AI / Experimental

LinkedIn Professional Presence System

A private, evidence-grounded workflow for preparing professional LinkedIn content with human approval, privacy gates, audit trails, and mock-only publishing.

StatusExperimentalPeriodLocal prototype · July 2026Primary toolsPython 3.12 · SQLite

01 / Problem

What the system needs to solve.

Online-presence automation can save time, but it can also invent claims, expose private information, repeat weak content, or publish before a person has approved the result.

Verified evidence

What this case study rests on.

21 automated tests pass across workflow, security, privacy, scheduling, and idempotency behavior
The current milestone uses Python standard-library components and SQLite
The prototype is local-only, uses synthetic fixtures, and cannot publish to LinkedIn
Source is retained privately while the project is reviewed for a future public repository

02 / Goals

A clear target for the work.

01Keep Peter in control of every approval decision
02Trace every public claim back to authorized evidence
03Block unsafe, private, duplicate, or unsupported content
04Enforce a maximum of one prepared post in any seven-day period

03 / Features

What the current system supports.

Local dashboard and SQLite data model

Claim, privacy, authorization, quality, and duplicate gates

CSRF-protected mutations and loopback-only hosting

State-transition audit log and idempotent mock publishing

Deterministic evaluation fixtures and regression tests

Python 3.12SQLiteunittestSQL migrationsHTML/CSS

04 / Process

How the work moves forward.

1
Define the product boundary, publishing rules, and threat model
2
Model the workflow as explicit states and release gates
3
Build mock providers before considering any live integration
4
Test privacy, scheduling, approval, failure, and idempotency paths
5
Keep the system in shadow mode until evidence supports a safer next step

05 / Lessons

What the work is teaching.

Responsible automation is mostly about boundaries, evidence, and failure behavior—not a publish button.

Human approval works best when the software makes unsupported claims and privacy risks visible before review.

06 / Next steps

Make the next version stronger.

Keep LinkedIn publishing disabled while Peter's profiles and evidence library are still being built
Add live providers only after OAuth, permissions, and security review
Measure usefulness in shadow mode before expanding the workflow

Evidence standard

Live links, repositories, dates, screenshots, and result claims appear only after they have been supplied and verified. This project’s current status is Experimental.

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