TAO rebounds strongly, here are 12 AI projects worth paying attention to on the subnet

原作者: TechFlow

The crypto market was devastated after this week’s “Black Monday,” but tokens in different sectors rebounded a day later.

Among them, the most popular one is Bittensor (TAO).

Coinmarketcap data showed that among the top 100 tokens by market value yesterday, Bittensor (TAO) rose 23.08%, ranking first on the rebound list.

Although the AI narrative is not as hot as it was at the beginning of the year, the cho氷 of hot money also represents optimism about the leading projects in the sector.

However, Bittensor has also suffered a certあいn degree of fud before. The community believes that the project is overrated and there is no practical application in the subnet.

Although the usefulness of a crypto project is not directly related to the token price, is Bittensor really just an empty shell?

In the past few months, 12 subnets have been added to Bittensor, and each subnet has promoted AI-related 発達 to a certain extent, and new Alpha projects may emerge among them.

We took a look at these new subnets to see how their fundamentals are changing while all attention is focused on TAOs price rebound.

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

Development Team: Agent Artificial

導入：

Sylliba is a translation app that supports both text and voice translation and can handle more than 70 languages.

It is worth mentioning that this program can be used by on-chain AI agents:

• Automated translation process: AI agents can automatically call this service to achieve cross-language information processing and communication.

• Enhance AI capabilities: Enable AI systems that do not have multilingual capabilities to handle multilingual tasks.

• Translation requests and results can be verified on the blockchain, adding credibility to the system.

• Incentive mechanism: Through the token economy, high-quality translation service providers can be incentivized.

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

Subnet 34: Bitmind, detects and distinguishes real content from fake synthetic content

Development team: @BitMindAI

導入：

BitMind is focused on developing decentralized deepfake detection technology. With the rapid development of generative AI models, distinguishing high-quality synthetic media from real content is becoming increasingly complex.

BitMind ’s Subnet solves this problem by deploying a powerful detection mechanism in the Bittensor network, using both generative and discriminative AI models to effectively identify deepfakes.

At the same time, the BitMind API enables the development of powerful consumer applications that leverage the deepfake detection capabilities of the subnet. A BitMind web application with an image upload interface can use the API to help users quickly identify whether a picture is real or fake, providing an easily accessible and easily explainable anti-spoofing tool.

Subnet 43: Graphite, intelligent path planning network

Development team: @GraphiteSubnet

導入：

Graphite is a subnet specifically designed to work with graph problems, with a particular focus on the Traveling Salesman Problem (TSP). The TSP is a classic optimization problem where the goal is to find the shortest possible route that visits a set of cities and returns to the starting point.

Graphite leverages Bittensor’s decentralized machine learning network to efficiently connect miners to handle 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 TSP using the algorithm they designed and sending the results back to the validators for evaluation.

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

Development team: @RizzoValidator , @FrankRizz 07

導入：

Gen 42 provides decentralized code generation services using the Bittensor Network. Their focus is on creating powerful, scalable tools for code-based question answering and code completion that are powered by open source large-scale language models.

main products:

a. Chat Application: Provides a chat front-end that allows users to interact with their subnets. The main functionality of this application is code-based QA.

b. Code completion: Provides an OpenAI-compatible API that can be used with continue.dev.

For details on how miners and validators can participate, please refer to the project Github

Subnet 41: Sportstensor, sports prediction model

Development team: @sportstensor

導入：

Sportstensor is a project dedicated to developing decentralized sports prediction algorithms, powered by the Bittensor Network.

The project provides basic models on the open source HuggingFace for miners to train and improve, while enabling strategic planning and performance analysis based on historical and real-time data, and rewards comprehensive data set collection and high-performance predictive model development.

Miner and validator functions:

• Miners: Receive requests from validators, access relevant data, and use machine learning models to make predictions.

• Validator: Collects miners’ predictions, compares them with actual results, and records verification results.

Subnet 29: coldint, niche AI model training

Developer: Not found yet, the official website is here

導入：

SN 29 coldint, the full name is Collective Distributed Incentivized Training.

Goal: Focus on pre-training of niche models. Niche models may refer to models that are not as widely used as large general models, but are very valuable in specific fields or tasks.

Participation and division of labor among miners and other roles:

a) Miners are primarily incentivized by publicly sharing training models.

b) Secondary incentives are given to miners or other contributors who share insights by contributing to the code base.

c) By rewarding small improvements, miners are encouraged to share their improved work regularly.

d) Highly reward code contributions that combine individual training efforts into better combined models.

Subnet 40: Chunking, optimizing the dataset for RAG (Retrieval-Augmented Generation) applications

Development team: @vectorchatai

Token: $CHAT

導入：

SN 40 Chunking is like a very smart librarian. The specific approach is to divide a large amount of information (text, pictures, sounds, etc.) into small chunks. This is done to make it easier for AI to understand and use this information. If the bookshelf is well organized, you can find it quickly.

SN 40 Chunking is helping AI organize the bookshelf.

Not only text, SN 40 Chunking can also handle multiple types of information such as pictures, sounds, etc. It is like an all-round librarian, managing not only books but also photo collections, music CDs, etc.

Subnet 39: EdgeMaxxing, optimizing AI models to run on consumer devices

Development Team: @WOMBO

Introduction: S N3 9 EdgeMaxxing is a subnet focused on optimizing AI models for consumer devices, from smartphones to laptops.

The EdgeMaxxing subnet uses a competitive reward system with daily competitions to encourage participants to continuously optimize the performance of AI models on consumer devices.

Participant roles and division of labor:

Miners:

The main task is to submit optimized AI model checkpoints

They use various algorithms and tools to improve model performance

Validators:

Must run on specified target hardware (e.g. NVIDIA GeForce RTX 4090), collect models submitted by all miners every day, benchmark each submitted model against a baseline checkpoint; score based on speed improvement, accuracy maintenance, and overall efficiency improvement, and select the best performing model of the day as the winner

Project open source repository: https://github.com/womboai/edge-maxxing

Subnet 30: Bettensor, a decentralized sports prediction market

Development team: @Bettensor

導入：

Bettensor allows sports fans to predict the outcomes of sports games, creating a decentralized sports prediction market based on blockchain.

Participant roles:

Miner: responsible for generating prediction results

Validator: Verify the accuracy of the prediction results

Data Collector: Collect sports event data from various sources

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

Subnet 06: Infinite Games, general prediction market

Development team: @Playinfgames

導入：

Infinite Games develops real-time and predictive tools for prediction markets. The project also arbitrages and aggregates events from platforms such as @Polymarket and @azuroprotocol.

Incentive system:

Using $TAO tokens as incentives

Reward providers of accurate predictions and valuable information

Overall, the project encourages users to participate in forecasting and information provision, forming an active forecasting community.

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

Development team: Taoverse @MacrocosmosAI

導入：

This is a subnet focused on fine-tuning Large Language Models (LLMs): miners are rewarded for fine-tuning LLMs and a continuous stream of synthetic data from subnet 18 is used for model evaluation.

Working Mechanism:

• Miners train models and publish them to the Hugging Face platform regularly.

• Validators download models from Hugging Face and continuously evaluate them using synthetic data.

• The evaluation results are recorded on the wandb platform.

• TAO tokens are rewarded to miners and validators based on their weights.

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

Subnet 21: Any to Any, creating advanced AI multimodal models

Development team: @omegalabsai

導入：

Any to Any in this project refers to the ability of a multimodal AI system to convert and understand between different types of data or information, such as text to image, image to text, audio to video, video to text.

The system can not only perform conversions, but also understand the relationship between different modalities. For example, it can understand the connection between a text description and an image, or the connection between a video and its corresponding audio.

In this subnet, incentive mechanisms are used to encourage AI researchers and developers around the world to participate in the project. Specifically:

• Contributors can earn token rewards by providing valuable models, data, or computing resources.

• This direct financial incentive makes high-quality AI research and development a sustainable endeavor.

Project repository address: https://github.com/omegalabsinc/omegalabs-anytoany-bittensor

Additional knowledge:

In case some readers dont know the meaning of Bittensor subnet, a simple explanation can be:

• A subnet is a specialized network in the Bittensor ecosystem.

• Each subnetwork focuses on a specific AI or machine learning task.

• Subnets allow developers to create and deploy AI models for specific purposes.

• They use cryptoeconomics to incentivize participants to provide computing resources and improve models.

オリジナルリンク

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