After the “Black Monday” of the crypto market this week, which saw a significant downturn, tokens across different sectors have experienced a rebound the following day. Among them, Bittensor (TAO) stands out as the most notable.
According to CoinMarketCap data, Bittensor (TAO) rose by 23.08% yesterday, making it the top performer in terms of rebound among the top 100 tokens by market capitalization.
Although the AI narrative is not as hot as it was at the beginning of the year, the choice of speculative capital indicates confidence in leading projects in this sector. However, Bittensor has faced some FUD (Fear, Uncertainty, and Doubt) in the past, with the community questioning the project’s name and the practical applications within its subnets.
(See also: FUD and Rumors: Will the New AI King Bittensor Fall from Grace?)
While the usefulness of a crypto project does not always correlate directly with its token price, is Bittensor just an empty shell?
In recent months, Bittensor has added 12 new subnets, each contributing to AI-related development to some extent, and potentially giving rise to new Alpha projects. We have reviewed these new subnets to observe their fundamental changes while focusing on the rebound in TAO prices.
Development Team: Agent Artificial
Introduction:
Sylliba is a translation application that supports both text and speech translation in over 70 languages. Notably, this application can be utilized by on-chain AI agents:
Project address: https://github.com/agent-artificial/sylliba-subnet
Development team:@BitMindAI
Introduction:
BitMind focuses on developing decentralized deepfake detection technology. With the rapid advancement of generative AI models, distinguishing between high-quality synthetic media and real content has become increasingly complex.
BitMind’s subnet addresses this issue by deploying robust detection mechanisms within the Bittensor network, using both generative and discriminative AI models to effectively identify deepfakes.
Additionally, the BitMind API allows developers to leverage the subnet’s deepfake detection capabilities to create powerful consumer applications. The BitMind web application, featuring an image upload interface, uses the API to help users quickly assess the likelihood that an image is real or fake, providing an accessible and interpretable anti-deception tool.
Development team:@GraphiteSubnet
Introduction:
Graphite is a subnet specifically designed to address graph-related problems, with a particular focus on the Traveling Salesman Problem (TSP). TSP is a classic optimization problem that aims to find the shortest possible route visiting a set of cities and returning to the starting point.
Graphite utilizes Bittensor’s decentralized machine learning network to efficiently connect miners for handling the computational demands of TSP and similar graph problems. Currently, validators generate synthetic requests and send them to miners in the network. Miners are responsible for solving the TSP using their algorithms and sending the results back to validators for evaluation.
Development team:@RizzoValidator,@FrankRizz07
Introduction:
Gen42 leverages the Bittensor network to offer decentralized code generation services. Their focus is on creating powerful, scalable tools for code-based question answering and code completion, driven by open-source large language models.
Main Products:
a. Chat Application: Provides a chat frontend that allows users to interact with their subnet. The primary feature of this application is code-based question answering.
b. Code Completion: Offers an OpenAI-compatible API that can be used with continue.dev.
Details on how miners and validators participate can be found on the project’s GitHub.
Development team:@sportstensor
Introduction:
Sportstensor is a project focused on developing decentralized sports prediction algorithms, supported by the Bittensor network.
The project offers foundational models on the open-source platform HuggingFace for miners to train and improve. It supports strategic planning and performance analysis based on historical and real-time data and rewards comprehensive dataset collection and high-performance prediction model development.
Miner and Validator Roles:
Developer: Not found yet,The official website is here
Introduction:
SN29 Coldint, short for Collective Distributed Incentivized Training, focuses on pre-training niche models. “Niche models” refer to those that may not be as widely applicable as large, general-purpose models but are highly valuable in specific domains or tasks.
Miner and Role Participation:
a) Miners primarily earn incentives by publicly sharing their trained models.
b) Secondary incentives are given to miners or other contributors who share insights by contributing to the codebase.
c) Miners are encouraged to regularly share their improvements through rewards for small enhancements.
d) Significant rewards are provided for code contributions that effectively combine individual training efforts into improved composite models.
Development Team: @vectorchatai
Token: $CHAT
Introduction:
SN40 Chunking functions like an exceptionally clever librarian, specifically designed to divide large volumes of information (text, images, audio, etc.) into smaller chunks. This approach makes it easier for AI to understand and utilize the information. Just as a well-organized bookshelf helps you quickly find what you’re looking for, SN40 Chunking helps “organize the bookshelf” for AI.
Not limited to text, SN40 Chunking can also handle various types of information, including images and audio. It’s akin to a versatile librarian managing not only books but also photo albums, music CDs, and more.
Development team:@WOMBO
Introduction: SN39 EdgeMaxxing is a subnet focused on optimizing AI models for consumer devices, ranging from smartphones to laptops. The EdgeMaxxing subnet employs a competitive reward system with daily contests to encourage participants to continually enhance the performance of AI models on consumer devices.
Participant Roles and Responsibilities:
Miners: The primary task is to submit optimized AI model checkpoints. They use various algorithms and tools to improve model performance.
Validators: Must run the submitted models on specified target hardware (e.g., NVIDIA GeForce RTX 4090). They collect all models submitted by miners daily, benchmark each model, and compare results with baseline checkpoints. Validators score models based on speed improvements, accuracy maintenance, and overall efficiency enhancements, selecting the top-performing model of the day as the winner.
Project open source repository: https://github.com/womboai/edge-maxxing
Development team:@Bettensor
Introduction:
Bittensor allows sports enthusiasts to predict the outcomes of sports events, creating a decentralized sports prediction market based on blockchain technology.
Participant Roles:
Miner: Responsible for generating prediction results.
Validator: Verifies the accuracy of the prediction results.
Data Collector: Gathers sports event data from various sources.
Development team:@Playinfgames
Introduction:
Infinite Games develops real-time and predictive tools for prediction markets. The project also engages in arbitrage and aggregation of events on platforms such as @Polymarket and @azuroprotocol.
Incentive System:
Uses $TAO tokens as incentives.
Rewards providers of accurate predictions and valuable information.
Overall, the project encourages user participation in prediction and information sharing, fostering an active prediction community.
Development Team: Taoverse &@MacrocosmosAI
Introduction:
This is a subnet focused on fine-tuning large language models (LLMs), rewarding miners for fine-tuning LLMs and using the continuous synthetic data stream from Subnet 18 for model evaluation.
Operational Mechanism:
Project warehouse address: https://github.com/macrocosm-os/finetuning
Development team:@omegalabsai
Introduction:
“Any to Any” in this project refers to a multimodal AI system’s capability to transform and understand different types of data or information, such as text to images, images to text, audio to video, and video to text. The system not only performs these transformations but also understands the relationships between different modalities. For example, it can comprehend the connection between a textual description and an image or between a video and its corresponding audio.
In this subnet, the incentive mechanism is designed to encourage global AI researchers and developers to participate in the project.
Project warehouse address: https://github.com/omegalabsinc/omegalabs-anytoany-bittensor
Additional knowledge:
Supplementary Knowledge:
In case some readers are unfamiliar with the concept of Bittensor subnets, here is a simple explanation:
This article is reproduced from [TechFlow], original title “TAO has the strongest rebound now, taking stock of 12 useful AI projects on the subnet”, the copyright belongs to the original author [深潮 TechFlow], if you have any objection to the reprint, please contact Gate Learn Team, the team will handle it as soon as possible according to relevant procedures.
Disclaimer: The views and opinions expressed in this article represent only the author’s personal views and do not constitute any investment advice.
Other language versions of the article are translated by the Gate Learn team, not mentioned in Gate.io, the translated article may not be reproduced, distributed or plagiarized.
After the “Black Monday” of the crypto market this week, which saw a significant downturn, tokens across different sectors have experienced a rebound the following day. Among them, Bittensor (TAO) stands out as the most notable.
According to CoinMarketCap data, Bittensor (TAO) rose by 23.08% yesterday, making it the top performer in terms of rebound among the top 100 tokens by market capitalization.
Although the AI narrative is not as hot as it was at the beginning of the year, the choice of speculative capital indicates confidence in leading projects in this sector. However, Bittensor has faced some FUD (Fear, Uncertainty, and Doubt) in the past, with the community questioning the project’s name and the practical applications within its subnets.
(See also: FUD and Rumors: Will the New AI King Bittensor Fall from Grace?)
While the usefulness of a crypto project does not always correlate directly with its token price, is Bittensor just an empty shell?
In recent months, Bittensor has added 12 new subnets, each contributing to AI-related development to some extent, and potentially giving rise to new Alpha projects. We have reviewed these new subnets to observe their fundamental changes while focusing on the rebound in TAO prices.
Development Team: Agent Artificial
Introduction:
Sylliba is a translation application that supports both text and speech translation in over 70 languages. Notably, this application can be utilized by on-chain AI agents:
Project address: https://github.com/agent-artificial/sylliba-subnet
Development team:@BitMindAI
Introduction:
BitMind focuses on developing decentralized deepfake detection technology. With the rapid advancement of generative AI models, distinguishing between high-quality synthetic media and real content has become increasingly complex.
BitMind’s subnet addresses this issue by deploying robust detection mechanisms within the Bittensor network, using both generative and discriminative AI models to effectively identify deepfakes.
Additionally, the BitMind API allows developers to leverage the subnet’s deepfake detection capabilities to create powerful consumer applications. The BitMind web application, featuring an image upload interface, uses the API to help users quickly assess the likelihood that an image is real or fake, providing an accessible and interpretable anti-deception tool.
Development team:@GraphiteSubnet
Introduction:
Graphite is a subnet specifically designed to address graph-related problems, with a particular focus on the Traveling Salesman Problem (TSP). TSP is a classic optimization problem that aims to find the shortest possible route visiting a set of cities and returning to the starting point.
Graphite utilizes Bittensor’s decentralized machine learning network to efficiently connect miners for handling the computational demands of TSP and similar graph problems. Currently, validators generate synthetic requests and send them to miners in the network. Miners are responsible for solving the TSP using their algorithms and sending the results back to validators for evaluation.
Development team:@RizzoValidator,@FrankRizz07
Introduction:
Gen42 leverages the Bittensor network to offer decentralized code generation services. Their focus is on creating powerful, scalable tools for code-based question answering and code completion, driven by open-source large language models.
Main Products:
a. Chat Application: Provides a chat frontend that allows users to interact with their subnet. The primary feature of this application is code-based question answering.
b. Code Completion: Offers an OpenAI-compatible API that can be used with continue.dev.
Details on how miners and validators participate can be found on the project’s GitHub.
Development team:@sportstensor
Introduction:
Sportstensor is a project focused on developing decentralized sports prediction algorithms, supported by the Bittensor network.
The project offers foundational models on the open-source platform HuggingFace for miners to train and improve. It supports strategic planning and performance analysis based on historical and real-time data and rewards comprehensive dataset collection and high-performance prediction model development.
Miner and Validator Roles:
Developer: Not found yet,The official website is here
Introduction:
SN29 Coldint, short for Collective Distributed Incentivized Training, focuses on pre-training niche models. “Niche models” refer to those that may not be as widely applicable as large, general-purpose models but are highly valuable in specific domains or tasks.
Miner and Role Participation:
a) Miners primarily earn incentives by publicly sharing their trained models.
b) Secondary incentives are given to miners or other contributors who share insights by contributing to the codebase.
c) Miners are encouraged to regularly share their improvements through rewards for small enhancements.
d) Significant rewards are provided for code contributions that effectively combine individual training efforts into improved composite models.
Development Team: @vectorchatai
Token: $CHAT
Introduction:
SN40 Chunking functions like an exceptionally clever librarian, specifically designed to divide large volumes of information (text, images, audio, etc.) into smaller chunks. This approach makes it easier for AI to understand and utilize the information. Just as a well-organized bookshelf helps you quickly find what you’re looking for, SN40 Chunking helps “organize the bookshelf” for AI.
Not limited to text, SN40 Chunking can also handle various types of information, including images and audio. It’s akin to a versatile librarian managing not only books but also photo albums, music CDs, and more.
Development team:@WOMBO
Introduction: SN39 EdgeMaxxing is a subnet focused on optimizing AI models for consumer devices, ranging from smartphones to laptops. The EdgeMaxxing subnet employs a competitive reward system with daily contests to encourage participants to continually enhance the performance of AI models on consumer devices.
Participant Roles and Responsibilities:
Miners: The primary task is to submit optimized AI model checkpoints. They use various algorithms and tools to improve model performance.
Validators: Must run the submitted models on specified target hardware (e.g., NVIDIA GeForce RTX 4090). They collect all models submitted by miners daily, benchmark each model, and compare results with baseline checkpoints. Validators score models based on speed improvements, accuracy maintenance, and overall efficiency enhancements, selecting the top-performing model of the day as the winner.
Project open source repository: https://github.com/womboai/edge-maxxing
Development team:@Bettensor
Introduction:
Bittensor allows sports enthusiasts to predict the outcomes of sports events, creating a decentralized sports prediction market based on blockchain technology.
Participant Roles:
Miner: Responsible for generating prediction results.
Validator: Verifies the accuracy of the prediction results.
Data Collector: Gathers sports event data from various sources.
Development team:@Playinfgames
Introduction:
Infinite Games develops real-time and predictive tools for prediction markets. The project also engages in arbitrage and aggregation of events on platforms such as @Polymarket and @azuroprotocol.
Incentive System:
Uses $TAO tokens as incentives.
Rewards providers of accurate predictions and valuable information.
Overall, the project encourages user participation in prediction and information sharing, fostering an active prediction community.
Development Team: Taoverse &@MacrocosmosAI
Introduction:
This is a subnet focused on fine-tuning large language models (LLMs), rewarding miners for fine-tuning LLMs and using the continuous synthetic data stream from Subnet 18 for model evaluation.
Operational Mechanism:
Project warehouse address: https://github.com/macrocosm-os/finetuning
Development team:@omegalabsai
Introduction:
“Any to Any” in this project refers to a multimodal AI system’s capability to transform and understand different types of data or information, such as text to images, images to text, audio to video, and video to text. The system not only performs these transformations but also understands the relationships between different modalities. For example, it can comprehend the connection between a textual description and an image or between a video and its corresponding audio.
In this subnet, the incentive mechanism is designed to encourage global AI researchers and developers to participate in the project.
Project warehouse address: https://github.com/omegalabsinc/omegalabs-anytoany-bittensor
Additional knowledge:
Supplementary Knowledge:
In case some readers are unfamiliar with the concept of Bittensor subnets, here is a simple explanation:
This article is reproduced from [TechFlow], original title “TAO has the strongest rebound now, taking stock of 12 useful AI projects on the subnet”, the copyright belongs to the original author [深潮 TechFlow], if you have any objection to the reprint, please contact Gate Learn Team, the team will handle it as soon as possible according to relevant procedures.
Disclaimer: The views and opinions expressed in this article represent only the author’s personal views and do not constitute any investment advice.
Other language versions of the article are translated by the Gate Learn team, not mentioned in Gate.io, the translated article may not be reproduced, distributed or plagiarized.