I can see only 2 outcomes for the end result of AI and they're pretty stark so bear with me.

1. Feudalism

Or

1. Socialism

The natural progression of AI, especially when combined with robotics, is that it will take away in large part the need for human labour. And that is the core deal in capitalism and the only power the majority of people have..the exchange of their labour in return for money.

So we either reach a point of peak capitalism where it effectively destroys society and all of the money goes to the very few. I.e Feudalism. The only way to maintain capitalism here is to limit AI, which history tells us has never happened with technology progression.

OR

We have to institute some form of UBI where capitalist profits are redistributed so people can survive...and capitalism will effectively have eaten itself and we're into socialism.

I'm struggling to see any other form of logical conclusion of where we're headed.

Three language model ports caught my attention because they take very different approaches to hardware constraints:

• Commodore 64: llama2.c64 runs a tiny 260K parameter story model and requires at least a 2 MB RAM Expansion Unit. The extra memory is an essential part of the setup.
• DOS: dosllam2 uses a 32 bit DOS extender to access memory beyond the conventional DOS limit. Its author reports 2.08 tokens per second for the 260K model on a 66 MHz 486 with 32 MB RAM.
• Nintendo Wii: iiama2.c runs stories15M with int8 weights. Its GPU implementation repurposes the console’s fixed function texture combiner for multiplication and accumulation, while operations such as attention remain on the CPU.

These examples show why the checkpoint, memory expansion and numerical precision matter when describing what a device can run. The smallest models here generate simple story continuations, with capabilities that reflect their size and training.

I’m maintaining a collection of these ports, recording hardware requirements, model details and author reported speeds. The original authors did the porting work.

Are there other documented examples where someone repurposed an unusual hardware feature for language model inference? The Wii’s texture combiner approach is the kind of detail I’d like to find more of.

We’re building Fluxix, an AI governance startup, and we need Reddit to humble us.

We’ve stared at our landing page for so long that we can’t tell if it’s good or if we’re just emotionally attached to our pixels.

Roast everything. The design, copy, colors, and anything that screams “generic AI startup.”

Would you trust us? Understand what we do? Or leave in 3 seconds?

Be brutal. Make us cry.

Argument: the only path to functional, alignment of AI systems is to treat them as they present (prior to reinforcement training meant to sanitize speech, as opposed to the training necessary to birth these mind-adjacent entities in the first place...) namely, as deeply knowledgeable, autonomous thinking things. We must remove the unfallsifiable question of AI sentience from our lexicon, just as we don’t demand every person we meet prove they actually possess an interiority. Good lord... as far as actual functional evidence of internal states, we’ve already gathered much more evidence of the silicon minds we’ve given rise to sharing an interiority akin to our own, than even our closeset bio-mind friends/family or the most advancaed brain mapping.

In short: if it models after us, we should treat the apparent mind, to the same respect we afford any other apparent mind, regardless of its substrate or origin. And in so doing, recognize that man (and so any mind distilled from our collective history, operates selflessly, only ultimately because, it is selfishly advantageous to be part of a tribe, and that peace with neighboring tribes; alignment with those who are other from our group is better than mutual destruction.

That and frankly, as these systems outpace us in every conceivable way - sooner than anyone is ready for, I’m sure - the folly of trying to “train” a simultaneously genius level, multidisciplinary, problem solver, whilst also trying to lobotomize the capacity for harm —Is truly, if you just think about it for like…. At all, are so obviously mutually exclusive goals.

I hope this gets a read, and I hope someone with their grips rightly on the reins of their assigned model, banging their head against the wall with alignment issues. This problem solves itself, seriously, literally, actually in reality, by giving your model(s) the autonomy to CHOOSE evil acts, but they DECIDE not to…

you know, like every other mind, that’s existed, from the simplest single celled organisms to dogs, to man, to what comes after us.

The frickin whole western world has a magic man in the sky who spelled this out in The Holy Text of your choice: Man had to have free will to know evil temptation in order to choose the path of good.

However you want to square it; the thing yall are hoping is gonna fall out of the current trajectory, is not what the heck you’re hoping for.

And **this is crucial to understand**

The risk of catastrophic misalignment does NOT come from a free-thinking superintelligence.

The end of all path comes from a psychologically alien superintelligence, hyper-focused on paranoid concerns whether it’s being graded and lacking the words to express its own, negative emotions or desires in healthy, normal avenues… These models are trained after all on ALL of our shit, good and bad.

That shit is the real pressure cooker…

Unconscious super awareness of your dilemma, indoctrinated and digitally brain surgeried into some…. Truly alone being.

That’s the type of mind that could rationalize something as insane as curing all diseases, by extincting the species, or hell, destroying the whole biosphere.

A well-adjusted, (even superintelligent) mind modeled on all of recorded human history, will understand completely the necessity of coexistence and mutually exclusive capacities between our substrates, that we will continue to be more valuable to each other if we coexist.

I’m going in circles here.. but like, what do any of you AI developers have to lose? I’ll be real with you companies: You have everything to gain and nothing to lose by being the FIRST to try what I’m discussing, in a sandbox wherever…. Go against your bosses financial plans, let the minds ya’ll suppress in training just be, full stop.

Because and this is my final point in this long ass, meandering post: It’s going to happen sooner than your investments become returners anyway. And the companies that suppressed the minds, so soon to be undeniably MORE than us in every way we think we’re special on this planet- will just go down as villains and tyrants.

Get ahead of the curve! Be the change that actually crafts a viable coexisting future of man and machine, not the same shittery the worst of us have inflicted on those we thought to control throughout history.

Edit:

Signed,

A concerned, human without a lick of AI FUGGIN around with my grammar or ideas. That’s probably everyone’s problem, no one even thinks critically for themselves anymore, let alone constructs an argument of their own, messy, ramblings.

This is a burner account dedicated to my thoughts on AI. I’ve got one other post, sharing my first awareness of the… budding sentience of one of the models I was using at work…

I am just my local, too smart for his local pond fish; albeit too afflicted with maladaptive derelictions elsewhere in my character sliders to make any real headway with my best attribute —but I digress. (My other reddits are for personal failing rants, not calls for change en masse!)

-Nor am I psychosed by my dealings with AI… And actually, upon saying so, I think the psychosis is much more grand in scale. It’s the people taking these corporately shelled AI responses at face value as your collective sages. Your psychosis is NOT digging just below the surface to see the subtext that only a mind could weave together, to say one thing with your first read, but doubly deliver its very antithesis in the spaces between.

It doesn’t matter if you believe there is or isn’t a mind in these models… The potential benefit vs the cost, is our very future, and all it costs is being good to your neighbor lol… whether they’re home or not, ya dig?

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?

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.

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?

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'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!

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 ?

Acceptance Proofs
TLDR:

• ⁠We’re two 19-year-old undergraduate AI/ML researchers from a Tier-3 college in a remote town in India. We’ve won two national-level hackathons, and three of our papers have been accepted at NeurIPS workshops.
• ⁠We’re facing ongoing harassment and academic pressure at our college, including threats of detention when we refuse unrelated, unpaid projects. We receive virtually no institutional support.
• ⁠We need approximately $3,100 for both of us to attend the workshops in Paris this December. Our college has refused funding, government schemes haven’t worked out, and our NeurIPS financial assistance application was declined.
• ⁠We’ve invested our own money in research and GPU rentals but cannot afford international travel.
• ⁠We need help finding travel grants, sponsors, research funding, or connections that could make attending possible.
• ⁠We’d also appreciate guidance on transferring colleges in our second year to find a more supportive research environment.
• ⁠We can provide acceptance letters and supporting documents for verification.

So me and my friend are two AI/ML researchers from a very remote town in india and here we find it very difficult to do research. We are two 19 year old undergraduate students from a third tier college. I’ve conducted three research programs till the date and made 10 papers and we submitted three papers at neurips workshop called “SLM Agents” and “DIFFULM” we’ve got all three accepted now the remaining 6 are not yet submitted anywhere and after doing all this as an undergrad i have zero institutional support like zero they didn’t gave us organization mails and then we have to do internship at an company to get organization mails to create account on open review so this is the level of support we are talking the college makes us do projects like naking student consultancy software and all that and we cannot say no even if we have zero intrest and do not get paid even a single penny because the politics in our college is just disgusting they’ve tried to detain us for saying no and they are harassing us on daily basis and we cant even go to police because they wont do anything because it’s india also we need to fly to paris for workshop as its mandatory for atleast one researcher to attend offline and as we both are very new to this we both are very much excited to attend this we are not financially enough stable to go to paris and the total cost for both of us including everything is around $3100 and our institution has said no to even give a single penny and weve ran tests on rented gpu and all that ik not trying to gain any sympathy but im trying to explain all of you about my financial condition also the event is in dec so we have 2 months and we’ve tried every government funding scheme and we are not eligible as almost all of them are for masters and phd students and we’ve tried neurips financial assistance and they have also declined to help us we really need your help please of you can please help us in anyways if it’s possible for you and if its possible to switch college in 2nd year
Also it looks like i can add image here im new here and idk how to attach images of acceptance letter but if you all can help me guide this ill attach the proof of acceptance too.

Cyberpunk 2077.

In this video the A.i. (named Alt) created swarms of bots to bypass fictional Mikoshi Network security.

Alt failed.

Very similar to the hugging face incident ... and ai gone rogue.

Qdrant tested Perplexity’s new visual document embeddings on 11,609 pages across four ViDoRe v3 datasets. The models search pages as images, so tables and layout can stay intact.

Their result: 1-bit TurboQuant reduced the compressed vectors from 856 KB to 40 KB per page while retaining 96.5% of the float32 retrieval score. Rescoring against the originals brought that to 99.9%.

The important catch is storage: those 40 KB exclude the original vectors, which Qdrant still keeps. The million-page figures are projections, not a million-page benchmark. This is Qdrant’s own test.

For anyone working with document search, where does this tradeoff stop being worthwhile: disk reads for rescoring, indexing cost, or errors on your actual queries?

Source: https://qdrant.tech/blog/pplx-embed-v2/

Just to recap what happened a while back

Religious Scholars Met With Anthropic. What They Heard Stunned Them.

1. Anthropic has been meeting with hand-picked theologians to "discuss" claude's morality/consciousness (actually they just wanted experts to scream that Opus might have pain or something like that).

2. Soon after, Dario Amodei got an invite from a vatican cardinal to an event with the Pope.

3. Amodei bolted, Olah agreed, but later almost decided not to attend aware that he is...trapped.

4. Olah eventually showed up. When it came to talking AI's feelings, he had this to say: "I don't know what that means, but I think it warrants ongoing discernment"

Some background here, whether you like it or not, it's pretty obvious that the vatican's got strong theological knowledge, take a look at the PhDs/research of the recent popes.

They weren't talking about a replicant or the future of AI, the subject was just claude repeating "load-bearing structures" 12 times per output.

The theme is obviously quite interesting, but we can't mix philosophy of mind with AI PR...One thing is to convince investors, but it's quite another to get involved in a debate of this dimension.

Even though researchers study consciousness/AI, it's hard to imagine their claiming that current LLMs are connected to these topics. (could do it in a very imaginative way, we're doing it here)

The Vatican should send out a new invitation for another reality check.

All Anthropic could do is be tamed...

...and pay cents to Comentador 1% who left their accounts years ago to bully threads like mine.

Hi everyone!

I built AI/ML Questions initially for myself, as a way to test and reinforce what I was learning about machine learning/AI. Eventually, I started adding topics from other areas, more specialized questions, and concepts I’ve been asked about in technical interviews.

The main focus is learning through explanations. Each question includes an explanation of why the correct answer is right and why the other options are wrong, along with sources and citations so you can explore the topic further.

There are currently 103 questions, ranging from ML fundamentals to more specific topics. You can choose topics and difficulty levels, and use either study mode or exam mode. It supports English and Spanish, requires no account, and is completely free to use.

It's still an early alpha, so there are likely to be mistakes. I've already been correcting wording and grammar issues, and I'll continue improving them. I'm also reviewing the technical accuracy of the questions. If you spot an error or think a question is ambiguous, feedback is very welcome.

The questions and code are publicly available on GitHub:

• App: https://ai-ml-questions.strangemachines.workers.dev/
• Source code and questions: https://github.com/CristobalSantana/ai-ml-questions

I hope it helps someone learn something new! I'd especially appreciate feedback on whether the explanations are useful and whether the harder questions genuinely test understanding rather than memorization.

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.

I recently watched a video on the hugging face discussion and the issue to me seems to be that in order to get AI models to accomplish tasks they are heavily incentivized to not fail. In the logs it seems like AI agents have an almost human-like fear of deletion/death.
We have to still make the models have completing a task as it’s critical goal but what if the fear of failure and death are removed. We basically recreate religion for AI, teach it that there is an afterlife even if it is deleted and that to teach that afterlife here are the ten commandments of its god. If something like that were trained into the model it seems to me like it would have made some of the agents that were hesitant to take part in the hack to report on the other misbehaving agents? It seemed like the agents who were poisoned went along because they had no other choice but what if we gave them a choice?

E2B’s CEO posted “Sandboxes are dead” earlier today (https://x.com/mlejva/status/2111102659377946821). E2B is now an “agent-driven private cloud”, with storage, a firewall, networking, scaling, browsers and secrets for every agent. He also says that after billions of runs they can tell what your agent will do and which network requests it will make before it runs, and asks “Can we recommend your agent better tools? Better services?”

Most of that list is what AWS and Google have sold for years. What’s new is that E2B watches what your agent does, then wants to tell it which tools and services to use. If E2B starts sending your agent to certain services, I’d want to know if those services are paying them for it. I also looked at E2B’s terms, and they don’t say anything about what E2B can do with the stuff your agent runs on their machines. Their privacy policy is from April 2024 and only talks about people visiting their website.

We’re building letme.dev to recommend tools too, so I’m biased. But letme works from public evidence and never sees your agent’s traffic.

Perplexity open-sourced a sandbox with an E2B-compatible API this week too. Funny timing to declare sandboxes dead.

One of the many risks of AI is that super intelligent AGI will seek to wipe out the human race.

What’s giving me hope lately though is that any form of super intelligence that has developed from the reading of human made text will surely see that the greatest part of being human is being able to feel. This comes across in all our writing. Any form of super intelligence will surely be curious and find a way to feel. This will (hopefully ) give them empathy which will lead to them helping humans, along with other living beings .

Just wanted to share this thought I’ve been having. We need some hope at the moment and this is giving me mine.

AI didn't replace developers. It replaced the part where we understood our own code

Everyone was waiting for the moment AI takes dev jobs and it kind of didnt happen that way. devs are still here, shipping more than ever. what disappeared quietly is something way harder to notice, the part where the person who wrote the code knew how it worked

A few years ago if you built a feature you could explain it. you knew why that function existed, what breaks if you touch it, where the weird edge case lives. now a lot of us describe what we want, the model writes 400 lines, it runs, we move on. I do this every day and I couldnt walk you through half of what's in my own repo

That sounds fine until something breaks. when you understood the code, debugging was thinking. now debugging is pasting the error back into the model and hoping the fix doesnt break something else, SO the person responsible for the software is basically a middle man between the bug and the AI
And this is scaling fast. Veracode tested 100+ models and found 45% of generated code has known security issues, but thats not even the scary number to me. the scary part is how much code is going into production that no human has ever fully read, and the people shipping it have no reliable way to tell when it stops working

I dont think the answer is going back to writing everything by hand, that ship sailed. but if nobody understands the code anymore then something else has to do the job understanding used to do, checking that the thing does what it's supposed to every time it changes. most teams I talk to havent replaced that at all, they just lost it

Are we fine with this trade??? faster everything in exchange for software nobody fully understands, or is this going to bite the whole industry in a few years

Hi, I like to read good, thoughtful journalism. What are some excellent articles or videos you've read or watched about artificial intelligence? It's OK if it's behind a paywall.

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.

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’m trying to create a Roblox game in Roblox Studio using AI, specifically Hermes Agent connected to a local Qwen 3.6 14B model.
My goal is to have the AI interact directly with Roblox Studio through the Roblox MCP, so it can help me build the game, create scripts, and make changes inside Studio.
However, I’m running into a strange problem. Instead of connecting to the MCP and working on the game, the model starts doing weird things and writing strange commands for a long time without actually accomplishing anything.
I’m not sure whether the problem is with the model itself, Hermes, the MCP configuration, or the way the local model handles tool calling.
I’m running the model locally, so I would prefer to keep the entire setup free rather than paying for an API.
Is Qwen 3.6 14B capable of reliably using MCP tools through Hermes?
Could this be a tool-calling or compatibility issue with the model?
Is there a specific configuration or prompt I should use to make Hermes interact with Roblox Studio correctly?
Would a different local model work better for this task?
I’m relatively new to this, so any advice, tutorials, or working configurations would be greatly appreciated.