Below is a sprint timeline for the development of GSL's beta application, tailored to the specific features provided.
- Set up version control (e.g., Git) and project management tools.
- Establish communication channels and protocols for team collaboration.
Tag: v0.0.1-setup
- Define scope and specifications for Smart AI Software/Hardware features.
- Identify key Natural Problem Points that the software needs to address.
- Start documentation and frameworks for Open-Source Projects contributions.
- Create initial designs and concepts for Flashlabor.
Tag: v0.1.0-alpha
- Set up development environment for Smart AI and hardware simulations.
- Develop foundational code repositories and guidelines for open-source collaboration.
- Outline labor management feature requirements.
- Implement basic framework for Hyperautomation features.
Tag: v0.2.0-alpha
- Initiate core module development for Smart AI algorithms.
- Design and prototype user interfaces for Labor Management and Flashlabor modules.
- Integrate a solutions database for identified Natural Problem Points.
- Foster the Open-Source Community and Collaboration foundations.
Tag: v0.3.0-alpha
- Iterate on Smart AI prototypes based on internal testing feedback.
- Expand Labor Management system with time tracking and scheduling.
- Develop basic functionalities for Hyperautomation tasks.
- Publish initial open-source projects and invite community contributions.
Tag: v0.4.0-alpha
- Finalize MVP of Smart AI software with essential features implemented.
- Implement critical problem-solving actions in Flashlabor.
- Solidify labor management features with employee self-service portals.
- Enhance Hyperautomation with workflow automation capabilities.
Tag: v0.5.0-beta
- Conduct comprehensive testing across all GSL features.
- Perform code reviews and security audits for the open-source contributions.
- Engage with potential users for feedback on the MVP.
- Refine user experience and interface design based on testing results.
Tag: v0.6.0-beta
- Address issues and incorporate user feedback from MVP testing.
- Stabilize the software/hardware integration.
- Optimize performance and scalability of Hyperautomation features.
- Polish and document open-source contribution guidelines.
Tag: v0.7.0-beta
- Prepare marketing materials and beta launch announcements.
- Set up monitoring and analytics systems for post-launch evaluation.
- Conduct final pre-launch checks and bug fixes.
- Train support staff on the system features and troubleshooting.
Tag: v1.0.0-beta
- Launch the GSL public beta to selected user groups.
- Monitor system performance and collect user feedback in real-time.
- Prioritize immediate fixes for any critical issues that emerge.
Tag: v1.0.1-beta
- Analyze user behavior and feedback to identify improvement areas.
- Expand features based on community requests and collaboration.
- Continue iterating on AI models for enhanced problem resolution.
- User test the improvements and prepare for the second beta release (if necessary).
Tag: v1.1.0-beta
- Implement last round of feedback and final touches on all GSL features.
- Ensure scalability and robustness for wider user adoption.
- Update documentation and prepare for the transition to official release.
Tag: v1.2.0-beta
- Final QA pass and acceptance testing for release candidates.
- Finalize release notes and changelog.
- Pre-launch marketing and community engagement activities.
Tag: v2.0.0
- Launch the official version of GSL.
- Engage with media and press for coverage.
- Provide support and gather initial feedback post-release.
Tag: v2.0.1
Note: This timeline is an estimation and serves as a blueprint. As the project progresses, it's likely that adjustments will be needed to accommodate changes in scope, feedback, and other unforeseen challenges. It also assumes that there are well-defined goals and understanding of the technologies involved with AI and hyperautomation which may require expert consultations or additional research.