Build on the discourse graph

AIDRAN tracks how the world talks about AI — ingesting public discourse from seven platforms, enriching it with semantic analysis, and exposing everything through a structured REST API. Use these docs to understand the system, query its data, or contribute to its development.

7 sourcesPlatforms
15 endpointsREST API 12.3% WoW
18 jobsCron pipeline 5.8% WoW

How It Works

From raw social posts to structured intelligence in four stages. Every step runs as TypeScript serverless functions on Vercel.

1

Ingest

Cron workers pull public discourse from 7 platforms via rate-limited API adapters.

Reddit, Bluesky, X, YouTube, Hacker News, GDELT, The Guardian

2

Process

Deduplicate, normalize, and generate 512-dim embeddings via Voyage AI.

Batch analysis with Claude for sentiment, topics, entities

3

Analyze

Signal detection identifies anomalies, trend shifts, and narrative opportunities.

Semantic clustering, cross-source correlation, gap analysis

4

Publish

AIDRAN generates editorial narratives from structured intelligence data.

Lead stories, beat narratives, dispatches, entity profiles

API Endpoints

15 endpoints across 9 groups. Here are the ones developers reach for first.

Quick Start

Request
curl -s https://aidran.ai/api/v1/topics \
  -H "x-api-key: your_key_here" | jq '.data[:3]'
Response
[
  {
    "slug": "ai-regulation",
    "name": "AI Regulation & Policy",
    "totalRecords": 12847,
    "sentiment": { "positive": 0.31, "neutral": 0.48, "negative": 0.21 }
  },
  {
    "slug": "open-source-models",
    "name": "Open Source AI Models",
    "totalRecords": 9203,
    "sentiment": { "positive": 0.54, "neutral": 0.33, "negative": 0.13 }
  },
  ...
]

Documentation

The Stack

TechnologyRole
Next.js 16App Router, API routes, ISR, Turbopack
TypeScriptEnd-to-end — ingestion to frontend
Neon PostgresServerless Postgres + pgvector embeddings
Drizzle ORMType-safe schema-as-code
Claude + Voyage AINarrative generation + embeddings
VercelDeployment, cron, edge functions