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Verathos
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Verathos
@verathos_ai
Verified Intelligence for Everyone. Cryptographically Verified AI inference & training on Bittensor Subnet 96.
Katılım Mart 2026
19 Takip Edilen329 Takipçiler
Verathos retweetledi
I looked at bittensor subnets:
#sn96 @verathos_ai (verified inference)
#sn35 @0x_Markets (dex)
#sn60 @bitsecai (agentic code security)
youtube.com/watch?v=M9G_EC…
YouTube
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Verathos retweetledi
SN96 verifies AI inference via cryptographic proofs and GPU capacity via hot-capacity audits. Results directly influence miner scores & rewards, ensuring genuine AI computation is rewarded. By linking verification with incentives, SN96 builds trustworthy decentralized AI services
Verathos@verathos_ai
the need for censorship resistant ai infra has never been higher, soon it will be the only viable option. bittensor:native
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the need for censorship resistant ai infra has never been higher, soon it will be the only viable option. bittensor:native
Jukan@jukan05
CHINA CONSIDERS RESTRICTING OVERSEAS ACCESS TO CUTTING-EDGE AI MODELS China’s Ministry of Commerce has led meetings over the past month with major AI companies, including Alibaba, ByteDance, and Z.ai, to discuss measures that would restrict overseas access to cutting-edge AI models, including models that have not yet been released. The discussions reportedly include not only closed-source models but also open-weight models. However, the scope of application is still under debate, and the rules may ultimately apply only to future frontier models. Officials have also discussed designating the leakage or theft of proprietary AI technologies as a national security crime, with stronger penalties, as well as restricting the types of foreign capital that can invest in Chinese AI startups. The backdrop is the U.S. move to strengthen export controls on AI models, along with national security concerns over cutting-edge models that could possess advanced cyberattack capabilities. Chinese authorities are reportedly concerned that advanced U.S. cybersecurity AI models could be used to exploit vulnerabilities in Chinese software. Since the beginning of this year, China has continued to tighten measures to prevent AI technology from being transferred overseas. Authorities have investigated whether Chinese AI startups that relocated abroad violated export control laws, while also strengthening oversight of overseas transactions involving Chinese investors, technology, data, and national security concerns. Future regulations could take the form of a tiered framework based on technological capability. Basic open-source AI models may be managed through a filing system, high-performance models may be subject to security reviews, and the most sensitive frontier models may be banned from public release or restricted to use within China.
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Verathos retweetledi
Verathos is not some empty subnet with a nice idea.
Dashboard already shows:
749k requests
2.7B tokens
93 miners
5 models live
The part I care about is simple:
Can decentralized AI compute be verified without trusting the machine?
That is what @verathos_ai is working on with SN96.
Showcasing proof that the work happened.
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Verathos retweetledi
@verathos_ai @OpenRouter That’s an interesting distinction.
Anyone can rent servers and offer inference.
Tapping into globally distributed, untrusted compute and making it usable feels much closer to the original promise of decentralization.
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Verathos retweetledi
Hot Capacity Audit Protocol is live.
Verathos now combines Proof of Inference and Proof of Capacity
• Proof of Inference — proving that AI outputs are correct.
• Proof of Capacity — proving that the compute behind them is real
The missing layer of global AI compute is trust
Verathos@verathos_ai
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Angry Davee@AngryDavee
Bittensor | $TAO You know why I still think TAO is undervalued? Because when I look at the problems in AI today, and solutions that subnets like @verathos_ai offer, I know we're on the right track. Model access is getting restricted (GPT-5.6, Fable 5), inference is expensive, and even when you pay for a model, you still can’t verify that the correct model weights and computation were actually used. @verathos_ai does not just return an AI output. The computation comes back with proof that the correct model and computation were used. That is the definition of untrusted compute. Most compute networks focus on cheaper inference, more supply, or better routing. Those matter, but the harder problem is proving that the work actually happened. Without proof, decentralized compute is just rented hardware with a trust assumption. With Bittensor, you get different subnets attacking different parts of AI like cheaper inference, open AI markets, and verified compute through Verathos. That is why TAO still looks undervalued to me.
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without verification we would be another inference provider on @OpenRouter using owned/rented compute
our approach changes the game, we tap into global untrusted compute then we turn them into clusters over the web running frontier open models by leveraging decentralized incentives on 96 bittensor:native
Verathos@verathos_ai
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Verathos retweetledi
Decentralized AI has a very simple problem:
How the hell do you know the GPU actually did the work?
Not because the miner said so.
Not because the server looks legit.
Not because the validator assumes it.
Actual proof.
That’s the part @verathos_ai is trying to solve with SN96.
If a miner runs inference, the network needs a way to verify that the computation happened correctly.
No reputation games.
No handshake deals.
No “close enough.”
Either the proof works or it doesn’t.
That matters because decentralized AI cannot scale properly if every layer quietly depends on trust again.
Random machines are only useful when the network can verify the work they produce.
That is the real unlock behind untrusted compute.
Verathos@verathos_ai
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@verathos_ai pipeline-parallel inference across................... nahhhh not yet milllzzzzzzzz
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@verathos_ai verifies that computation is being useful. It's part of my stacccccc. Building with $TAO
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@LordRagnarao without verification we would be another inference provider on @OpenRouter using owned/rented compute
our approach changes the game, we tap into global untrusted compute then we turn them into clusters over the web running frontier open models.
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Everyone talks about compute when discussing decentralized AI.
More GPUs 🖥️
More hardware ⚙️
More infrastructure 💾
But after learning about SN96 @verathos_ai, I’m starting to wonder if verification is the real bottleneck. Because if anyone can contribute compute on #Bittensor, how do you prove the work actually happened?
Feels like one of the more important problems being tackled in the $TAO ecosystem.
Ragnarao (ᚱᚱ)@LordRagnarao
One thing I’m enjoying about my first Stitch3 campaign is that I’m mining and learning about #Bittensor at the same time 🧠 A week ago I was buying my first $TAO. Today I’m diving into SN96 @verathos_ai. The more I read, the more I think they’re tackling one of the biggest problems in decentralized AI: trust. If anyone can contribute compute, how do you know the work was actually performed? Most projects solve this with trusted hardware. Verathos uses cryptographic proofs to verify the computation itself. No trusted enclaves. No special hardware. Just math. Trust the math, not the server ⚔️ Who’s building something similar, and how does Verathos compare?
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AI needs more compute every year.
@verathos_ai is making untrusted compute usable through cryptographic verification.
Their Verified Chat is actually pretty slick too.
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Pairing @verathos_ai proof with, say, GLM 5.2... and you got something really special.
Because renting inference from some random GPU today and you're basically hoping it ran the real model, no shortcuts, no swapped-out weights.
Verathos kills that guessing game because with every response, it ships with a proof baked right in. So this stops mattering whose hardware touched your prompt.
The proof (cryptographic proof bundle) has a small size of ~50-100KB and it guarantees:
1. The exact model weights were used (committed on-chain via Merkle root)
2. The computation was correct (not shortcut)
3. The output wasn't tampered with
Sketchy rig, unknown miner, doesn't matter, the math either checks out or it doesn't.
$TAO #Bittensor
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Every AI computation on Bittensor should be verifiable, not blindly trusted.
@verathos_ai SN96 on Bittensor makes that possible by committing model state on-chain, generating cryptographic proofs for inference and training, and letting validators verify results on CPU in milliseconds.
That gives every subnet a trust-minimized verification layer they can plug into. This is massive because it enables provably correct AI, stronger incentives, and a more credible decentralized intelligence network.
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Verathos retweetledi
$TAO Bittensor SN96 Verathos is attacking one of the hardest problems in decentralized AI: how do you trust compute you did not run yourself?
Their answer is verified inference. The Verathos API is live on mainnet, uses pay-per-token verified inference, and points toward machine-payable AI through x402.
The important claim goes beyond "decentralized inference." Every response can carry a proof tied to the model call, so the provider cannot simply lie about which model ran.
This is a serious problem to work on. If decentralized AI is going to matter beyond ideology, users need more than cheap tokens and open endpoints.
@verathos_ai is one of the $TAO Bittensor subnets trying to make that legible.
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Verathos retweetledi
How many times has AI made something up?
argued, confidently wrong?
given you garbage that looked clean till you checked?
you blamed the model. fair
but as AI moves onto cheap decentralized networks, theres a darker option: it wasnt the model failing, it was a weaker one swapped in behind your back, and you never knew
because thats the part nobody tells you. when you hit an AI on an open network, you have no idea which model actually answered. you trust the label
a stranger runs your request and has every reason to swap in something cheaper and keep the difference. the output still looks plausible. you just eat worse quality and call it a bad day
@verathos_ai (SN96 on Bittensor|$TAO ) ends the guessing. every response comes back with a cryptographic proof of which model actually ran it
validators check it on CPU in milliseconds, the math does the gatekeeping, not a company
and they took it to production, not to a whitepaper. 730,000 requests, 2.7 billion tokens, live, 89 miners, real models
most verified compute is a pitch deck. this one has a dashboard
you dont argue with the model anymore. you verify it
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Verathos retweetledi
Most people talk about decentralized AI needing more GPUs
The harder problem is making sure the work that gets done is actually real
Right now in most decentralized setups, validators often have to re-run parts of the model just to check if a miner cheated. That’s expensive and doesn’t scale.
@verathos_ai (SN96) flipped this
They generate a cryptographic proof during the actual inference. Validators don’t need to rerun the model, they just verify the proof on a normal CPU in milliseconds. If the proof fails, the miner gets zeroed out immediately.
This changes two important things:
- Miners can’t cheaply fake work anymore because creating a valid proof is tied to doing the actual computation
- Validation becomes extremely cheap and accessible which keeps the network more decentralized
It’s still early but this approach feels more sustainable than relying on expensive trusted hardware or heavy recomputation by validators
Currently live for verified inference with plans to expand into training
#Bittensor bittensor:native
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