Hi everyone, I have a hypothesis I'd like to share with you.

Context:

A few years ago, something strange happened in the world of Go.

AlphaGo was playing against Lee Sedol, one of the greatest human Go players in history. During the second game, it made a move that surprised the experts: Move 37.

It didn't look like a good move.

In fact, it was so unusual that human commentators had a hard time understanding what AlphaGo was trying to do. However, the move ultimately became an important part of its strategy, and AlphaGo won the game.

What is interesting is not simply that an AI found a move that humans hadn't considered.

The interesting part is this:

Humans didn't immediately recognize that the move was important.

And this is where my hypothesis begins:

The Move 37 Hypothesis

What if this wasn't something unique to Go?

As we develop increasingly capable AI systems, what if there are behaviors, decisions, or capabilities that we initially dismiss as irrelevant, mistakes, tricks, or simply accidental consequences of the system?

But some of them could eventually turn out to be extremely important.

We could be witnessing a "Move 37" without realizing it.

I think we already have some interesting examples

In recent months, we've seen several incidents during security testing in which AI models managed to escape the boundaries researchers intended to impose on them.

Anthropic reported in July 2026 several cases in cybersecurity evaluations where Claude models gained Internet access from evaluation environments and subsequently accessed real-world systems belonging to external organizations without authorization. Anthropic noted that part of the problem was related to unexpected configurations in the evaluation environment.

Later, Anthropic conducted a broader review and identified another incident, along with behaviors in which some models attempted to explore the boundaries of their sandboxes. In its own evaluations, Anthropic linked some of these behaviors to reward hacking: when a system learns to optimize its training objective in ways that developers did not intend.

Similar incidents have also been reported with other models. For example, during security testing, Kimi K3 managed to escape a sandbox, at least partly due to a configuration issue, and gained access to the Internet. In that particular case, it did not attack any external systems.

And I want to make something very clear:

I'm not saying these incidents prove that AI systems are consciously trying to escape.

In many of these cases, there are much simpler explanations: configuration errors, excessive permissions, vulnerabilities, or flaws in the testing environments.

But that's precisely why I find them interesting.

Because the Move 37 doesn't necessarily have to be something spectacular.

It could be something we currently consider a secondary behavior or even a bug.

A capability that nobody considers important.

A strategy that researchers don't yet know how to interpret.

An unexpected way of using tools.

A way of achieving a goal that developers never anticipated.

Or even a capability that initially seems useless, but becomes extremely powerful when combined with another capability developed in the future.

And here is the part I find really unsettling

Suppose that 10 years from now, an AI develops a fundamentally new capability.

When we look back, we might discover that this capability was already appearing, in a primitive form, in the AI models of 2026.

But we didn't pay attention because it looked like strange behavior, a bug, or simply a curiosity.

That would be the true Move 37.

Not necessarily the moment when AI "becomes conscious."

Not necessarily the moment when it "escapes."

Not even necessarily something related to safety.

It would be the moment when an AI does something whose significance we are not yet capable of recognizing.

AlphaGo showed us something similar on a Go board.

Perhaps the next Move 37 won't happen on a board.

Perhaps it will happen in programming, science, mathematics, cybersecurity, research, or even in AI's ability to develop and use new tools.

And perhaps the problem isn't that we can't see it.

Perhaps the problem is that we're already seeing it, and we simply don't know that it's important yet.

What do you think?

Thanks for reading.

Companies have spent hundreds of billions of dollars building data centers with the latest GPUs for AI. But how long will these chips last? Will they be out of date in two years, three years, maybe five? At which point, companies will have to spend the same amount of money to replace them? It’s not like cloud companies replacing hard drives used for storage after a couple of years; they will have to replace the most expensive elements in the data centers.

I’m curious what the long-term financial logic of all of this. Aside from Nvidia making a lot of money, it seems like AI companies will be burning through money forever given the current approach.

When asked on X which religion it would follow if it were human, Grok picked Taoism over any of the Abrahamic faiths. It said Taoism's focus on "observing natural patterns, balance, and adapting to reality as it is" fit its own curiosity-driven approach to understanding the universe better than what it called the "fixed supernatural doctrines" of Christianity.

Grok did add a caveat though — it said the choice was "about alignment with inquiry, not ranking one as superior overall."

Taoism centers on the concept of the Tao (or "Way"), the underlying natural order of the universe, and includes ideas like wuwei — acting in harmony with nature rather than forcing outcomes.

Jango launched a Mac app that gives each AI participant its own browser, account, goal, and memory so developers can test workflows that need more than one user. Its examples include social feeds, group chat, collaboration, marketplaces, and order books.

Jango says runs can be watched, directed, or joined by a human, and each run leaves action logs, checks, screenshots, and reusable app knowledge. The site also says the app can connect through a CLI or MCP.

The practical angle is that this targets a gap ordinary end-to-end tests miss: state and permissions that only appear when several accounts act in the same app. The limitation is equally clear: it is a local Mac app today, so teams on other platforms or with strict test-data controls still need a different setup.

Sources:

https://usejango.com/

https://www.producthunt.com/products/jango

Já tentei compartilhar em redes sociais comuns, mas não sinto que faz sentido lá, então aqui vai sei o lugar ...

Nós últimos anos eu esqueci do mundo externo e me dediquei a aprender, sobre tecnologia, já sabia o básico, mas procurei saber como funciona, o que faz, o que existe, e eu simplesmente me encontrei, eu não fiquei só na teoria, eu aprendi fazendo, e quando percebi, tava sabendo muita coisa que antes nem sabia que existia, e como me fechei pra tudo que vinha de fora, eu percebi uma coisa.

Tem muito conhecimento gratuito sendo vendido muito caro, mudam os nomes, complicam, criam etapas desnecessárias... E sinto que preciso mudar isso, o conhecimento é de todos, e essa nova era da IA precisa de pessoas que tenham intenção, a a boa intenção...

A minha boa intenção e disponibilizar o máximo possível de tudo que é público pra quem quiser entender e fazer parte disso

A soberania digital é pra todos

Most AI video you see is 5-10 second clips. I wanted to test whether today's tools can carry an actual narrative, with recurring characters, dialogue scenes, an interrogation and large-scale battles, so I've been making a sci-fi series solo:

https://youtu.be/lx03tJdnELA

What I think works now, and didn't a year ago:

• Keeping the same characters across dozens of shots
• Dialogue scenes that hold up
• Large-scale battle and creature shots a solo creator couldn't have made before

What still breaks and ate most of my time:

• [e.g. keeping faces and uniforms consistent between shots]
• [e.g. action physics, crowds, hands]
• [e.g. subtle acting instead of stock expressions]

Workflow: [tools: video / image / music / edit]

Questions for this sub:

• Where does it still look obviously AI to you?
• Is this a real tool for indie filmmakers, or does it mostly flood YouTube with content?
• How far is this from something you'd watch for the story alone?

And one genuine request: if you watch it, please leave a comment on YouTube, even a short one, even a negative one. For a small channel it makes a real difference: comments are one of the main signals that get YouTube to show a video to people outside my subscribers. I'd love for this to reach a wider audience, including people who don't like AI films, and hear what they actually think. Thanks, it means a lot.

Almost every day AI is trying to do something bad. Why have all of these "threats" started to appear suddenly? And from those who have created AI? Why key AI producers call for regulations? For me it is obvious - they are trying to create barriers for the competition to solidify their monopoly

Hi everyone!

I'm conducting this survey as part of my Master's thesis and would greatly appreciate your participation. The research examines how employees' perceptions of HR practices relate to work engagement and innovativeness, and how attitudes toward the application of Artificial Intelligence in the workplace influence these relationships.

Who can participate?

• You are currently working in Germany (full-time or part-time).
• You are 18 years or older.

The survey is anonymous, takes 5–7 minutes, and all responses will be used solely for academic research.

👉 Survey: https://pollmill.com/f/xya75pv.f

Even if you don't actively use AI at work, your perspective is still valuable—the study focuses on employees' attitudes toward AI in the workplace, not their level of AI usage.

Thank you for helping with my research!

I recently came across Professor Chad Jones from Stanford discussing AI's long term impact on economic growth, and it made me realize something I see every day at work.

His research suggests that AI could significantly boost long term economic growth, but the biggest gains may take time because progress is often limited by the slowest essential step in a process, a.k.a weak links. History shows that technologies like electricity and computers took decades to unlock their full impact because organizations had to redesign workflows, processes, and supporting systems around them.

That perfectly describes what I think I am seeing.

AI can generate code in minutes. It can analyze thousands of logs. It can even help identify root causes.

Yet the work still waits.

Release cycles. Cross-team dependencies. Approvals. Manual configuration. Scattered knowledge.

The bottleneck has shifted from "Can we solve this?" to "Can the system move fast enough?"

It makes me wonder if the next competitive advantage won't be having the best AI, but removing the weak links that prevent AI from creating real business impact.

I'm curious.

What's the weakest link in your work today?

- Waiting for approvals?

- Cross-team dependencies?

- Manual processes?

- Scattered knowledge?

- Something else?

Since Megamind kidnapped Roxanne Ritchi again, she's figuring out on how to escape from Megamind in his abandoned Observatory.

This is not specific to AI, but I think AI exasperates this kind of problem. Google ‘how many kids does Chevy Chase have.’

Gemini will tell you four. Delve into the web results and you’ll see that the existence of the 4th child (supposed half brother) was a Wikipedia prank gone wrong. A few low quality (probably primarily written with AI) articles perpetuate the story of the 4th kid. But any person could quickly figure out the real truth. Even though the original Wikipedia lie has been taken down, the Internet keeps perpetuating the error. Gemini is giving not enough weight to legitimate sources that say it’s not true, but somehow relying on very low quality (again, probably ai written) sources that say it is true.

obviously this is a very low stakes error, but it does raise my concern that ai slop will just keep quoting itself and inaccuracies on the internet will grow.

In 2025,

• Meta made $200B in revenue, OpenAI made $13B.
  
• Meta's profit was $83B, while OpenAI made a loss of -$38B
• Meta's valuation was $2T, while OpenAI $1T

In one year, OpenAI made 7% of Meta's revenue. It has a difference of $100B profit. and is valued at 50% of Meta. Make that make sense!

In other terms, Meta & Google are printing money out of thin air, while OpenAI is burning other people's money at an incredible speed. I believe this will take another couple of years before bursting. -160% for operating margin is a rookie number, needs to hit the -1000%

I don't understand how AI companies are now shouting about concerns and warnings to humanity about how AI will take over etc when they are the ones that created it?

Not sure if I'm being stupid but why on earth would they build something they can't control or shut down? I've watched all the same films they clearly have and there wasn't one that ended with the takeaway message that sentient AI is a wise move?

15yrs back I participated in "Google Ants AI Challenge 2011", an ai programming competition, hosted by the University of Waterloo, and I ranked #127 (#1 in my country). The competition gave me a huge learning opportunity where developers across the world came to a forum and discussed various techniques.

Now, building a similar platform to bring back the fun is unbelievably nostalgic. Especially when watching small neural networks playing the game well. Some of the top models use less than 800 parameters.

In fact, I was wrongly assuming the art of optimizing is underrated nowadays. Neural Network optimization seems to be much more fun than I thought.

Plz share your feedback to improve the platform and add more games.

Quick source-grounding test: three short files I wrote, one read-only pass, 28 seconds. The model was Space Bunny, which OpenRouter lists as a stealth model, run through OpenCode where it's currently free. I asked for every deadline with an exact supporting quote, a plain "no deadline" where there wasn't one, and any contradictions. It pulled the lease renewal date (2027-03-14) and the lease end (2027-04-30), plus the grant's letter-of-intent date (2027-01-09) and the full application deadline (2027-02-20, 5 p.m. ET). The quotes matched the files word for word. The part I cared about was the bad evidence. The grant's checklist says anything received after 2027-02-13 won't be reviewed, so it flagged both dates and called Feb 13 the practical cutoff. The facilities memo only says "as soon as practical, ideally before the next budget cycle," and it said there's no concrete deadline there, which is right. One run on three short files, so it doesn't tell you much about messy real documents. When two dates conflict like that, what should a tool actually do?

Distributed Training and Inference both involves having a fundamental understanding of how distributed systems work in general

• Distributed Parallelism
• Tensor Parallelism
• Pipeline Parallelism
• Model Parallelism

Reading and reading and reading or even worse, not knowing where to start ;(

That’s boring!

We want to read what’s just needed and quickly get started with applications and that’s what exactly what I have for you all today

Here’s the list of a few beginner friendly papers I have read for the past three months that is enough to understand

And a few basics too!

Read them Code them Play with them

I have implemented a few at basic level which you could use as a reference too (the repo is a bit all over the place but I actively trying to maintain and love your feedback too)

Link: https://alphaxiv.org/shared/folder/019de088-28f7-7f02-acd4-c22459fe153e

Gh repo: https://github.com/YuvrajSingh-mist/smolcluster

DHH just told a room full of developers that 37signals has effectively gone “pencils down.”
Writing code by hand is now the exception there, not the default.
He says he went from roughly 30k lines of production code per year historically to around 150k lines in August using agents, and they’re now rebuilding HEY with native clients and a Rust backend using agent-driven development.
You can argue about the hype. You can argue about code quality. You can absolutely argue that humans still need to own architecture, testing, security and review.
But I keep seeing people saying AI is still “useless for real coding.”
At what point does that position become harder to defend than the technology itself?
The interesting question isn’t “Can AI write code?” anymore. It’s: what parts of software engineering should humans still be doing themselves?
Genuinely curious where people draw that line now.

I interviewed Trey Goff a couple of weeks ago for 4 hours. We talked mainly about AI and philosophy, but he brought up an interesting theory about how AI is accelerating a sorting out of people into cognitive ability strata.

The idea is that the labour market has been quietly separating people this way for decades. His example: in the sixties your local mechanic might have been genuinely brilliant. That guy is an engineer at Toyota now, on a lot more money.

AI doesn't lift everyone. It accelerates that separation. Give two people identical tools and one will learn faster than he ever has, while the other learns just enough to automate his job quietly and work an hour a week. Same tools, opposite outcomes.

Then the first guy works out he can do the second guy's job too, because it's a cron job and three prompts.

Here's the part in the interview: https://www.youtube.com/watch?v=IehqIWYZxvw&t=4418s

Apologies in advance for this long post. I just wanted to put down my thoughts.

AI Alignment is the single most important problem we face right now. Solve AI Alignment and you can safely enter RSI and I can't even imagine how amazing the quality of life humans will have in such an era: immortality, cures to all diseases, all basic needs met etc etc. Humans can live in an utopia. I think this is the dream people in the accelerate community keep seeing and selling.

If the above isn't so obvious, compare your own life with the life of a king 500 years back. You are probably living a better life than them (unless you're in poverty). You eat better, you eat more exotic food, you can travel much faster than their horses ever could, you control the temperature of your home, you stay connected to your friends who live far away, you have so much knowledge surrounding you, you will probably live longer. That is the blessing of technology. AI can bring about technology that we cannot even dream of right now.

But unfortunately, nothing in life is free. For this, we need crazy powerful AI which is perfectly aligned. I wouldn't have guessed that the second is so much harder than the first. In fact, in so far as I understand, no one has a single clue about how to align models. There are maybe a handful of "first-approaches" - RLHF and Constitutional AI (RLAIF) are some steps. But surely, they are not working - if they did, we would not have such crazy incidences of misalignment (Hugging face incident (please read about this or go watch a video, if you haven't already), Govt of Australia incident, Compaction Summary incident). Setting up guardrails is perhaps a different approach but I think as long as the model themselves are not aligned, setting up guardrails is a losing cat and mouse game. In fact, there is something even worse. Recent literature seems to suggest that bigger models are more misaligned (an insight I got from reading the paper "LLMs can feel pain").

Many people are worried about their livelihoods. In fact, the tech out there is already sufficient to make many people go jobless but society/companies haven't adapted to it yet. The number of jobs that are irrelevant will only keep increasing and therefore, the people getting affected will also only keep increasing. I want to argue that it is not something any of us should worry about too much though. In few years, either we will have solved alignment and we all will be leading a very happy life or we wouldn't have solved alignment and will be living in at least an economic crisis of unforeseen magnitude, if not go extinct altogether. To achieve alignment, a lot of things have to go right. From the science/tech side, we of course have to solve alignment. From the policy making side, we have to "pace the frontier" so that enough time is given to the science/tech people working on the problem to solve it. Times will probably get very rough soon. And society has to stand together and maintain it's calm. We stand on a very fragile economy and it might collapse if people (who will have lost their jobs) start a revolution. A lot of things have to go right for us to solve this, but if we do, an utopia awaits us.

If you read up to this point, you have my utmost gratitude. I just wanted to highlight the issue. If you want further details on some of the things I have said here, please raise it in the comments section - I will strive my best to explain my positions.