🤡🤡🤡.eth (τ, τ) (π) (☯️)

One piece of news from the Thorchain pod this morning: Thorchain is working on a Bittensor integration. This means that native Bitcoin (and other chains / assets) will be able to swap into native $TAO via DEX -- no centralized exchange required (!) $BTC in Bitcoin wallet --> $TAO in Bittensor wallet. One step. Big volumes, no problem.

🚨 MASSIVE: @THORChain is integrating $TAO This means you’ll soon swap native BTC → native TAO directly onchain with no CEX, no wrapped tokens, no middleman. 🔁 One step: $BTC from your Bitcoin wallet → $TAO in your Bittensor wallet. This unlocks: •Deep cross-chain liquidity between Bitcoin and AI compute markets •Direct on-chain access to deAI via TAO •Massive volume potential as BTC holders enter the Bittensor ecosystem Decentralized money + decentralized intelligence = the future of crypto. Thorchain x Bittensor is a liquidity bridge between Bitcoin and AI. The AI economy just became trustless.

@Rujira_Intern @FranLegon @RujiraNetwork So after 5 April I should just withdraw my RUJI as there is no reason to leave it in that contract anymore

@FranLegon @EnsClown @RujiraNetwork There won't be a 'final distribution' of remaining RUJI. Over the course of 12 months there was a decay rate and the conversion rate went from 1x last year, to 0x now, along with 'token bonus' distribution to mergers. This is a gradual process that ends 5 April.

Only 1 week left to merge KUJI and other eligible assets into RUJI. After April 5, 2026, the conversion rate will reach 0% and it will no longer be possible to convert your merge tokens into $RUJI. If you still hold eligible tokens, this is the final week to merge them 👇

Ohhhh this is what we love to see. 1 million followers being told about $TAO

Targon is unlocking millions of GPUs for TAO. @TargonCompute announced TargonOS today. Here's what that actually means. Decentralized compute networks have had one problem since day one. The host operator can see everything. Your data. Your model weights. Your code. That kills enterprise deals. And without enterprise, there's no sustainable flywheel. Targon already fixed this for datacenter hardware with TVM. • Intel TDX integration, full memory encryption at the hardware level • Intel co-authored the whitepaper validating the architecture • Runs on H100s, H200s, B200s paired with Xeon server CPUs But that left out millions of GPUs that can't run TDX. RTX 4090s. 3090s. AMD cards. Racks of consumer hardware sitting idle. TargonOS changes that. • Flash a USB, boot, enter your hotkey. Done. • Any machine with a TPM 2.0 chip can join (basically every machine made in the last decade) • Customer VMs fully encrypted with LUKS, each with their own unique key • Operator never sees the keys. Ever. • OS locks itself down. No shell access, no package manager, auto-updates via A/B partitions Two tiers. One API. Enterprise node (H100 + TDX) for sensitive workloads. Community node (4090 + TPM) for training runs and batch processing. Same billing. Same lifecycle management. You just pick the hardware class. The TAO compute subnet is going to get a lot bigger TargonOS is still in development. Beta is weeks away. Worth watching.

@FranLegon @RujiraNetwork But once the merge is done, at some point any remaining RUJI will get allocated pro rata to the stakers left at the end, right? And then we can/should finally collect our tokens

@EnsClown @RujiraNetwork Nope. You can leave it there indefinitely if you want to.

@cyrilXBT What timeframe is that measuring?

🚨 HUGE inflow for ETH something must be cooking

The Guy Who Made Millions From Uber Says This $300 Coin Could Hit $32,000 Jason Calacanis Predicts 200x for $TAO: Here's What You Need to Know Early Uber investor Jason Calacanis just made a bold call on #TAO during his "This Week In Startups" podcast. He believes TAO could deliver a 200x return over the next 5-10 years, targeting a $500 billion market cap. He called it the "better Bitcoin" and compared its potential to Ethereum and Solana. But here's what most people are Missing: ➤ Calacanis is NOT a neutral observer. He has invested ~$500K in TAO personally and is a consulting partner at Stillcore Capital, a fund built specifically around Bittensor. ➤ This is a classic "talking his own book" situation. What makes TAO interesting regardless: ➤ Fixed supply of 21M tokens (same as Bitcoin) ➤ First halving completed in Dec 2024 ➤ Nvidia CEO Jensen Huang recently endorsed the decentralized AI model ➤ Currently trading around ~$300 with ~$3B market cap ➤ Ranked #32 on CoinGecko What CryptoPatel community already did: ➤ We shared #TAOUSDT spot entry setup with chart around $150-$160 ➤ Already delivered 160%+ profit from our entry ➤ Our community was positioned well before mainstream attention A 200x from here means $500B market cap. For context, that's roughly where Ethereum sits today. Possible? Yes. Guaranteed? Absolutely not. Always check who benefits from a prediction before you act on it. TA Only. Not Financial Advice. ALWAYS DYOR.

@venture_charts Awesome. Thanks for sharing.

$TAO update These videos are designed to be for educational purposes only and should never be considered investment or trading advice. Always make your own analysis before entering a position. $TAO continues to trade in a wide range and in this video I cover some very basic supply and demand concepts in order to help ppl understand how and why price moves and what can happen to the asset. As always, thanks to those few who support the feed.

🚨 The most impressive $TAO subnet founder story Babelbit SN59 @babelbit isn't a crypto project that discovered AI. It's 30 years of speech technology research that discovered Bittensor. Bittensor is bigger than crypto or what it should be, it acts like a magnet. It brings together people who would have never naturally met, researchers, founders, developers, domain experts, and aligns them around a common mission. They support each other’s progress because every breakthrough strengthens the whole. That is a very powerful thing. Matthew Karas @matthew_karas built one of the UK's first multilingual search engines at BBC News Online in 1997, covering 47 languages. Two years before Baidu existed. He worked alongside Mike Lynch, founder of Autonomy an £11 billion company built on the thesis that statistical analysis could extract meaning from text better than grammatical parsing. For three decades, Matthew worked on one problem: making recorded and live speech as useful as text. He built systems that cut documentary editing time by 75%. He deployed speech indexing across corporate markets. He kept pushing the frontier. In real-time speech, speed is everything. Then in August 2024, his colleague Josh Greifer called with news that changed everything: 50 milliseconds of latency for speech transformation. Potentially 25ms. That was the kind of number that makes an experienced person stop and realize: this could finally be good enough to change the whole category. Mike Lynch was supposed to hear about it over a pint the following week. He died in a yacht accident four days later. This breakthrough was not just technical, it was also deeply personal. That breakthrough became @babelbit. Here's why this is different from everything else in AI translation: Every translation system you've ever used works word by word. It waits for you to finish speaking, converts each word, and outputs the result. Every error, every mishearing, every confusion gets repeated. That’s translation. Babelbit is building interpretation. When someone says, “I pledge allegiance to the...” a human interpreter already knows where it’s going. They don’t wait for “flag.” They translate the thought, not just the words. Babelbit’s LLMs aim to do the same thing. Not next-word prediction. Utterance completion. The system commits to a translation as soon as it can adequately predict the rest of the sentence. Sub-3-second latency. Interpretation-grade quality. Self-corrections, which happen constantly in real conversation, get handled the way a human interpreter would: process the context, catch the correction, output only the final clean version. The architecture is serious: a two-stream design with one low-latency stream for live conversation and one high-accuracy stream for a trusted translation of record. Custom metrics like EATP, Lead, and ACS do not just measure accuracy. They measure how early accurate predictions can be made. Matthew said it himself: building this as a centralized company in 2024 meant going head-to-head with Google, Meta, and OpenAI. Bittensor offered a different path: Babelbit was built using @AffineSN120 decentralized training at scale, incentivized iteration, and an ecosystem of complementary subnets like @chutes_ai, @MacrocosmosAI, and @hippius_subnet. The real-time translation market is projected to exceed $29B by 2030 French-English real-time interpretation is launching next week. V2 infrastructure is deployed. This is what lt real use case looks like. Decades of domain expertise. Human interpreters immediately recognize. Mathematical. There is nothing like this in crypto. There is barely anything like it in centralized AI. Babelbit did not come to Bittensor because it was trendy. It came because the architecture fit the problem. That’s what many miss. When world-class builders choose Bittensor not for the token, but for the infrastructure, it starts proving itself. $TAO DYOR

He’s using SparseLoco invented by Subnet 3 @tplr_ai