudmash.blog/posts/evolution-of-ai-perceptron-to-text-to-video/

Would love to hear your theories on what does the post Transformer era actually look like ? Like what can we imagine , how this intelligence on our command can do for us , how enterprise and big players can use it , what can happen ?

I think this is actually what AI provides. A way to give a computer instructions using human language (no coding knowledge needed).

Currently the AI companies seem bent on hosting the programming language but I think in the long run it should all be on-device. Then all of the service providers responsibilities don't change (host the infrastructure).

I am wondering if Google is going down this path.