TSC maxi ~ roundtrip king & cook ~ Temple | Sanctuary NFA DYOR etc | base.eth
on ze blox
Joined April 2022
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just had an utmost important thought while working
gm
ga
gn
or just
gman
to greet all everywhere
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This take is so dumb. Obviously they get paid to just hodl the wallet and hodl it all the way down - look at all the hodling!~
meanwhile their side-wallets are breaking bank but nobody can see the actual extraction in the background
Being early means nothing if you don’t sell
@pointfarmcap round-tripped the hardest, from $13M down to $400K upnL (zero sells)
@theunipcs gave back $8.5M (no sells)
@cryptogle and @the__solstice both went from deep green to red
The round trips are CRAZYY
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0xfffaf retweeted
This week @samjavadi and I are at the Digital Asset Summit in London, 10-11 Nov.
If you’d like to talk about verified compute on @Tensorcash, the roadblocks to trading inference, and how we’re solving them with @inferenceFI and @AskTillyAI, feel free to DM.
We’re looking forward to meeting you there.
Day one of the Digital Asset Summit in London with @enrico_otta.
Looking forward to connecting with like-minded people on verified compute and inference markets, and with anyone who’d like to know more about what we’re building at @AskTillyAI and @inferenceFI on @Tensorcash.
Come find us.
tensorcash:native miners bring the supply. @inferenceFI is building the market to sell it.
Mining rewards help get GPUs online. Paying customers give them a reason to stay beyond the block reward.
This is the part of the thesis I'm watching.
0xfffaf retweeted
Read this as a miner not as a tweet.
Bitcoin burns gigawatts on hashes nobody uses. Every proof of work since secured itself with something the world does not need. Elon just named what the world will need and it is Inference almost all of it.
One chain already mines with it. On $TSC a block is won by serving a real answer from a real model with a receipt proving which model wrote it. Independently measured: 90 to 97 percent of the GPU time is the proof itself under 1 percent overhead. SERVING IS THE MINING.
So if 99 percent of compute becomes inference every GPU serving an open model can secure this chain and gets paid twice for one job. Block reward from the chain payment from the customer. The pipe is already proven and first paid session settled on chain last month.
Bitcoin had to convince the world to waste energy. $TSC only has to be there when the world does what @elonmusk says it will do anyway.
The richest man alive just told you which side to stand on. Hyperliquid of inference is coming for all global inference anon ❤️
0xfffaf retweeted
a coding agent asked for a website, and a miner on @Tensorcash wrote it.
that miner got paid in $TSC for the answer. the same run also counted toward the next block.
so the power that's securing the network is doing real ai work too
A miner on the TensorCash network wrote a website for a coding agent on Qwen3.8-27B, and received 0.01136 TSC for this inference.
Inference and a shot at the block reward, from the same unit of electricity.
A live run by @boblabs_eu, an independent team building on the network.
Mira tests the provider; tensorcash:native is designed to let you verify which model produced your actual answer.
0xfffaf retweeted
前两天看到 @AnthropicAI 用 ZK 技术探索 AI 推理的可验证性，今天又看到 @miranetwork 推出 Sterling，开始解决 AI 推理服务商的可信度问题。
方向都没错，但我觉得这些还不够酷。
一个在研究如何证明 AI 推理过程，一个在解决 AI 推理服务的可信交付。
而 @Tensorcash 想做的是更进一步：
让 AI 推理本身成为 PoW 共识工作。
不需要在完成推理之后，再额外花费大量算力去生成证明。
而是让真实的 AI 模型推理直接参与区块链安全，通过推理轨迹、统计验证、随机抽检和经济激励，实现低开销的可验证推理。
推理即工作，工作即共识。
让维护区块链安全的算力，同时创造真实的 AI 生产力。
这才是我认为更酷的 AI × Crypto 结合方式。 tensorcash:native
AI推理正在成为一种商品。但商品市场首先要解决一个问题：你付费买的，和实际收到的，是不是同一种东西？
石油有品质标准，白银有成色检验。AI推理也需要自己的“验货机制”——你买的是某个模型的推理，交付的结果究竟是不是由它产生的？
@Tensorcash 这条官方推文，讲的就是这个问题：把推理验证作为协议的核心。
要让AI推理像石油、电力一样可以定价、交易，甚至进一步衍生品化、金融化，可验证的交付是一个重要前提。
验证本身不是金融化，但它可能是连接两者的一块关键底座。
这也是我认为 tensorcash:native 值得关注的地方：不只是“AI＋挖矿”，而是让AI推理从黑箱服务，走向可信商品的可能性。
0xfffaf retweeted
In an anonymous market nobody trusts you. Without a good reputation it is hard to sell anything - doesn’t matter how good your product might be.
So compute gets bought from a handful of names everyone knows. Buyers can’t check the product, so they pay for the brand... and they just pay “the extra”.
Beyond this there’s another problem. Even for the same open model, picking a good provider is surprisingly hard. GLM 5.3 on OpenRouter: 78% to 89% on GPQA, depending on who serves it. So even if you pay the extra, there are big differences - own quantisation, custom kernels, silent bugs in the stack. Without your own reference you will never notice.
@Tensorcash solves this. Every answer carries a proof of the model that produced it, so a provider can’t swap in a heavily quantised or fine-tuned version without being caught. A GPU at home can sell next to a data centre, and nobody needs to know who runs it.
Bringing more supply, all of it verifiable, to a market with ever-growing demand.
This is why we are building @inferenceFI on @Tensorcash.
Picking a good inference provider is difficult.
Picking the best inference provider for an open model is surprisingly hard: The reason: no single provider does it all. On OpenRouter, GLM 5.3’s GPQA score ranges from 78% to 89% (quite a large gap, despite the caveats†), and the trends in GPQA is not strongly correlated with other tasks, so it varies by workload.
Why discuss this now?
Recently, Jon Barron and colleagues showed that standard softmax implementations hide two silent bugs: Softmax Collapse, where the target gradient rounds to zero, and Softmax Cancellation, where identical logits still produce a gradient. Widely speaking, inference has the same problem, except it's harder to get something agreed to be a 'correct' outcome.
In my experience, providers on alternative chips tend to have worse silent framework bugs, though these issues are apparent on NVIDIA inference frameworks as well.
MoE models are especially fragile: the router bias and router output need fp32, etc. (there's a long list of half-patched issues). As deployment engines grow more complex, these bugs get almost exponentially harder to catch.
(Note†: SGLang or vLLM in their default configurations should give the best accuracy. The issue come from what providers add, like some custom quantization, speculative decoding, and custom kernels, or even simply forgetting to update to the latest versions of the frameworks that improve accuracy. And consider the case where you have no open reference at all (i.e they take care of both post-training and inference, so you don't own your own canon copy of the model, where you wouldn't know if there's some error in the stack silently reducing your quality).
"i don't think that useful work is required for AI labs to mine it"
"my bullcase rests on i think AI labs will mine Pearl"
gl pearl-2:native maxis
The $PEARL debate turns to a key question, how much of its hash rate is actually doing useful AI work today?
A recent @Blockworks report found that less than 1% of blocks match real AI models, raising questions about whether @prlnet's current valuation already assumes useful work is happening before it's been verified
The bull case is that PEARL doesn't need useful work to succeed, but needs AI labs to adopt and mine the network
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0xfffaf retweeted
On TensorCash, two people can trade directly, with no one else in between. The funds stay in their own wallets until the trade settles, removing the risk of an exchange losing or freezing them.
This video follows one of those trades, from the offer to the block it settled in.
One unit of a call option on the network’s difficulty was traded for 0.9 TSC. The option and the TSC moved in one transaction, straight from one wallet to the other.
Each trade on the protocol is trustless, and neither side can end up paying without receiving. All in an ordinary transaction, with no escrow, no multisig and no trace of the negotiation, which keeps it cheap and private.
0xfffaf retweeted
As @enrico_otta said on @MCGlive, the @Tensorcash protocol lets you prove which AI model ran a prompt, and that it wasn’t swapped for a cheaper, heavily quantised version.
That matters for two reasons.
1. Decentralized markets. Participants in decentralized markets, marked by their anonymity, can’t sell things that aren’t verifiable. The result is a closed market with a few “trusted” sellers, which brings a lot of flaws. The fix from the protocol is that sellers can use it to generate a proof that credentials what they sell, so the buyer now knows what he gets.
2. Security. TensorCash is a layer one where the proof of work is the inference itself. That only works if the work (the inference) can be verified. Without it, everything collapses.
So at the same time you allow an efficient and decentralized market to take place and a new market structure to emerge on a massive new commodity, while securing a blockchain and incentivizing miners to participate, through the verification protocol.
Creating a system where the chain is secured by one of the most in-demand commodities out there atm: AI inference.
0xfffaf retweeted
Verifiable inference is the cornerstone for an open and decentralised market, what we are building with @inferenceFI.
@Tensorcash protocol is already delivering verifiable inference at scale - great to see attention on the topic growing.
New Anthropic research: progress on provable inference for large language models.
Here, we introduce FlashProve. Building on established cryptographic techniques, FlashProve allows developers to verify whether a given response came from a given AI model. It’s substantially more efficient than prior open-source methods for doing so.
These proofs have a range of applications. They can, for instance, make it harder for attackers to hijack a chat, or enable safety evaluators to verify that the model they’re testing is the same one being served to customers.
For our full write-up, see here: anthropic.com/research/prova…
Further progress in provable inference will likely be an important part of improving trust in AI deployment. To that end, we're open-sourcing FlashProve’s code, here: github.com/anthropics/FlashP…
We’re grateful to our collaborators: Alessandro Chiesa, @GiacomoFenzi, @gwberg95, @miranda__christ, Rahul Dalal (rdalal.github.io) and Ben Mayer (@transmissions11).
0xfffaf retweeted
He is right and here is why.
Every exchange has the same hidden product and that is the queue. Prices are public and matching rules are public so the only thing left to fight over is who came first. Whoever controls the sequence owns the market.
On a centralised exchange one engine sets the sequence and the race goes to whoever rents the rack next to it but on a chain the block producer reads every pending order and picks the sequence himself. That is MEV the sandwich in every block and every DEX pays it. For example Hyperliquid handles it with a small validator set.
The TSC exchange design does it the other way around. Decide the order before anyone can read it so your order is encrypted the key split in ten pieces and each piece sent to a random validator. Each one stamps the arrival time on something it cannot read. Only when six pieces are public does the order become readable and by then its time is already signed. A colocated market maker cannot cancel in time bcs the key pieces have to cross his own link first. Being first becomes physics not a referee.
Now add what sits on the same chain. Your coins never leave your wallet they lock under your own key with no deposit to anybody. The inference price index and the CFD on it already live on the L1 so the exchange is not inventing a product it is giving one a venue. And by design fees in TSC, validators stake TSC and earn them, and the ticket to validate comes from mining shares. The miners who serve inference are the ones who run the exchange.
Hyperliquid for inference with fair sequencing you can verify instead of trust run by the people producing the commodity, settling in the coin that pays for it.
Still research stage the paper is public a sidechain anchored to the L1. The order book is standard stuff but the sequencing is the rare part. Still early but that is case when you frontruning biggest tradfi trade with $TSC in real time. Hyperliquid of Inference anon ❤️
0xfffaf retweeted
this is the part of @Tensorcash i keep explaining to friends. the mining is the ai work.
a miner's gpu answers a prompt, and that same answer is a try at the next block. so it earns $TSC for the block and can sell the answer to a customer.
that's one power bill paid twice
$TSC
