Nexus AI News

Nexus

A Jev-powered AI news feed.

Nexus ingests AI news from multiple sources, dedupes it, classifies each item with a single Jev (TypeSafe System One) call, and serves the result as a reverse-chronological feed with filters and a guarded, extractive chat search. No graph database, no generative LLM in the request path.

Sources (arxiv, hackernews, github, twitter, rss)
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dedup.ts (URL → title → arXiv-id → Jaccard + entity fingerprints)
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PostgreSQL raw_items
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Jev enrichment (TypeSafe System One, model jev-latest, 1 call/item)
   relevant? (Noul) · vertical (Choice) · event_type (Choice) · significance (Score)
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PostgreSQL feed_items (reverse-chron, indexed)
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Fastify API — GET /api/feed · GET /api/feed/meta · POST /api/chat
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React feed client — ActivityFeed, FilterBar, HotCards, guarded ChatBox

Quick Start

# 1. Start infrastructure (PostgreSQL)
docker compose up -d

# 2. Install dependencies
pnpm install

# 3. Build all packages
pnpm build

# 4. Start API and client dev servers (separate terminals)
pnpm dev:api
pnpm dev:client

# 5. Run the agent (source polling + Jev enrichment) separately
pnpm --filter @nexus/agent start

The client opens at http://localhost:5173 and the API serves at http://localhost:3001.

Monorepo Layout

Adding a Source Adapter

The primary contribution path is adding new data sources. Every adapter feeds raw items into the dedup + Jev enrichment pipeline, which classifies and inserts them into the feed automatically.

The RawItem Interface

// packages/agent/src/sources/types.ts
interface RawItem {
  source: string;        // adapter name, e.g. "arxiv"
  source_url: string;    // canonical URL for deduplication
  title: string;         // headline / paper title
  content: string;       // body text (Jev classifies, excerpt is sliced from here)
  published_at: string;  // ISO 8601 timestamp
  raw_metadata: Record<string, unknown>; // source-specific fields
}

Write Your Adapter

Extend BaseAdapter and implement fetchItems(). You get rate limiting, exponential retry with backoff, and URL-based deduplication for free.

// packages/agent/src/sources/my-source.ts
import type { RawItem } from "./types.js";
import { BaseAdapter } from "./base-adapter.js";

export class MySourceAdapter extends BaseAdapter {
  name = "my-source";
  priority = "P1" as const;

  constructor() {
    super({
      pollIntervalMs: 30 * 60 * 1000, // how often to poll
      rateLimitMs: 2000,               // min delay between requests
    });
  }

  protected async fetchItems(): Promise<RawItem[]> {
    const response = await fetch("https://api.example.com/items");
    if (!response.ok) throw new Error(`Fetch failed: ${response.status}`);

    const data = await response.json();
    return this.dedupeByUrl(
      data.map((item: any) => ({
        source: this.name,
        source_url: item.url,
        title: item.title,
        content: item.body,
        published_at: new Date(item.date).toISOString(),
        raw_metadata: { id: item.id },
      }))
    );
  }
}

Existing Adapters

Register Your Adapter

1. Add your adapter to the adapter list in packages/agent/src/start.ts:

import { MySourceAdapter } from "./sources/my-source.js";

const adapters: SourceAdapter[] = [
  new HackerNewsAdapter(),
  new ArxivAdapter(),
  new GitHubTrendingAdapter(),
  new MySourceAdapter(),          // add here
];

1. Write a test in packages/agent/src/sources/__tests__/ following existing patterns.
2. Run tests: pnpm --filter @nexus/agent test

Jev Enrichment

Every deduped RawItem gets exactly one call to TypeSafe's System One API (POST https://api.typesafe.ai/v1/systemone, model jev-latest, Bearer TYPESAFE_API_KEY). Jev is a decision model — it answers typed questions (Noul, Choice, Score) against structured JSON, it does not generate text. One call answers all four questions for an item:

• is_ai_relevant (Noul) — dropped if confidence < 0.6
• vertical (Choice) — one of the 21 Vertical values, or none
• event_type (Choice) — launch | funding | release | acquisition | paper | update | shutdown | other
• significance (Score) — a 5-point rubric mapped to 0.2–1.0

The excerpt shown in the feed is the first 280 characters of the source content — never an LLM summary, so it can't hallucinate.

Chat Search

POST /api/chat is a guarded, extractive search box, not a chatbot. It classifies the query with one Jev call (on-topic check, vertical/event-type/timeframe extraction), then returns real rows from feed_items via Postgres trigram search. There is no generative model anywhere in the chat path — there's nothing for a prompt injection to talk to.

MCP Server

The feed is exposed to agents as a public, read-only Model Context Protocol server over Streamable HTTP:

https://nexus.carapace.bot/mcp

Add it to Claude Code with claude mcp add --transport http nexus https://nexus.carapace.bot/mcp. Stateless (no sessions), extractive only — same validation as GET /api/feed, no model calls. Registry manifest: server.json.

Data Model

feed_items

id (uuid) · title · url (unique) · source · published_at · excerpt · vertical · event_type · significance (0.0–1.0) · created_at

Verticals

21 spatial-cluster categories, defined in packages/shared/src/types.ts (e.g. foundation_models, agents, safety_alignment, consumer_products, ...).

Event Types

launch | funding | release | acquisition | paper | update | shutdown | other

Contributing

1. Fork the repo and create a branch: feat/my-feature, fix/some-bug, or chore/cleanup
2. Write conventional commit messages: feat: add devto adapter, fix: handle empty RSS feed
3. Run checks before opening a PR:

pnpm test          # run all tests
pnpm lint          # eslint
pnpm typecheck     # tsc --noEmit

1. Keep PRs focused — one adapter or feature per PR.

Environment Variables

Tech Stack

• Database — PostgreSQL 16 (raw_items, feed_items, pg_trgm search)
• API — Fastify
• Client — Vite + React
• Search — Fuse.js (client-side, loaded items), pg_trgm (server, chat + feed search)
• Agent — TypeSafe Jev (System One) for enrichment and chat classification
• Deploy — Railway (Postgres + API) + Vercel (client)

Cost

Enrichment is roughly 1 Jev call per new deduped item, at ~50–100 items/day across all sources, plus 1 Jev call per chat query. There is no Neo4j, no Redis, and no generative LLM anywhere in the pipeline — that's the entire compute bill.

License

MIT