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.

Subnet 38: Sylliba, a text-to-speech translation tool supporting 70+ languages

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:

• Automated Translation Processes: AI agents can automatically call this service for cross-language information processing and communication.
• Enhanced AI Capabilities: Allows AI systems without multilingual capabilities to handle multilingual tasks.
• Blockchain Verification: Translation requests and results can be verified on the blockchain, increasing system credibility.
• Incentive Mechanism: Through token economics, it can incentivize high-quality translation service providers.

Project address: https://github.com/agent-artificial/sylliba-subnet

Subnet 34: Bitmind, a tool for detecting and distinguishing between real and fake synthetic content

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.

Subnet 43: Graphite, intelligent path planning network

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.

Subnet 42: Gen42, GitHub’s open source AI coding assistant

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.

Subnet 41: Sportstensor, sports prediction model

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:

• Miners: Receive requests from validators, access relevant data, and make predictions using machine learning models.
• Validators: Collect predictions from miners, compare them with actual results, and record validation outcomes.

Subnet 29: coldint, niche AI ​​model training

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.

Subnet 40: Chunking, optimized data set for RAG (Retrieval-Augmented Generation) application

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.

Subnet 39: EdgeMaxxing, optimizing AI models for operation on consumer devices

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

Subnet 30: Bettensor, decentralized sports prediction market

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.

Project open source repository: https://github.com/Bettensor/bettensor (appears to be still under development)

Subnet 06: Infinite Games, general prediction market

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.

Subnet 37: LLM Fine-tuning, large language model fine-tuning

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:

• Miners train models and regularly publish them on the Hugging Face platform.
• Validators download models from Hugging Face and continuously evaluate them using synthetic data.
• Evaluation results are recorded on the wandb platform.
• TAO tokens are distributed as rewards to miners and validators based on their performance.

Project warehouse address: https://github.com/macrocosm-os/finetuning

Subnet 21: Any to Any, creating advanced AI multi-modal models

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.

• Contributors can earn token rewards by providing valuable models, data, or computational resources.
• This direct economic incentive makes high-quality AI research and development a sustainable endeavor.

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:

• A subnet is a specialized network within the Bittensor ecosystem.
• Each subnet focuses on specific AI or machine learning tasks.
• Subnets allow developers to create and deploy AI models for particular purposes.
• They use cryptoeconomics to incentivize participants to provide computational resources and improve models.

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1. 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.

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