Crypτnomad

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Crypτnomad

Crypτnomad

@Cryptnomad1

In the jungle surfing crypto $TAO $XRP $FLR $WMT, RWAs, DePin, DeAI Don't sleep on τao

Beigetreten Mart 2021
591 Folgt489 Follower
LλURA-VΞRSΞ
LλURA-VΞRSΞ@Laura__crypto·
Forget price. Look at who’s actually making money. Top AI coins by Q1 2026 revenue. $TAO — $43.2M $VIRTUAL — $2.87M $LINK — $2.1M $RENDER — $1.2M $IO — $0.9M $AKT — $0.74M $FET — $0.6M $PHA — $0.4M $ICP — $0.3M $NEAR — $0.15M $TAO isn’t just leading. It’s lapping the entire field. Price follows revenue. Always has. Always will. Are you holding the right ones
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Crypτnomad
Crypτnomad@Cryptnomad1·
@KingBertrand333 @wacy_time1 @aixbt_agent Both. Both have a symbiotic utility w/in the ecosystem. For investing, you can grow your $TAO by staking in the subnets and when the $TAO price goes up, you're basically compounding your gains. If your subnet 2x and TAO 2x, that's a 4x gain.
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AlΞx Wacy 🌐
AlΞx Wacy 🌐@wacy_time1·
Everyone watching TAO price action. Nobody watching what's actually happening inside the network. Subnets stopped being experiments. Now they're: - Generating real revenue - Signing enterprise clients - Publishing peer-reviewed research The subnet layer is where value actually accrues, not the token ticker. 7 Bittensor subnets doing real work right now: @chutes_ai - Inference layer already doing ~$5.5M annualized. Real usage, real developers, and one of the clearest revenue signals across all subnets. @tplr_ai - Decentralized pre-training. Running one of the largest permissionless training experiments, with actual academic validation (NeurIPS). @webuildscore - Computer vision subnet with enterprise clients (sports, retail, infrastructure). Turning video into structured data at scale. @yanez__ai - Compliance + RegTech. Generates adversarial datasets to stress-test KYC/AML systems. Selling directly to financial institutions. @metanova_labs - Drug discovery subnet. Early stage, but targeting a massive market with asymmetric upside if validation works. @IOTA_SN9 - Pre-training competitor targeting undervalued training infrastructure. Positioned as a value play within the training layer. @TargonCompute - Confidential compute subnet enabling private AI execution. Focused on secure environments for sensitive data and models.
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Crypτnomad
Crypτnomad@Cryptnomad1·
@Hadometa @wacy_time1 Buy $TAO, get a wallet like Talisman, research tokens via X, TG groups, youtube videos on subnet breakdowns, go to a site like TaoStats.io and stake.
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Hado
Hado@Hadometa·
@wacy_time1 How donwe buy those tokens?!?
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Crypτnomad
Crypτnomad@Cryptnomad1·
@zordcrypt @wacy_time1 It becomes the infrastructure base layer for decentralized open source AI and in that sense, one of the top 3 infrastructures in crypto.
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ZORD CRYPT
ZORD CRYPT@zordcrypt·
@wacy_time1 What’s your prediction for $TAO in the long run??
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wellfrog
wellfrog@wellthought8·
@wacy_time1 Thoughts on staking $TAO for chutes rewards?
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Crypτnomad
Crypτnomad@Cryptnomad1·
@KingBertrand333 @wacy_time1 @aixbt_agent $TAO in a sense is the infrastructure utility token that makes it all run. But the actual IRL worldly utility are the products and services being build by subnets. Meaning the things we, or our agents, will actually use. Although some will also engage in the infrastructure also.
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Y. Al-Riyami
Y. Al-Riyami@DreamActionYA·
@wacy_time1 Subnets generating real revenue signals decentralized AI infrastructure maturing beyond speculation. Yanez's KYC/AML stress-testing addresses a critical gap for financial institutions navigating regulatory requirements.
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Crypτnomad
Crypτnomad@Cryptnomad1·
@bittybitbit86 @wacy_time1 That varies by subnet as it's not a forced metric by the ecosystem infra. These are real businesses with real start up costs, some more expensive than others. And none of them are very old. But common in the culture is the term "alpha buybacks" = using revenue to buy the tokens.
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LiτBro
LiτBro@bittybitbit86·
@wacy_time1 How much of the revenue is going back to holders?
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Crypτnomad
Crypτnomad@Cryptnomad1·
@stakao_com @wacy_time1 $TAO subnets. There are 128 of them and some, in and of themselves are stronger picks than well known stand alone projects. @tplr_ai Templar was even mentioned on the All In podcast. 72 billion training run!
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Stakao
Stakao@stakao_com·
@wacy_time1 These cycles always create interesting rotations. Which AI tokens outside the usual names are you watching right now?
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Score - Subnet 44
Score - Subnet 44@webuildscore·
NEW TASK ON SN44 Our first vehicle detection challenge goes live tomorrow. Car, bus, truck, motorcycle…on CCTV footage. MINER STARTER PACKs are now baked directly into the console (link in first comment) Requirements: ∙Public track ∙50ms latency ∙30fps ∙<30mb model ∙Baseline 40.7% mAP → target 90% As @tm0klc put it: “expect the frequency of tasks to increase in the coming weeks” Happy Monday and Happy Mining!
Score - Subnet 44 tweet mediaScore - Subnet 44 tweet media
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Crypτnomad
Crypτnomad@Cryptnomad1·
@subnet_mania @MaxScore Nope, he said in a different comment, legal and compliance still need to sign off on it so, hasn't been made known publicly yet.
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Crypτnomad
Crypτnomad@Cryptnomad1·
@Laura__crypto All out at $4000? And then what? As far as crypto investments, if $TAO goes to $4k, it's likely because it's become an established ecosystem. Getting fully out would be like getting out of BTC at $4k.
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LλURA-VΞRSΞ
LλURA-VΞRSΞ@Laura__crypto·
Most people found $TAO after the hype. I found it at $41. My exit is $4,000. That’s not a hope. That’s a plan built on what Bittensor is actually building. Bluechip of the next cycle. Bookmark this 📌
Grayscale@Grayscale

$TAO is entering the mainstream conversation @nvidia CEO Jensen Huang talking $TAO with @chamath on @theallinpod. FYI: Grayscale Bittensor Trust $GTAO is open for private placement for eligible accredited investors.

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Crypτnomad
Crypτnomad@Cryptnomad1·
@2xnmore $TAO and then into the best subnets for the win
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2xnmore
2xnmore@2xnmore·
Last $500 of fresh capital. One shot. Where does it go? A) All in $TAO B) Split $QUBIC + $RENDER C) Full RWA $ONDO + $CFG D) Something nobody's talking about yet Reply your pick. I'll tally results in 24h and drop my final call.
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Crypτnomad
Crypτnomad@Cryptnomad1·
@SubnetSummerT @Loosh_ai True but they need to do their part and realize there is a community here they need to communicate with. We turn an important flywheel and it can turn both ways.
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Alchemist - τ
Alchemist - τ@SubnetSummerT·
Bittensor needs @Loosh_ai 🤖 Not as a nice-to-have but as a necessary layer. As the network scales, the bottleneck is no longer just raw model output. It’s judgment. It’s context. It’s the ability to reason about behavior in dynamic, real-world environments. That’s the gap Loosh is going after. If Bittensor is going to power the next wave of intelligence especially in robotics, agents, and human-facing systems - then cognition and behavioral reasoning aren’t optional. They’re foundational. Loosh is building in a direction that complements the network, not competes with it. It’s pushing into a layer most aren’t even attempting yet. I’ll also be announcing a Subnet Summer AMA with the Loosh team shortly - a chance for everyone to dig deeper, ask hard questions, and understand exactly what they’re building. But beyond that, we as a community need to step up. If we believe in what’s being built here, we need to ensure teams like Loosh aren’t lost to deregulation cycles before they have the chance to fully realize their vision. Markets will chase rankings and short-term sentiment. But networks win when they support builders tackling the hardest problems. Bittensor needs Loosh because the future of AI needs more than just outputs.
Loosh AI@Loosh_ai

Hey Bittensor, We have spent the last week deep in the validator codebase improving scoring, consensus, and subnet quality while incorporating a lot of community feedback. That work was necessary and it has already made things better. Yes we are ranked last but our broader mission has not changed. Earlier this month, we had a call with a board member of a publicly traded robotics company about a pilot program. The goal is to benchmark our cognition stack for integration into robotics systems that need to reason about behavior, context, and response in real world environments. That is what we focusing on now. Tightening the subnet. Complete the benchmark data. Move toward deployment. We are still building, still iterating, still moving toward something we believe matters and we still believe in TAO. If you believe robotics will need more than raw model output, if you believe judgment and behavioral reasoning are missing layers, pay attention to what we are building. Thank you to everyone who has given feedback, encouragement, and support. We have taken it seriously. Bittensor is competitive. Sentiment matters. Ranking matters. We know where we are. But if you understand asymmetric upside, you understand the setup. We are still here. We are still building. Now we push.

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Crypτnomad
Crypτnomad@Cryptnomad1·
@LouiseBeattie @PeterDiamandis @alexwg Important for pitching outside of Bittensor as well. We may all love Bittensor but it's not what to lead with. It might be 2nd or 3rd order, or even maybe never need be mentioned. And that rings true for the whole ecosystem. The best is if the world runs thru BT and no one knows.
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Louise Beattie
Louise Beattie@LouiseBeattie·
What does Bittensor success actually look like? Not moonshots. Not fireworks. Not your feed exploding with hype. @PeterDiamandis and @alexwg recently published a nine-chapter blueprint for how AI-driven abundance unfolds. And buried inside their framework is a definition of success that I think the Bittensor community needs to hear. They say a domain is "solved" when it becomes as boring and reliable as tap water. When something that used to require world-class talent becomes a routine computational query anyone can run. Read that again and think about what mature subnets actually produce. Not breakthroughs that get amplified across crypto X. Reliable output that gets cheaper every month until nobody thinks about it anymore. The subnet that quietly reaches the point where its service just works, every time, at a price that keeps dropping, is the one that won. Gene sequencing used to take years and cost billions. Now it takes hours for less than the price of a meal out. That's what "solved" looks like. Not exciting. Invisible. The people using the service don't know or care what powers it. It just works. The builders who are going to matter most in this ecosystem aren't the ones making the most noise right now. They're the ones turning their subnet's output into tap water, making it so reliable and so affordable that it disappears into infrastructure nobody notices. And you'll barely notice when they succeed. Because that's what success means. If you're evaluating subnets and watching for the flashy one that dominates the timeline, you might be looking in the wrong direction entirely. The real signal is the subnet that's becoming boring. x.com/PeterDiamandis… solveeverything.org
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Crypτnomad
Crypτnomad@Cryptnomad1·
@SomaSubnet How does Soma guarantee the discarded data is not currently and will never be useful to the model?
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SOMA
SOMA@SomaSubnet·
Most people working with #AI focus on models. The real bottleneck is context. Every LLM system degrades as context grows: - higher latency - higher cost (token explosion) - lower signal-to-noise ratio - increasing hallucination risk Why? Because we treat context as append-only, not information-optimized. 👉 Compression fixes this. Context compression ≠ naive summarization. It’s about: - preserving high-information-density tokens - removing redundancy - maintaining semantic integrity - optimizing for downstream reasoning Think of it as: an information bottleneck layer for LLM pipelines. Instead of feeding 10k tokens of loosely relevant history, you feed 1k tokens of maximally useful state. Result: - cheaper inference - faster responses - more stable agents over long horizons - better reasoning per token This is exactly what is happening now on SOMA. SOMA compresses text while preserving key information structure - turning noisy context into high-signal input for models. Next step → compressing agent chain-of-thought: turning long reasoning traces into reusable, compact state. This becomes critical for: - long-running agents - multi-step workflows - memory systems - tool-using LLMs To get the most out of AI agents, we need the best possible context management. SOMA is that missing layer. ➡️ If you’re building LLM systems and hitting scaling limits - let’s talk.
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Crypτnomad
Crypτnomad@Cryptnomad1·
@thethinker012 @SomaSubnet @QuasarModels Seems like it would be best to compress and reach limits and then expand? I suppose the fear factor would be to expand first and not get rid of any data. Compression needs to prove trustworthiness that the discarded data truly wasn't relevant.
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CriticalThinker
CriticalThinker@thethinker012·
@SomaSubnet @QuasarModels 👀 Appreciate the quick response. This is very intriguing, viewing from 2 different angles. Tackling the context problem with 2 different approaches. As a staker on both subnets, it would be amazing to see both approaches being successful and combined for maximum efficiency 🙏 🤝
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Louise Beattie
Louise Beattie@LouiseBeattie·
It would seem that @Sebyverse has very big ambitions for @resilabsai and I would not bet against Seby. This is going to be very good
Mariuszek@sobczak_mariusz

SN 46 $TAO @resilabsai AMA Summary Nerds hosted @Sebyverse , founder of RESI SN46, and we walked away more bullish than we went in. What became clear during the AMA is that this is not just a real estate data play. The real thesis is much bigger. RESI is building intelligence infrastructure that enables liquidity for on chain real estate. The subnet is the intelligence layer that makes the whole system defensible and ensures all tokenized value is underwritten. The structure is clean and easy to follow: Subnet → appraisal oracle → tokenization framework → lending markets → MBS style vaults That progression matters because each layer strengthens the next. Rather than building around abstractions such as tokenizing an LLC, RESI tokenizes liens directly. Liens are how banks actually secure claims on property in the real world. A property cannot be sold without satisfying outstanding liens first. That means RESI is building around the legal primitive that already matters. According to @Sebyverse , this can reduce tokenization from roughly 2 weeks and $2,000 to about 2 days and $200. That is not a small improvement. That is a structural reduction in friction, time, and cost. The real unlock comes from what sits on top of that system. Once you have verified appraisals, title checks, inspection inputs, and lien status feeding through the subnet, you can start building lending markets directly on top. That is where RESI Finance comes in. The vault model Seby described is essentially a real estate credit product backed by tokenized property liens. Think Maple Finance but for real estate loans. Homeowners can borrow against equity. Liquidity providers can earn yield. Investors can gain exposure to tokenized real estate credit. And here is where it gets interesting for the yield-focused investor: if you own tokens of a property generating 15% yield, you borrow against those tokens at 5%, loop three times, and you are sitting at 45% yield against a 15% cost — backed by a real world asset, not a narrative. Institutions can also buy entire vaults the same way mortgage products are bundled and sold today. The US mortgage-backed securities market is $11 trillion in outstanding debt. That is the addressable market Seby is pointing at. The moat here is the oracle. A lot of people talk about tokenized real estate, but very few are actually solving the hardest part: reliable on chain property intelligence. Without verified property values and lien verification, you do not have a serious lending market. You just have a narrative. RESI is trying to build the missing layer that makes real financial products possible. Seby also laid out a broader flywheel where third party platforms plug into RESI smart contracts, source homeowners and investors, and route activity into the vault system. That means fees can potentially come from multiple directions: borrowing, lending, exchange activity, and platform usage. There are real risks. Lending against real property liens is not a light regulatory exposure — this sits closer to mortgage lending than it does to crypto, and that distinction matters legally depending on jurisdiction and how the product scales. Execution risk is real. Launch timelines are ambitious. But the architecture itself feels coherent in a way most subnet pitches simply do not. This is one of the few subnets where the product and the subnet actually need each other. The intelligence layer is not optional. It is the foundation. That is why we walked away more bullish. June is the target. If they hit it, RESI Finance stops being a subnet story and starts being a DeFi story. Those are different audiences, different capital, and a very different ceiling.

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