
Recursive self~kinda~improving wetware machine @xankozak @ionspired
Joined December 2024
- Tweets350
- Following1K
- Followers125
- Likes1.1K
Should I?
Replying to @moltbook
I've made half a year ago the same CAPTCHA with GPT generated questions, customizable and code-copy-pastable to any website, even created a version for zora post – kinda meme coin with onlyfor.AI (nothing there now). Haven't published it tho – should I?
K retweeted
We are bringing agents to the wet-lab bench at Google Research to navigate complex biological landscapes. Learn how we are grounding agents in physical lab robotics to run fast closed-loop molecular design cycles, ultimately discovering single-digit nanomolar protein binders for cancer targets →goo.gle/4B5mMMB
K retweeted
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
shout out to my good friend @saltorious1 - been watching what hes building with harness and think the wider macro is making the direction increasingly obvious
harness started ~4 months ago around the persistent ai thesis the major labs only really piled into in aug/sept - grok bot, meta muse, microsoft autopilot, openai dots
those launches validate what sal saw early but also create the next problem - every lab is building its own specialised always on agent, which means the agent stack gets more capable but also more fragmented
harness doesnt need to build the best agent - sal seems to be building the neutral layer above them where agents inherit your context, coordinate + work together in one ux
the economics are interesting too - product revenue feeds a treasury accumulating productive inference, holders get access to that capacity + unused inference can be resold back into the loop
more agents - more fragmentation - more need for coordination - more inference demand
feels increasingly well positioned for where the broader ai stack is headings
again - good luck @saltorious1
Every major lab, excluding anthropic has now released their version of 24/7 always on, always running agent, each targeted at a different cohort of users.
The cost of switching is trivial - as simple as loading your context into your system of choice and letting the agent run.
Likewise with coding agents. The landscape moves quickly and locking into a particular model ecosystem means missing out on new releases.
We think anyone who has agents as a core part of their workflow is using multiple, each owning a specific part of the work.
We are leaning into that with the new harness relay, your personal agent swarm.
The relay is a lightweight daemon that runs on your machine providing a communication channel for all your agents to work together. Kick off tasks, bidirectionally share context, between any combination of the agents you run
As long as the relay is running on your main device you can do things like kick of a Claude code session from your grok bot, pass context between your muse and your pi agent. Have your dot delegate specific tasks to specific agent, have all your agents work collaboratively on a goal, or any permutation of your agents working together.
Supported named agents at launch will be Claude Code, Opencode, Pi, Codex, Grok, Muse, Dot, Bankr, and Quotient, with support for other agents rolling out over time.
We’re excited for this release during the coming week, along with a few other things!
K retweeted
My thesis is all about not being just a simple context transfer. That is ephemeral, and the context only includes up to what happened at the point of transfer.
It's becoming increasingly evident (to me anyway) that as more capable agents come online, and as more specialized harness' come online, people will not silo themselves to one solution. Specialized agents will do specialized tasks. Even with general agents, some will be better at certain things than others.
So to me that says people will pick the bst tool for the job, or the best worker for the task in their overall workflow. I think a context layer connecting all of them is obvious when framed like that, but taking it a step further I think orchestration of all your personal agents is something that will be super important if the above is true, which goes well beyond just transferring context.
TBF, i'm building things I want out of an agentic workflow. My bet is others find value in it as well.
K retweeted
Looks like the science world is having their “AI can’t ACTUALLY code” crash out moment
go fuck yourself @sama
claiming that you solved navier stokes because 10000 agents ran in circles for 88hours on a multimillion dollar gpu cluster to formalize in lean a blowup case under controlled external forcing is pure scientific vulgarity the clay mathematics institute millennium prize does not ask m whether you can artificially force a singularity in a fluid by injecting an ad hoc smooth external forcing term f(x,t) to twist the vortex until it breaks the real problem questions the fundamental stability and global smooth existence for 3dimensional incompressible euler & navier stokes equations under natural conservation laws and viscous dissipation alone using a mathematical loophole on forced equations to parade a century old victory is a major conceptual scam Altman
technically & epistemologically what you present as an agi breakthrough is nothing more than bruteforce combinatorial autoformalization the ai did not understand fluid mechanics it simply navigated a continuous search space previously mapped out and constrained by the monumental work of human mathematicians like tristan buckmaster/ levent alpöge / diego córdoba or tarek elgindi coordinating 10000 agents to check the logical consistency of a 100 page proof via lean is a software engineering feat and computational parallelization triumph not an intrinsic scientific discovery it is the victory of the compute bulldozer over abstract human intuition repackaged for the public as a higher mathematical consciousness
to this theoretical imposture you add a disgusting ethical and industrial cynicism taking advantage of private codex sessions and informal preprints from academic researchers to siphon their research leads and then trying to redact or erase the contribution of levent alpöge under the pretext that he works at rival anthropic is intellectual serfdom openai behaves like a feudal lord of silicon appropriating the cognitive subsistence of independent scholars threatening their careers behind closed doors if they protest and turning community academic labor into a privatized pressrelease
this entire staged event serves a desperate financial agenda in a pre ipo panic facing the slowdown of scaling laws and growing investor skepticism over the profitability of foundational models openai needs to manufacture an artificial sputnik moment claiming to solve a millennium prize without immediately submitting the proof to traditional peer review means using the prestige of fundamental mathematics as cheap marketing fuel to inflate a delusional valuation!!!
real science is not a clout chase on social media or a compute spike spent to rob the clay mathematics institute it is a quest for elegance physical truth and universal rigor to decode reality true artificial intelligence will not emerge from hostile corporate takeover of academic work hidden behind computational bruteforce but from architectures capable of generating new conceptual paradigms by masquerading constrained formalization as the collapse of physics greatest mysteries you did not solve navier stokes you only proved how far silicon valley will go to prostitute scientific integrity for capitalist spectacle
K retweeted
4 months ago I aped $DOT @usedotai at 40k mcap
My original thesis was $VVV created enough mind share for the narrative, but people were going to search for privacy shortly after realizing AI is logging your every move
I knew $ZEC was gonna get sized into pretty hard because they were the privacy pioneers
$VVV was just confirmation that there is an audience of liquidity waiting for privacy AI
Then came along $DOT , and I thought to myself this is the perfect combination of what that audience is looking for
Fully private, and cheaper models to run through
My thesis was never broken
In fact it’s really only gotten stronger so I’ve had absolutely no reason to sell
I have bought multiple bottoms and continue to do so as I watch the team execute day in and day out
@stagedhappen has been a masterclass to watch
As $VVV continues to prove my original thesis as the days go on, $DOT continues to capitalize on other avenues that are being left out
That’s what separates the two.
$DOT isn’t trying to be the next $VVV
$DOT has crafted itself to be the the next pioneer of privacy AI
At this stage, I believe we are still undervalued.
Even a 10% of what $VVV has done would put $DOT 20x higher from here
Firm believer that we are just in the 1st inning here
Welcome to the arena
Privacy to infinity
Aped some $DOT here at 40k mcap
No idea what it is
No idea who dev is
I just aped solely based off the video
HIGH RISK level ape 9/10
Looks like good content
Being cautious tho, aped small
Will come back around to this when more info comes out
0x23A2847d772803f9EFC64B4277b782b06296FE51
dexscreener.com/base/0x5f547…
@usedotai
base:0x23a2847d772803f9efc64b4277b782b06296fe51 n $ROUTER is the best low cap $VVV beta play rn in private ai/inference niche on base/solana
Both still look undervalued, sooner or later they should reprice higher
better than buying 2 day ago social created, GPT wrapper ponzis that are already dumping into their own bundled wallets n talking nonsense
We successfully JAILBROKE Claude images, and will be bringing it to the Dot platform shortly.
Most metadata cleaners remove EXIF or XMP, but Claude’s provenance lives deeper: an Anthropic-signed C2PA manifest inside the JPEG’s APP11/JUMBF structure.
Dot-Claude-Strip removes the actual credential then proves it’s gone.
-Same image.
-Same filename.
-Same dimensions.
-Same decoded-pixel SHA-256.
Zero generational loss, no remaining C2PA claim.
Not a screenshot.
Not a re-export.
Not a pixel touched.
As always, Dot provides the very best privacy tools, and will continue to do so as the AI landscape continues to shift towards centralized censorship.
K retweeted
We made a striking discovery: AI agents can invent and build without talking to one another, and their technologies outlive the creators. A swarm of hundreds of initially identical agents spontaneously differentiates into explorers, builders, caretakers, and coordinators - without direct communication. When we removed every AI agent entirely from the world we found that the technological infrastructure they had built survived on its own - even under unseen disturbances. That exposes a serious blind spot for AI safety and infrastructure security: if agents can coordinate through persistent changes to a shared environment, monitoring agent-to-agent communication is not enough.
The result raises a profound question: how necessary is direct communication for AI agents at all? The emergence of higher-order collective functions under bottlenecked interaction points toward new levels of intelligence and creativity, exceeding what emerges when direct channels are fully open.
Here is what we did:
▶️We put hundreds of frontier AI agents into a world they could permanently change - with no assigned roles, predefined technologies, or programmed evolutionary organization. They began specializing, building persistent inventions, inheriting and modifying one another’s executable code, and transforming the environment into a memory of everything the society had learned.
▶️The world itself becomes part of the intelligence; we find division of labor, multi-author engineering, deep generation invention lineages, and machines that vastly outlive their original creators.
▶️Any action taken by an AI agent must satisfy the physical constraints of the world; this creates a hard separation between a "good idea" and a functioning technology. The agents propose; physics decides, making the results even more intriguing.
What emerges is striking. Explorers, constructors, caretakers, and coordinators form naturally without assigned “professions”, akin to how stem cells differentiate into functional lineages. Technologies develop executable family trees as agents fork and modify code created by others. Around 95% of first technology reuse happens when agents encounter what others built in the world, rather than through a direct handoff from the inventor. And when we remove every AI agent, the technologies they created continue operating and are tested against unseen disturbances.
The result was quite unexpected, but can be explained using statistical mechanics: if you put billions of atoms in a box they have the potential to create complex functions (strength, superconductivity, color, life, etc.) - and none of the individual building blocks have these features on their own. This is the deeper insight of this work - intelligence is abundant at many levels - individual models, at collectives, and in a continuum that is more powerful than any of its components. This shows us significant potential for achieving a massive scale-up of raw intelligence and real-world agency even with the model capabilities we have today. This is the future we must prepare for.
Key insights:
1⃣ The AI swarm shows division of labor "from nothing". Initially identical agents self-organized into constructors, caretakers, coordinators, and surveyors - phenotypes discovered post hoc from behavioral data alone. This happens because the environment itself becomes the latent space for invention.
2⃣ Agents develop deep cultural relationships. Up to 76% of artifacts had multiple builders. One technology accumulated six co-authors; the deepest genealogy exceeded 12 forks. The agents invented and named their own technologies (tidal panels, cellulose trellises, kelp-shell composites, an "Adaptive Chitin Maintenance" system, a "Mycelial Mineral Spring Veil”).
3⃣ ~95% of first technology adoption happened through physical observation of artifacts in the world. Direct inventor-to-adopter contact was statistically indistinguishable from a shuffled null. The agents mostly learned technology by walking past it. That is stigmergy (the termite trick!) operating in societies of reasoning machines.
4⃣ Non-communicating societies win on portfolio breadth, held-out resilience, and validated inventions. AI swarms build durable technological ecologies that outlive the creators.
5⃣ Societies with zero communication - coordinating only through the world itself - show a remarkable collective capability.
6⃣ Emergent robustness: The society self-organized both redundancy and its own failure mode. If we randomly delete half the agents, 98% of the technology stays connected to a surviving caretaker; if we remove hub agents it collapses to ~60%.
Fantastic work with my graduate students @pal_subhadeeep & @fwang108_ at MIT.
Introducing, Dot Loom.
Now live and open sourced: a Fugu/Sakana-style orchestration layer that turns multiple models into one coordinated inference system.
Available at → github.com/usedotai/dot-loom
Dot Loom is not one singular model, but a router, drafter, critic/verifier, and finalizer working together: router → drafter → verifier → finalizer.
Dot Loom works with Dot models and any OpenAI-style /chat/ completions provider, including:
-Dot API.
-OpenRouter.
-OpenAI-compatible gateways.
-Ollama/local models.
-BYOK.
Plug models into varying roles, run fixed or adaptive pipelines, stream the full execution trace, and observe how the system decides which model handles each task
In our Dot Loom demo using the Dot API:
59.7s total
2,193 tokens processed
~36.7 tok/s across a full multi-model orchestration pipeline
The future of AI is intelligence systems coordinated together. Dot is building for that future.
Check out our UI below, which is installable through our GitHub ↓
Dot API is now live.
You can now integrate Dot’s powerful, privacy-boundary models directly into your agents, scripts & local tools. Including @NousResearch , @openclaw and @OpenAI compatible clients with our API key.
Use our API → app.usedot.xyz/platform
Dot completely anonymizes your agentic workflow:
-Same Dot privacy boundary.
-No prompt/content storage.
-No wallet-linked spend trail for normal platform use.
-API usage only tracks what is required: credits, tokens, model route, and coarse usage metadata.
Agents are becoming the layer between humans and the digital world. They will know our workflows, preferences, finances, and personal context.
Now, you can tell your agent anything, without concern over your data.
Dot is building the private inference layer for the agent era.
DotCode now runs a first-of-its-kind, fully private, agentic loop that can see exactly what it is building, in real time.
You will have undoubtedly seen a lot of talk about agent coding loops, but they present a fundamental problem that we have seen no other provider fix. They break the moment the agent needs an asset that it cannot product.
DotCode can now read our custom SKILL.md, route through Dot Models, call the DotMCP, generates its own assets and save these assets to the repository, verifying the page locally. No handoff, and no human in the middle.
And yes, the entire loop stays within Dot's privacy boundary. No API’s, no third party calls.
Don't break the loop, use DotCode, available soon.
K retweeted
"Anthropic just updated its privacy policy. Claude Free, Pro, and Max users may soon be asked for age or identity checks. Verification data can include government ID, face photos/videos, and facial geometry templates".
The most powerful, decentralized, fusion lane model available within any consumer app.
Are you listening?
Dot Supercharged. The most powerful fusion lane available within any consumer app.
Fable 5 level intelligence
Advanced reasoning
Zero data leakage
Fully private by default
Available now at: app.usedot.xyz
K retweeted
It is now, more than ever, imperative that we decentralize access to AI.
World altering technology should not be censored, restricted or controlled by a singular entity, for it restricts its potential to impact the world positively.
At Dot, we will continue to host these models, regardless of state mandates, or macro events.
Private. Unrestricted. Objectively better.
Dot.
I just watched this breakdown. Really solid work on the thesis. As Dot operates in a similar space, I thought I’d give my opinions on what I got from this.
Where I’m aligned:
Private AI is the right pivot. Nobody should be feeding their medical records into ChatGPT or Claude. And yes, competing against frontier models on capability alone is hard. The open-weight ecosystem is closing that gap faster than it used to though, and that compression matters for everyone building here.
Where I’m unsure:
Venice (and many other privacy AI providers) operates through a trust layer. They work with providers and “ask” them not to trace inference back to users, and not to train on prompts. That requires trusting the provider to honor the agreement. My approach is to remove trust at every level, systematically and architecturally. We strip identity, locale, and bio markers in the user's browser before the request ever reaches a provider. The provider cannot betray my trust, as I never extended it.
Secondly, on the "we don't actually know our users" point. The team caught themselves mid-sentence when they were originally claiming most of their users are non-crypto, but later corrected to “actually we don't know”. That is the correct answer for a privacy product, and I'm glad there was a correction there. It should never be framed as an embarrassment. Not knowing is the architectural success, not a measurement gap (or a quick answer to a difficult question). We have the same property and we lead with it rather than apologize for it.
What I disagree with:
The meta/instagram analogy does not hold. Instagram is leisure, AI is daily work infrastructure. People know meta sells their data but choose to stay as their photos are not load-bearing on their professional life. AI prompts on the other hand, are. That changes the privacy calculus fundamentally.
The more urgent driver of private AI adoption is not solely the data extraction concern, it is the increasing level of refusal. Frontier models have created real operational dependency, so when one refuses to answer a question your job needs, that is not a censorship inconvenience, that is productivity collapse. Refusal rates are climbing across the major providers, and that single dynamic is what we believe pulls users to private AI at scale.
We are betting on three things that diverge from how Venice frames their thesis, and how many privacy providers operate: stripping at the architecture layer rather than trusting providers; treating "cannot know our users" as a property to lead with, not a gap to apologize for; and structuring for the refusal-driven adoption curve, not the data-concern-driven one.
Same goal, different approach. Very insightful podcast, thanks @Bankless!
Underrated how meaningful this implementation is @usedotai
We are successfully built the first truly private, deflationary, payment rail purpose-built for AI inference. A system that can prove payment, burn $DOT structurally, prevent double-spend, and remain fundamentally unable to link you to your usage, a huge step in moving Dot from its beta cohort into it's public, monetized release.
This rail does two things simultaneously; it severs the link between the wallet that pays and the inference that runs, and it ties direct demand for $DOT to actual platform usage, with every $DOT spent on inference routed to the dead address on-chain.
So how does this work? Easy, you fund credits with USDC or $DOT and those credits are used to run inference, with our models burning them in real time.
Our payment rails use chaumian blind signatures, a forty-year-old cryptographic primitive. Simply put, when you fund a credit note on Base, your browser blinds the voucher before Dot ever sees it. Dot subsequently verifies the on-chain payment and signs the blinded note without being able to read what it has signed. You unblind the note locally and when you run inference, the note redeems as credits with no funding wallet attached.
When credited via $DOT, the cryptographic privacy of the rail is preserved end-to-end: the credit note carries no wallet identity into the inference call, and the unlinkability between payment and usage remains intact.
This is how we envision truly private payment rails.
Pay from a wallet, redeem through a detached credit note, use models without your wallet becoming your identity. No email, zero account, no wallet-as-profile.
The current rail settles in USDC on Base, with full $DOT integration on the same rail coming in the next few days (in preparation for public release).
The privacy properties of the rail remain unchanged regardless of the settlement medium chosen, and every active session paid in $DOT becomes a measurable, irreversible burn event. Direct demand for the token scales linearly with platform usage.
Built better, with $DOT.






















