Time-TokenAIzer
Where Your Time Becomes A Purchasable Token.
Demo
About
The problem it solves ## What Problem Does Time-TokenAlzer Solve? ### The Problem Traditional platforms fail to efficiently utilize fragmented time, cross-timezone talent, and instant skill access. This results in: * **Underutilized micro-skills and time slots** > 1–2 hour daily skills go unused, No micro-gig platforms to monetize them * **Ineffective cross-timezone collaboration** > Real-time skill matching doesn’t exist, Geographic and time zone barriers persist * **Lack of real-time, on-demand skill marketplaces** > No platform for “I need help NOW” moments, No tokenized access to skill + time combo * **Disconnected social intent and on-chain action** > People express demand on platforms like X, but there’s no way to automatically turn that intent into tokenized and verifiable actions ## What Can People Use Time-TokenAlzer For? Time-TokenAlzer transforms **idle time** into **tokenized skill units** that can be traded or used instantly across the globe. ### Key Use Cases: * **Freelancers** can monetize short bursts of availability (1–2 hrs) * **Remote teams** can instantly hire verified micro-experts across time zones * **Students & learners** can access on-demand mentors without subscription platforms * **Emergency projects** can find and onboard help in real-time * **Communities** can build tokenized time banks or skill exchanges * **Creators & DAOs** can auto-generate tokens from social activity using ElizaOS ## What's special? * **Real-time matching** with smart contracts and automation * **Trustless transactions** with blockchain-backed identity and time proof * **Global access** to skill-time units with no intermediaries * **Verified value** through skill and time tokenization * **Composable infrastructure** that connects social platforms, intent detection, and on-chain token logic
How it's made
Challenges we ran into While building this project, we faced several technical hurdles: **Chainlink Functions – KYC Check Issues** One major challenge was integrating Chainlink Functions for performing KYC checks. Specifically, we encountered problems with **gasLimit** exceeding constraints during execution, which caused repeated failures. We also struggled with securely uploading secrets to Chainlink for off-chain computation. We resolved this by optimizing the function's logic to reduce gas usage and followed Chainlink’s secret management guidelines to successfully encrypt and upload API keys. **AI-Recommended Token Bundler Bug** Another issue arose while implementing the AI-recommended token bundler. Every time a new token bundle was created, the system assigned the same token name due to a state mismanagement in the token generation logic. We fixed this by updating the naming mechanism to generate unique names dynamically based on the AI strategy ID and timestamp. **Model Override – Google Gemini Quota Issue** We also encountered a configuration issue where the ElizaOS runtime defaulted to using the **Google Gemini Large** model. Since this model is resource-intensive, it quickly exceeded our usage quota and caused the project to crash at startup. After investigating the issue, we found that the model could be overridden through environment configuration. By adding *LARGE_GOOGLE_MODEL=gemini-1.5-flash* to our **.env** file, we successfully switched to a quota-friendly model under the free API tier. These challenges helped us better understand how to use Chainlink Functions and AI-generated logic in smart contract development.