🚀 TAO.BOT is Officially Live—Explore and Trade dTAO Subnet Tokens Now The wait is over. TAO.BOT is here, your gateway to the rapidly evolving decentralized intelligence economy of Bittensor. 🔵 Seamlessly Bridge to Bittensor $TAO Trade subnet tokens effortlessly, directly from Ethereum. No more complex setups or technical barriers—swap ETH and stablecoins into your favorite subnets in minutes. 📊 Discover the Best Subnets Explore detailed subnet pages, real-time data analytics, and unique insights into each subnet's performance. Make informed decisions to stay ahead in the emerging decentralized AI market. ⚙️ Advanced Trading Tools Smoothly enter and exit positions with TWAP orders, reducing slippage and optimizing your trades. 🌐 Decentralized AI is the Future—Claim Your Place Today The next era of AI-driven value creation has begun. $TAOBOT is your key to participating in the decentralized intelligence revolution. Trade, explore, and thrive now: 🔗 tao.bot/

🧠 Bittensor $TAO upgrade check-in: Conviction Bittensor’s latest chain upgrade introduces Conviction — an on-chain locked-stake system for subnet alpha. Coldkey holders can now lock alpha stake to a specific hotkey on a subnet. Over time, that locked stake builds a conviction score, creating a public signal of long-term commitment. The goal is simple: make subnet alignment more visible. 1⃣ Long-term commitment becomes measurable Subnet owners, large stakers, and community members can show commitment by locking alpha on-chain. 2⃣ Large exits become harder to hide If someone wants to move away from a committed position, the lock has to move through a public decay process rather than being silently unwound. 3⃣ Subnet governance gets a new primitive Conviction creates a measurable signal for which hotkeys have the strongest long-term backing within a subnet. Ownership transfers are not active yet, but this lays important groundwork for future subnet governance. 4⃣ Emissions are unchanged Locking stake does not increase emissions directly. This is a transparency and governance layer — not an APY boost. Conviction is an important step in making Bittensor’s subnet economy more legible. More commitment moves on-chain. More alignment becomes measurable. More of the network becomes easier to analyze. Trade & research subnets → tao.bot/explore

🧭 TAO.BOT Validator Explorer is now fully live The first release made it easier to view validators across the Bittensor $TAO network and compare high-level network performance. This update expands the page into a full validator analysis dashboard. Users can now open individual validator profiles and analyze: 🔵performance across Root and subnets 🔵staking distribution 🔵delegator growth 🔵validator fees and yield 🔵active subnet coverage 🔵network allocation across Bittensor As the network grows, the validator layer becomes more important, delegators need better tools to understand where stake is going, how validators are performing, and how each validator is positioned across the network. The Validator Explorer is designed to be the one-stop interface for researching Bittensor validators. Explore validators → tao.bot/validators

🔎 Bittensor $TAO subnet check-in: top 5 by market cap @chutes_ai (SN64) — still the market cap leader. Chutes is Bittensor’s serverless AI compute layer for deploying and running open-source models at scale. Recent updates have focused on making the system more sustainable: improving revenue efficiency, pruning underused models, expanding compute supply, moving further toward TEE infrastructure, and exploring more efficient training through Parallax. @TargonCompute (SN4) — confidential compute on decentralized hardware. Targon’s stack is built around TVM, Intel TDX, Intel Trust Authority, and NVIDIA Confidential Computing, with recent momentum from its Intel-linked whitepaper, Supply Portal launch, and real AI workloads being run through Targon compute. @webuildscore (SN44) — decentralized computer vision and real-world evaluation. Score has moved into the top 3 as Manako, built by the Score team, pushes enterprise vision AI into production use cases. The recent PwC France / Manako alliance is one of the clearer enterprise-facing examples of a Bittensor subnet being taken to market. @lium_io (SN51) — decentralized GPU rental marketplace. Lium connects GPU providers with users who need compute for training, inference, and other heavy workloads. Its position near the top shows how strongly the market continues to value raw GPU access as a core Bittensor primitive. @affine_io (SN120) — incentivized RL and model improvement. Affine rewards miners for measurable improvements across reasoning and coding-style environments, with the goal of turning model improvement into an open, competitive market. The top of Bittensor by market cap now spans inference, confidential compute, enterprise vision AI, GPU rentals, and reinforcement learning. Capital is increasingly concentrating around subnets that look more like infrastructure than narratives. Trade & research subnets → tao.bot/explore

🧭 TAO.BOT Validator Explorer is live You can now view validators across the Bittensor $TAO network directly on TAO.BOT. The new page makes it easier to compare validator performance across Root and individual subnets, with key stats like APY, total stake, root stake, alpha stake, staker count, active subnets, fees, and recent yield. You can also filter by subnet, search validators, and switch between table, bubbles, and heatmap views to better understand how stake and performance are distributed across the network. As Bittensor grows, validator visibility becomes increasingly important. The Validator Explorer is designed to make that layer easier to understand — for delegators, subnet teams, and anyone researching the network. Explore validators → tao.bot/validators

🧠 Bittensor $TAO — Monthly Recap The last month was a major infrastructure month for the network. 1️⃣ TAO went live on Solana via Wormhole Sunrise A canonical version of TAO is now live on Solana, giving the asset access to Solana DeFi through platforms like Jupiter and Meteora, with wallet support through Phantom and Solflare. This expands TAO’s surface area beyond its native chain and makes Bittensor more accessible to a much larger liquidity ecosystem. 2️⃣ TaonSquare launched TaonSquare is an early directory for products and services powered by Bittensor, covering subnet categories like inference, training, data, compute, storage, and more. As subnet products continue to grow, discovery becomes a major bottleneck. TaonSquare is a step toward making the network easier to navigate for users, developers, and agents. 3️⃣ Targon expanded compute supply @TargonCompute launched its Supply Portal, giving hardware providers a simpler way to monetize idle GPUs/CPUs through SN4. Operators can onboard permissionlessly or use Targon Managed, where blockchain operations are handled for them. For decentralized compute markets, supply onboarding is just as important as demand. 4️⃣ Core protocol development continued Bittensor’s docs now track a neuron registration rework, moving non-root neuron registration toward a continuous TAO-burn model with slippage controls and owner-tunable pricing parameters. Less flashy than product launches, but important protocol plumbing for a growing network. 5️⃣ Institutional rails kept forming Grayscale Bittensor Trust continued issuing shares through private placements to accredited investors, showing that traditional access points around TAO are continuing to develop. The main theme: Bittensor is becoming more usable. More liquidity rails, better discovery, stronger compute supply, and continued protocol work all point in the same direction — the network is moving from narrative into infrastructure. Trade & research subnets → tao.bot/explore

🔎 Bittensor $TAO subnet spotlight: @TargonCompute (SN4) Targon is one of Bittensor’s leading confidential compute subnets, built by @manifoldlabs. The subnet provides secure GPU/CPU rentals for AI training and deployment, with the goal of letting developers run workloads on decentralized hardware without exposing data, model weights, or execution state to the underlying machine operator. The core idea is simple: decentralized compute is much more valuable when it can also be private and verifiable. Targon’s stack is built around the Targon Virtual Machine (TVM), using Intel TDX, Intel Trust Authority, NVIDIA Confidential Computing, AMD SEV, encrypted CVMs, and remote attestation to support confidential AI workloads on third-party hardware. Recently, Targon has had several major updates: 1️⃣ Intel x TVM whitepaper Targon / Manifold Labs published a confidential compute paper with Intel engineers, outlining how sensitive AI workloads can run on untrusted decentralized hardware using Intel TDX, Intel Trust Authority, and NVIDIA Confidential Computing. 2️⃣ Targon Supply Portal Targon launched a new onboarding portal for compute suppliers, giving hardware operators a simpler way to monetize idle GPUs/CPUs through SN4. Suppliers can onboard permissionlessly or use Targon Managed for weekly payouts and handled blockchain operations. 3️⃣ Venice Uncensored 1.2 @AskVenice and @dphnAI trained Venice Uncensored 1.2 using Targon compute — a Mistral 24B-based model with vision support, a 4x larger context window, and stronger tool-use capabilities. 4️⃣ Ecosystem usage Targon has also been powering infrastructure for teams across Bittensor and adjacent AI projects, including Bitstarter ML teams, MinosVM, and BrainPlay’s real-world AI evaluation layer. Targon is one of the clearest examples of Bittensor moving from experimental subnet design toward production AI infrastructure. Not just cheaper compute — private, attestable compute that real AI teams can use. Trade & research subnets → tao.bot/explore

📈 Bittensor $TAO Flow Leaders — Weekly Check-In Top weekly net inflows (1W): templar (SN3) — τ3.20K Appears at the top on 1W flow, but this is largely residual activity — the subnet has been deprecated following the Covenant exit, and longer-term flow remains deeply negative. Short-term inflows here don’t reflect ongoing network demand. @lium_io (SN51) — τ3.10K Continues to see strong inflows as a core infrastructure layer across the network. Consistent demand sustained 1M inflows suggest real underlying usage rather than short-term rotation. @actualinc (SN95) — τ1.44K Actual Computer — a research-focused subnet exploring new compute and model paradigms. One of the more notable recent movers, with strong inflows across 1W and 1M pointing to growing early-stage interest. MVT (@taos_im - SN79) — τ1.23K Seeing steady inflows alongside strong short-term price performance. Continues to attract capital as participation increases across the subnet. HODL (@subnet118 - SN118) — τ941 Smaller subnet but with consistent inflows and strong relative price movement over the past week. Likely early accumulation rather than large-scale capital rotation. Under TaoFlow, emissions are driven by EMA-smoothed net $TAO flow. Sustained inflows increase emissions, while sustained outflows push emissions toward zero. Flow is one of the clearest signals for where capital — and attention — is moving across Bittensor. Trade & research subnets → tao.bot/explore

🔎 Bittensor $TAO subnet check-in: top 5 by emissions @TargonCompute (SN4) — Confidential AI compute: focused on running sensitive AI workloads on decentralized hardware. Targon continues to build around Intel TDX / Trust Authority and NVIDIA Confidential Computing, with recent momentum driven by its Intel-linked research and growing demand for private inference. @lium_io (SN51) — Decentralized bandwidth / infrastructure layer: positioned around enabling network-level throughput and connectivity across the Bittensor stack. It has been steadily maintaining high emissions with consistent usage, suggesting strong underlying demand for its role in the network. distil (@arbos_born - SN97) — Model distillation optimization: focused on compressing and improving model efficiency across Bittensor. As more subnets push toward production use, distillation becomes increasingly important for lowering costs and improving deployability. @webuildscore (SN44) — Evaluation / scoring infrastructure: provides the layer that measures model outputs and performance. As emissions become more tightly tied to measurable results, scoring subnets like SN44 play a critical role in determining where value flows. ORO (@oroagents - SN15) — Decentralized data / pretraining: focused on large-scale data pipelines and model training across distributed compute. Continues to see strong emissions as data and training remain core primitives of the network. Across compute (Targon), bandwidth (lium), optimization (distil), evaluation (Score), and data/training (ORO), the top of Bittensor emissions is spread across the full AI stack. Trade & research subnets

🔎 Bittensor $TAO — Top 3 subnets by market cap @chutes_ai (SN64) — continues to hold the top spot as one of the clearest examples of real product-market fit on Bittensor. Chutes is a decentralized, serverless AI compute platform for deploying and running open-source models at scale, and its sustained usage liquidity has kept it leading the network even through recent volatility. @TargonCompute (SN4) — remains one of the strongest infrastructure plays in the ecosystem, focused on confidential AI workloads on decentralized hardware. Its recent work around Intel TDX / Trust Authority and broader visibility from that stack continues to position SN4 as a core piece of production-ready AI infrastructure. @affine_io (SN120) — has moved into the top tier backed by steady inflows and emissions, with a focus on RL / evaluation-driven model improvement across reasoning and coding environments. Its positioning around measurable model performance has made it one of the more consistent recent climbers. Across Chutes’ scale, Targon’s confidential compute stack, and Affine’s evaluation layer, the top of Bittensor is increasingly made up of distinct infrastructure primitives rather than overlapping narratives. Trade & research subnets → tao.bot/explore

There’s been a lot of discussion around the Covenant situation over the past ~24 hours. From what we can tell, this was largely a builder exit event combined with a sizable sale (~37k TAO) that hit the market at the same time, which triggered the sharp move and the wave of panic selling that followed. Events like this tend to create short-term volatility, but they’re also part of the natural stress testing that happens in open networks. One team leaving doesn’t change the fact that Bittensor now has 100 subnets, thousands of participants, and a rapidly expanding decentralized AI ecosystem. The builders that continue shipping through moments like this usually end up defining the next phase of the network. We're still very bullish on the long-term trajectory of Bittensor $TAO.

⚙️ Subnet Spotlight — SN3 (templar) @tplr_ai is one of Bittensor’s decentralized training subnets — built to coordinate large-scale model training over the internet through an incentive mechanism that rewards honest, high-quality contributions from distributed compute. The team is best known for Covenant-72B, which templar describes as a 72B parameter model trained over the internet, and its docs frame the subnet as a system for incentivized distributed training of large language models. What it does: - Decentralized large-model training across heterogeneous internet-connected hardware. - Uses miners, validators, and aggregators to coordinate training and score contributions on-chain. - Today, SN3 is also running Crusades, an MFU optimization competition on the same subnet, after the Covenant-72B run completed Why it matters: templar currently sits at the top of the network by emissions and is one of the two largest subnets by market cap. More broadly, it anchors a larger stack from the same team: Templar (SN3) for decentralized pre-training, Basilica (SN39) for decentralized compute, and Grail (SN81) for decentralized RL post-training. That vertical integration is part of what makes SN3 one of the most important infrastructure subnets on Bittensor $TAO right now. Trade & research subnets → tao.bot/explore

📈 Bittensor $TAO Flow Leaders — Weekly Check-In @gradients_ai (SN56) — $815.3K / τ2.63K Gradients keeps attracting flow as one of the easiest ways to train image and text models on Bittensor. The core pitch is simple: pick a base model, dataset, and training time in a few clicks, then compete in recurring training tournaments. @TrajectoryRL (SN11) — $796.7K / τ2.57K TrajectoryRL is a decentralized prompt and policy optimization subnet for AI agents. It runs an open competition around improving OpenClaw agent instructions, with the goal of making agents cheaper, faster, and more reliable. @404gen_ (SN17) — $678.9K / τ2.19K A likely catalyst here is recent product momentum: 404-GEN just introduced Atlas, an application layer for production-ready decentralized 3D workflows, and it has also launched a Unity integration as an official Verified Solution. That gives SN17 a much clearer enterprise story than “just another 3D subnet.” Swap (SN10) — $573.5K / τ1.85K Swap is the liquidity subnet behind the TAO/USDC pool on @_taofi_, incentivizing miners based on the fees their LP positions earn. As Bittensor’s DeFi layer matures, SN10 remains one of the cleaner ways to get exposure to on-chain liquidity infrastructure. @Bitcast_network (SN93) — $393.7K / τ1.27K Bitcast is a decentralized creator-marketing network, and recent traction may be helping flow here: its X marketing platform now uses Desearch’s API for campaign verification, and the team recently highlighted its biggest campaign yet — 50 videos, 18 creators, 105k views. Trade & research subnets → tao.bot/explore

🧠 Bittensor $TAO — March Recap March was one of the most important months Bittensor has had so far. 1⃣ SN3 / @tplr_ai trained Covenant-72B A 72B model, trained permissionlessly over the internet on Bittensor — one of the clearest proofs yet that decentralized AI coordination works at meaningful scale. 2⃣ Jensen Huang put Bittensor on the map Calling it a “modern version of Folding@home” was a huge external validation moment for the network and for decentralized AI more broadly. 3⃣ @TargonCompute published a confidential-compute paper with Intel This is a big deal. It shows decentralized AI workloads can run on untrusted hardware using Intel TDX, Intel Trust Authority, and NVIDIA Confidential Computing — exactly the kind of infrastructure the network needs to support serious production use cases. 4⃣ The network itself kept moving Core chain releases shipped, OTF hackathon winners were announced, and the ecosystem kept getting deeper. March made one thing clearer than ever: Bittensor is becoming real infrastructure. Trade & research subnets → tao.bot/explore

This is a huge step for Bittensor $TAO. One of the hardest problems in decentralized compute is making it possible to run secure workloads on untrusted machines. @TargonCompute building TVM around that problem is exactly the kind of infrastructure the network needs as it grows from interesting experiments into real-world AI systems. Excited for what comes next.

🔎 Bittensor $TAO subnet check-in: the top 3 @chutes_ai (SN64) — still the clearest example of product-market fit on Bittensor. Chutes is a decentralized, serverless AI compute platform for deploying and running open-source models at scale, and its own site describes it as powering trillions of tokens per month. Even with market rotations underneath it, SN64 remains one of the network’s core infrastructure leaders. @tplr_ai (SN3) — has rapidly moved into the top tier after one of the biggest technical achievements the network has seen so far: Covenant-72B, the largest collaborative globally distributed pre-training run to allow open, permissionless participation over the internet, trained on roughly 1.1T tokens and competitive with centralized 70B-class baselines. That milestone helps explain the huge monthly flow and recent repricing. @TargonCompute (SN4) — has pushed itself firmly into the top 3 on the back of the confidential-compute narrative becoming real. Targon’s stack is built around Intel TDX / Intel Trust Authority / NVIDIA Confidential Computing, with the subnet focused on confidential AI workloads on decentralized hardware. The recent Intel-linked paper and broader visibility around that work have made SN4 one of the strongest recent movers in the network. Between Chutes’ scale, templar’s 72B breakthrough, and Targon’s confidential-compute push, the top of Bittensor is starting to look a lot more like real infrastructure than just rotating narratives. Trade & research subnets → tao.bot/explore

📈 Bittensor $TAO Flow Leaders — Weekly Check-In Top weekly flow gainers (strongest inflows): @TargonCompute (SN4) — $6.43M weekly flow Targon leads this week after a major confidential-compute milestone: they published a paper with @intel engineers describing how sensitive AI workloads can run on untrusted, decentralized hardware using Intel TDX, Intel Trust Authority, and NVIDIA Confidential Computing. Intel’s official account also amplified the work — a strong signal for both Targon and Bittensor more broadly. @tplr_ai (SN3) — $3.92M weekly flow templar continues to benefit from the momentum around its recent 72B permissionless pretraining milestone — one of the strongest technical achievements Bittensor has seen so far. @gradients_ai (SN56) — $1.54M weekly flow A subnet focused on decentralized model training, making it easier to participate in competitive AI training through Bittensor. @Affine (SN120) — $1.34M weekly flow An incentivized RL / evaluation subnet built around measurable model improvement across reasoning and coding-style environments. @IOTA_SN9 (SN9) — $1.27M weekly flow A decentralized pretraining subnet focused on large-scale model training across distributed compute. Under TaoFlow, subnet emissions are driven by EMA-smoothed net $TAO flow. Sustained inflows help a subnet capture emissions, while sustained outflows push emissions toward zero until demand returns. Trade & research subnets → tao.bot/explore

This is why we’re bullish on Bittensor $TAO. @TargonCompute is building a decentralized compute network where workloads stay protected at rest, in transit, and in use — even on hardware operated by anonymous providers. If decentralized AI is going to compete with centralized clouds, this is exactly the kind of infrastructure it needs.

⚙️ Subnet Spotlight — SN64 (@chutes_ai) Chutes is a decentralized, serverless AI compute platform built on Bittensor $TAO. It lets developers deploy, scale, and run open-source models in production without managing infrastructure, using a single API for inference, batch jobs, and custom deployments. What it does: - Serverless inference for open-source AI models, with hot models ready for scale and support for long-running jobs. - Trusted Execution Environments for private, verifiable AI compute. - Consumer and developer products like Chutes Search, Chutes Chat, and custom model deployment tooling. Why it matters: Chutes has become one of the clearest examples of real product-market fit on Bittensor: open-source AI infrastructure, production usage at scale, and a team that ships relentlessly, led by @jon_durbin. Trade & research subnets → tao.bot/explore

This is a massive milestone for Bittensor. A 72B model, trained permissionlessly over the internet on @tplr_ai (subnet 3), with performance competitive with centralized baselines like Meta’s LLaMA-2-70B and LLM360 K2. Achievements like this are why we’re so bullish on the network — real proof that decentralized AI coordination works.

📈 Bittensor $TAO Flow Leaders — Weekly Check-In Under TaoFlow, subnet emissions are driven by EMA-smoothed net $TAO flow. Sustained inflows help a subnet capture emissions, while sustained outflows push emissions toward zero until demand returns. Top weekly flow gainers (strongest inflows): Targon (SN4) — τ4.64K weekly flow Confidential AI cloud inference infrastructure focused on secure, high-performance model serving. iota (SN9) — τ1.58K weekly flow A distributed pretraining subnet focused on large-scale model training across decentralized compute. Bitcast (SN93) — τ1.45K weekly flow A decentralized creator-marketing subnet connecting brands with creators and rewarding content performance on-chain. NOVA (SN68) — τ1.33K weekly flow A decentralized drug-discovery subnet from MetaNova Labs, built to crowdsource early-stage therapeutic screening. Data Universe (SN13) — τ1.20K weekly flow Macrocosmos’ large-scale social data subnet, indexing and serving structured data from sources like X, Reddit, and YouTube. Trade & research subnets → tao.bot/explore