Bittensor (TAO)

The Most Bullish Crypto Projects Right Now (Ranked by Sentiment) $TAO : 91.9% $KAS : 90.7% $SHIB : 86.4% $SUI : 84.4% $XRP : 82.5% $ICP : 82.3% $ADA : 79.9% $SOL : 79.5% $PI : 79.2% $XMR : 78.8% Data : As per CoinMarketCap https://t.co/753Oui2zWv

I have been following @Pritensor for a few weeks now. He's like the @JacobKinge of $tao. He also reminds me of Crypto capo around oct' 2022 when he was constantly right when btc was nuking from 69k to 15k Capo was calling for 14k , 12k 10k I believe even 5k $BTC. He made a brand by calling lower, and lower and lower with the celius, luna, ftx debacles. Pritensor is now doing the same thing with tao and is CONSTANTLY right. Making it appear as if he knows something EXTRA we don't, or he's CONSTANTLY right, or he has the latest edges, news, bloomberg terminals or wall street connections. In reality I use his personality and posts as a metric to buy. When he sounds REALLY right and genious and I told you vibes ALL THE TIME about tao's price (not once but always in a short span of time, and this is key) , this simply means that tao is most likely oversold, everybody is panicking and are irrationnal so they fumble their tokens at fire deals because they don't know what they hold. Accounts like these have value

Friendly reminder that it is never too late to read something that doesn't try to please everyone. There are a lot of people posting their thesis, few do it without being on payroll for the guys pulling the strings on $TAO.

The $TAO Ecosystem Explained ft. Bittensor Halving > Bittensor isn’t one AI network. > It’s an AI economy. > Made of specialized subnets competing for TAO. Each subnet does one job. Do it well → earn TAO. Do it poorly → get starved. Here’s how the ecosystem actually breaks down. 1️⃣ Core Compute & Training Subnets The backbone of decentralized AI. These subnets provide raw compute, training, and inference. • Chutes (SN64) – serverless AI compute and data routing • Celium (SN51) – decentralized GPU compute • τemplar (SN3) – distributed training infrastructure • Gradients (SN56) – on-demand gradient computation • Distributed Training (SN38) – single-LLM training at scale • NI Compute (SN27) – GPU commoditization • Compute Horde (SN12) – aggregated compute power These are the AWS equivalents of Bittensor. 2️⃣ Foundation Models & LLM Subnets Training the brains. • iota (SN9) – LLM pretraining • Apex (SN1) – conversational AI • Zeus (SN18) – text + image responses via API • Nineteen AI (SN19) – image ge

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