MyZubster Is Not Trying to Build Another App — We're Exploring a Verifiable Digital Ecosystem For years, software development has largely followed the same pattern: User → Application → Database → Service AI changed part of that equation.
IoT changed another part.
Blockchain introduced new models for provenance and ownership.
But there is still a difficult problem connecting all of them: How can a digital system verify what actually happened in the real world?
This is one of the questions driving the development of MyZubster.
MyZubster is an Italian open-source digital ecosystem currently under development.
It hasn't reached its final public form yet.
And that's important.
Because we're not presenting a finished platform.
We're documenting how the architecture evolves.
From application to ecosystem Calling MyZubster simply an "app" increasingly feels incomplete.
The architecture we're exploring connects several layers: The goal isn't to put every technology imaginable into one application.
The interesting part is the connection between these layers.
AI needs evidence Generative AI can produce extraordinary outputs.
But generation and verification are fundamentally different operations.
An AI system can say: "This intervention reduced water consumption by 30%." But where did that number come from?
What sensor produced the original measurement?
What period was compared?
What methodology was used?
Was the dataset modified?
Can somebody reproduce the calculation?
This leads to a principle we're increasingly using when thinking about MyZubster: AI ≠ Source of Truth Instead: Evidence ↓ AI ↓ Interpretation ↓ Verification ↓ Decision AI becomes a tool operating on evidence, rather than a machine expected to manufacture truth.
Connecting software to physical reality This becomes particularly interesting with IoT.
Imagine an environmental pilot containing: soil sensors water meters weather data field observations images timestamps GPS/context information Collecting those values isn't enough.
We need provenance.
A measurement should ideally answer: WHAT was measured?
WHERE?
WHEN?
BY WHICH DEVICE?
USING WHICH METHOD?
WHO/WHAT processed it?
WHAT transformation occurred?
CAN IT BE REPRODUCED?
Only then can we begin transforming raw measurements into meaningful digital evidence.
Why we're exploring MRV This is also why we're increasingly interested in MRV: Measurement, Reporting and Verification.
A simplified pipeline might look like: REAL EVENT ↓ MEASUREMENT ↓ RAW DATA ↓ PROVENANCE ↓ PROCESSING ↓ KPI ↓ REPORT ↓ VERIFICATION This model is useful far beyond environmental projects.
It could eventually matter for: sustainability; circular economy; agriculture; digital identity; decentralized systems; IoT; supply chains; community contributions; public-interest infrastructure.
The LIFE 2027 direction We're currently exploring whether a focused part of this architecture could eventually support a future LIFE 2027 proposal.
The important word is exploring.
This is not an announcement of EU funding or a finalized consortium.
The work happening now is about understanding what could realistically be measured and validated.
We're beginning conversations around areas such as environmental data, agronomic information, circular water, irrigation reuse, scientific methodology, KPI/MRV and data governance.
The question isn't: How do we fit MyZubster into a European project?
The better question is: Is there a measurable environmental problem where this architecture can demonstrate something useful?
That's a much harder question.
And therefore a much more interesting engineering problem.
Open source becomes part of verification Open source normally means that people can inspect the code.
But imagine extending that principle.
Developers inspect the implementation.
Researchers inspect the methodology.
Machines inspect structured evidence.
Communities inspect results.
Independent contributors attempt reproduction.
CODE + DATA + METHODOLOGY + EVIDENCE + REPRODUCI