MindsEye Hunting Engine — AI-Built, Human-Refined, and Production-Ready Submission for the Xano AI-Powered Backend Challenge

Source: Dev.to

Overview

The MindsEye Hunting Engine is a production‑grade backend system designed to analyze distributed system events, detect failures, group related occurrences, run investigations (hunts), and provide a simple public API for external developers. This submission falls under the Production‑Ready Public API challenge.

The MVP’s goal is to prove that AI can generate a complete backend foundation and that a human developer can transform it into a reliable, scalable system.

Public API Endpoint (Live)

Debug Counts Endpoint

curl -X GET \
  'https://x8ki-letl-twmt.n7.xano.io/api:Mx6Nh7jm/debug_mindseye_counts' \
  -H 'Content-Type: application/json'

Example Response

{
  "result1": {
    "source_count": 16,
    "stream_count": 36,
    "events_count": 36,
    "hunts_count": 1,
    "hunt_run_count": 4,
    "event_annotation_count": 49
  },
  "debug_summary": 4
}

This endpoint confirms database health, dataset completeness, hunt readiness, event density, and annotation activity, demonstrating that all tables are correctly linked.

Screenshots and Diagrams

Screenshot 1 – API Response

Screenshot 2 – Function Stack

Screenshot 3 – Workflow Canvas Diagram

Screenshot 4 – Database Schema

Backend Architecture

Data Model Summary

• source – Represents origins of system events (key, name, kind, environment).
• stream – Logical channels tied to sources (key, name, source reference, description).
• events – Core log dataset (timestamps, severity, JSON payload, source and stream relationships).
• hunts – Definitions of investigative queries (time windows, labels, linked event sets, result metadata).
• hunt_run – Execution history for hunts (run status, matched event counts, detailed JSON summaries).
• event_annotation – Human or AI tagging system for events (notes, tags, metadata enrichment).

AI Prompts Used During Development

• Prompt A – Database Schema Generation – Produced the full relational schema with indexes and reference fields.
• Prompt B – Seed Data Generation – Created a large synthetic dataset (sources, streams, events, hunts, annotations) aligned with the schema.
• Prompt C – API Workflow for Debug Counts – Generated the initial version of the debug endpoint, later refined manually.

These prompts illustrate the human‑in‑the‑loop pipeline.

Human Refinements After AI Generation

• Fixed foreign‑key mismatches between tables.
• Added missing indexes for performance.
• Corrected invalid field types (e.g., timestamps vs. integers).
• Ensured events correctly reference source_id and stream_id.
• Repaired hunt linkage arrays.
• Implemented proper time‑window validation logic.
• Built a structured debug response for public consumption.
• Guaranteed that no function silently returned empty data.
• Verified all relational mappings with live runs.

This hybrid approach reflects Xano’s intended model: AI sparks, human completes.

API Usage for External Developers

• Validate backend health.
• Retrieve counts across all subsystems.
• Confirm relational integrity.
• Build dashboards around event volumes.
• Extend the hunting engine with specialized endpoints.

The design encourages future expansion.

Experience Using Xano

Xano provided a strong foundation for mixing AI generation with human engineering. The XanoScript extension enabled rapid structure creation, while the visual function stack made refinements straightforward. Debugging tools were especially helpful for diagnosing empty responses or misaligned joins. Overall, Xano felt like an AI‑powered junior engineer paired with a senior human operator.

Future Scaling of the MindsEye Hunting Engine

Planned enhancements include:

• Real‑Time Ingestion – Streaming data via webhooks or event‑bus integrations.
• Hunt Templates – Reusable investigations (e.g., “error bursts,” “spike detection,” “severity clustering”).
• ML‑Based Classification – Vector embeddings for anomaly grouping, automatic labeling, and event clustering.
• Multi‑Tenant Support – Isolated hunts for multiple developers or clients.
• Frontend Dashboard – Timeline visualizations, heatmaps, hunt analytics, and annotation tools.
• Auto‑Healing – Automated remediation triggered by hunt outcomes.
• API Documentation Portal – Full developer‑facing docs with usage examples.

These additions position MindsEye as a lightweight observability engine suitable for small teams, student projects, and AI‑powered systems.