CryptoTrigger

It was clear from the start how important this level was, and how much liquidity we were going to need to clear. It may fail, resulting in new lows. Bulls better hope we find support quick to return and have another go at it $TOTAL $BTC $ETH

@JesusMartinez “Q4 will be unbelievable for your bags”

we’re so down bad we’re not even using price charts anymore

@RadoTsc @chutes_ai Been 100% of mine for a few months now. Comfy

After researching chutes for a week, this if proof im putting my money where my mouth is, @chutes_ai is now 61% of my #dtao bag (it was 0%) before. and i'm looking towards 80%.

In the last 30 days, @chutes_ai has bought back and burned 22,000+ Alpha Tokens Worth roughly 341,000 base:0x833589fcd6edb6e08f4c7c32d4f71b54bda02913 or 1610 bittensor:native

Is it just me or does it feel like @chutes_ai is really accelerating on all fronts. $TAO

The world doesn't need YALMs (yet another LLM). Nor does it need an LLM "but trained decentralized". The world needs a truly sovereign, secure, open source platform for building, serving, and fine-tuning models that is always available, to everyone, forever, borderlessly. The mission could not be more clear.

Your AI chats don't belong to you. They belong to whoever's server they sit on. Proof: in January 2026 a federal judge ordered OpenAI to hand 20 million private ChatGPT conversations to lawyers in the NYT copyright case. The original demand was 1.4 billion. An earlier order forced OpenAI to keep chats users had deleted. The court's reasoning: you gave your words to a company, so they're discoverable. On Chutes, TEE inference means the GPU operators serving the model can't see your prompts or outputs. Words nobody holds can't be subpoenaed. who should own your chat history?

Jon Durbin is going on the Hash Rate Podcast with @markjeffrey. On the table: Parallax, decentralized inference, and what it takes to train models across distributed compute. Live Friday, July 10. What should Mark ask Jon? Drop it below and we'll send him the best ones.

Crypto is so cucked that the up move in and endless chop is considering a bull market $BTC $ETH

Training strong AI models outside large data centers requires solving a hard coordination problem between distributed GPUs. @chutes_ai just announced they trained a recurrent model with Parallax across distributed GPUs in a fully non-blocking training setup, staying within only a 0.6% quality gap versus centralized training. A fully non-blocking setup means each GPU keeps training instead of pausing until every other GPU has finished synchronizing. That matters because recurrent models are harder to split across many GPUs than transformers, which makes this a strong test case for decentralized training. Another concrete milestone for decentralized AI training from a Bittensor subnet.

As expected, this ended up a significant zone of liquidity. It looks like bears were too strong for now and we’re rejected. Need to find support below to go have another go. Lift off above. Zone is weaker with each touch $TOTAL

@chutes_ai Insane. Congrats to the Chutes team. Keep pushing $TAO forward

We have achieved fully non-blocking decentralized training on a recurrent model, within 0.6% of centralized quality. To our knowledge, a worldwide first. In plain terms: training AI across distributed GPUs normally forces a choice. Either the GPUs pause and wait to sync with each other (slow, expensive) or you skip the sync and quality drops. We just showed you can have both. No blocking, no meaningful quality loss. We chose the hardest test case on purpose. Recurrent models are sequential by nature, every step depends on the last. Transformers are far easier to parallelize. If our approach holds on the hardest case, the easier architectures should follow. To our knowledge, no one has published decentralized non-blocking training for a recurrent architecture before. Parallax is the first. This is new ground. Only on Chutes. $TAO

Now live: SN64 @chutes_ai is the leading serverless AI compute network on @bittensor, powering trillions of tokens per month across open-source LLMs and every open-source modality, from image and video to speech and music. Start trading today → app.kraken.com/JDNW/SN64

Given you the most precise levels all of 2026. Not just random lines but actionable areas of liquidity. We continue to chisel $BTC $ETH $SOL

Inference is it. It's the whole game. At the end of the day, any fine-tuning, RL, pretraining, prediction models, whatever else you're trying to build, if you don't use it, it's useless. Tooling almost exclusively focuses on test-time compute now vs. better base models. And, at the same time, inference needs to be more efficient. DFlash/DSpark are great, but we need algorithmic/architectural breakthroughs, not just extra add-ons post-hoc. Creating/curating data pipelines and synthetic augmentation/etc. also need inference as a prerequisite to train the model. Using agents = inference. RL = largely inference. The goal of training a model = inference. That's parallax's "why" - we need to address the compute/cost/energy efficiency crisis facing the AI space right now. Maximally compact and efficient models to extract every last IQ point per watt out of the hardware, vs. just throwing more hardware at the problem. A̶t̶t̶e̶n̶t̶i̶o̶n̶ inference is all you need.

@jon_durbin @futileturtle @AlgodTrading Thanks for taking the time to share such extensive thoughts

I think the SEC would have words, not legal advice, of course. sec.gov/files/rules/in… Chutes is a DePIN, and the token is a utility token, aka it's a digital commodity not a security/etc. The chutes token being burned is what provides access to the commodity (hence any revenue from any payment type ultimately ends in burning of equivalent alpha token). This then also makes accounting less dubious because it's a COGS model, the revenue is immediately converted to alpha and burned, so there's no refunds/etc, and we have a much cleaner accounting posture because revenue is immediately matched to the tokenized cost required to deliver compute. It's programmatic purchase-and-burn for consumptive use. There is no discretionary treasury accumulation, no customer balance liability, no refundable stored value model, and no ambiguous promise that the OpCo will later use revenue in ways that benefit token holders, etc. (§ III.A): "A digital commodity is a crypto asset that is intrinsically linked to and derives its value from the programmatic operation of a crypto system that is 'functional,' as well as supply and demand dynamics, rather than from the expectation of profits from the essential managerial efforts of others." And critically, it "does not have intrinsic economic properties or rights, such as generating a passive yield or conveying rights to future income, profits, or assets of a business enterprise or other entity, promisor, or obligor" § III.E and § IV.A relevant as well. Projects claiming to be digital commodities but then not having token utility could be potentially violating those guidelines or have extraordinarily dubious interpretations that I would not want to be on the defending end of personally. Projects that then do have utility to the token but that then take the revenue, at least by my reading... well I wouldn't want to defend that fact pattern because it looks more like a web2 OpCo with an associated token whose value depends on the company’s managerial decisions. "the Federal securities laws generally do not apply to items that are purchased for use or consumption," whether physical or digital (§ II) and consumption = token purchase (and then we'd not want to be custodians and complicate things with yield etc. so burn) And if it's instead the OpCo holding staked assets for customers, then... (from same sec link) "Further, the deposited digital commodities: (1) are not used by the Custodian for operational or general business purposes; (2) are not lent, pledged, or rehypothecated for any reason; and (3) are held in a manner designed not to subject them to claims by third parties. To this end, the Custodian may not use the deposited digital commodities to engage in leverage, trading, speculation, or discretionary activities." The cleanest and lowest-risk way I’d personally want to defend using revenue operating as a bittensor subnet if you want to be classified as a digital commodity and not risk the ire of the SEC/etc. Anything else... Same with projects explicitly tying the token to equity in the OpCo, how can anyone interpret that as anything other than clearly a security? Again, not legal advice and perhaps there are some other interpretations, but that's why, in a nutshell. Another minor note here, just purely logistically it doesn't really make sense, we would have had to buy the servers with revenue we've accumulated until now when the servers were actually available and there was rack space/power/etc. to even use them in the first place, and unfortunately we don't have a time machine, or have taken out a massive loan etc. And of course, we're not trying to build a web2 company that happens to have a coin. Some people/projects want to own a DC or have huge colocated racks etc. and fully self-mine and limit access to the network etc., we want the opposite of those things. The facts are wrong, the premise is wrong, the idea is misguided, potentially illegal, etc. This is the same type of reasoning that has been driving teams away from bittensor historically, short-sighted lowest common denominator logic. Would it be better for hermes agent/nous research to be on bittensor today? Several teams doing the most ambitious work have decided this ecosystem isn't where they build, in some cases because of the constant toxicity/lambasting/cabaling from a handful of people including those who can't see or think past short term revenue maxxing. We must dream bigger and do better.

A question regarding Chutes, imagine instead of buying back the token they would have bought their own hardware, with that hardware they would now be able to generate $10m ‘profit’ a year Do you think this would have better or not

How do you price an AI app when your heaviest user costs you 4x what they pay? Flat $20/mo. Most users cost you $1 in inference. The power user burns $80 and you eat it. The more they love the product, the more they cost. Sign in with Chutes flips it. Users bring their own Chutes account and credits. The tokens come out of their balance, not your margin. Cost per user trends toward zero. Won't fit every product. But if your unit economics get worse with every active user, it's worth a look. What's your AI cost per user?

Do you see the importance of these levels? First major rejection. Again, we need to chisel away at all the liquidity stacked there. We need to find support in the move up and go back and have another shot at it. $TOTAL