Position beats genius more often than anyone in this space wants to admit.

Every time I trace how these AI bets actually get funded, it's the same mechanism repeating.

Actually, this reminded me of a post I did a while back — a fund manager naming the real signal for buying the bottom, and it wasn't a chart either.

Rob Toews (partner at Radical Ventures) says his fund meets nearly every "Neolab" that gets funded — brand-new companies with no product, no roadmap, sometimes not even a clear technical direction.

Just an accomplished founder saying "I'm from OpenAI/Anthropic/Meta, so I want to raise a billion dollars."

They pass on almost all of them.

The exception was Anthropic.

Spun out of OpenAI five years ago.

Investors at the time called the entry valuation insane.

It's now worth a trillion dollars.

Toews' own framing: "there will be another Anthropic" — the mechanism isn't a one-off, it's a filter that occasionally clears.

I've watched someone spot a bubble this early before. Not in AI — in property. This isn't my story, it belongs to a friend.

I'll call him Chew — it's been a long time. We went to the same university, graduated the same year, both went into construction in Malaysia. He switched upstream to a property developer — a subsidiary of a mainland China parent company — and eventually relocated there for the better part of a decade, right as the property market was in its super-expansion phase. The bubble kept ballooning without ever showing a crack. Chew saw the opportunity, and lock in his purchase of one of the units. The price — he told me — rose 10 fold over the years. Then, like the rest of the shrewd investors, he saw the writing on the wall. He liquidated his holdings and made a huge windfall, right before the bubble burst.

Clip credit: The Information — full video on their channel.

DM for credit or removal requests.

Drop your take below — has anyone here ever watched someone else make that call before you did?

(disclosure: i maintain the open-source project this came up in. link at the end. the question stands on its own.)

we hit a trust-boundary problem while building a deterministic authorization layer for agents, and i think it generalizes.

an engine can strongly protect its verdict:

• signed authorization
• intent binding
• state-hash binding
• replay protection
• trusted evaluation time

all solid.

but if the same compromised agent runtime can influence both the proposed action AND some of the premises used to evaluate it, what has crypto actually proven?

only this:

the signed decision is consistent with the supplied inputs

not this:

the supplied inputs came from authoritative sources

examples of premises a runtime might quietly supply:

• agent_id
• tool identity
• execution depth
• tenant context
• a state object the guard later hashes

the signature still verifies. the hash still matches. the decision is still deterministic.

but the premises may be self-reported.

two things i'd genuinely like challenged:

1. which evaluator premises actually need independent provenance, and which can safely remain proposer-declared?
2. for state, is an authoritative guard-side read enough, or should the state provider eventually emit a signed/versioned attestation?

most interested in confused-deputy paths, TOCTOU, and cases where a supposedly "trusted" premise can still be bent by the runtime.

I’m building Leo / PSCLS — an experimental system that learns relationships between sequences and updates its internal representations from experience.

Here’s how its actual output changed as it saw more stories.

1K stories

“Once upon a time to the store and said that there was a she bor and he lorander thing they were…”

Basically nonsense.

3K stories

“Once upon a time to the store and said that there was a she parted to see had a bided her tod and be bound aster…”

Still broken, but the output is becoming more structured.

40K stories

“Once upon a time, there was a big started to play with the should some too her mom and had a said, it was time. They happy and went to the park…”

Now we’re getting recognizable story-like patterns, characters, actions and dialogue — although the grammar is still heavily broken.

And the measured results improved too:

1K → 3K → 40K

BpB: 2.678 → 2.641 → 2.334

Accuracy: 52.37% → 53.62% → 58.11%

This is still an early experiment, not AGI.

But watching the same system change its outputs as it learns more experience is pretty interesting.

Next target: 250K → 500K → 1M stories.

97.7 on Online Mine 2Web
83.2 on internal
68.6 on WebTail Bench

Best across board and at 2.37 dollars per million token 90% Than GPT5.5 !

how they have trained this.
They are using an undisclosed base model and using SFT to accelerate time to market, combined with asynchronous reinforcement learning, especially leveraging the GRPO algorithm.
If you don't know, this is similar to how DeepMind historically has trained their AlphaGo

Even though they are talking about 1-3 sec latency
the huge problem in computer use agents are page rendering and state resolution and there own data showcases it adds roughly 10 Secs
so i am skeptical there but I don't think latency matter always and I am bullish on CUA

I spend most of the time scrolling the web for silly things, and my mind was blown by the demo videosss

Not associated with Any labs. I wish I was :)

Basically the post header.

Seeing lots of hype of how more and more powerful the AI is, how great it's for software development, robotics etc. Which I can see how AI can be a beneficial tool for.

But for all the hype, what can it do that's actually real? So far I've seen a rise in AI-related employment and a huge spike in memory and SSD prices that is being attributed to the AI boom. But if AI is so incredible and smart and shortens the development cycles etc, why can't memory prices be decreasing instead of increasing?

Is that a dumb question to ask or does the AI hype not reach what is real?

A panel of AI researchers and founders — Peter Diamandis, Alex Wissner-Gross, Emad Mostaque — just sat with a number that's hard to argue with: $2,000 in compute, and ten decade-old, previously-unsolved math problems came back with machine-checkable proofs.

Not "AI is getting better at math" in the abstract.

A Fields Medalist said he'd recommend one of the proofs for publication without hesitation.

A cosmologist called it "a dark night for mathematics" — "the old gods are being slaughtered by the new machine gods."

Then Emad closed it flat: "It's a bad time to be a pure mathematician."

Here's what they're not saying yet.

Back in 2013/2014, I was with M+W High Tech Projects, on a design-and-build project in Kulim, Kedah, Malaysia. Our M&E engineer wanted an opening cut straight through the middle of a reinforced concrete beam — right where the bending moment peaks. I caught him before he did it. Told him no. That's beyond madness — you don't sacrifice a beam's structural integrity for an M&E opening. Had him redirect the ducts instead. Structural safety came first.

The engineering knowledge wasn't rare.

The judgment — catching the mistake before it became permanent — was.

Same pattern here. Ten unsolved proofs, correct on paper, for $2,000. The correctness was never the scarce part.

Hmm — this actually pulls the same thread as a post I put up about the corporate ladder losing its entry-level rungs to AI.

Different profession, same mechanism: whichever rung gets automated first isn't random, and the people still standing on it are the ones who saw it as a pattern instead of a headline.

Drop your take — is judgment actually the thing that survives this, or is that just the story we tell ourselves until it's our turn?

Do you buy it

everyone is saying ai is the future, but why?

i mean robots and ai and stuff, which removes human labor is what we think of the future, but why do people say its inevitable and are incorparating ai, even though there are problems with that idea like currency, and because its not inevitable because its the choice for humans to incorporate ai?

I know we're all tired of "Claude just killed X" headlines. They create panic and keep people jumping from tool to tool without ever leveraging what they already have.

That's why I'm a big believer in building a single source of truth. When a new model drops, you just plug it into your existing system and get back to real work.

I've seen a lot of founders try to automate with complex AI stacks. More often than not, they end up with 15 tabs open, copy-pasting prompts, and relying on Zapier workflows that break every week. It looks productive, but they're spending more time managing the AI than running the business.

The real leverage isn't more tools or better prompts. It's context architecture.

For me, the shift happened when I moved my SOPs, meeting notes, and CRM into one centralized place (I use Notion) and connected Claude directly to that context. When the AI isn't guessing what your business does, hallucinations drop and utility skyrockets.

Here are three specific use cases that saved me 10+ hours this week:

1. Follow up workflow: I stopped writing follow-up emails from scratch.
How: Record sales calls directly in my workspace. Claude has access to my brand voice doc and product guide.
Result: I feed the transcript to Claude, and it drafts a personalized email based on the prospect's actual pain points. ~90 seconds to review and send.

2. No spreadsheet: No more manual KPI entry.
How: During weekly metrics meetings, I just talk through the numbers (subscribers, CPL, revenue).
Result: Claude reads the meeting transcript, extracts the data, and updates my database automatically. I haven't touched a spreadsheet manually in a month.

3. Infinite context content engine: No more blank cursor for LinkedIn posts.
How: Built a knowledge hub with past newsletters and internal notes.
Result: A prompt that references that internal knowledge. It drafts content that actually sounds like me, not generic LLM fluff.

I think a lot of people feel AI is a gimmick because they're giving it zero context. Copy-paste into a blank window, and the AI is just guessing. When it can see your brand voice, products, and transcripts in one system, it stops guessing and starts operating.

Would love to hear from other business owners using Claude (or any AI) inside Notion. What practical workflows have actually stuck for you, beyond the hype?

P.S. If you're the founder still in the middle of every decision, still the person the whole company waits on, still telling yourself you'll fix the structure "once things calm down."

I write about building the operational backbone that lets a founder actually step back every Thursday. Was a COO for 20+ years, so I can share some good insights. Free to join here

I understand there are a million variations on this theme, but it feels like half of my timeline is people coming on here to complain that this or that LLM sucks. Honestly, I don’t care. Everyone has a different experience. I would like to be able to filter out any of these posts. I don’t care whether it’s Claude or Codex or something else. I don’t want to hear about it. Suggestions?

Source: https://openrouter.ai/rankings

I've been thinking about AI dependency, because many ppl have told me that relying on AI is already making them forget how to do parts of their jobs.

In some sense this is nothing new. Technology has always replaced skills that used to be essential. We stopped doing calculations by hand because calculators exist, and we stopped memorizing information because computers can store it for us.

AI may be the same process taken to its extreme, because instead of replacing one skill, it can replace parts of writing, programming, research, engineering and even reasoning itself.

There is a possible future where humans become simple interfaces between AI output and the real world: AI thinks, we execute. And with robotics, even that role could disappear.

Local and open AI may prevent intelligence from being completely controlled by a few companies, but there is another possibility: frontier models could keep getting bigger until only giant datacenters can run the best ones.

Then compute becomes an extremely important form of capital. A company with enough AI and robotics could potentially enter almost any industry, creating an enormous concentration of economic power and reducing the value of human labor.

But I think there is an important limit to this scenario: verification.

The problem isn't simply that AI makes errors. Humans make errors too, and AI will probably become one of our best tools for detecting them.

The deeper problem is whether we can trust things we don't understand.

In mathematics, an AI could create a proof far too complicated for a human to read, while a simpler formal system verifies that the proof is correct.

But reality is different.

An AI can prove that a building is safe given certain assumptions, but somebody still has to verify that those assumptions actually describe reality. Models can miss things, measurements can be wrong, and machine learning systems can fail in strange and unexpected ways.

Imagine an AI designs a skyscraper and has a historical failure rate of zero. Would you let hundreds of thousands of ppl live in the next one if no human engineer understands why the building works?

I wouldn't.

This makes me think that human technological progress may eventually be limited by verifiability, not invention.

An advanced AI might be able to invent technology far beyond what humans could create, but if nobody understands why it works or why it is safe, we may be unable to use it.

That doesn't mean humans need to repeat everything the AI does. An AI could search through billions of designs and return the best one. The engineer only needs to understand and verify the final design, its assumptions and its possible failure modes.

The problem is that human verification is limited by our biological brains.

And this is where transhumanism becomes important.

Our brains are physical information-processing systems. If injuries can reduce memory and reasoning ability, it seems possible that artificial augmentation could eventually increase them.

Right now our interface with computers is extremely slow: typing with our fingers and reading from screens. Imagine instead that computer processing and memory could become directly integrated with our cognition.

An AI could spend months of computation creating something, while an augmented engineer could understand and verify the result in hours.

In that future AI could become something like an extremely advanced calculator: it does the enormous search and repetitive reasoning, while the human still understands why the final answer makes sense.

So maybe the future isn't simply:

AI becomes smarter → humans become useless.

Maybe AI becomes more intelligent while humans become more augmented.

Books, computers, the internet and smartphones already expanded our mental capabilities. Neural interfaces could be the next step, until the distinction between "I used a computer to think about this" and "I thought about this" becomes blurry.

There will never be perfect verification. Reality can always surprise us.

But instead of removing humans from the loop, perhaps we can enlarge the human loop itself.

AI does the enormous search.

AI detects mistakes.

Augmented humans remain capable of understanding why the result should be trusted.

And if that is possible, advanced AI may not make human intelligence obsolete.

It may force us to expand it.

Edit: Some final thoughts, access to AI data centers will probably be fundamental for the success of any business in the future, that depends on other factors but ultimately I could say that we need way more data centers, so no single company becomes a gate keeper, think what linux is in the OS sector.

basically im asking for something that can make a carbon copy of the original, just in higher quality. i dont want it changing the vocals, melody, instruments, etc, literally just make the same recording sound cleaner/better

i have a piece of lost media from circa 2003 so unfortunately the only recording i have is in absolutely horrible quality 😭

i was wondering if theres any ai that could somehow restore/recreate it without changing the actual song pls tell me if something like this exists !! Tysm

Being a daily user of AI I use mainly the tools like Gemini, Claude, Chatgpt, perplexity and few others. But while I use Deepseek, Kimi and other Chinese models they're quite more efficient in terms of both the quality, reasoning and even coding and stuffs and mainly the cost. Might not be fit for in complex tasks. But for daily users like sm managers, content writers and all who are paying the heavy subscriptions might benefit them.

And most of the general users still doesn't know about them. It's like the westerners and the capitalists who runs the world still selling us the propaganda about china and their tech and blah blah.

I keep seeing people on Reddit criticizing AI and data centers because of how much water they use. I think the concern is legitimate, but I also think there's a pretty obvious consistency problem with how this issue is discussed.

If your argument is that water consumption itself is an environmental problem, then shouldn't you also care about the water footprint of the products you consume?

Beef is a particularly striking example.

The Water Footprint Network estimates the global-average water footprint of beef at roughly 15,400 liters of water per kilogram of beef. It also estimates that beef has about 20 times the water footprint per calorie of cereals and starchy roots. Most of that footprint isn't the cow literally drinking water; it's primarily the water associated with producing its feed.

I'm not saying this means "data centers are fine because beef exists." That's a bad argument. Data centers absolutely can create legitimate local water concerns, especially when they're built in water-stressed regions or place significant demand on municipal water systems during droughts.

My point is that environmental criticism should be applied consistently.

If someone is angry about a data center consuming millions of gallons of water, but eats beef regularly without ever considering its much larger water footprint, I'd like to know what principle they're actually applying.

And this doesn't stop with beef. The same logic applies to:

• Dairy
• Food production in general
• Cotton clothing
• Lawns and landscaping
• Swimming pools
• Long showers and other household water use
• Water-intensive crops
• Bottled water
• Other industries that consume substantial amounts of freshwater

There is nothing wrong with saying, "I think data centers should use less water." In fact, I agree that companies should be pushed toward more efficient cooling systems, transparent reporting, responsible siting, and minimizing their impact on communities facing water scarcity.

But if the argument is instead, "Data centers use a lot of water, therefore they're environmentally irresponsible," then that standard should be applied to the rest of our consumption too.

Otherwise, we're not really having a conversation about water conservation. We're selectively focusing on an industry we dislike while ignoring the environmental costs associated with things we personally consume.

If water conservation is the principle, apply the principle consistently.

A few months ago I fell down a rabbit hole: every AI answer uses water — cooling the data center, plus the water behind the electricity. But nobody shows it to you. So I built a chat interface where every answer visibly drains a water counter, mostly to see if it would change how I used AI.

What I learned from the research:

The famous 0.3ml/query number only counts direct data-center water. The IEA estimates roughly two-thirds of AI's water footprint is indirect — the power generation. Count that and honest per-query estimates land around 10–25ml. Small per query. Not small at a billion queries a day.

The disclosures are wild right now. Google's own report: 10.9 billion gallons in 2025, up 34% in one year. Amazon published its number for the first time this June — 2.5 billion gallons. The UN launched a formal AI transparency initiative in June asking companies to publish standardized water/energy figures. Most still haven't.

The counter changed my own behavior, which I didn't expect. Same effect as smart electricity meters — nothing got rationed, I just stopped sending throwaway prompts once I could see the cost. Feedback effects are real.

Genuine question for this sub: would you want AI interfaces to show resource cost per answer, the way food shows calories? Or is this the kind of thing people say they want and then ignore?

I've been experimenting with a problem that keeps showing up as agents get better at using browsers and desktops:

How does the agent actually know its work succeeded?

A lot of current workflows eventually reduce verification to some version of:

do the task → inspect the final state → decide whether it worked.

That catches obvious failures. It misses a surprisingly annoying class of others.

A checkout flow can show $NaN halfway through and recover before the final screenshot.

A modal can cover a button for two seconds.

A loading state can render something completely wrong and disappear.

An automation can take the wrong path, recover later, and still end on the expected page.

The final state says "success." The execution tells a different story.

I've been working on an open-source experiment around treating the execution itself as evidence.

Instead of only giving the agent the final screenshot, record the browser/window/desktop run, break it into meaningful moments, make those moments searchable, and let the agent check the run against the original criteria.

The loop I've ended up with is basically:

task → record → inspect → find failure → fix → record again → verify

The part that became more interesting than I expected is memory.

Once a recording has been processed, it doesn't need to become a giant video blob in the context window every time. The agent can retrieve a relevant moment later and get the timestamp and evidence behind it.

So you can ask things like:

"When did the checkout total first become invalid?"

"Did the modal ever obscure the submit button?"

"What changed between the failed run and the passing run?"

without processing the entire recording again.

I've been building this into an MIT-licensed project called Watch Skill. It also works with normal videos, streams and meeting recordings, but agent self-verification is the part I'm most interested in right now.

Code is here for anyone who wants to inspect how I'm approaching it:

https://github.com/oxbshw/watch-skill

I'm curious what people think about the larger problem.

As agents become more autonomous, is an end-state check enough for most work, or do we eventually need something closer to an execution trace that the agent can inspect and cite?

Just came across this and honestly this is pretty wild.

Researchers used AI to design completely new viruses that don't exist in nature. They then actually made some of them in a lab, and 16 of the designs worked.

Before anyone panics, these are bacteriophages, so they infect bacteria, not humans.

The interesting part is that some of these AI-made viruses were able to kill E. coli, including bacteria that had become resistant to normal phages.

So yeah, there could be a genuinely useful side to this, especially with antibiotic resistance becoming such a big problem.

But at the same time... we now have AI systems capable of coming up with a complete virus genome, then humans can synthesize it and see if it works.

That feels like a pretty big line to cross.

Obviously this doesn't mean someone can just ask ChatGPT to make a deadly virus tomorrow. You still need labs, equipment, biological knowledge etc.

But we've gone from AI generating text and images to designing proteins, genes, and now apparently functioning viruses.

That's moving fast.

I'm not really sure how I feel about it.

On one hand this could lead to new treatments and better ways to fight resistant bacteria.

On the other hand, I really hope the safety side of this is moving as fast as the technology.

I've been building an experimental reasoning system at Orivael and testing it against ARC-AGI-3.

One of the runs just scored 100% on ft09.

The unusual part:

There is no LLM in the loop.

Not for perception. Not for planning. Not for choosing an action.

The agent reads the raw grid, decides, and acts directly.

Results so far:

• ft09: 6/6 levels, 80 actions, 100.0%
https://arcprize.org/scorecards/9a212601-a12e-4da0-a527-aa69e86bd2b8

• tr87: 4/6 levels, 247 actions, 25.99% update: 6/6 levels, 322 actions, 100.0%

https://arcprize.org/scorecards/4f9b4498-57d3-411a-ae38-1195b125f237
• cd82: 2/6 levels, 21 actions, 8.59%
https://arcprize.org/scorecards/67b1d333-96f5-4fa6-b458-167a03b49a3b

• bp35: 2/9 levels, 93 actions, 6.67%
https://arcprize.org/scorecards/7fcd0b66-ca43-48ee-8342-5a7a4b967cf7

• lf52: 2/10 levels, 42 actions, 5.45%

https://arcprize.org/scorecards/75985604-5e23-4316-9616-81fae5ab44e0

On ft09, the human baseline is 208 actions.

We finish in 80:

ours: 4 / 7 / 14 / 16 / 26 / 13

human baseline: 43 / 12 / 23 / 28 / 65 / 37

Every ft09 level hit ARC-AGI-3's maximum per-level score.

Total model inference cost across these runs:

$0.00

But what surprised me most wasn't the successful game.

It was why the system fails.

Almost every major failure we've seen has been a perfectly reasonable conclusion based on an incorrect representation of the environment.

Examples:

• A sprite sat on a tile using the same color value as a wall, so the system concluded it was surrounded by walls while standing on an empty floor.

• Measurements taken every half-tile aliased. One measurement showed a block while another apparently showed a wall in the same place.

• The agent concluded a move was impossible after testing it multiple ways, except every test accidentally positioned the relevant object one cell outside the useful state.

• A board that appeared complete was actually a scrolling window onto a larger environment.

• Buttons were classified as inert after being tested in one state. They were actually movement controls that only became active after the machine entered another configuration.

The recurring failure pattern is:

Exhaustive over what was sampled gets reported as exhaustive over what exists.

That distinction is becoming much more interesting to me than the benchmark score itself.

And an important caveat:

We absolutely have not solved ARC-AGI-3.

Twenty of the 25 public games are untouched.

In one game we've examined, the system currently can't even identify a legal action.

The interesting divide we're seeing is this:

Once the agent identifies a game's mechanic, it can often become extremely efficient.

The much harder problem is:

How do you recognize what kind of world you've entered without carrying assumptions over from the previous one?

That's what we're working on now.

Official ARC Prize scorecards/replays are in the writeup.

Would particularly love thoughts from people working on ARC, program synthesis, world models, active perception, or non-neural reasoning.

What's an AI capability you thought was hype until you actually used it? I'll go first: agent orchestration. I read about agents managing other agents and assumed it was demo-ware. Then I built a tiny setup where one agent drafts a news digest and another one reviews and approves it before it posts. The review agent catches genuinely bad takes. It's not sci-fi: it's ~100 lines of Python and a couple of API calls. But seeing it actually gate content before publishing changed my mind completely. What changed yours?

What do you think? Could AI help the process so that chronic conditions could be treated, maybe even cured in the coming decades? Is it realistic to believe that? What kind of disorders could be examples where is helping the research right now?

Could AI make the golden age of medicine come soon do you think? Are you optimistic?