<div align="center">

RECON — AI Research Engine That Finds What Others Miss

Free alternative to Perplexity Pro, Elicit, Consensus, SciSpace

One command. YouTube, papers, podcasts — scored, filtered, and loaded into a queryable NotebookLM notebook.

</div>

---

What This Does

One command:

research("prompt engineering techniques", sources=["youtube", "papers"])

What happens:

1. Searches YouTube (25 candidates), OpenAlex (474M papers), Podcast Index
2. Grabs transcripts and scores them for substance — hedging language, failure discussion, specific data
3. Scores channel credibility — clickbait detection, upload consistency, description depth
4. Auto-searches for contrarian viewpoints (criticism, limitations, risks)
5. Selects top sources across all types, creates a NotebookLM notebook
6. Updates your knowledge graph — connects concepts across sessions
7. Registers in the feedback loop — learns which channels actually produce results

Next research session? The system already knows what worked last time.

---

Why Not Just Use Perplexity?

Feature	Perplexity Pro	Elicit	RECON
Source scoring	No — shows top results	Citation count only	8-factor scoring (substance, credibility, engagement, recency, velocity, relevance, transcript, feedback)
Transcript analysis	No	No	Analyzes what the speaker actually said — hedging, failures, specificity
Clickbait detection	No	N/A	Penalizes ALL CAPS titles, sensational language, empty descriptions
Knowledge graph	No	No	Connects concepts across sessions, finds gaps, suggests next research
Feedback loop	No	No	Tracks which sources produced real results — boosts them in future searches
Expert mode	No	No	Inverts popularity scoring to find practitioners over influencers
NotebookLM integration	No	No	Creates queryable notebooks you can have conversations with
Contrarian search	No	No	Auto-searches for criticism and opposing viewpoints
Cost	$20/month	$10/month	$0/month

---

Expert Mode

Most research tools find you popular content. Popular = mainstream consensus. RECON has an expert mode that inverts popularity scoring to find practitioners instead of influencers:

Signal	General Mode	Expert Mode
Views	High views = good	5K-50K sweet spot (practitioners, not mainstream)
Velocity	Trending = surface first	Removed entirely (trending = consensus)
Transcript	14% weight	24% weight (substance over hype)
Credibility	9% weight	20% weight (practitioners over influencers)
Engagement	Like ratio	Like ratio (same — honest signal either way)

The thesis: a 12K-view video from someone who discusses what went wrong and cites specific data is worth more than a 500K-view video that says "this technique is GUARANTEED to work."

research("RAG architecture", mode="expert", sources=["youtube", "papers"])

---

Architecture

research("prompt engineering")
         |
    +----+--------------------+
    v                         v
[YouTube API]           [OpenAlex API]          [Podcast Index]
 25 videos               20 papers               20 episodes
    |                         |                       |
    v                         v                       v
[Transcript              [Paper                  [Podcast
 Analyzer]                Scorer]                  Scorer]
 - substance              - citations             - episode count
 - hedging                - open access           - regularity
 - failures               - journal tier          - description
 - specificity            - recency               - relevance
    |                         |                       |
    +----+--------------------+-----------------------+
         v
[Credibility Scorer]
 - clickbait detection (45%)
 - upload consistency (25%)
 - description substance (20%)
 - channel age (10%)
         |
         v
[Contrarian Search]
 - auto-searches "topic + criticism/problems/risks"
 - reserves 1 slot for the best opposing viewpoint
         |
         v
[Top N Sources Selected]
         |
    +----+----+
    v         v
[NotebookLM]  [Knowledge Graph]
 notebook       entities
 created        edges
    |            |
    +----+-------+
         v
[Feedback Loop]          [Outcome Tracker]
 learns which             tracks which research
 channels work            produced real results

---

9 MCP Tools

Tool	What It Does
research	Full pipeline: multi-source search, notebook creation, graph update
search_videos	YouTube search with transcript + credibility scoring
search_papers	OpenAlex academic paper search (free, 474M+ papers)
list_research_notebooks	List all auto-created NotebookLM notebooks
rate_research	Rate a notebook 1-5 — feeds back into future scoring
suggest_research	AI-suggested topics from knowledge graph gaps
knowledge_map	Visualize concept connections across all research
track_edge	Record whether research produced real results
edge_report	ROI report — which sources/channels actually deliver

---

Substance Detection

Most YouTube scoring looks at views and likes. RECON looks at what the person actually said.

Three signals that are hard to fake in a 20-minute video:

1. Hedging Language (30% of substance score)

Nuanced thinkers acknowledge complexity:

"on the other hand", "it depends", "the tradeoff", "there are exceptions", "context matters"

2. Failure Discussion (35% — heaviest factor)

Practitioners talk about what went wrong:

"doesn't work when", "the risk is", "I was wrong", "lesson learned", "the hard way"

3. Specificity (35%)

Grounded speakers cite data:

Years, percentages, dollar amounts, "according to", "research shows", "study found"

A video can have 1M views and perfect engagement but still score low on substance if the speaker never hedges, never discusses failures, and never cites specific data.

---

Credibility Scoring

What got dropped: Self-reported credentials. Anyone can type "10 years experience" in their channel description. Meaningless.

What replaced it:

Factor	Weight	Why
Clickbait Detection	45%	Most honest signal. "GUARANTEED RESULTS" is unfakeable garbage.
Upload Consistency	25%	Regular uploads over years = committed to the craft
Description Substance	20%	Length + specificity (URLs, dates, contact info)
Channel Age	10%	Weak signal — punishes early adopters of new topics

Red flags that trigger penalties:

• Channel under 3 months old
• Fewer than 10 videos
• 30%+ clickbait titles
• 50%+ ALL CAPS titles
• Empty channel description

---

Knowledge Graph

Every research session extracts entities (topics, concepts, channels, authors) and links them.

After 5+ sessions, the graph reveals:

• Knowledge Gaps — concepts that keep appearing but haven't been researched directly
• Cross-Domain Insights — entities that bridge different research areas (e.g., "embeddings" appears in both NLP AND image generation research)
• Bridge Concepts — central nodes that connect many topics in your expertise
• Stale Notebooks — time-sensitive research decays in 14 days, general in 30

suggest_research()
  -> "You've researched 'RAG' 3 times — go deeper"
  -> "'vector databases' appears across 4 notebooks but was never researched directly"
  -> "'embeddings' bridges NLP and computer vision research — cross-domain opportunity"
  -> "Research on 'LLM fine-tuning' is 18 days old — URGENT: refresh"

---

Outcome Tracker

The feedback loop most research tools are missing:

Research -> Implement -> Results -> Better Research

After you implement something from a research notebook:

track_edge(notebook_id="research-rag-architecture", result="edge", notes="New chunking strategy improved retrieval accuracy 23%")

The system learns:

• Which channels produce real results (boosted in future searches)
• Which topics have high success rates (surfaced first in suggestions)
• Which source types deliver (YouTube vs papers vs podcasts)
• Overall research ROI — what % of sessions produced actionable insights

edge_report()
  -> "3Blue1Brown: 3/4 sessions produced results (75%)"
  -> "AI topic success rate: 42% (above average)"
  -> "YouTube delivers 2x more actionable content than papers for applied topics"

---

Setup

1. Clone and install

git clone https://github.com/itsjwill/RECON.git
cd RECON
python -m venv .venv && source .venv/bin/activate
pip install -e .

2. Environment variables

cp .env.example .env
# Edit .env:
YOUTUBE_API_KEY=your_key_here  # https://console.cloud.google.com/apis/credentials

YouTube API key is the only requirement. OpenAlex (papers) needs no key. Podcast Index is optional.

3. Add to Claude Code MCP config

{
  "mcpServers": {
    "auto-research": {
      "command": "/path/to/RECON/.venv/bin/python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/RECON"
    }
  }
}

4. Use it

research("prompt engineering techniques", sources=["youtube", "papers"])

---

Cost

Component	Cost
YouTube Data API	Free (10,000 units/day)
OpenAlex Papers	Free (no key, 100K req/day)
Transcripts	Free (youtube-transcript-api)
NotebookLM	Free (Google account)
Podcast Index	Free (optional, needs registration)
Total	$0/month

---

File Structure

RECON/
├── src/
│   ├── server.py            # MCP server — 9 tools
│   ├── config.py            # Environment + settings
│   ├── youtube_search.py    # YouTube API + 8-factor scoring + expert mode
│   ├── transcript.py        # Transcript extraction + substance detection
│   ├── credibility.py       # Channel credibility (clickbait, consistency)
│   ├── paper_search.py      # OpenAlex academic paper search
│   ├── podcast_search.py    # Podcast Index search
│   ├── notebook_manager.py  # NotebookLM browser automation
│   ├── library_sync.py      # Shared library.json management
│   ├── feedback.py          # Usage tracking + stale detection
│   ├── knowledge_graph.py   # Cross-notebook entity graph
│   └── edge_tracker.py      # Research outcome tracking
├── data/
│   ├── knowledge_graph.json # Entity graph (auto-created)
│   └── feedback.json        # Usage data (auto-created)
├── pyproject.toml
├── requirements.txt
├── .env.example             # API key template
└── .env                     # Your API keys (not committed)

---

Join The Community

Built by the team at The Agentic Advantage

Get help setting up RECON, share research workflows, and connect with other builders using AI tools that actually work.

---

More From itsjwill

Repo	What It Does
vanta	Open source AI video engine — voice cloning, AI avatars, auto-captions
seoctopus	8-armed SEO intelligence — MCP server with 23 tools
ghosthacker	Adversarial AI pentester — CHAOS vs ORDER dual-agent system
claude-memory	Persistent memory for Claude Code — auto-capture decisions and patterns

---

<div align="center">

Built for finding what matters, not collecting bookmarks.

</div>