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Daily Edge - Technical Intelligence Automation
Plain-English summary: A private automation pipeline for daily technical intelligence reports, validation, publishing handoff, and downstream workflow readiness checks.
Executive summary
Built a private automation runner that turns daily technical research into validated report artifacts and controlled downstream workflow handoff.
Python
CLI
Docker
n8n
Validation
Automation
Security Hygiene
Technical Architecture
Problem
- Daily technical intelligence work needed repeatable structure instead of ad hoc manual handling.
- Generated reports needed fail-closed validation before publishing or downstream processing.
- Automation boundaries had to keep private content, credentials, and connected-service details out of public artifacts.
What I built
- Implemented CLI commands for report initialization, validation, publishing handoff, and workflow readiness checks.
- Added validation for report naming, structure, placeholders, private-data patterns, and credential-like content.
- Separated content generation, validation, publishing, and downstream automation responsibilities for safer operation.
My role
- I designed the automation architecture, CLI behavior, validation rules, and container-friendly operation model.
- I kept private reports and connected-service internals out of public documentation.
- I treated workflow readiness as an operational control, not a marketing metric.
Constraints
- This is private automation, not a public news product.
- Raw report content, connected-service internals, automation endpoint details, and runtime state files remain private.
- No public usage or adoption claims are made.
Outcomes
- A repeatable report workflow with stronger validation and safer publishing handoff.
- A clearer separation between research output, automation control, and downstream processing.
- A useful proof point for practical AI-assisted workflow operations without exposing private content.
Architecture decisions
- Fail-closed validation for generated artifacts.
- Automation boundary design for private workflows.
- Operational readiness checks before downstream handoff.
Public evidence
This page documents the runner model, CLI stages, validation boundaries, and controlled publishing handoff. Raw reports and connected-service state remain private.
Proof limits
No private report content, webhook, credential, runtime state, public adoption, or autonomous publishing claim is exposed. The system assists a controlled workflow.
Related work
AI Workflow Library · Evidence Capture MCP