Subnet 26
Kinitro
ThreeTau
Kinitro accelerates robotic intelligence via incentivized competitions, training AI agents for real-world tasks.
SN26 : Storb
| Subnet | Description | Category | Company |
|---|---|---|---|
| SN26 : Kinitro | Embodied intelligence | Al Agent Robotic | ThreeTau |
Kinitro is Bittensor’s Subnet 26, a project focused on advancing robotic intelligence through incentivized competitions. (This subnet was previously known as Storb, which centered on decentralized storage, before being sold and rebranded as Kinitro in mid-2025.) In its new form, Kinitro creates a decentralized competition platform where AI researchers and developers train embodied AI agents (robots or simulation agents) to perform tasks, and are rewarded based on performance. The aim is to accelerate the development of embodied intelligence – AI that can understand and act in the physical world – by tapping into Bittensor’s decentralized network of miners and validators.
In simpler terms, Kinitro turns AI training into a competition. Network participants called validators propose specific tasks or challenges (for example, a robotic navigation or planning problem) along with how success is measured and what rewards are offered. Miners (developers) then build and train AI agents to tackle these tasks and submit their trained models as entries. The validators evaluate each submitted agent and automatically dispense rewards to the best-performing ones. This process incentivizes continuous improvement: better-performing agents earn more, encouraging miners to keep enhancing their models, while validators earn rewards for providing accurate and timely evaluations.
To outline the process more concretely, Kinitro’s competition workflow involves three key steps:
- Define: Validators post a new task (challenge) with clear metrics for success and a reward bounty.
- Compete: Miners train AI agents to solve the task and submit their agents (solutions) for evaluation.
- Validate & Reward: Validators run evaluations on the submitted agents; the highest performers are verified and payouts are automatically distributed to those miners in Kinitro’s native token rewards.
By structuring AI development as above, Kinitro “drives the future of robotic policy and planning models with incentivized competitions”. This directed incentive model (the project’s motto is cerebrum machinae, meaning “mind of the machine”) is intended to push the frontier of robotics AI. Over time, tasks can become more complex or change (“benchmarks evolve”), and miners must adapt their agents, ensuring a continuous improvement cycle in the collective embodied AI capability.
Kinitro’s product is essentially a decentralized platform for AI competitions built on the Bittensor blockchain. Technically, it consists of a few core components working together: a backend platform, a network of validator nodes, and participating miner nodes (which run the AI agents). The backend and blockchain logic orchestrate the posting of tasks, submission of models, and distribution of rewards. Validators are responsible for hosting and evaluating tasks (for example, running a robot simulation or test environment to judge an agent’s performance), while miners focus on developing the agents. All of this is underpinned by Bittensor’s substrate-based network, meaning the coordination and rewards are handled in a decentralized manner (with Kinitro’s own α (alpha) subnet token as the unit of reward).
The technical architecture of Kinitro is actively evolving. Initially, the developers experimented with a “parent-child validator” design – possibly where a primary validator could spawn subtasks – but they decided to refactor this into a clearer platform + validator architecture. In the refactored architecture, a dedicated competition backend (platform) coordinates multiple validators more directly, improving scalability and clarity. Each validator can independently evaluate agents and report results back to the platform, rather than one validator hierarchically handling all subtasks. This modular approach makes it easier to add or remove validators and ensures the system can handle many simultaneous competitions.
Under the hood, Kinitro’s codebase is written mostly in Python. It likely leverages existing Bittensor frameworks for networking and consensus, while adding new logic for managing competitions. For example, smart contracts or on-chain logic may be used to record when a task is posted, which miners have submitted models, and how rewards are allocated, ensuring transparency and trustlessness. Off-chain, there may be integration with AI frameworks or simulators to actually run the robots’ evaluations (since training and testing AI agents involves significant compute). Kinitro’s repository provides a “Kinitro agent template” for miners to start building their agents, indicating that there are defined APIs or guidelines for how an agent should be packaged and submitted to the network.
The incentive structure of the build is an important aspect: Kinitro’s blockchain logic automatically mints and distributes α rewards based on performance. Validators earn emission rewards for doing the work of evaluation (and doing it accurately and promptly), while miners earn the posted task bounties in proportion to how well their agent did. This creates a balanced economy where both creating good AI models and honestly assessing them are rewarded. All transactions and rewards occur on-chain in a decentralized fashion.
From a security and infrastructure standpoint, the team is working on hardening the system. For instance, secure communication between the backend platform and validators is being improved to prevent tampering or cheating in the evaluation process. They are also integrating monitoring and telemetry tools (like PostHog) to track the performance of the network and detect any issues in real-time. Logging has been identified as an area to improve as well, so that the behavior of agents and validators can be audited if needed. All these technical enhancements are part of ensuring that Kinitro’s platform is robust and enterprise-grade as it moves from prototype toward production.
In summary, the “product” of Kinitro is a full-stack decentralized AI competition system. On the front end (conceptually) it looks like a leaderboard of AI agents competing to solve robotic tasks. On the back end, it is a combination of blockchain smart contracts, networking infrastructure, and AI evaluation pipelines. This build allows anyone with the skills to become a miner (AI trainer) or a validator (competition host) by running the Kinitro code, thereby decentralizing the advancement of robotics AI beyond any single lab or company.
Kinitro is developed and maintained by a small, specialized team under the banner of ThreeTau. ThreeTau is the entity that took over Subnet 26 and pivoted it to the Kinitro project. The open-source repository lists three core contributors to Kinitro’s code: Ray Okamoto, Syeam Bin Abdullah, and Rishi.
Ray Okamoto – Ray appears to be one of the lead developers; he opened several of the project’s key issues and features (including documentation and security improvements) which suggests he is deeply involved in the architecture and direction of the project.
Syeam Bin Abdullah (GitHub handle “Shr1ftyy”) – Syeam is another principal engineer on Kinitro. He has contributed major code refactors (for example, overhauling the validator architecture and integrating the competition system). His involvement indicates expertise in backend/platform development for the subnet.
Rishi (GitHub handle “rishiad”) – Rishi is also listed as a contributor and presumably provides development support or research expertise to the project.
Together, this team is building Kinitro in the open via the ThreeTau GitHub organization. The ThreeTau name and the presence of a domain (threetau.net) suggest it might be a small startup or collective formed around building Bittensor subnets, with Kinitro being a flagship project. The team actively engages with the Bittensor community; for example, they communicate progress updates and calls for miners on X (Twitter), and they coordinate with the broader Bittensor developer ecosystem (e.g., via Discord, as noted in their README).
It’s worth noting that prior to ThreeTau’s involvement, Subnet 26’s previous project (Storb) had a different team focused on storage. With the transition to Kinitro, the ThreeTau team has effectively assumed ownership of this subnet. This change in leadership brought a new vision (robotics competitions) and the technical expertise needed to implement it. The current team’s mix of systems programming skills and AI knowledge is well-suited for Kinitro’s ambitious goal of merging blockchain incentives with robotics training.