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Have you guys noticed how quickly everything is getting solved in the world since AI... Something we’ve never seen before 🤯 bittensor:native

Zcash co-founder Sean Bowe wants to end the automatic 8% subsidy going to ZCG But first, what is ZCG? The Zcash Community Grants (ZCG) program funds developers, infrastructure and projects that help grow the Zcash ecosystem Bowe's argument is that ZCG already has a substantial treasury It holds around 91,000 ZEC plus cash. At $1,300 per ZEC, that's roughly $125M in assets Its actual spending last quarter was around $1.5M, or roughly $6M annualized Meanwhile, the 8% allocation brings in around 52,000 ZEC every year, worth roughly $60–70M at the prices cited That's a huge gap between what ZCG spends and what it receives Bowe wants that 8% redirected to the Network Sustainability Mechanism (NSM), where funds remain locked and can later support mining security. ZCG could continue operating from its existing treasury Josh Swihart has suggested going further: redirect both funding streams entirely to NSM instead of splitting them One important detail: this is still a draft proposal, not part of NU7 The bigger debate is how Zcash should balance funding ecosystem development with long-term network security zcash:native

A London Stock Exchange-listed company just staked 5,000 TAO on Bittensor And its message is pretty clear: if you're serious about AI and blockchain, Bittensor deserves a place in your portfolio KR1 plc (ticker: KR1) has acquired 5,000 TAO and started root staking This isn't just another crypto trade. It reflects a bigger investment thesis: as AI turns increasingly complex work into software, demand for the supply of intelligence could grow significantly And Bittensor is building exactly that supply layer Miners compete to provide AI services, validators evaluate performance, and TAO rewards the strongest contributors. An open market for intelligence. Why root staking? - Principal remains in TAO - Rewards come from a validator-managed basket of subnets - No need to take individual subnet exposure - No unbonding period, so unstaking gives immediate access - Estimated annualised infrastructure income of around 6.6% at current rates KR1's strategy is interesting because it's not simply holding TAO. It's putting the asset to work through staking and generating rewards. Their thesis is that Bittensor brings open competition to the supply of AI services, turning intelligence into an open market. One important caveat: rewards currently come largely from emissions. The real test will be whether customer revenue eventually grows beyond those emissions. Still, seeing institutional capital take an active position in decentralized AI infrastructure is a meaningful signal. AI agents, compute and inference are becoming more than just a technology narrative. They're developing into on-chain markets. KR1 has made its move. What's your take? #Bittensor bittensor:native

Binance is investigating what could become one of the largest security incidents in crypto history On-chain analysts have tracked suspicious outflows worth up to $6.3B across wallets linked to the incident. This is an estimated exposure, not a confirmed loss. The recovery effort is already significant: - Tether froze more than $1.567B in USDT - Circle froze over $800M in USDC - Ripple froze around $200M - World Liberty Financial also helped freeze additional assets Binance reported $3B in verified frozen funds, with more potentially recoverable The exchange says user funds are safe and all hacking losses will be covered. Deposits and withdrawals were temporarily suspended across multiple networks, while Binance Alpha trading was also paused as a precaution. Another important detail: CoinDesk's review found that some affected wallets appeared in Binance's October proof-of-reserves data. The exchange will need to demonstrate that its reserves remain fully backed after the incident. The attacker and exact entry point remain unknown. Binance is working with law enforcement, issuers and blockchain security firms. This is still developing. The next big questions are how much actually escaped, how the compromise happened, and how quickly Binance restores normal operations.

Big thanks to the @Bitcast_network team 🙏 Even with significant changes happening behind the scenes, they never shut the platform down because they understand how much creators depend on it No campaigns were removed and there was no downtime. We did not experience any disruption on our side. We truly appreciate the team's effort #Bittensor bittensor:native

30% off the same model is a 30% bigger runway 🔥 Most startups die from burn, not bad ideas bittensor:native

Open AI infrastructure depends on more than models and compute. Data needs to move reliably between clouds, storage systems, agents, and machines. Yet data movement is still handled through brittle pipelines, centralized services, and custom integrations that break as systems change. Beam (@b1m_ai) approaches this differently by treating data movement as an open coordination layer. Independent operators compete to execute transfers, performance is measured, and applications can respond to verified delivery. Data stays at its existing endpoints. The network coordinates how it moves. This creates a foundation for multi-cloud workflows, agent-to-agent handoffs, and event-driven applications without making data movement another infrastructure bottleneck. #Bittensor bittensor:native

Everywhere you look, there’s demand for bittensor:native

Gold remains gold 😎 Pure metal is what the market ultimately values, regardless of how it’s produced. Unlike diamonds, where origin and branding created a lasting premium between natural and lab-grown, refined gold is judged primarily on purity. Whether it comes from mining or a controlled process, the metal itself is what matters. The real questions ahead are cost, reliable scale, and any residual technical constraints. Everything else is secondary to that.

Who becomes the Michael Saylor of Bittensor? bittensor:native

A great story with weak execution flops. Remake the same story years later with proper execution and it becomes a massive hit. Startups work the same way: the idea matters, but execution decides everything.

Interesting setup 🔥 THOR Operator lets you fund with $THOR → auto mints USDC → collateralizes against $KAS yield. You earn real yield (claimable anytime) + extra $THOR if you lock. Basically a THOR → USDC → $KAS yield loop with optional locking rewards. Curious how the collateralization ratio and liquidation mechanics work under the hood.

What project is this? 👀

Big move in decentralized communication 🔥 Komms has officially acquired Bittensor Subnet 27 Komms is building a private, censorship-resistant workspace and community app, an alternative to Slack and Discord that puts control back in the hands of teams and communities. How it works: Messages are end-to-end encrypted on your device. Independent operators (miners) handle delivery, storage, and indexing, paid through SN27. Kaspa records identities and message order as an immutable truth layer. No single company controls the network or can read your chats. Why this matters: Acquiring SN27 gives Komms its own decentralized infrastructure. It reduces reliance on centralized servers, improves resilience, and aligns incentives with the network that actually powers the product. Key benefits for users: - True privacy (even Komms can’t read your messages) - Flat pricing per workspace/server - unlimited members, no per-seat fees - Censorship resistance and data ownership - Passkey login, verifiable history, and exportable data The app is currently on testnet, with public beta expected soon solana:UKbXwN3ySC2jP5p9TyQ91yXYXmnnDsjiZQ55QCqpump bittensor:native

Most robotics demos keep the scene fixed so the result looks clean. This playground does the opposite. You set the environment first materials, lighting, table height, object positions then watch a single demonstration in Room A and the current leading policy attempt the same task in your rearranged Room B. Multiple camera angles, live execution, visible successes and failures. I spent some time testing it. The useful part is how quickly the generalization problem becomes concrete: rearrange a few objects and you immediately see what the policy can transfer and what still breaks. It makes the “one demonstration, weights frozen, new scene” constraint easier to evaluate than reading about it. Worth trying if you want a clearer sense of where current in-context robot learning stands bittensor:native