Pulse JSON Feeds: Plug Real-Time Authority Intelligence Into Anything

Amygdala Pulse already tracks real-time intelligence across topics like AI, crypto, cybersecurity, healthcare, gaming, and geopolitics. Every finding is attributed to a verified authority, categorised, tagged, and ranked by impact. Most people encounter it through the web interface: browse a topic, read what authorities are saying, follow the signals.

What is less obvious: every Pulse topic has a structured JSON feed behind it. Append .json to any Pulse query and you get back the full dataset. Hundreds of findings, organised by auto-generated category taxonomy, with source attribution, popularity scores, raw signals (the actual quotes), and the ranked authorities behind each insight. All of it machine-readable, all of it ready to pipe into whatever you are building.

This post shows exactly what is in that JSON and what you can do with it.

How to access the JSON feed

One URL pattern:

https://amygdala.eu/pulse?q={topic}.json

For example:

https://amygdala.eu/pulse?q=artificial%20intelligence.json
https://amygdala.eu/pulse?q=crypto.json
https://amygdala.eu/pulse?q=cyber%20security.json

No API key needed for the public feed. Returns application/json. Works in any HTTP client, script, or browser. The q parameter is the topic name and the .json suffix is appended directly to it, not to the path.

What is in the JSON

The top-level structure looks like this:

{
  "topic": "artificial intelligence",
  "created": "2026-03-20T17:20:22.426944Z",
  "categories": { ... },
  "category_display_names": { ... },
  "source_ids": [ ... ],
  "top_terms": [ ... ],
  "authorities": [ ... ],
  "errors": [ ... ]
}

Here is what each key gives you.

categories: the core content

Each key in categories is a slug containing an array of findings. The AI topic currently has 20 categories and 685 total findings. Categories are dynamically generated per topic: the crypto feed has entirely different categories (bitcoin_btc_talk, altcoins_and_tokens, market_price_action, regulation_and_sec, stablecoins_payments, mining_hashrate_home_mining) that would not exist on an AI or healthcare feed. Amygdala auto-generates the taxonomy that fits the domain.

Some of the categories in the AI feed:

Each finding

Every item in a category array follows this structure:

{
  "subcategory": "trend",
  "title": "World models hype after Nvidia GTC",
  "description": "In r/artificial, Nvidia GTC chatter drives renewed interest in world
    models, while commenters argue the practical path is combining them with language
    models rather than replacing LLMs outright.",
  "popularity": 111.72,
  "date": "2026-03-30",
  "attributes": {
    "date": "2026-03-30",
    "tags": ["world models", "nvidia", "language models"],
    "description": "..."
  },
  "source_accounts": [
    {
      "sdu": null,
      "name": null,
      "subreddit": "artificial",
      "channel_id": null
    }
  ],
  "signals": [
    "world modelling is the actual GOAT of AI right now",
    "Your LLM may invoke a world model to simulate..."
  ]
}

Field by field:

• title: human-readable finding headline, suitable for display directly.
• description: synthesised narrative of what authorities are saying around this finding, already written for readability.
• popularity: impact score combining reach, authority weight, and engagement across sources. Higher means more signal from more authoritative voices.
• date: when this finding was captured. Use this field to build timelines or filter for recency.
• tags: topic tags nested under attributes. Useful for filtering, routing, or building tag clouds.
• source_accounts: attribution, who said it. Includes SDU identifiers linkable to the Authority Index, names, subreddit sources, and channel IDs.
• signals: the actual quotes and statements from authorities that formed this finding. Raw signal data, the building blocks before synthesis.

authorities

A ranked list of the authorities contributing to the topic's findings. Each entry includes the SDU (the Authority Index identifier), the authority's name, and an array of the findings they appear in:

{
  "sdu": "e7788a98-1087-4926-9dd7-fad2b8c0be7b",
  "name": "OpenAI",
  "appearances": [
    {
      "title": "AI engineering department run by manager agents",
      "category": "coworker_automation"
    },
    {
      "title": "ChatGPT in CarPlay",
      "category": "viral"
    }
  ]
}

The sdu field links directly to the Authority Index. Pass it to /api/v1/detail/ to get the full profile: bio, verified social handles with follower counts, authority score, and more. This makes the Pulse feed and the Authority Index a complete pipeline: what is happening right now, who is saying it, and how authoritative they actually are.

top_terms

The most frequent terms across all findings for this topic. Useful for tag clouds, trend detection across time, or routing logic that needs to know what the dominant concepts in a topic are right now.

What you can build with this

Here are eight concrete applications, mapped to the specific JSON fields they depend on.

1. AI-powered newsletter generator

Pull the top findings by popularity score, group them by category, and feed them into an LLM to generate a weekly digest. The signals array gives you raw quotes for attribution. The source_accounts tell you who to credit. category_display_names give you ready-made section headers.

A concrete version: a crypto newsletter that auto-generates sections for "Market Moves", "Regulation", "Bitcoin Talk", and "Altcoins", with every item attributed to the authority who flagged it, with the actual quote included.

2. Slack or Discord alert bot

Poll a Pulse JSON feed on a schedule. When popularity exceeds a threshold, fire an alert to a channel. Include the finding title, description, and the source authority. Filter by category slug to route alerts intelligently: send security_safety_and_policy findings to the policy team's channel, and industry_market_and_company_moves findings to the trading desk or competitive intelligence channel.

3. Competitive intelligence dashboard

Fetch multiple Pulse topics simultaneously and display findings across the industry_market_and_company_moves category with popularity trends over time. The date field on each finding lets you build timelines. The authorities array shows whose voices dominate each topic, useful for tracking which competitors, analysts, or organisations are shaping the conversation in your market.

4. RAG pipeline with real-time context

Inject Pulse findings as real-time context into your retrieval-augmented generation pipeline. When a user asks about a trending topic, check the Pulse JSON for the latest findings first, then use the signals array as grounding data for your LLM. This gives your AI current, attributed information instead of stale training data. For the full pattern, see our post on building a RAG pipeline with trusted, authority-ranked data.

5. Trend detection and early warning system

Track top_terms over time to spot emerging concepts before they hit mainstream coverage. Compare the trending category findings week-over-week to identify acceleration. The popularity scores give you a quantitative threshold to alert against: you decide what level of signal constitutes "breaking".

6. Content research and ideation tool

For content teams: pull findings from a relevant topic, use the tags to cluster related themes, and feed the description fields into an LLM with a prompt like "Based on these authority signals, suggest five article angles we should cover this week." Every angle comes pre-attributed to real authorities, so you know the ideas are grounded in actual discourse rather than hallucinated trends.

7. Authority monitoring: track who is shaping a field

Use the authorities array to track which voices appear most frequently across findings. Build a leaderboard of who is driving the conversation on any topic you care about. Cross-reference the SDUs with the Authority Index API to enrich with follower counts, bios, and authority scores. For how authority scoring works, see our methodology page.

8. Multi-topic intelligence aggregator

Fetch JSON feeds for multiple topics simultaneously (AI, crypto, cybersecurity, healthcare) and merge them into a single intelligence view. Use category_display_names as section headers and popularity to rank findings across topics. This is the pattern for building a command center that covers everything your organisation tracks, without maintaining separate data pipelines per domain.

Pulse vs. traditional news APIs

Traditional news APIs like NewsAPI or GDELT give you articles: headlines, URLs, and publication timestamps. Pulse gives you synthesised intelligence from verified authorities. Not a list of links to news articles, but structured findings with attributed quotes, impact scores, and an auto-generated category taxonomy that reflects what is actually being discussed in that domain right now.

The difference is the gap between "here are 500 articles about AI published today" and "here are the three things the most authoritative voices in AI are actively debating right now, with the actual quotes, ranked by impact." For building things that people want to read or act on, the second is the useful one.

The JSON feed is also topic-agnostic. Request any topic Pulse tracks and you get the same rich structure back. And new topics can be requested directly on the Pulse page. If a domain you need is not yet tracked, you can ask for it.

Getting started

Five steps to get your first feed running:

• Pick a topic from amygdala.eu/pulse, or request a new one on the same page.
• Append .json to the query: https://amygdala.eu/pulse?q=your%20topic.json
• Parse the response: findings are in categories, ranked authorities in authorities, trending terms in top_terms.
• Go deeper on any authority by passing their sdu to the Authority Index API for the full profile.
• Integrate with Claude or ChatGPT directly using the MCP server, no code required for conversational use cases.

A minimal Python script to pull and rank the top five findings across all categories:

import requests

# Fetch the latest AI Pulse findings
resp = requests.get("https://amygdala.eu/pulse?q=artificial%20intelligence.json")
data = resp.json()

# Collect all findings across categories and rank by popularity
all_findings = []
for category, findings in data["categories"].items():
    for f in findings:
        f["_category"] = category
        all_findings.append(f)

top_5 = sorted(all_findings, key=lambda f: f["popularity"], reverse=True)[:5]

for f in top_5:
    print(f"[{f['popularity']:.0f}] {f['title']}")
    print(f"  Category: {data['category_display_names'].get(f['_category'], f['_category'])}")
    print(f"  {f['description'][:120]}...")
    print()

Or a one-liner to inspect any topic feed:

curl -s "https://amygdala.eu/pulse?q=crypto.json" | python3 -m json.tool | head -50

Pulse JSON feeds are GDPR-compliant by design: all data is processed on European infrastructure, no personal data leaves the EEA, and every finding is derived from publicly available signals. For teams building in the EU, this matters. See our post on reducing LLM hallucinations with authority-ranked sources for more on why the provenance of your data layer is a product decision, not just a legal one.

If you are already using the MCP integration to query Amygdala from Claude, the MCP research post covers how the same authority data works across every domain: the Pulse feed and the Authority Index are two surfaces of the same underlying dataset.