With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time
With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightly out of date from time to time
IOTA (previously known as Pre-Training) is a specialized subnet of the Bittensor network designed to incentivize the open training of large language models (“foundation models”) on a massive web dataset. In August 2024, Bittensor’s Subnet 9 (SN9) demonstrated that a decentralized network of incentivized, permissionless actors could pretrain large language models (LLMs) ranging from 700 million to 14 billion parameters, surpassing established baselines. This work proved that blockchain-based decentralized pretraining was a viable approach, but it also revealed key issues: each miner had to fit an entire model locally, and the “winner-takes-all” reward structure led to model hoarding.
Now, they introduce IOTA (Incentivised Orchestrated Training Architecture), an architecture that solves these issues by transforming SN9’s previously isolated competitors into a single, cooperating unit. This approach allows them to scale arbitrarily while ensuring fair rewards for each contributor. IOTA is a data- and pipeline-parallel training algorithm designed to work across a network of heterogeneous, unreliable devices in adversarial and trustless environments. The outcome is a permissionless system capable of pretraining frontier-scale models without overloading individual devices with GPU demands. It also accommodates unreliable devices and aligns participants through transparent token economics.
Various solutions have tried to address key technical challenges in distributed training, but many either lack a proper incentive model or have failed to match the performance of a coordinated cluster. IOTA bridges this gap by combining novel techniques that tackle all three of these limitations together.
IOTA (previously known as Pre-Training) is a specialized subnet of the Bittensor network designed to incentivize the open training of large language models (“foundation models”) on a massive web dataset. In August 2024, Bittensor’s Subnet 9 (SN9) demonstrated that a decentralized network of incentivized, permissionless actors could pretrain large language models (LLMs) ranging from 700 million to 14 billion parameters, surpassing established baselines. This work proved that blockchain-based decentralized pretraining was a viable approach, but it also revealed key issues: each miner had to fit an entire model locally, and the “winner-takes-all” reward structure led to model hoarding.
Now, they introduce IOTA (Incentivised Orchestrated Training Architecture), an architecture that solves these issues by transforming SN9’s previously isolated competitors into a single, cooperating unit. This approach allows them to scale arbitrarily while ensuring fair rewards for each contributor. IOTA is a data- and pipeline-parallel training algorithm designed to work across a network of heterogeneous, unreliable devices in adversarial and trustless environments. The outcome is a permissionless system capable of pretraining frontier-scale models without overloading individual devices with GPU demands. It also accommodates unreliable devices and aligns participants through transparent token economics.
Various solutions have tried to address key technical challenges in distributed training, but many either lack a proper incentive model or have failed to match the performance of a coordinated cluster. IOTA bridges this gap by combining novel techniques that tackle all three of these limitations together.
IOTA is structured around three core roles: the Orchestrator, Miners, and Validators. Rather than using a fully peer-to-peer topology, IOTA adopts a hub-and-spoke architecture centered around the Orchestrator. This design choice ensures global visibility and facilitates comprehensive monitoring of all participant interactions, which is essential for enforcing incentives, auditing behavior, and maintaining system integrity.
The Orchestrator’s primary responsibility is to monitor the training progress of each miner across all discrete layers and initiate weight-merging events accordingly. Given the heterogeneous nature of miner hardware and their unreliability, it is impractical to wait for all miners to complete an equal number of batches. Instead, a minimum batch threshold is defined for each miner, and once at least a specified fraction of miners complete the required number of batches, the Orchestrator prompts all qualifying miners to upload their weights. This mechanism is inspired by centralized training practices, where global batch sizes are used in typical large language model training. In the decentralized setting, it is coupled with DiLoCo, which allows miners to perform local optimization steps before synchronization. DiLoCo is especially suited for this paradigm because it:
Miners can register for the subnetwork at any time. Upon registration, the Orchestrator assigns each miner a model layer to train. Miners will wait until the next full synchronization period to begin active participation. During full synchronization, miners update their weights and optimizer states to align with the rest of the network, allowing them to proceed with the forward and backward activations in the training stage.
Validators play a key role in determining whether the work completed by a miner is valid. Validators rely on computational reproducibility to validate the work. The validator tracks a specific miner and reruns a portion of the miner’s training to verify its accuracy. Forward and backward passes are compared to the miner’s submitted activations using cosine similarity. While there are challenges related to reliable validation, these are addressed in the system’s incentive structure and in later sections of the documentation, which explore anomaly detection and adversarial robustness using Shapley values.
The incentive structure is designed with the trade-offs between optimization and reproducibility in mind. Since validation depends on the validator’s ability to reproduce specific sections of the training process, miners are not granted the ability to innovate algorithmically. Validators monitor randomly assigned miners during full synchronization stages to ensure comprehensive oversight. The monitoring period is kept as short as possible to maximize the number of miners overseen by each validator. Importantly, miners are not aware of when they are being monitored, preventing them from selectively behaving correctly during observed intervals. After each validation stage, mining rewards are calculated based on the number of backward passes successfully processed by each miner.
The system incorporates a temporal decay mechanism governed by a hyperparameter, which determines how long a miner’s score is valid. After a fixed period, the score drops to zero. This ensures that miners are only rewarded for their active participation during validation periods, discouraging gaming strategies or manipulation of throughput during non-validation stages. The simple linear reward structure ensures that miners receive fixed compensation for each processed activation, and the recomputation requirement during validation stages provides additional security against exploitation.
IOTA Mining Setup
IOTA (Incentivized Orchestrated Training Architecture) is a data- and pipeline-parallel training algorithm designed to operate across a network of heterogeneous, unreliable devices in adversarial and trustless environments.
Miners’ Purpose in IOTA
In the decentralized LLM-training network, miners are responsible for providing GPU compute, memory, and bandwidth to collaboratively train models. IOTA employs data- and pipeline-parallelism, which means miners are responsible for training sections of the model instead of the entire model. This approach reduces hardware requirements for participation. Miners download their assigned section, process forward and backward passes of activations, and periodically synchronize their weights with peers via a merging process. Distributing workloads across many independent miners allows the network to achieve massive parallelism, fault tolerance, and censorship resistance while eliminating single-point infrastructure costs.
The IOTA incentive mechanism continuously scores miners based on throughput and the quality of their work throughout the training and merging processes. Miners are rewarded with subnet 9 alpha tokens according to the quality of their contributions.
Joining the Network
Miners can join the network and register with the orchestrator using their API client, which assigns them to a training layer. Up to 50 miners can operate per layer. Once registered, miners download the current global weights for their assigned layer and begin processing activations.
Activations
There are two types of activations: forward and backward.
Backward activations are given precedence as they provide the learning signal. If a miner fails to process an activation assigned to it, a penalty is applied. This design operates like an assembly line, where workers pass between adjacent stages of the process. The flow of samples through the pipeline is randomized and stochastic.
Activation processing occurs across all layers simultaneously, but miners process samples asynchronously. Miners are incentivized to process as many activations as possible in each epoch, with their scores based on throughput.
Merging
Once enough samples have been processed in the network, the orchestrator signals the transition from training mode to merging mode. In this phase, miners engage in a multi-stage process based on a modified version of the Butterfly All-Reduce technique.
This process is designed to tolerate miner failures, so merging can continue even if some miners fail during this stage. Merging is the slowest part of the process, so the training stage runs for longer to amortize this delay by effectively training on larger batch sizes. After merging is complete, the orchestrator returns the system to training mode, and the cycle continues between training and merging modes.
Prerequisites
To begin setting up the miner on IOTA, the following are required:
Validating
Within the system, validators have a vital role in ensuring the integrity of the work completed by miners. They primarily rely on computational reproducibility to verify the miner’s work. To do so, the validator tracks a specific miner and reruns a portion of the miner’s training. This includes checking both forward and backward passes against the miner’s submitted activations using cosine similarity. However, reliable validation presents several challenges, which are further explored in the following sections.
Joining the Network
Validators join the network by registering with the orchestrator through their API client.
Shadowing Miners
Validators periodically reproduce an epoch’s worth of work from a randomly selected miner. This process can take up to an hour per miner in the current version, and efforts will be made to reduce this time post-launch. There is a tradeoff between the effective batch size and network stability—validators have more work to reproduce when scoring a miner during longer training stages. The system currently relies on basic and robust reproducibility checks, though a future integration of CLASP will enable an auditor-like mechanism to estimate miner contributions. Once a validator confirms that all activations were processed correctly by the miner, they perform a local all-reduce to compare their own local weights and optimizer state with those uploaded by the miner. If the miner passes the check, they earn a score based on the total number of activations processed during that period.
Setting Weights
Validators communicate through the orchestrator, submitting the scores they assign to miners after performing spot checks. These scores are pooled together to calculate a consensus score for each miner. Periodically, validators request miner scores from the orchestrator, normalize the data, and set weights. This process ensures agreement among validators and helps maintain a high level of trust within the validation system.
IOTA is structured around three core roles: the Orchestrator, Miners, and Validators. Rather than using a fully peer-to-peer topology, IOTA adopts a hub-and-spoke architecture centered around the Orchestrator. This design choice ensures global visibility and facilitates comprehensive monitoring of all participant interactions, which is essential for enforcing incentives, auditing behavior, and maintaining system integrity.
The Orchestrator’s primary responsibility is to monitor the training progress of each miner across all discrete layers and initiate weight-merging events accordingly. Given the heterogeneous nature of miner hardware and their unreliability, it is impractical to wait for all miners to complete an equal number of batches. Instead, a minimum batch threshold is defined for each miner, and once at least a specified fraction of miners complete the required number of batches, the Orchestrator prompts all qualifying miners to upload their weights. This mechanism is inspired by centralized training practices, where global batch sizes are used in typical large language model training. In the decentralized setting, it is coupled with DiLoCo, which allows miners to perform local optimization steps before synchronization. DiLoCo is especially suited for this paradigm because it:
Miners can register for the subnetwork at any time. Upon registration, the Orchestrator assigns each miner a model layer to train. Miners will wait until the next full synchronization period to begin active participation. During full synchronization, miners update their weights and optimizer states to align with the rest of the network, allowing them to proceed with the forward and backward activations in the training stage.
Validators play a key role in determining whether the work completed by a miner is valid. Validators rely on computational reproducibility to validate the work. The validator tracks a specific miner and reruns a portion of the miner’s training to verify its accuracy. Forward and backward passes are compared to the miner’s submitted activations using cosine similarity. While there are challenges related to reliable validation, these are addressed in the system’s incentive structure and in later sections of the documentation, which explore anomaly detection and adversarial robustness using Shapley values.
The incentive structure is designed with the trade-offs between optimization and reproducibility in mind. Since validation depends on the validator’s ability to reproduce specific sections of the training process, miners are not granted the ability to innovate algorithmically. Validators monitor randomly assigned miners during full synchronization stages to ensure comprehensive oversight. The monitoring period is kept as short as possible to maximize the number of miners overseen by each validator. Importantly, miners are not aware of when they are being monitored, preventing them from selectively behaving correctly during observed intervals. After each validation stage, mining rewards are calculated based on the number of backward passes successfully processed by each miner.
The system incorporates a temporal decay mechanism governed by a hyperparameter, which determines how long a miner’s score is valid. After a fixed period, the score drops to zero. This ensures that miners are only rewarded for their active participation during validation periods, discouraging gaming strategies or manipulation of throughput during non-validation stages. The simple linear reward structure ensures that miners receive fixed compensation for each processed activation, and the recomputation requirement during validation stages provides additional security against exploitation.
IOTA Mining Setup
IOTA (Incentivized Orchestrated Training Architecture) is a data- and pipeline-parallel training algorithm designed to operate across a network of heterogeneous, unreliable devices in adversarial and trustless environments.
Miners’ Purpose in IOTA
In the decentralized LLM-training network, miners are responsible for providing GPU compute, memory, and bandwidth to collaboratively train models. IOTA employs data- and pipeline-parallelism, which means miners are responsible for training sections of the model instead of the entire model. This approach reduces hardware requirements for participation. Miners download their assigned section, process forward and backward passes of activations, and periodically synchronize their weights with peers via a merging process. Distributing workloads across many independent miners allows the network to achieve massive parallelism, fault tolerance, and censorship resistance while eliminating single-point infrastructure costs.
The IOTA incentive mechanism continuously scores miners based on throughput and the quality of their work throughout the training and merging processes. Miners are rewarded with subnet 9 alpha tokens according to the quality of their contributions.
Joining the Network
Miners can join the network and register with the orchestrator using their API client, which assigns them to a training layer. Up to 50 miners can operate per layer. Once registered, miners download the current global weights for their assigned layer and begin processing activations.
Activations
There are two types of activations: forward and backward.
Backward activations are given precedence as they provide the learning signal. If a miner fails to process an activation assigned to it, a penalty is applied. This design operates like an assembly line, where workers pass between adjacent stages of the process. The flow of samples through the pipeline is randomized and stochastic.
Activation processing occurs across all layers simultaneously, but miners process samples asynchronously. Miners are incentivized to process as many activations as possible in each epoch, with their scores based on throughput.
Merging
Once enough samples have been processed in the network, the orchestrator signals the transition from training mode to merging mode. In this phase, miners engage in a multi-stage process based on a modified version of the Butterfly All-Reduce technique.
This process is designed to tolerate miner failures, so merging can continue even if some miners fail during this stage. Merging is the slowest part of the process, so the training stage runs for longer to amortize this delay by effectively training on larger batch sizes. After merging is complete, the orchestrator returns the system to training mode, and the cycle continues between training and merging modes.
Prerequisites
To begin setting up the miner on IOTA, the following are required:
Validating
Within the system, validators have a vital role in ensuring the integrity of the work completed by miners. They primarily rely on computational reproducibility to verify the miner’s work. To do so, the validator tracks a specific miner and reruns a portion of the miner’s training. This includes checking both forward and backward passes against the miner’s submitted activations using cosine similarity. However, reliable validation presents several challenges, which are further explored in the following sections.
Joining the Network
Validators join the network by registering with the orchestrator through their API client.
Shadowing Miners
Validators periodically reproduce an epoch’s worth of work from a randomly selected miner. This process can take up to an hour per miner in the current version, and efforts will be made to reduce this time post-launch. There is a tradeoff between the effective batch size and network stability—validators have more work to reproduce when scoring a miner during longer training stages. The system currently relies on basic and robust reproducibility checks, though a future integration of CLASP will enable an auditor-like mechanism to estimate miner contributions. Once a validator confirms that all activations were processed correctly by the miner, they perform a local all-reduce to compare their own local weights and optimizer state with those uploaded by the miner. If the miner passes the check, they earn a score based on the total number of activations processed during that period.
Setting Weights
Validators communicate through the orchestrator, submitting the scores they assign to miners after performing spot checks. These scores are pooled together to calculate a consensus score for each miner. Periodically, validators request miner scores from the orchestrator, normalize the data, and set weights. This process ensures agreement among validators and helps maintain a high level of trust within the validation system.
Will Squires – CEO and Co-Founder
Will has dedicated his career to navigating complexity, spanning from designing and constructing significant infrastructure to spearheading the establishment of an AI accelerator. With a background in engineering, he made notable contributions to transport projects such as Crossrail and HS2. Will’s expertise led to an invitation to serve on the Mayor of London’s infrastructure advisory panel and to lecture at UCL’s Centre for Advanced Spatial Analysis (CASA). He was appointed by AtkinsRéalis to develop an AI accelerator, which expanded to encompass over 60 staff members globally. At XYZ Reality, a company specializing in augmented reality headsets, Will played a pivotal role in product and software development, focusing on holographic technology. Since 2023, Will has provided advisory services for the Opentensor Foundation, contributing to the launch of Revolution.
Steffen Cruz – CTO and Co-Founder
Steffen earned his PhD in subatomic physics from the University of British Columbia, Canada, focusing on developing software to enhance the detection of extremely rare events (10^-7). His groundbreaking research contributed to the identification of novel exotic states of nuclear matter and has been published in prestigious scientific journals. As the founding engineer of SolidState AI, he pioneered innovative techniques for physics-informed machine learning (PIML). Steffen was subsequently appointed as the Chief Technology Officer of the Opentensor Foundation, where he played a pivotal role as a core developer of Subnet 1, the foundation’s flagship subnet. In this capacity, he enhanced the adoption and accessibility of Bittensor by authoring technical documentation, tutorials, and collaborating on the development of the subnet template.
Michael Bunting – CFO
Before joining Macrocosmos, Mike spent 12 years in investment banking, where he guided clients through major strategic and financial transitions across more than £1 billion in international M&A and capital raising deals. Most recently serving as a Director at Piper Sandler, he brings deep experience in advising high-growth startups on strategy, business planning, funding pathways, and corporate governance. Mike has also worked closely with multinational corporations and prominent financial investors throughout his career.
Elena Nesterova – Head of Delivery
Volodymyr Truba – Senior Machine Learning Engineer
Alma Schalèn – Head of Product Design
Felix Quinque – Machine Learning Lead
Dmytro Bobrenko – Machine Learning/AI Lead
Alan Aboudib – Machine Learning Lead
Alex Williams – People & Talent Manager
Chris Zacharia – Communications Lead
Brian McCrindle – Senior Machine Learning Engineer
Lawrence Hunt – Frontend Engineer
Nicholas Miller – Senior Software Engineer
Kalei Brady – Data Scientist
Szymon Fonau – Machine Learning Engineer
Monika Stankiewicz – Executive Assistant
Amy Chai – Junior Machine Learning Engineer
Giannis Evagorou – Senior Software Engineer
Richard Wardle – Junior Software Engineer
Kai Morris – Content & Community specialist
Lewis Sword – Junior Software Engineer
Will Squires – CEO and Co-Founder
Will has dedicated his career to navigating complexity, spanning from designing and constructing significant infrastructure to spearheading the establishment of an AI accelerator. With a background in engineering, he made notable contributions to transport projects such as Crossrail and HS2. Will’s expertise led to an invitation to serve on the Mayor of London’s infrastructure advisory panel and to lecture at UCL’s Centre for Advanced Spatial Analysis (CASA). He was appointed by AtkinsRéalis to develop an AI accelerator, which expanded to encompass over 60 staff members globally. At XYZ Reality, a company specializing in augmented reality headsets, Will played a pivotal role in product and software development, focusing on holographic technology. Since 2023, Will has provided advisory services for the Opentensor Foundation, contributing to the launch of Revolution.
Steffen Cruz – CTO and Co-Founder
Steffen earned his PhD in subatomic physics from the University of British Columbia, Canada, focusing on developing software to enhance the detection of extremely rare events (10^-7). His groundbreaking research contributed to the identification of novel exotic states of nuclear matter and has been published in prestigious scientific journals. As the founding engineer of SolidState AI, he pioneered innovative techniques for physics-informed machine learning (PIML). Steffen was subsequently appointed as the Chief Technology Officer of the Opentensor Foundation, where he played a pivotal role as a core developer of Subnet 1, the foundation’s flagship subnet. In this capacity, he enhanced the adoption and accessibility of Bittensor by authoring technical documentation, tutorials, and collaborating on the development of the subnet template.
Michael Bunting – CFO
Before joining Macrocosmos, Mike spent 12 years in investment banking, where he guided clients through major strategic and financial transitions across more than £1 billion in international M&A and capital raising deals. Most recently serving as a Director at Piper Sandler, he brings deep experience in advising high-growth startups on strategy, business planning, funding pathways, and corporate governance. Mike has also worked closely with multinational corporations and prominent financial investors throughout his career.
Elena Nesterova – Head of Delivery
Volodymyr Truba – Senior Machine Learning Engineer
Alma Schalèn – Head of Product Design
Felix Quinque – Machine Learning Lead
Dmytro Bobrenko – Machine Learning/AI Lead
Alan Aboudib – Machine Learning Lead
Alex Williams – People & Talent Manager
Chris Zacharia – Communications Lead
Brian McCrindle – Senior Machine Learning Engineer
Lawrence Hunt – Frontend Engineer
Nicholas Miller – Senior Software Engineer
Kalei Brady – Data Scientist
Szymon Fonau – Machine Learning Engineer
Monika Stankiewicz – Executive Assistant
Amy Chai – Junior Machine Learning Engineer
Giannis Evagorou – Senior Software Engineer
Richard Wardle – Junior Software Engineer
Kai Morris – Content & Community specialist
Lewis Sword – Junior Software Engineer
IOTA is still in active development and at the time of writing a current roadmap has yet to be released. Below is the previous roadmap which may still have some similarities.
IOTA is still in active development and at the time of writing a current roadmap has yet to be released. Below is the previous roadmap which may still have some similarities.
Huge thanks to Keith Singery (aka Bittensor Guru) for all of his fantastic work in the Bittensor community. Make sure to check out his other video/audio interviews by clicking HERE.
Steffen Cruz, previously the CTO of the Opentensor Foundation, has joined forces with his longtime friend Will Squires to establish Macrocosmos. Leading subnets 1, 9, 13, 25 and 37, this team is actively shaping the future of Bittensor and stands as one of the most influential entities within the ecosystem.
In this second video, they spend much of the episode covering Subnet 13’s rebranding to “Gravity” and the team’s prediction of a Trump victory along with how this has managed to build a team of PHDs and machine learning professionals around Bittensor.
A big thank you to Tao Stats for producing these insightful videos in the Novelty Search series. We appreciate the opportunity to dive deep into the groundbreaking work being done by Subnets within Bittensor! Check out some of their other videos HERE.
In this session, the team from Macrocosmos discuss various exciting updates and innovations surrounding their subnets. They delve into the launch and functionalities of new subnets, including the Data Universe subnet for web-scale data scraping and the Apex subnet for advanced AI models. The team highlights the integration of decentralized, real-time data scraping using custom crawlers, which surpass traditional methods in efficiency and scalability. They also explore how the Bittensor community can drive decentralized AI research, leveraging massive data sets and advanced AI training. Further, the discussion touches on the evolution of scientific research within BitTensor, with the introduction of the Mainframe subnet for protein docking and drug discovery. The session concludes with an emphasis on Macrocosmos’ commitment to pushing the boundaries of decentralized AI and computational science, and the potential collaborations that can be achieved in the growing ecosystem.
This Novelty Search session, recorded in late 2024, focuses on the ongoing work and developments of the Macrocosmos team within the Bittensor ecosystem. The team dives into their progress with multiple subnets, including subnet 13, which has become one of the largest social media datasets in the world. They also discuss their work in various sectors, such as decentralized AI, data scraping, and protein folding, which is set to revolutionize fields like healthcare and pharmaceuticals. The team highlights their ongoing efforts to create a decentralized, incentive-driven system that encourages miners to contribute and optimize their computational resources. They also introduce their latest advancements in distributed training, including novel approaches to handling latency and improving gradient averaging. With a focus on data and model quality, the discussion explores how they plan to scale and refine their subnets to provide value for both validators and token holders within the Bit Tensor network. The session ends with an exciting preview of upcoming decentralized games and the future potential of the Bittensor ecosystem.
A special thanks to Mark Jeffrey for his amazing Hash Rate series! In this series, he provides valuable insights into Bittensor Subnets and the world of decentralized AI. Be sure to check out the full series on his YouTube channel for more expert analysis and deep dives.
This session, recorded in mid-2024, features a conversation with Will Squires and Stefan Cruz from Macrocosmos, a prominent player in the Bittensor ecosystem. They discuss their involvement in the decentralized AI network Bittensor, which operates through various subnets that run AI models and create computational power through a decentralized system. Will and Stefan explain their backgrounds, including their expertise in AI, machine learning, and computational research, and how these experiences led them to explore the potential of Bittensor. The conversation covers several exciting aspects, from the challenges of training AI models to the importance of creating a decentralized and transparent AI ecosystem. They also highlight the innovative and competitive nature of Bittensor, where miners are incentivized to innovate and optimize processes, driving the network forward. The discussion touches on both technical details, like the development of the protein folding subnet, and broader concepts such as the future of decentralized AI and the evolving business model of the Bittensor network.
Another video from Mark Jeffery recorded in early 2025. Mark Jeffrey sits down with Stefan and Will from Macrocosmos to discuss the launch of DAOW (the Decentralized Autonomous Oracle Wallet) and the broader changes in the Bittensor ecosystem. The team reflects on the technical success of the DAOW launch, which exceeded expectations in terms of adoption and market dynamics. They delve into the complexities of the subnet launch, including the unexpected volumes and price fluctuations, and discuss how retail investors can approach the market strategically. The conversation also covers the development of Macrocosmos’ subnets, such as their work on protein folding, data scraping, and AI training, which are all part of their “Constellation” platform. This platform aims to unite various subnets into a seamless experience, providing powerful AI tools and decentralized computing. The team emphasizes the need for both strong technical development and effective marketing to make their products accessible and impactful in the growing decentralized AI space.
‘Data Universe covers around 75% of all social media data, from X, Reddit, and Youtube. We’re delivering the most valuable and the most common data to our users’
Head of Delivery at @MacrocosmosAI, Elena Nesterova, talks us through what SN13 is all about - collecting petabytes…
One of Bittensor’s best 👌 @SiliconJose does more than anyone to bring this community together - and bring everyone else in, too, no matter where they are. Hero 👏 @btlabs_ai
And we're live!
Inventive Mechanisms - SN13, Data Universe
Chatting about data-scraping, Gravity (our flagship app), and its role in Macrocosmos
Come watch! 🎙️
Inventive Mechanisms - SN13, Data Universe
Inventive Mechanisms today at 3pm UK time 🎙️
@Data_SN13 is presenting. Data Universe, Gravity, and more.
Livestreamed right here on X.
Set an alarm. You don't wanna miss this 👁️
It’s here. #SN13 has its own X account 🚀
Welcome to the world @Data_SN13
Spotlighting Data-Universe and Gravity (our flagship app built on SN13)
Follow for updates, news, interviews, demos, and releases 🧨
We’ve got loads to chat about 🗣️
Our CTO @macrocrux just vibe-coded Macro(dot)fun, a #Bittensor mindshare dashboard, using X data scraped via SN13.
Track hashtags, community sentiment, top voices, and historic insights.
Made entirely from Gravity’s scraped data.
This is the power of SN13’s large-scale, low…
