KnowledgeOS Local AI Workstation Baseline — 2026-07-09

Status: Public-safe documentation summary

Scope: KnowledgeOS private governance layer, LocalAI, Open WebUI and OpenCode-style local workflows

Runtime impact: none

Sensitive data involved: none

Change type: documentation / governance model

Purpose

This document records a public-safe summary of the KnowledgeOS local AI workstation baseline.

The purpose is to document how local AI tooling can be introduced into a private knowledge and repository workflow without bypassing Git traceability, human review, public/private boundaries or data classification.

This is not a production AI platform description. It is a documentation and governance note for a local workstation model.

Executive summary

A new local AI workstation baseline was added to the private KnowledgeOS control layer.

The baseline defines how these components should relate to each other:

KnowledgeOS
  = private memory, decisions, prompts, templates and governance

technical-documentation
  = public-safe method, evidence and documentation hub

OpenCode-style workflow
  = repo-aware AI assistance with ask-before-write boundaries

LocalAI
  = local OpenAI-compatible inference/API layer

Open WebUI
  = local browser UI for AI interaction

GitHub
  = branch, pull request, review and audit trail layer

The key decision is that local AI tools are introduced only after boundaries are documented.

Why this update matters

Local AI tooling can easily blur boundaries between:

• private notes and public documentation
• local runtime data and source-controlled evidence
• model output and verified facts
• AI-assisted drafting and human-approved documentation
• safe local tests and uncontrolled automation

The KnowledgeOS baseline reduces this risk by making the architecture, data classification, AI permissions and first safe test path explicit before any local AI stack is treated as operational.

Documents added in KnowledgeOS

The KnowledgeOS update introduced documentation for:

Control model

The control model is intentionally conservative:

Git baseline
-> documented boundaries
-> local-only harmless test
-> reviewed output
-> KnowledgeOS summary
-> optional public-safe promotion
-> branch
-> pull request

Raw private notes do not move directly into public documentation, Open WebUI knowledge bases or LocalAI test datasets.

Public/private boundary

KnowledgeOS remains the private working-memory and governance layer.

This documentation hub receives only reviewed, public-safe summaries.

Material that should remain private includes:

• raw KnowledgeOS notes
• job-search context
• personal profile details
• private AI chat logs
• customer or employer material
• credentials, tokens or recovery material
• unredacted logs or screenshots
• confidential operational context

If a note is useful but private, the safe path is to write a sanitized summary rather than promoting the source material.

Data classification summary

The local AI workstation baseline separates data classes before tool use.

The core rule is that local AI tools may help structure knowledge, but they must not become an uncontrolled memory store, secret sink or evidence repository.

Tool boundaries

OpenCode-style workflow

Allowed role:

• read-first repository orientation
• documentation drafting
• runbook clarification
• repo inventory support
• PR summary drafting
• controlled branch-based work

Boundary:

• ask before writes
• no direct pushes to main
• no independent merges
• no secret handling
• no unattended runtime-impacting changes

LocalAI

Allowed role:

• local inference experiments
• local model API testing
• non-sensitive prompt testing
• backend provider for local AI UI workflows

Boundary:

• localhost-only by default
• no customer data
• no secrets
• no raw vault ingestion
• no committed model/cache data

Open WebUI

Allowed role:

• local browser UI for AI interaction
• safe prompt experiments
• model behavior comparison
• sanitized documentation drafting

Boundary:

• no public exposure by default
• no raw KnowledgeOS mirror
• no unreviewed file upload or knowledge-base ingestion
• no confidential chat-history accumulation

Recommended local service map

A recommended local port map was documented to reduce service collisions:

The default network posture is localhost-only unless a separate hardening decision is documented.

What changed and what did not

Changed

• local AI workstation architecture was documented
• LocalAI and Open WebUI boundaries were documented
• local AI data classification was documented
• a safe first-test runbook was added
• OpenCode project config template was added
• local model/runtime data exclusions were added to .gitignore

Did not change

• no runtime code was added
• no Docker Compose stack was started or modified
• no production deployment path was changed
• no customer data was introduced
• no secrets were introduced
• no raw private KnowledgeOS notes were promoted
• no automation was enabled

Evidence and review path

The KnowledgeOS change was created as a branch-based documentation update and opened for pull request review.

Evidence model:

KnowledgeOS branch
-> documentation-only commits
-> PR review
-> no merge until human approval

This follows the same operating principle as the earlier KnowledgeOS AI workflow rollout: boundaries before automation and review before promotion.

Portfolio interpretation

This update strengthens the portfolio theme from documentation-driven DevSecOps toward controlled local AI adoption.

It demonstrates:

• local AI governance thinking
• private/public data separation
• repo-aware AI permission boundaries
• Git-based review discipline
• AI-assisted work without overclaiming autonomy
• practical workstation-level risk classification

The correct claim is not:

I built a production local AI platform.

The correct claim is:

I documented a controlled local AI workstation baseline that defines how local AI tools can be introduced with Git traceability, data classification, human review and public/private boundaries.

Next safe steps

Recommended follow-up actions:

1. Review and merge the KnowledgeOS local AI workstation PR only after human review.

2. Keep this documentation hub summary public-safe and separate from raw private notes.

3. Consider linking the new KnowledgeOS local AI workstation baseline from the portfolio narrative.

4. Run only a harmless local-first test before using any private material.

5. Record future test results as summaries, not raw AI chat dumps.

One-sentence summary

The KnowledgeOS local AI workstation baseline documents how OpenCode-style workflows, LocalAI and Open WebUI can be introduced as controlled local tools without turning private notes, model outputs or runtime data into unmanaged evidence or public documentation.