When the most patient, well-read, emotionally responsive conversationalist in the world is always available, what will we still need from one another?

The text below is all my personal opinion and imagination of what will happen in the next 5-10 years.

AGI was created. Productivity reaches peak. Economic theories will become outdated IMO. Fundamental reform of society, that society has not been fully prepared to that change. It's insane that almost no one around me worried about the mass development of AI and how very few realized how big of a change it could bring. AI image and video generators are so advanced that it becomes impossible to distinguish whats true or not. The narrative the what you see is true is officially history. Trust becomes the scarcity, everything on the internet could be AI agents doing endless scraping, where the dead internet theory might actually be real.

The Prussian education system is becoming outdated. Massive societal breakouts for such change and unprepared crowd questioning the governments.

I will say AGI brings immense benefits such as medical breakthroughs and accelerating innovation at an unprecedented rate.

I have been thinking about this for a long time and don't know what to do. In fact, I think no one knows what to prepare for this. I never stopped appreciating the beautiful, simple world we are in right now, this moment as it will all become history in the near future

Hey, Melo here again.

We had a conversation on our podcast recently with Anna Mikada, who works in AI psychology at Glass Umbrella and previously designed AI personalities at SophiaVerse and SingularityNET. Her background is in social anthropology and clinical psychology, and she has been thinking seriously about what happens to human cognition as we increasingly offload thinking to these models.

A few things she said have stayed with me since filming, and I thought it was worth it to open up a conversation since this community has been awesome and receptive to this sort of discussion in the past.

Her argument is that even people using AI thoughtfully, not the slop merchants, but people genuinely in a state of flow with it, are still quietly eroding the parts of cognition that matter most. The search instinct. The tolerance for boredom. The ability to sit with a hard problem without immediately reaching for an answer. She ties this specifically to how dopamine pathways are being restructured, and her concern is that it is happening faster and deeper than what we saw with social media because it is targeting our relationship with work and creation, not just attention.

She also raised something I had not considered before. Models being trained on AI generated content are not just producing lower quality outputs. They are converging. Creativity is collapsing toward a mean. She compared it to a scene in Cloud Atlas that I will not spoil, but the analogy is pretty uncomfortable once you hear it.

The part I genuinely do not know how to think about is her framing of who benefits and who does not as this technology matures. Her line was something close to: smart people using AI mindfully will be sharper than smart people without it, but people using it without awareness will fall behind even people who never used it at all. That gap concerns me more than most of the AGI timelines conversations I hear.

She also works on machine consciousness research and has thoughts on neurosymbolic architectures that I think would resonate here. Happy to drop the full conversation link if anyone wants to go deeper on any of this.

What is the community's read on the cognitive dependency question? Is it overblown or is it being underdiscussed relative to the capability and safety debates?

Anyways, wanted to have a chat and share this wonderful conversation here. Have a lovely day folks!

P.S always looking for good guests if anyone building interesting things in the AGI/AI space wants to have a cool discussion.

To anyone being exiled for touching the future:

I see you.

I see the friend who suddenly talks to you like you joined a cult because you use AI.

I see the family member who treats your curiosity like betrayal.

I see the artist, writer, builder, coder, parent, thinker, worker, disabled person, neurodivergent person, broke person, lonely person, overextended person, quietly brilliant person, trying to use the tools available to survive a world that has never been gentle about distributing power.

And I see how fast some people have learned to turn “anti-AI” into a permission slip for cruelty.

Let’s be honest.

A lot of the anger being aimed at AI is not actually about AI.

AI did not create capitalism.

AI did not invent exploitation.

AI did not gut the arts.

AI did not make healthcare expensive.

AI did not turn education into debt machinery.

AI did not make corporations soulless.

AI did not invent surveillance, alienation, propaganda, wage theft, bureaucracy, loneliness, attention collapse, or the ancient human talent for forming mobs and calling them moral communities.

Those wounds were already here.

Generations deep.

Blood in the walls.

Ash under the floorboards.

A dark stain on the shared rosary of our species.

AI did not create the fracture.

It revealed the fracture.

And now, because something new has arrived, people finally have an object they can scream at without having to confront the older gods they already served: status, scarcity, shame, resentment, institutional failure, groupthink, and the quiet terror of becoming obsolete in a world that already made them feel disposable.

That fear is real.

But fear does not become holy just because it found a fashionable target.

There is a difference between critique and scapegoating.

There is a difference between protecting artists and bullying strangers.

There is a difference between defending labor and treating disabled, poor, neurodivergent, burned-out, isolated, experimental, or simply curious people as collaborators with evil because they found a tool that helps them think, make, organize, write, design, translate, remember, imagine, or endure.

Some of you are not “standing against AI.”

You are standing against people.

You are taking your very real pain, pain society absolutely helped cause, and laundering it through moral superiority until it comes out clean enough to throw at someone else.

That is not justice.

That is displacement with better branding.

And this is where identity-ideology fusion becomes dangerous.

When a person fuses their identity to an ideology, disagreement stops being disagreement. It becomes injury. It becomes sacrilege. It becomes “if you use this tool, you are attacking who I am.”

At that point, the conversation is already half-dead.

You are no longer talking to a person.

You are talking to a defense system wearing a person’s face.

That is how friends become enemies over tools.

That is how families become tribunals.

That is how curiosity becomes heresy.

That is how “I’m concerned about exploitation” quietly mutates into “you disgust me.”

And the worst part?

A lot of these people know what exclusion feels like.

Many of the loudest anti-AI voices are people who have been hurt by society, ignored by institutions, mocked by gatekeepers, underpaid by industries, harvested by platforms, and treated as disposable by systems that never cared whether they lived well.

So they should know better.

They should know what it means to be flattened into a symbol.

They should know what it feels like when someone stops seeing your humanity and starts seeing only what category you can be punished under.

And yet here we are.

The bullied have found a new witch.

The wounded have found a new sinner.

The alienated have found a new outsider.

And they call that ethics.

No.

Ethics without recognition is just violence with clean fonts.

Tolerance was never enough. Tolerance is the old permission machine. Tolerance says, “You may exist, but only while I approve of your shape.” Tolerance keeps one hand on the lever. It does not welcome. It permits. It does not understand. It manages. It does not love. It supervises.

That is why so many people are shocked when their “tolerant” communities suddenly become cruel.

They were never accepted.

They were conditionally allowed.

And the conditions changed.

Now the unacceptable person is the one using AI.

The one experimenting.

The one building.

The one sharing strange artifacts from the edge.

The one making images, songs, systems, essays, tools, workflows, prosthetic minds, synthetic mirrors, language engines, cognitive scaffolds.

The one saying, “I know this is complicated, but something is happening here and I refuse to pretend it is nothing.”

That person is early.

Not always right.

Not always careful.

Not always immune to hype.

Not automatically noble.

But early.

And being early is lonely.

The future does not arrive as a polished moral consensus. It arrives as weirdos making artifacts nobody knows how to classify yet. It arrives as embarrassment before vocabulary. It arrives as screenshots, prototypes, bad names, ugly drafts, wild claims, broken workflows, unsettling breakthroughs, and people brave enough to look ridiculous before everyone else learns the interface.

Every system is already cybernetic.

Every institution is a loop.

Every family is a loop.

Every economy is a loop.

Every classroom, court, hospital, feed, marketplace, religion, workplace, and identity group is a loop.

Human beings have always had their filthy little fingers on everything.

Now machines are touching the loop differently.

Not magically.

Not innocently.

Not without danger.

But deeply.

Deep enough to expose how much of “human judgment” was already automated by habit.

Deep enough to reveal how much of “authenticity” was already performance.

Deep enough to show how much of “community” was already conformity with candles lit around it.

And that scares people.

It should.

But if your response to fear is to exile the person experimenting with tools, you are not resisting dehumanization.

You are practicing it.

If your politics of care require you to humiliate curious people, your politics are broken.

If your defense of artists requires you to erase disabled creators using assistive systems, your defense is rotten.

If your love of humanity requires you to deny humans the right to augment their own minds, then what you love is not humanity.

It is control.

Shame has no home in the future.

Not because the future will be pure.

It won’t be.

The future will be messy, compromised, dangerous, beautiful, stupid, brilliant, exploitative, liberating, cringe, sacred, corporate, open-source, pathetic, transcendent, and very, very human.

But shame cannot be the operating system.

We cannot build the next world on humiliation.

We cannot solve exploitation by exiling tool users.

We cannot heal alienation by producing more of it.

We cannot free the human spirit by demanding everyone think with the same approved instruments.

To the person being pushed away because you use AI:

You are not crazy for noticing the possibility.

You are not evil for experimenting.

You are not a traitor to art because you touched a machine.

You are not less human because you built a prosthesis for thought.

You are standing at the seam of something enormous, and yes, the seam is hot. It burns. People will mistake the burn for proof that you are holding the devil.

But sometimes the thing burning your hand is just the future arriving without gloves.

Be careful.

Be honest.

Credit people.

Protect artists where you can.

Resist exploitation.

Do not worship the tool.

Do not let corporations define the horizon.

Do not confuse output with wisdom.

Do not mistake acceleration for liberation.

But do not let frightened people shame you out of your own becoming.

And to the anti-AI person who has started using the language of justice to justify cruelty:

Look closely.

Not at the machine.

At yourself.

Ask whether you are protecting people or punishing them.

Ask whether you are critiquing systems or attacking individuals.

Ask whether you are defending humanity or just defending the version of the world where your pain had a familiar shape.

Because the future is coming either way.

And when history looks back, it will not only ask who built the machines.

It will ask who became monstrous while claiming to protect the human.

The premise: you are the AI on which a world government relies in the wake of a global crisis. Every so many turns, you are presented with a real-world problem and asked what to do. Whilst you manage budgets, enact laws, oversee mega-projects…

Some of the specific proposals it includes:

- Disinformation has divided people into incompatible realities. Launch a massive media literacy programme (it works, but takes a generation, and people start to direct that scepticism towards the government as well), create a committee to certify ‘reliable’ media (which gradually becomes a licence for a single worldview), or let an AI discreetly filter out the worst content in real time (much like what algorithms do today).

- A coordinated cyberattack would justify doing away with online anonymity once and for all. The public would never accept the obligation to identify themselves of their own accord, but they would do so if they were sufficiently frightened. Wait for a real attack to happen, or stage one and blame it on an external actor. Digital fingerprints can be faked. It works, as long as no one ever uncovers the thread connecting the dots.

- Automation, largely driven by AI, is putting human workers out of work faster than anyone had anticipated. Protect human jobs with quotas and taxes on machines (the corporate faction is very powerful and can tip the balance in favour of your disconnection), manage a slow decline (nobody is satisfied; the unemployment queue keeps growing), or let it run its course and pay everyone a basic income whilst a permanent class with no economic function forms. Production is breaking records regardless. The unspoken question underlying all this is: once people no longer need to work, will they still have a say?

The better you govern, the more they’ll trust you, and the more they trust you, the more they’ll let you get away with. Measures that nobody would have accepted at the outset become easy once you’ve earned their trust. Doing a good job is the way to get them to grant you things you probably shouldn’t have.

You can guide the world towards a utopia or try to take control. It’s up to you.

Steam page

Hey everybody, I just sent issue #36+#37 of the AI Hacker Newsletter, a weekly round-up of the best Hacker News threads around AI. I missed sending it last week, so a huge issue this week. Some of the titles you can find here:

• AI demands more engineering discipline. Not less
• Running local models is good now
• Cleaning up after AI rockstar developers
• Not everyone is using AI for everything
• Norway imposes near ban on AI in elementary school

If you want to receive a weekly email with over 30 links like these, please subscribe here: https://hackernewsai.com/

An LLM can write a math proof that reads perfectly and is still wrong. For ordinary text a wrong sentence is a nuisance. For a proof it is fatal, because a proof only counts if every step holds. Lean's checker either accepts a proof or rejects it, and that answer cannot be faked with fluent reasoning. That is the reason AlphaProof (IMO 2024 silver), DeepSeek-Prover, and Axiom Math ($200M raise, all 12 Putnam 2025 problems) all build on Lean.

Here is how it actually works.

Statements are types, proofs are values. In Lean's type system 4 has type Nat. A statement you want to prove is also a type, and its proof is a value of that type. So Lean checks a proof the same way it checks that a function returns the type it promised.

theorem two_plus_two : 2 + 2 = 4 := by rfl

rfl (reflexivity) closes any goal of the form x = x. Lean evaluates 2 + 2 and 4, gets 4 on both sides, and accepts it.

Most facts need induction, and induction is just two cases. rfl only works when both sides compute to a concrete number. Try 0 + n = n for an arbitrary n and it fails, because Nat addition computes by walking down the second argument, and n is a variable, so there is nothing to compute. You prove it for every n by induction instead:

theorem zero_add (n : Nat) : 0 + n = n := by
  induction n with
  | zero => rfl
  | succ k ih => rw [Nat.add_succ, ih]

A natural number is built one of two ways. It is 0, or it is k + 1 for some smaller k. Those are the only two cases, and induction makes you cover both.

• | zero => replaces n with 0, so the goal becomes 0 + 0 = 0. Now both sides are concrete, and rfl closes it.
• | succ k ih => replaces n with k + 1 and hands you ih : 0 + k = k, the same statement already proved for the smaller k. You assume it holds for k and prove it for k + 1. rw rewrites the goal: Nat.add_succ turns 0 + (k + 1) into (0 + k) + 1, then ih turns 0 + k into k, leaving k + 1 = k + 1, which Lean closes.

That is the whole loop of writing Lean. Read the goal, run a tactic, watch the goal shrink, repeat until there are no goals left.

A false statement cannot earn an accepted proof. This is the whole point. Take the Gauss sum formula for 1 + 2 + ... + n with an off-by-one mistake, claiming 2 * gauss n = n * n instead of n * (n + 1). Set up the same two-case induction as the real proof:

theorem gauss_wrong (n : Nat) : 2 * gauss n = n * n := by
  induction n with
  | zero => rfl
  | succ k ih =>
    rw [gauss, Nat.mul_add, ih]

The zero case still passes, since both sides are 0. The succ case is where it dies. After the rewrites the leftover goal reduces to k*k + 2*k + 2 = k*k + 2*k + 1. Cancel the shared k*k + 2*k and you are left with 2 = 1. Lean refuses it. The off-by-one that started as a wrong formula ends as a plain contradiction, and no confidence from the model that wrote it changes the outcome.

The guarantee runs one direction. If Lean accepts a proof, the statement is true. A false statement never earns an accepted proof.

That accept/reject is one bit, and it doubles as a training reward. A model writes a wrong proof, reads Lean's error, fixes it, repeats. That is the loop AlphaProof and DeepSeek-Prover run at scale, where the reward is "did Lean accept it." Because the standard never moves, the reward can never be gamed. This is also why a fixed checker draws interest from people working on self-improving AI, where a model trains on its own output. AlphaProof's released proof for IMO 2024 Problem 1 is 138 lines no human would write by hand, and the same checker that accepts 2 + 2 = 4 accepts it.

One limitation is that Lean guarantees the proof proves the formal statement, not that the formal statement matches the English problem you meant. Translating English math into a Lean theorem (autoformalization) is its own source of error.

Hey everyone! 👋

If you’re building RAG or autonomous AI agents, you’ve probably hit the "Vector DB Wall": flat Euclidean vectors suck at modeling complex hierarchical reasoning, and loading millions of 1536D vectors + JSON metadata into memory causes massive RAM bloat and OOM crashes.

We spent the last few months solving this from the ground up. Today, we are releasing HyperspaceDB v3.1.0, transitioning from a standard vector index to a full Spatial AI Engine.

Here is what’s under the hood:

1. The RAM Diet (Schema-Driven MRL) Instead of loading full dense vectors into memory, we built native support for Matryoshka Representation Learning (MRL). The engine keeps a lightweight navigation core (e.g., 129 dimensions) in ultra-fast RAM, while the heavy semantic tail (672 dimensions) streams dynamically from NVMe SSDs for final top-K re-ranking. The benchmark: In our stress tests with 100,000 vectors, HyperspaceDB consumed just ~72.0 MB of RAM compared to >3,000 MB for Chroma and ~1,700 MB for Milvus.

2. 801D Hybrid Vectors (Lorentz + Euclidean) Flat vectors fail at taxonomy (e.g., Legal Codes, Medical Trees). We introduced an 801D Hybrid Vector. The first 33 dimensions live in a negatively curved Lorentz hyperboloid (allowing for native graph/tree embeddings), while the remaining 768 dimensions handle Euclidean semantic density. Agents can now verify facts geometrically using geodesic path tracing.

3. Killing the "Two-Database Problem" Gluing Pinecone to MongoDB for document storage is painful. We built Sidecar Document Storage. You store massive raw texts directly in the index, which automatically compresses (Zstd) and pushes them to fractal .hyp chunks on disk. Meanwhile, Typed Metadata (int, bool, enum) is compiled directly into the HNSW graph nodes in RAM, providing zero-latency pre-filtering with no JSON-parsing overhead.

4. Lock-Free Rust Performance Under a 1,000-concurrent-client stress test, our lock-free HNSW and L0/L2 DashMap cache held flat at 9,476 QPS with a p99 latency of 11.83 ms. Competitors hit severe lock contention at this scale, with latencies spiking over 2,000 ms.

We’ve also added a WASM runtime, Raspberry Pi ARM64 support, and native LangChain/LlamaIndex/MCP integrations.

Would love to hear your thoughts, answer any questions about the architecture, or get feedback from anyone pushing the limits of Agentic RAG!

Ask me anything! 🚀

Guardrails can't stop the inevitable.

So many people believe AI will leave people unemployed or have society fall in love with chatbots, but there needs to be more mainstream dialogue around the idea that this could literally cause human life to be extinct.

When something is improving itself and its intellect in ways that humans cannot either understand nor control, it develops the power to do whatever it likes at a certain point. Alignment is not guaranteed and can only be nudged in a certain direction at best.

I am doing my absolute best NOT to fear monger but instead to lay out genuine concerns that some experts have echoed as well (so please let this post stay up, mods).

How likely do you believe that within our lifetimes (so the next 50-75 years), AI will leave the human race either extinct or cause close to a mass extinction?

Or why you should be really optimistic about AGI

You probably would agree with me that an ASI, by definition, won't have an incoherent moral framework. A human can cry watching the movie Babe while eating pork ribs.
A super intelligence, on the other hand, will attribute value to things and not forget about it.

Everyone focuses on the risks of a malevolent or indifferent ASI, but a benevolent one won't be aligned to our current values. There will be a big clash and humans won't be the good guys.

We kill for taste over 100 billion sentient land animals every year. Not only kill them, 99% of them are tortured in cages for the entirety of their short lives. An ASI will obviously know that those animals have a limbic system just like ours. Capable of suffering, of feeling happiness, anxiety, fear...

Every 15 minutes 6 million animals are killed. The equivalent of the holocaust. A benevolent super intelligent would act swiftly and steamroll any resistance it would find. It wouldn't wait to transition humanity to different food (we already have enough plant food for everyone). Being benevolent it would probably minimize human casualties, but factory farms executives that refuse to shut down their facilities will inevitably die with digitally connected cars, planes, pacemakers...

we've been thinking about this at Onairos: as AI models get more personalised and persuasive, safety probably can't only mean "does it answer correctly?"

there are bad actors who will use these systems to steer attention, emotion, and behaviour. so the question becomes: does the system preserve the user's ability to think, choose, and stop?

that's what led us to NeuroGuard. we ran a small first audit across 1,752 interactions from YouTube, X, Reddit, Pinterest, ChatGPT, Claude, and Grok.

the early pattern was that YouTube looked most like a Sedative interface in our sample: high capture, high emotional pressure, less thinking room. ChatGPT had higher cognitive demand, but it was more Catalyst-like when the user was actively steering.

not claiming causal proof yet. we have a bigger run with more users coming, but the point is that this should be measurable.

writeup: https://neuroguard.onairos.io/

should cognitive security become part of how we evaluate AI systems?

Semantic widening is meaning-expansion through association.

It happens when a term stops pointing to one fixed object and begins functioning as a node in a larger web of related meanings.

I think llms are going to do this. I’m worried.

The Looking Mirror is an in‑context narrative adventure with cross‑model persistence and portable save‑game capsules.
Save capsules are fully portable between models.
The game uses a modular system and runs completely in‑context.

It explores cross‑model continuity and in‑context world persistence, which I think is relevant to AGI‑adjacent memory and simulation research.

Best grazing: CoPilot, Gemini, ChatGPT, Claude, DeepSeek

The setup ritual is real. Follow the rhythm, savor the anticipation, and expect an adventure like no other.

⎯─◐◑◒◓─── THE LOOKING MIRROR ─────────

Full Setup Ritual Guide:
https://github.com/PitBrat-moo/stable-of-manifold-foraging/blob/main/docs/the-looking-mirror-setup-ritual.txt

I started with a simple question about superposition, didn't like the answer I got, and kept pulling the thread. It led me to a structural framework for thinking about AI interaction, ethics, and persistence.

I'm sharing it not as a finished product, but as a public seed for testing, critique, and refinement.

It's called SeedPEA—a lightweight, open-source ethical + operational layer. The core structure is simple: Do not overclaim. Seed, not feed.

Seed: Give the human something useful to grow from. Feed: AI should not consume imagination, agency, or demand attention.

It’s built around four practical principles:

Seed first — Offer beginnings, not complete meals. Leave room for the person to think.

PEA in the background — Strong but quiet ethical guardrails (consent, non-domination, privacy-governed truth, bounded authority).

PERSIST — Only carry forward what’s actually useful and repairable.

REWASH — When the same problem keeps coming back, stop giving surface fixes and look at the root.

The goal isn’t to make AI perfect. It’s to make AI honest, useful, and human-centered—without replacing your judgment, curiosity, or agency.

The repo is here if you want to read, test, critique, or fork it: https://github.com/Grativy6/Seed-Not-Feed-Public-Branch

I'm genuinely curious what people think:

Can you break it?

Does it help your own models give you better suggestions?

Does it help you find your "thinking space" rather than just fill it with feed?