All work

Q Ideas

Content recommendations arrived as large generated batches, while review decisions, revision reasons, production details, and outcomes needed to stay connected without changing the source research.

Role
Product Engineer
Type and status
Internal tool · Active development
Period
2026

See the system.

Actual interface evidence is embedded here. External links are secondary.

Q Ideas desktop review queue populated with fictional clients and ideas.
Synthetic demo data

The daily surface answers one question: what needs a decision now?

Local runtime, captured September 3, 2026
Q IdeasReview queue
Q Ideas desktop review queue populated with fictional clients and ideas.

The daily surface answers one question: what needs a decision now?

The operational problem

Content recommendations arrived as large generated batches, while review decisions, revision reasons, production details, and outcomes needed to stay connected without changing the source research.

A mobile-first Que Media workspace that turns immutable research batches into review decisions, production plans, and an auditable idea history.

Why this system exists

Separating immutable source batches from append-only review events protects provenance while giving the team a focused daily interface for deciding what should move toward production.

Research producers create checksummed batches. Q Ideas projects them into a review model, stores pending decisions locally, and imports validated review or outcome events into an append-only log.

Who uses itQue Media reviewers, strategists, and production operators.

How the work moves

  1. Receive research batch
  2. Validate checksum and schema
  3. Project review queue
  4. Inspect idea and evidence
  5. Approve, revise, or reject
  6. Plan production
  7. Append outcome
  8. Refresh history

What shaped the solution

Original repository images contain internal client ideas.

The public portfolio must use synthetic records.

What I owned

Designed

Immutable batch, review-event, production-plan, and client-workspace model

Developed

Review queue, idea library, client views, detail pages, and mobile navigation

Integrated

Batch exporters, approval import, and append-only outcome events

Tested

Checksum validation, immutability, event routing, deduplication, and responsive UI

Context matters. The work happened with teams and stakeholders. These statements describe my contribution without turning shared delivery into a solo claim.

Where AI fits

Model or agent role

Upstream research agents can discover, analyze, adapt, score, and structure content ideas.

Structured contract

Agents return a versioned, checksummed batch rather than free-form state changes.

Deterministic safeguards

Strict schemas, confidence, evidence references, duplicate checks, and append-only events protect the decision boundary.

Human decision

A person decides approval, revision, rejection, filmed, scheduled, or published state.

Decisions that carried the work

Keep source and decision separate

Checksummed research batches remain immutable while append-only events record review and production state.

Verified outcome

One review queue across clients and batches.

Review and production state remains separate from immutable source research.

Desktop, tablet, and mobile layouts are implemented.

What I will not overclaim

Portfolio captures intercept the data request and supply fictional clients, ideas, and metrics while preserving the real interface.

Verified from the Q Ideas repository, product documentation, local runtime, tests, and synthetic capture script.

Tools used where they fit

ReactViteJavaScriptJSONSHA-256Vitest

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