luckdragon

A people explorer for a conference room. Each attendee gets their own FalkorDB graph, built from their public footprint. You pick someone, see their graph next to yours, and ask what you have in common. Every claim cites the nodes it came from.

You walk into a room of strangers with name tags. What they have shipped, talked about, and worked on is spread across LinkedIn, GitHub, talks, and posts. Luckdragon pulls that public footprint into one graph per person and answers questions against those graphs.

Features

Before you walk up to someone here, luckdragon tells you who to meet, why now, what you share, and what to say, and shows you the evidence. Blue is you, amber is them, everywhere.

1. Who to meet (the home screen)

• Who to meet: people ranked by what you have in common in public work, each with why now (their newest post, talk, or repo on something you share), you share, and a line to say.
• How it works: a three-step explainer beside the list for first-time visitors.
• People: a plain directory with search; "+ Add person" takes a name, LinkedIn URL, or Luma URL and builds their graph live.

2. About this person (the person page)

• The Brief: Who, Lately, Common ground, How you connect, and a Best opener weighed against two alternates and scored with its reason.
• Follow-ups: ask anything about them; the agent answers from their graph with read-only Cypher.
• How you connect: the path FalkorDB found, drawn: direct (you → shared topic → them) and intro paths through a mutual person, plus what you share and why now.
• Your own Brief: select yourself to see what the room sees about you.

3. Why you can trust it (the evidence)

• Citations on every line: each claim ends with chips for the facts it uses; hover to see which graph they're in.
• How I know this: a one-line trace under each answer opens the exact graph queries: what each looked for, the hop pattern, rows, time, and the Cypher.
• Full graphs: both people's graphs side by side, with a card for every fact (source URL, date, confidence, how the account was matched).
• Data sources: when each graph was fetched, which sources succeeded or failed, and which Bright Data product produced which facts.
• Sensitive topics hidden: a header switch, on by default (its i explains it); graphs, matches, and answers leave out health, religion, politics, ethnicity, orientation, family, and age.

Under the hood

• Live web data: Bright Data Web Scraper API (LinkedIn, X, YouTube, GitHub, Instagram, Threads, Bluesky, Reddit, Crunchbase, npm/PyPI), SERP API, and Web Unlocker; each source fails on its own with a retry, and the rest are kept.
• One FalkorDB graph per person (person:<slug>) plus a room graph projected from them, so who to meet, why now, and intro paths are multi-hop Cypher.
• MCP server: /api/mcp gives Claude, Cursor, or any MCP client list_people, best_matches, brief, ask, run_cypher, and graph_summary.
• Live: https://luckdragon.nishilfaldu.site (read-only; ingest runs on the presenter's laptop).

How it works

One FalkorDB graph per person

Graph name: person:<slug> (graphName in lib/falkor.ts). There is no shared room graph. Re-ingesting the same slug hits the same graph.

The root Person node id is always person_root. Every other id is <type-lowercase>_<first 10 hex of sha1(type + '|' + key)>, where key is the source URL when there is one, otherwise the normalized name (lib/graph/ids.ts).

Every node stores id, label, source_url, fetched_at, and confidence (0–1). Account nodes also store matched_via: luma, search, or agent.

Schema

Node	Properties
Person (exactly one, id person_root)	name, headline, avatar_url, location, luma_url
Account	platform, handle, url, followers, matched_via
Organization	name, kind (company / school / community), url, industry, size
Post	platform, text, url, posted_at, likes
Video	title, url, published_at, views, kind (talk / podcast / video)
Repo	name, url, description, stars, language, pushed_at
Package	registry, name, url, downloads
Article	title, url, published_at, outlet
Topic	name (free text from the model)
Skill	name
Location	name

Edge	From → To	Properties
HAS_ACCOUNT	Person → Account
WORKS_AT / WORKED_AT	Person → Organization	title, start, end
STUDIED_AT	Person → Organization	degree, start, end
AUTHORED	Person → Post, Video, Repo, Package, or Article	at
POSTED_ON	Post or Video → Account
ABOUT	Post, Video, Repo, or Article → Topic
USES	Repo → Skill
HAS_SKILL	Person → Skill
INTERESTED_IN	Person → Topic	weight
BASED_IN	Person → Location

Writes go through writeGraph in lib/graph/write.ts: batched MERGE on id, 80 rows at a time. Running ingest again is safe.

Ingest

lib/ingest/pipeline.ts, started by npm run ingest or POST /api/ingest/[slug].

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