pleasehammerdonthurtem

@BubblegumLtng, @rhubarbmedia, @Kaspa_HypeMan Any chance of getting this $KAS tee added to the official merch on @PoolPartyNodes ? Otherwise I might have to create a bootleg one for myself 😅 #KASPA #KAS #CRYPTO #GRATEFUL

@MMAJunkie @MMAJunkieGeorge @thegoze Respect 👏

Cutman Jacob "Stitch" Duran explains why he stopped Nate Diaz's fight vs. Mike Perry on the stool. ""Nate thanked me. ... I said, 'Of course.' I was there to take care of him. That was a proper move." (via @MMAJunkieGeorge, @TheGoze)

A New Lending Protocol is Launching its Mainnet on Kaspa Kaskad (@AppKaskad) is officially opening its public mainnet on May 24 at 12:00 UTC, marking the start of the decentralized finance era for the Kaspa ecosystem. The Token Generation Event allocation claim will go live simultaneously with $KSKD deposit support on @MEXC. Official listing and trading are scheduled to begin on May 25 at 12:00 UTC as the protocol initiates its first operational epoch.

#Bittensor is taking over Paris... again.

@cptdankkk What does billions of views translate into, in dollar amount?

Dana White reveals he took $3,000,000 and turned Power Slap into a billion views a month "I called the Fertitta brothers and we all put up a million bucks, $3 million. Got the commission involved and turned it into a real sport" "It does a billion views a month on social media, and it is a sponsorship home run" "I invited all the influencers to come. If you had over 5 million followers, you got invited to the event and I let these kids come in, do your thing and create content" "Power Slap is the most successful thing I’ve ever been a part of in such a short amount of time. The first two years it had more sponsorship than the UFC did in 10 years. Power Slap is a beast"

Kaspa Initiates Final Hardfork Test Ahead Of Toccata Mainnet Launch @Kaspaunchained announced that Kaspa is conducting the final hardfork rehearsal on Testnet-10 today to finalize the Toccata mainnet transition. Developers are utilizing the latest Rusty-Kaspa source code to stress-test consensus stability and high-throughput block processing under simulated load. This phase includes deploying dedicated $KAS CPU mining software to ensure decentralized network participation during the validation window.

@sobczak_mariusz @ConnitoAI Is there a video to this AMA?

🧠 AMA Recap — Isabella Liu $TAO subnet @ConnitoAI SN102 The Nerds hosted Isabella Liu, subnet owner of @ConnitoAI and founding engineer at OTF since 2021. She was there before subnets existed. The first OTF network was a distributed training task. Five years later, she is back to finish what she started. This is not an agent subnet. Not inference. Not data scraping. This is distributed training, the hardest problem in the Bittensor stack. The core issue is simple. Traditional distributed training tops out around 40B to 80B parameters. SOTA models are now 600B to 1T. A single miner cannot afford to train at that scale. If Bittensor relies on traditional architectures, it will always be playing catch-up with centralized labs. Isabella’s answer is to stop forcing distributed training into architectures built for centralized compute. Reinvent the architecture for distributed from the ground up. That is where Mixture of Experts comes in. Each miner trains different experts locally with no communication during training. At step X, the pieces merge back into one model through weight-based merging in a DiLoCo-style setup. Real example from the AMA: take a 16B model with 64 experts per layer. A miner grabs 8 experts most relevant to a math task, finetunes locally, then passes weight updates back to the validator for loss evaluation. Cost per miner drops compared to data parallel training. Communication overhead drops compared to pipeline parallel training. To prevent drift between independently trained experts, there is a shared expert acting as a communication channel. Attention layers are frozen to serve a similar stabilizing role. This builds on Meta’s Branch-Train-Mix, AI2’s FlexoLMoE, and DeepSeek’s ESFT paper, which showed expert-specific training can outperform LoRA. The new part is combining those ideas with a decentralized incentive mechanism. Isabella made a point that stuck with me. The current miner-as-GPU model is limiting. If you want a subnet’s output model to match OpenAI, you would need the owner’s research team to be as big as OpenAI’s. That is not happening. But if miners have flexibility in what they train on, and you treat Bittensor’s talent pool as the research team, then you have a real shot. @ConnitoAI is designed around that thesis. The team is Isabella and George, her university alumni who did post-grad research in distributed training. They are backed by Crucible Labs for packaging and go-to-market. The roadmap is research-first. Next deliverable is a research paper analyzing design choices from the whitepaper. Purpose is to build credibility and attract collaboration with research firms. Then comes a working pilot. Then the goal is to convert collaborations into paying customers. They are open to B2B and B2C, anyone who needs a model trained on specialized data. Long term, the vision is training-as-a-service with the modular advantages MoE provides. Isabella was direct about marketing. Product before marketing. Engineers before marketing hires. The whitepaper dropped before this AMA. The honest gaps: Data privacy for defense and healthcare is still unsolved, and she said so. Man hours are a real constraint. Two-person research team with more to build than they can currently cover. No paying customer yet. Go-to-market is a plan, not a pipeline. This is early stage, research-heavy, and technically ambitious. But Isabella has been thinking about this problem longer than most people have known Bittensor exists. The architecture is differentiated from every other distributed training attempt in the ecosystem. I am willing to bet that she will succeed

Nerds will have an AMA with $TAO subnet 59 @babelbit on Monday May 18 at 11 am EDT. Please submit any questions I will ask for you. On a personal note I saw a preview of what @babelbit is working on and it’s fantastic

Blockchain Banter - Episode 103 @Chris_Hutch7 & myself are hosting yet another BANGER episode! We will be diving deep into the importance of grassroots events with builders from $KAS, $ETH & $SOL!! Set That Reminder 👇 x.com/i/spaces/1kKzD…

@sobczak_mariusz Double trouble

Yesterday, DSV unveiled that they bought $TAO Subnet 69. Finally, one of the most iconic and sacred numbers to nerds worldwide has been claimed. To honor this historic milestone, and since Bittensor is made up of 99.99% nerds, I think we should run a subnet naming contest. Choices are 1. Sydney 2. Nerds in Paradise 3. Sex 4. Sixty Nine This is just for fun, so let’s enjoy it 😂, best comment wins bragging rights.

@sjdedic Thanks for calling this out and keeping us informed

In case you’re wondering why CEXs aren’t stopping the massive and painfully obvious scam pump and dump of $LAB: OKX, Gate, KuCoin, Mirana (venture arm of Bybit), GSR as their market maker: they’re all part of it. They’re sitting on a 323x of their early investment right now. So they have every incentive to look the other way, or worse, to actively contribute. These are the institutions that should be leading this industry forward. Instead they’re squeezing it dry every single day and wondering why there’s nothing left to grow.

Big update for the Kaspa community 🚀 Toccata Hard Fork is coming soon This will be a major milestone for the Kaspa ecosystem. 📅 Mark the date: 6th June Big things ahead. #Kaspa #Toccata

Fast, secure, and simple: swap Kaspa (KAS) in Ledger Wallet! No need to move your assets to an exchange. Approve your swap directly on your Ledger signer and keep your private keys offline 🔐 Head over to Ledger Wallet now to swap KAS.

We’re excited to share the Connito whitepaper V1: a framework for decentralized, composable MoE adaptation. We trains sparse expert subsets, validates updates through Proof-of-Loss, and turns open-model improvement into a distributed expert-level market. Read the whitepaper: connito.ai/whitepaper

✅️✅️✅️

#Bittensor Conviction is coming soon Understand what that means 👉 youtube.com/watch?v=R0tJwW… The goal is to make sure rug pulls no longer happen within the ecosystem $TAO

Most $TAO holders are flying blind. They bought the token. They watched the price. They read the threads. But they have never opened the one tool that shows them everything happening inside the Bittensor network in real time. It is called Taostats. It is free. And after reading this, you will never look at $TAO the same way again. Here is exactly how to use it. Step 1: Start at the Subnets page. This is the heartbeat of the entire network. Every subnet running on Bittensor is listed here with: - its current emission rate - the number of active miners and validators - real-time performance data The emission rate is the most important number on this page. It tells you exactly how much TAO is flowing into each subnet every block. High emission means the network is directing significant resources toward that subnet's commodity. Low emission means the market has not yet recognised its value, or the subnet has not yet proven itself. Watch which subnets are gaining emission share over time. That movement tells you where the network believes the most valuable work is being done, before any headline announces it. Step 2: Use the Subnet pages to go deeper. Click any subnet, and you enter a complete dashboard for that individual market. - The TradingView chart shows you the alpha token price history for that subnet. Alpha tokens are the subnet-specific tokens that sit inside TAO's broader economy. Their price relative to TAO tells you how the market is valuing that subnet's specific commodity. - The Metagraph is the full list of every miner and validator currently active in the subnet: their UID, their stake, their trust score, their emission share. This is the raw intelligence layer. The miners consistently earning the most emissions are producing the work the validators collectively agree is the most valuable. - The Sentiment Index gives you a real-time community temperature reading on each subnet. Not price sentiment. Ecosystem sentiment. Whether the participants building inside the subnet believe it is healthy and improving. Step 3: Check Validators before you stake anything. This is the step most people skip and regret. The Validators page on Taostats shows you the performance history of every validator on the network: their VTrust score, their emission consistency, and their weight-setting behaviour across subnets. VTrust is the metric that matters most. It measures how closely a validator's judgments align with the honest stake-weighted majority across the network. High VTrust means the validator is doing genuine work and being rewarded for it. Low VTrust means the validator is either lazy, copying other validators' weights, or attempting to manipulate the system. When you delegate your TAO to a validator, you are trusting them with your emissions. Taostats shows you exactly which validators have earned that trust over time, and which ones have not. Never stake blind again. Step 4: Use the Blockchain explorer to track real movement. The Blockchain section of Taostats logs every transfer, every staking transaction, and every extrinsic called on the Bittensor chain in real time. This is where you track what wallets are actually doing: - Large staking transactions from unknown addresses - Subnet registration events that signal a new market is about to go live - Neuron registration burns that show demand for participation in a specific subnet is accelerating The people who read on-chain data before the narrative catches up to it are the ones who position correctly before the crowd notices the move. Step 5: Track your own portfolio inside the Dashboard. Connect your coldkey address, and Taostats builds you a complete portfolio view: - Your TAO balance - Your staking positions - Your delegation returns - Your yield over time The yield calculator is particularly useful. It shows you the actual return you are generating from your staking position in real TAO terms, not in percentage estimates that assume conditions that may not hold. If your yield is lower than the network average for your validator tier, Taostats shows you that too. Switching validators takes one transaction. The data to make that decision intelligently is right in front of you. The bigger picture. Most people holding $TAO are making decisions based on price charts and social media sentiment. Both of those inputs are downstream of what is actually happening inside the network. Subnet emission shifts. Validator VTrust changes. On-chain registration events. Neuron burn rates. Alpha token price movements relative to TAO. All of it is live on Taostats right now. All of it is free. All of it tells you something the price chart cannot. The investors who understand Bittensor at the data layer will always be positioned ahead of the investors who understand it at the narrative layer. Taostats is the data layer. Bookmark it. Open it daily. The network is telling you exactly what it is doing if you know where to look.

Blockchain Banter - Episode 102 Myself & @Chris_Hutch7 are hosting yet another BANGER of a show! Join us as we will be hearing from a few $KAS community members & builders! Set That Reminder 👇 x.com/i/spaces/1Xxyg…

$TAO's SN102 just dropped alpha code. Watch this one. Connito AI. Subnet 102. Distributed Mixture-of-Experts training for 100B+ parameter models. Here's what makes this different from every other training subnet. Most decentralized training requires miners to hold and train a full model. Connito splits it. Each miner trains only a small subset of expert modules their slice of a Mixture-of-Experts architecture. The results get aggregated back into a single, full model. Low communication overhead. Small compute requirement per miner. And the expert modules are composable meaning customers can customize the model by swapping or fine-tuning specific expert groups without retraining everything. This is how you train at 100B+ parameter scale without being Google. SN3 Teutonic just proved decentralized training can reach 72B parameters. Connito is building the infrastructure to go further with an architecture designed so that the cost and complexity scales across the network instead of sitting on any one machine. What's live right now: alpha code, active mining, validator coordination, background round evaluation shipped in v0.1.17, and Hugging Face as the checkpoint transport layer. What's coming: Whitepaper May 12. Dashboard May 26. Early days. Small team. Clean architecture. Real technical depth. The training layer of decentralized AI is being built right now subnet by subnet. Connito is one of the more technically credible attempts at solving the hardest part of it. $TAO Not financial advice — always DYOR. 🔗github.com/Connito-AI/Con… 🔗connito.ai

What benchmark would move you off Opus 4.7 or GPT-5.5? Kimi K2.6 TEE on Chutes ties GPT-5.5 on SWE-Bench Pro at 58.6%. Opus 4.7 leads at 64.3%. 1T total params, 32B active. 256K context. Native vision and video. Top-ranked open-weights model on Artificial Analysis Intelligence Index. $0.95 in / $4.00 out. Opus 4.7: $5/$25. GPT-5.5: $5/$30. 5M in + 5M out per day: Opus 4.7: $4,500/month GPT-5.5: $5,250/month K2.6 TEE: $742.50/month $45K to $54K back per year. With hardware-attested privacy neither closed model offers. If your agentic coding runs on Opus 4.7 or GPT-5.5, what's the eval keeping you there? 🔗 chutes.ai/app/chute/aac0…