MyZubster has recently completed one of its most important development phases so far.
We worked across the entire stack: production infrastructure, authentication, Marketplace visibility, economic APIs, digital entrepreneurship workflows, security automation, and public Metaverse identities.
This is a technical overview of what we built, what broke along the way, and what is now running in production.
The MyZubster ecosystem
MyZubster is an open-source ecosystem that combines several connected services:
- A community Marketplace
- GitHub-based user identities
- ZORGAX, an assistant and economic experimentation layer
- A guided digital entrepreneurship program called LIFE Pilot
- A persistent Metaverse called Neon Plaza
- Public contributor and project records
- Automated testing, security, and deployment workflows
The project is available on GitHub:
github.com/MyZubster-Ecosystem/myzubster
The production platform is available at:
www.myzubster.com
Fixing the production foundation
Before adding new functionality, we had to solve several production-level problems.
The application was deployed, but some important features were not working reliably.
The main issues included:
- MongoDB operations timing out
- GitHub OAuth failing after authorization
- Marketplace data not loading in production
- Backend requests being captured by frontend routes
- Node.js runtime incompatibility with the MongoDB driver
- Dependency lockfile differences during clean Vercel builds
MongoDB and GitHub OAuth
GitHub authentication could start correctly, but the login process failed when the application attempted to query the users collection.
The operation remained buffered until it timed out.
We corrected the production MongoDB configuration and verified that the application could connect to the database before executing authentication queries.
We also aligned the production runtime with Node.js 20, which provides a stable environment for the current MongoDB driver and application dependencies.
After the fix:
- GitHub OAuth completes successfully
- User records can be retrieved or created
- Marketplace listings can access persistent data
- API routes no longer fail because of buffered database operations
Making the Marketplace visible
The Marketplace was already present in the repository, but it was not correctly exposed through the production application.
We restored its route, connected it to the live API, and verified that the listing endpoint returned a successful response from production.
The Marketplace is now accessible here:
Open the MyZubster Marketplace
This provides the base for future services, contributor offers, digital products, and community exchanges.
ZORGAX pricing and access model
ZORGAX now exposes a public pricing API with three plans:
- Zorgax Free
- Zorgax Pro
- Zorgax Developer
The current Pro reference price is €9.90 per month.
The Developer plan is intended for users who need:
- API access
- Automation
- Higher usage limits
- Advanced workspace functionality
The payment architecture is explicitly non-custodial.
MyZubster does not request or accept users’ private keys, and it does not automatically sign blockchain transactions.
External settlement must be independently verified before paid access is activated.
This separation is critical:
A payment request is not proof of payment, and an unconfirmed transaction is not confirmed revenue.
Building the ZORGAX economic layer
ZORGAX has evolved from a basic assistant into a more structured economic experimentation system.
We added services and APIs for:
- Confirmed economic inflows
- Capital-allocation proposals
- Advisory capital recommendations
- Capital decision tracking
- Outcome recording
- Decision memory
- Bounded learning
Confirmed inflows only
The economic layer does not treat every payment intent as available capital.
Only independently confirmed inflows are included in the capital model.
This prevents pending, rejected, or unverified transactions from influencing recommendations.
Advisory capital allocation
ZORGAX can generate proposals about how available resources could be allocated.
These recommendations are advisory. They do not automatically transfer money or execute financial operations.
A server-side policy constrains how the recommendations are generated.
Decision and outcome memory
Capital decisions can be recorded together with their eventual outcomes.
The system can use this history to improve later recommendations through bounded learning.
Bounded learning means the system can learn from evidence while remaining inside explicit operational limits.
It cannot silently expand its own authority.
From idea to digital product
We also built a structured digital entrepreneurship workflow.
The workflow can guide a project through the following stages:
- Project creation
- Idea intake
- Idea validation
- Product blueprint generation
- Offer creation
- Launch preparation
- Metrics collection
- Evidence-based learning
- Product iteration
Each stage has dedicated backend services, API routes, data models, and automated tests.
Digital idea validation
A user can submit a digital product idea and receive a structured validation result.
The validation engine evaluates the idea and creates a persistent snapshot.
This makes it possible to compare different iterations instead of overwriting previous reasoning.
The workflow connects validation results to the project so that later stages can use real evidence.
Product blueprint engine
A validated idea can be converted into a digital product blueprint.
The blueprint provides a structured foundation for the product, including its direction, audience, and execution model.
The latest blueprint is persisted with the project and exposed through a dedicated API.
This creates a bridge between an idea and an actionable product plan.
Launch and offer engine
The launch-and-offer engine converts the product blueprint into a controlled offer package.
Instead of stopping at strategy, the workflow prepares something that can be presented to real users and tested.
The resulting package can then be connected to product metrics and learning events.
Metrics and learning
A digital product needs more than a launch plan. It needs measurable feedback.
We added:
- Product metric event models
- Metric collection services
- Public API routes
- Learning summaries
- Tests for metric aggregation and learning behavior
The objective is to create a loop:
Idea
↓
Validation
↓
Blueprint
↓
Offer
↓
Launch
↓
Metrics
↓
Learning
↓
Next iteration
The public response does not expose:
- Email addresses
- Password data
- Password hashes
- Internal MongoDB identifiers
- Private GitHub identifiers
- Private verification metadata
- Authentication credentials
The database query also uses an explicit field projection so that unnecessary data is not loaded before the response is created.
Security automation
Security work was added directly to the development workflow.
The repository now includes:
- Automated security checks
- Continuous evidence gates
- Pull-request security verification
- Post-remediation security records
- Public security documentation
- Regression protection for critical routes
The latest Marketplace remediation recorded zero remaining HIGH-severity findings.
The goal is to make security evidence reproducible.
Instead of saying that a feature is secure, the project records what was tested and protects the result with automated checks.
Test coverage
Tests were added across the new services and integration boundaries.
The coverage includes:
- Marketplace behavior and security
- Metaverse public statistics
- Verified explorer responses
- ZORGAX pricing and product APIs
- Capital recommendations
- Capital decision lifecycle
- Confirmed inflow aggregation
- Digital idea validation
- Product blueprint generation
- Launch offer generation
- Metrics and learning
- Pilot enrollment
- Pilot onboarding
- First-project creation
- Idea ranking
- LIFE Pilot API routing
- Vercel route precedence
The final H4x0r release passed backend route tests and frontend syntax validation before deployment.
Production verification
The latest changes were committed to the main branch and deployed automatically through Vercel.
After deployment, we verified:
- The deployment reached the Ready state
- The production API returned HTTP 200
- H4x0r appeared in featuredCharacters
- Only approved public fields were present
- No sensitive fields were exposed
- The frontend displayed the verified explorer card
- The public GitHub link worked correctly
The release commit is available here:
feat(metaverse): publish verified explorer profiles
Engineering lessons
This development phase produced several useful lessons.
1. Production configuration is part of the codebase
A correct application can still fail because of runtime versions, environment variables, dependency metadata, or proxy routing.
2. Public APIs need explicit boundaries
Never expose complete database documents by default.
A public API should intentionally select and transform every field it returns.
3. Payment detection and payment confirmation are different states
Paid access should not be activated from a payment intent alone.
Settlement must be independently verified.
4. Automation needs authority limits
An intelligent system can generate recommendations and learn from outcomes without receiving permission to execute financial actions.
5. Integration tests protect the real product
Many important failures happen between components rather than inside individual functions.
Authentication, databases, frontend routing, deployment proxies, and settlement verification all require integration-level protection.
What comes next?
The next stages of MyZubster development include:
- Completing verified paid-access activation
- Expanding Marketplace services
- Adding more verified explorers to Neon Plaza
- Improving the LIFE Pilot user experience
- Strengthening production monitoring
- Publishing clearer governance and contributor records
- Exploring resilient and decentralized service distribution
- Continuing security and privacy improvements
MyZubster is still evolving, but it now has a much stronger foundation.
The ecosystem has persistent identities, working authentication, public services, economic experiments, guided project workflows, privacy-safe APIs, automated tests, and a verified production deployment.
If you want to inspect the code or contribute, visit:
MyZubster Ecosystem on GitHub
You can explore the live platform here:
www.myzubster.com