🚀 Building MyZubster: What if AI preserved uncertainty instead of inventing missing data?

Thu, 17 Sep 2026 01:19:10 +0000

🚀 Building MyZubster: What if AI preserved uncertainty instead of inventing missing data?

I'm building MyZubster, and I'm using a surprisingly simple real-world experiment to explore a much bigger software problem: milk kefir and community knowledge.

The question behind the project is:

How can we transform personal experience into structured community knowledge without turning experience into “verified truth”?

The problem

Imagine a user submits:

“I leave my kefir in the refrigerator for a few days.”

An AI system could easily transform that into:

duration: 72 hours
temperature: 4°C

It looks structured.

It also contains information the user never provided.

For MyZubster, that is a data-integrity problem.

Instead, the system should preserve:

duration:
  reported: "a few days"
  exact_value: UNKNOWN

temperature:
  environment: refrigerator
  measured_value: UNKNOWN

UNKNOWN is not an error. UNKNOWN is data.

It tells us what should be measured during the next real experiment.

Evidence-aware Knowledge Cards

I'm building the first version as a Git-based Community Knowledge Library.

Every practice receives a stable ID:

KF-001
KF-002
KF-003
...

The current repository contains cards for:

base milk-kefir practice
strained kefir
whey reuse experiments
pizza, focaccia and baking experiments
dehydrated kefir mixtures
a real community pilot
a reusable community contribution template

But these aren't treated as automatically verified recipes.

Each card carries an evidence state:

PERSONAL_PRACTICE
TRADITIONAL_PRACTICE
OBSERVATION
EXTERNAL_SOURCE
VERIFIED_GUIDANCE

A contributor saying:

“I did this.”

is useful information.

But it is not equivalent to:

“This has been scientifically verified.”

That distinction is part of the data model.

AI should normalize — not manufacture certainty

The future pipeline looks like this:

community submission
        ↓
preserve raw contribution
        ↓
AI-assisted normalization
        ↓
mark missing fields UNKNOWN
        ↓
validate against invented data
        ↓
assign KF-###
        ↓
generate Knowledge Card
        ↓
community reproduction
        ↓
observations
        ↓
external evidence

The AI contract is intentionally constrained:

DO:

extract
structure
connect
identify missing information
suggest what to measure next

DON'T:

invent
erase provenance
silently upgrade evidence
confuse inference with observation

One of the validation mechanisms I'm exploring is detecting transformations such as:

SOURCE:
"a few days"

GENERATED:
"72 hours"

→ UNSUPPORTED_PRECISION

or:

SOURCE:
"kept in refrigerator"

GENERATED:
"4°C"

→ UNSUPPORTED_PRECISION

Other potential validation states:

UNSUPPORTED_CAUSATION
UNSUPPORTED_SAFETY_CLAIM
MISSING_PROVENANCE
MISSING_CONSENT
EVIDENCE_STATE_ESCALATION
SOURCE_NOT_TRACEABLE

Why Git first?

Right now, the knowledge layer is intentionally simple: Markdown + Git.

That immediately gives the project:

human-readable data
version history
diffs
stable files
commit history
auditability
portability

If somebody changes:

temperature: UNKNOWN

to a measured value after a real experiment, the history remains visible.

Git isn't necessarily the final database.

It's a very useful first knowledge ledger.

From recipes to a knowledge graph

The interesting part is that these objects aren't isolated.

For example:

milk + culture
      ↓
    kefir
      ↓
   draining
    ↙    ↘
thick     whey
fraction    ↓
   ↓      reuse
spread     ↓
   ↓    experiment
   └──────┬──────┘
          ↓
      observation
          ↓
   community knowledge

Eventually this can become a graph:

Contributor
    │
    │ contributed
    ▼
  KF-002
    │
    │ produces
    ▼
   Whey
    │
    │ reused in
    ▼
  KF-003
    │
    │ reproduced by
    ▼
Participant B

And evidence can eventually move from card-level evidence to claim-level evidence.

The bigger idea

Kefir is only the pilot.

The same architecture could potentially represent community knowledge around:

fermentation
gardening
seed saving
cooking
repair
craft
maker projects
local observations

The underlying problem remains the same:

How do we preserve useful human knowledge without erasing where it came from or pretending it has stronger evidence than it actually does?

My current rule for MyZubster is:

Don't manufacture certainty. Preserve provenance, record observations, expose unknowns, and let evidence accumulate.

The experiment started with a jar of kefir.

Now I'm using it to explore what an evidence-aware, AI-assisted community knowledge system could look like.

🔗 Project:
https://github.com/DanielIoni-creator/Myzubster-fermentation-kefir

I'd especially like feedback from developers working on AI data pipelines, knowledge graphs, provenance, community platforms, or evidence-aware systems.

How would you design the boundary between AI-assisted normalization and unsupported inference?

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