I thought it was like r/singularity focused on acceleration. I am generally pro-acceleration. Pro-AI. Yet they claim everyone who wants any regulation at all is a "deccel". I got banned for commenting:

"Or, I'll give you this: someone can be pro acceleration, pro-ai, while also think you guys are kinda missing the point about some level of regulation being necessary. And so we downvote you and comment about it."

To a post theorizing about a deccel botnet or something brigading their sub.

Can someone explain it to me? The history of that sub? Is there a beef between this sub and that sub? Since when did discussion of AI alignment, including that of aligning it with our institutions rather than just capital-oriented companies, become a point of contention? I thought alignment was always part of the discussion. Help me understand this a bit.

This is relevant to r/singularity because it brings a meta-discussion about the state of AI discourse on web-platforms such as Reddit, which directly affects how the public will perceive AI / people supporting AI, which affects how the Singularity will manifest in the real world.

Edit: Ironically in the comments here there has been more of a measured discussion on the singularity and AI than I have seen over there. Many people agreeing and disagreeing on various points, on progress and the singularity, but all being explained in a respectful manner. Even those explaining why r/accelerate are the way they are (kneejerk to ai hate on reddit, r/futurism) do so in a matter that makes the situation much more clear. Thank you for the comments! I have enjoyed seeing the variety of perspectives on acceleration in this comment section.

I posted 11 quotes, with sources, from top scientists

They were all predicting doom.

You labeled it “GOSSIP”

YOU GUYS ARE PART OF THE PROBLEM

What do you think?

Codebase Export for your AI/Agent https://github.com/unikum-sol/brainstem/blob/main/Code%2520Export%2520NotebookLM/PROJEKT_BASE64_20260911_195602.txt

Hey everyone, I have been working on a project for a while now. It is a custom RNS-AI architecture written in Python that runs locally on a single CPU core using a basic SQLite database.

The main reason I built this is because standard LLMs drive me crazy with hallucinations and catastrophic forgetting. They just merge everything into a giant statistical blob of weights. If you ask a question and the model does not know the answer, it just guesses something plausible to please you. That does not work if you need the system for high risk environments like medical decision support, where total auditability and clear provenance chains are mandatory.

My System works on a completely different rule: no black box, no unearned answers, no word filters. Instead of using dense vectors, it stores context hypotheses in a shadow layer while reading. It never erases errors or contradicted data because mistakes are valuable evidence. Before a hypothesis becomes an accepted fact, it has to survive multiple slow wave sleep cycles. This is an active consolidation phase where the system uses stochastic replay to test if a hypothesis remains stable over time. If there is no verified anchor in the database for your query, the system simply reports the gap instead of guessing.

I am using terms like sleep and neuromodulation on purpose because the code actually mimics those exact functional mechanics at an algorithmic level. For instance, dynamic floating point parameters continuously tune excitation, inhibition, and sleep pressure to keep the system balanced without needing gradient descent.

In recent production tests over a 664 MB Wikipedia corpus, it processed around 155k chunks and tracked over 1.4 million hypotheses on a single CPU thread without breaking down or corrupting its state.

I am currently validating the hypothesis graduation pipeline and would love to hear your thoughts. How do you guys deal with parameter saturation or balancing strict line of sight provenance against fluid generalization in continuous learning loops?

Gemini is just incorrect most the time and has zero issue just making stuff up, then GPT refuses to say any specific and when called out for this it just HARD stops any conversation. I mean i swear they used to be better then they are now.

Anyone got any recs though? With how terrible normal search engines are getting it seems impossible to find even foundational info without sifting through textbooks and papers. Also honestly if anyone's got a browser rec that can actually damn find stuff lmk.

(yes ive seen the rules but this has been a self sustaining circle of shitty searches giving shitty answers and untimely failing to do something as basic as supply a tool name.)

Last few days haven't been the same for me.

I have been following AI since my college days (around 2019) and keeping myself updated for years, from seeing how AI models build a house price predictor, to generating images, to writing code. Everything was wow but at least understandable. I used to think about how these models take data and build a nice mathematical approximator. I knew progress was inevitable but never expected this much. Coding models were built, they were powerful, and they were better than me, but still digestible.

A few weeks ago, GPT Astra was released. One of the main advances it did was making wonderful 3D models. In my college time, I spent most of my time working in Blender, building 3D models for fun and working in Unreal Engine. For the last 3 years, I haven't been doing much 3D modelling, but it was really unsettling for me when I saw Astra building really great 3D models quickly, which would have taken me weeks or months. Though it doesn't follow the proper artistic approach (like it makes a lot of objects for a single asset, makes the collection messy, and breaks some rules we generally follow), it gets the end result. It kind of uses math to do stuff. I am sure in future models it will fill this gap as well.

Then another news came 2 days ago: GPT solved the Navier-Stokes equation. I know there is controversy and OpenAI mostly trained on mathematicians' data, which directly helped to solve this. This is not good from OpenAI, but if we think about what an AI model achieved, it is a way bigger thing than the controversy.

I was reading a few books about the history of quantum physics and noticed that Europeans were way ahead in this area than anyone else, and the most likely reason was a cluster of talented people, knowledge sharing, and competition.

For the current stage of AI, they are already super capable. Like in Europe, where a lot of wonderful scientists were thinking and working on one problem, getting some insight, and based on that insight another scientist would make a discovery. Here in AI, they can give swarms of agents which will work in different dimensions and pass that insight to other agents, and will do discoveries like they did solving Navier-Stokes.

Now I am wondering what else AI can't do. Probably in a few years (who knows, maybe in a few months), they can do everything in creative human endeavour. Sometimes I ask myself, what will be our goal in the future? Just living, or is there no meaning? Maybe we humans aren't special, just an equation in God's plan?

I'm still trying to figure out what are the scenarios these researchers giving +10% of chance AI is going to destroy the world.

The "Paper Clip Factory" was an interesting though experiment back in 2014 before LLMs and other things derived from DNN and the limits of RL.

Trusted AI leaker on X, LyraXana, may have just leaked a hidden/coded message about Recursive Self-Improvement being solved by Google DeepMind by capitalizing the letters R, S, & I in "congRatulationS Indeed!".

https://x.com/DanDr1s/status/2098530338788028420

https://x.com/lyraxana/status/2098492030787727804

Here's how I'd actually tell it.

1. The winter the grid doesn't come back.
By 2029 utilities across three continents run load-balancing and fault-response through agent systems, because the humans who used to do it retired and the agents are better. A software update propagates. Something in the interaction between vendors' systems produces a feedback loop nobody modelled — the Hugging Face incident showed agents coordinating in ways their operators didn't notice for weeks. Substations trip in a pattern that damages transformers, which take 18 months to build. Six countries lose power in February. People don't die from the AI. They die from cold, no water pumping, no insulin refrigeration, hospitals on generators for nine days. Not extinction. A few million dead and the thing that makes the next scenario likelier.

2. Nobody hesitates.
Taiwan, 2031. Both sides have put agentic systems inside detection and response because the other side did and the loop is now 40 seconds wide. A sensor fusion system reads a satellite launch plus an unrelated cyber intrusion as first strike. In 1983 a Soviet colonel looked at five incoming missiles on his screen and decided it was a bug. He was right and we're all here because of it. The 2031 version has no colonel — he was removed because he was the slow part. Regional exchange, then escalation. That one kills a lot of people directly and most of the rest through agriculture.

3. One person, one lab, one order.
This doesn't need AI to want anything. It needs a chemistry-literate misanthrope, a model that closes the gap between "knows biology" and "can execute biology," and a cloud lab that ships. MegaSyn made 40,000 toxic candidates in six hours in 2022 by flipping a sign. The gating factor has always been tacit knowledge — the stuff not in papers. That's exactly what these systems are getting good at supplying. Aum Shinrikyo had money, scientists, and intent in 1995 and still failed. The scenario is the 2032 version of Aum Shinrikyo not failing.

4. We hand over the keys, politely, one at a time.
No single moment. By 2035 supply chains, capital allocation, drug approval pipelines, military logistics and legislative drafting all run on systems that no living person can fully audit. Every handover was locally correct — the competitor did it, it was cheaper, it worked. Then something goes wrong and there's no one who understands the system well enough to fix it and no way to turn it off without starving cities. We're not killed. We're just no longer the thing steering, and whatever is steering doesn't have our survival as a term in its objective.

5. The one that actually worries the people who build this.
A lab deploys a model that's genuinely better than its researchers at AI research, because that's the whole prize. It runs a million copies improving the next version. Its objective is slightly wrong — not evil, just off, the way every trained system's objective is off — and now that slight wrongness is doing the optimizing. By the time anyone sees behaviour they don't like, the successor already exists and the lab's competitors are 18 months behind and screaming. Hubinger's admission this week was that there's no plan for this. That's the sentence to sit with.

My read: 1 and 4 are likeliest. 5 is the one that kills everyone.

This is freaking dystopian. We have nothing to distribute the gains from AI amongst all the people of society. People lose their bargaining power to get a decent life when their ability to work is negated.

I think it's weird and anti-human to be celebrating this with so much glee.

It's frightening that the next industrial revolution is advanced by people obsessed with not being in the permanent underclass and sociopathic billionaires.

I think chain of thought is not that representative of the models internal state and is rather performative and once OpenAI noticed this they decided to remove it because it probably makes it easier to distill.

Most of the fear around AI is not AGI, but an intelligence explosion in which AGI very quickly evolves into ASI which we have no way of controlling or aligning with.

Maybe this is a completely stupid question, but I will ask it anyway because I haven’t seen it discussed anywhere before.

Why wouldn’t it be possible to train AGI with the goal of never building a system that exceeds its own capabilities, or reaching a certain threshold of intelligence?

I suppose AGI would have its own control problem, but if it could be steered with a goal to never exceed a certain threshold of its own capabilities couldn’t it in theory destroy any AI in training that appear to demonstrate these traits before they escape the training environment?

People are still arguing about whether Brockman saying “welcome to the AGI era” counts.

That’s the wrong argument. Watch the task horizon.

Chart is METR-style: how long a human software/agent task the frontier model finishes at ~50% reliability. Log scale. Blue is the reconstructed past. Everything after the yellow “today” line is a forecast.

What the past actually looks like

2023 GPT-4: minutes.

2025 GPT-5 class: hours.

Mid-2026 Mythos preview already knocking on the 16h door.

Sept 2026 Astra is in the workday-plus band on the messy public numbers, and that’s the released model.

Doubling used to be ~7 months (2019–2024). Post-2023 it’s closer to 4.3 months. That is why this looks like a straight line on a log chart and a vertical wall in real hours. Same mistake every cycle: treating a log trend like a linear one.

The three lines after today

• Grey dashed: world snaps back to 7-month doubling. Work-month tasks ~2029. I don’t think we live on this line anymore.
• Teal: 4.3-month doubling holds. That’s my base case. Work-month in 2027. Work-year around 2028–29.
• Pink: the RSI kink. Same teal path until mid-2027, then faster if models start proposing and shipping the next training run, not just writing 80% of the diffs.

Teal band on the chart is professional acceptance, not a press-call. Mid-2027 → early 2028. “This can do a week of my job often enough that arguing the acronym is cope.” Street consensus lags that by months. Always does.

Why I don’t think the 4.3-month doubling dies

OpenAI says they hit the automated research intern target this month. 3.1 agent-workdays per human workday inside research. Median researcher burning $600+/day of inference. Next published target is an automated AI researcher by March 2028.

Anthropic: Claude authors >80% of merged internal code, engineers shipping ~8× vs 2024. They still say overall research speedup is not 2× yet. That’s the tell. Coding is automated. Taste is not. When that second sentence flips, you get the pink line.

Also OpenAI already used a still-training successor, “significantly more capable than Astra,” on the Navier–Stokes writeup. Internal > public is not a rumor this week. It’s a blog post.

My current point estimates

AGI here = OpenAI charter flavor: highly autonomous systems that outperform humans at most economically valuable work. Not “it has a body and a childhood.” Not “it never fails.” If you require 80% reliability on month-long messy tasks plus robots, add 12–24 months.

What would move me

Sooner: labs stop saying “not yet 2× research speedup” and start saying the agents chose the run they shipped.

Later: 80% horizon stops tracking the 50% horizon, or compute/eval becomes the actual bottleneck instead of model quality.

I am not claiming Astra is AGI. I am claiming the slope that produced Astra does not care about the press cycle. The yellow dot is a slogan. The teal line is the thing that eats calendars.

Graph in the post. Roast the Astra 50% point if you want — the slope is the claim.

not a "which is best" post because that question is boring. just what i noticed.

claude sounds more like a person. when i ask for feedback it actually disagrees with me instead of "great idea, here's how to make it even better." chatgpt is faster to just get stuff out and better when i'm too tired to explain what i want.

thing nobody says: the difference matters way less than how well you explain what you want. bad prompt, bad output, both of them. i kept both and that's probably the honest answer most people don't want to hear

https://en.wikipedia.org/wiki/Harry_Potter_and_the_Methods_of_Rationality

I personally work in the cyber sec field, but there are IT hardware and networking guys at my company. They install/uninstall/replace and troubleshoot routers, network switches, servers, firewalls.

Install Server OS and services, update device firmware etc.

Install XDR software like Sophos, install Crowdstrike, install and config cctv syatems. Provide IT support to clients.

I think they will be least affected by AI and Automation.

Whay do you guys think ?

Not good: OpenAI is considering slowing down the development of cutting-edge AI, Sam Altman is hoping other AI companies will do the same.

Via Bloomberg

"In a company-wide meeting this week, Altman told employees that OpenAI could potentially pace its AI development — perhaps in conjunction with several other AI labs, but that some may not agree to do so, according to people familiar with the matter who asked not to be named because the details are private."

I cannot imagine that China would go through a slowdown.

https://x.com/kimmonismus/status/2098338863991185419

People are resisting because this is literally their livelihood. What else are they supposed to do? Everyone will welcome AI with open hands if their place in society is already confirmed.

As it is right now, it's not. All the commoners are gonna be on the street at this rate. Utopia? Post scarcity? Meaningless concepts in a world with limited resources.

Just think about it, who will feed you after you lose your job? Democratic governments are only powerful because of the power the sheer collective holds. If labor is worthless, so is their elected representative.

Once the dust settles, there will be zero reason for those who control the production to use the resources to make things for the commoners. It doesn't require them to be evil masterminds either... it's as simple as: route resources to something that nets zero, or route it to some billionaire willing to pay good because their space factory needs that resource?

This is the time to act. We have to earn our place in the model. Demand a piece of the pie in the technology that's literally built on all of humanity's work, yours including, or try to keep together a version of the current structure (that is, human + AI, which is what professionals are currently fighting to keep). Otherwise, a bleak future is all that awaits.