# GraQle — Social Media Punchlines & Captions

> Copy-paste hooks, captions, and CTAs for every platform. Pair with the assets in this folder.

---

## Viral Hooks (First Line — Stops the Scroll)

### The Contrarian

> **"RAG is dead for code. Graph-of-agents is 100x cheaper."**

> **"Your AI reads 50,000 tokens to guess. Mine reads 500 and knows."**

> **"I stopped paying $0.15 per query. Now I pay $0.0003. Same model. Same question."**

> **"Copilot reads files. That's the problem."**

### The Proof

> **"I asked 'what breaks if I change auth?' — 8 AI agents debated for 5 seconds."**

> **"847 files. 312 nodes. 178 edges. 10 seconds. My AI now understands my architecture."**

> **"Claude broke my auth module 3 times last week. Then I gave it a brain."**

> **"92% confidence. Not a guess — a consensus of 8 agents who checked the graph."**

### The Curiosity Gap

> **"This is what happens inside your code when AI nodes negotiate with each other:"**

> **"What if your AI could see every dependency before answering?"**

> **"There's a reason your AI halluccinates about your codebase. Here's the fix."**

> **"I found a way to make any AI coding tool 100x cheaper. Here's the architecture."**

---

## Full Captions by Platform

### LinkedIn (Professional, Value-First)

**Post 1 — The Problem/Solution**
```
Your AI reads 50,000 tokens to guess.
Mine reads 500 and knows.

The difference? A knowledge graph.

GraQle scans your codebase in 10 seconds and builds
a dependency graph that your AI tools query instead
of reading files one at a time.

Results:
→ 100x fewer tokens
→ 24x faster
→ 92% confidence (scored, not claimed)
→ $0.0003 per query instead of $0.15

Works with Claude Code, Cursor, VS Code Copilot.
No config. No cloud. No account needed.

pip install graqle

https://graqle.com
```

**Post 2 — The Story (pair with query-journey series)**
```
What happens when you ask your AI "what breaks if I change auth?"

Without GraQle:
→ Reads 50 files, burns 50K tokens, guesses

With GraQle:
→ 8 agents activate on the knowledge graph
→ auth.py talks to routes.py: "Do you use my JWT validation?"
→ routes.py responds: "3 endpoints depend on @auth_required"
→ api.py confirms: "I import verify_token() and AuthError"
→ All agents reach consensus: 92% confidence

500 tokens. $0.0003. 5.2 seconds.

The AI didn't guess. It checked.

Try it: pip install graqle
```

**Post 3 — The Technical Deep Dive (pair with node-anatomy)**
```
Inside every GraQle node, three things happen:

1. MEMORY ($0)
Graph traversal — who depends on this node?
Edges, consumers, import chains, history.
Pure graph, zero LLM cost.

2. EMBEDDING (~$0.00001)
One vector call for the query.
Cosine similarity against cached chunk embeddings.
Not recomputed — loaded from .npz cache.

3. LLM REASONING ($0.0003)
Only the relevant subgraph goes to the model.
500 tokens, not 50,000.
Each node reasons, then they exchange messages.
Consensus emerges.

That's why it's 100x cheaper.
Memory first. Embeddings second. LLM last.

https://graqle.com
```

### Twitter/X (Short, Punchy)

**Thread starter:**
```
Your AI reads files one at a time.
It has no idea what connects to what.

That's why it halluccinates about your codebase.

GraQle fixes this. 🧵
```

**Tweet 1:**
```
pip install graqle && graq init

10 seconds later:
✓ 847 files scanned
✓ 312 nodes, 178 edges
✓ Your AI now has a brain

graqle.com
```

**Tweet 2:**
```
Before GraQle:
- 50,000 tokens per query
- $0.15/question
- "I think auth is used by..."

After GraQle:
- 500 tokens per query
- $0.0003/question
- "auth.py is consumed by routes (3), api (2), middleware (1) — 92% confidence"
```

**Tweet 3 (with image):**
```
What happens inside a GraQle node when you ask a question:

1. Memory: graph traversal ($0)
2. Embedding: 1 vector call ($0.00001)
3. LLM: 500 tokens ($0.0003)

Memory first. LLM last.
That's 100x cheaper than RAG.

[attach: story-node-square.png]
```

### Reddit (r/Python, r/programming, r/MachineLearning)

**Title options:**
```
pip install graqle — turns your codebase into a queryable knowledge graph. 500 tokens instead of 50,000.
```
```
I built an open-source tool that gives AI coding assistants a knowledge graph. 100x cheaper queries.
```
```
Show HN: GraQle — graph-of-agents reasoning for codebases. Works with Claude Code, Cursor, Copilot.
```

**Body:**
```
I was tired of Claude burning 50K tokens to answer "what depends on auth?"
and still getting it wrong.

So I built GraQle. It scans your codebase, builds a dependency graph,
and exposes it to your AI tools via MCP.

What changed:
- Token usage: 50,000 → 500 (100x reduction)
- Cost: $0.15 → $0.0003 per query
- Accuracy: guessing → 92% confidence with evidence chains
- Speed: ~2 min → 5.2 seconds

How it works:
1. `pip install graqle && graq init` (10 seconds)
2. Your AI tools automatically get graph-aware context
3. Each query activates relevant nodes as "agents"
4. Agents exchange messages and reach consensus
5. Answer comes with confidence score and evidence

Works with: Claude Code, Cursor, VS Code Copilot, any MCP client
Backends: 14 LLM providers (Anthropic, OpenAI, Ollama, Bedrock, etc.)
License: Proprietary (free tier: 500 nodes, 3 queries/month)

GitHub: github.com/quantamixsol/graqle
PyPI: pypi.org/project/graqle
Docs: graqle.com
```

### Facebook Groups (Dev/AI communities)

**Post 1 — The Hook**
```
"Your AI reads 50,000 tokens to guess. Mine reads 500 and knows."

I built something that gives Claude/Cursor/Copilot a knowledge graph
of your entire codebase.

Instead of reading files one by one and guessing, your AI queries
a graph that knows every dependency, every import, every connection.

Result:
✓ 100x cheaper
✓ 24x faster
✓ 92% confidence (not a guess — a scored consensus)

10 seconds to install. No account. No cloud. Runs on your machine.

pip install graqle
https://graqle.com
```

**Post 2 — Question Format (drives comments)**
```
Genuine question for devs using AI coding tools:

How many tokens does your AI burn when you ask
"what depends on this module?"

Because I measured mine:
- Without GraQle: 50,000 tokens, ~$0.15, took 2 minutes
- With GraQle: 500 tokens, $0.0003, took 5 seconds

Same model. Same question. 100x cheaper.

The trick? A knowledge graph.

Anyone else working on reducing AI token waste?
```

### Instagram (Carousel — pair images with captions)

**Slide 1** (story-traversal-1-query.png)
```
THE QUERY ENTERS 🔵

You ask: "What breaks if I change auth?"

Your AI doesn't read 50 files.
It activates ONE node on the knowledge graph.
```

**Slide 2** (story-traversal-2-messages.png)
```
AGENTS TALK 🟣

That node talks to its neighbors:
"Do you use my JWT validation?"
"Yes — 3 endpoints depend on it."

Nodes negotiate. Not guess.
```

**Slide 3** (story-traversal-3-consensus.png)
```
CONSENSUS 🟢

8 agents agree:
→ routes.py: 3 endpoints break
→ api.py: 2 imports break
→ middleware.py: request wrapper breaks

92% confidence. $0.0003. 5.2 seconds.
```

**Slide 4** (story-node-square.png)
```
INSIDE EACH NODE

Memory ($0) → Embedding ($0.00001) → LLM ($0.0003)

Memory first. LLM last.
That's why it's 100x cheaper.

pip install graqle
graqle.com
```

---

## Hashtags

**Primary:**
```
#GraQle #DevTools #AI #KnowledgeGraph #CodeIntelligence
```

**Extended:**
```
#ClaudeCode #Cursor #VSCode #Copilot #MCP #Python #OpenSource
#GraphOfAgents #DependencyAnalysis #AIForDevelopers #CodeAnalysis
#VibeCoding #DevExperience #LLM #RAG #BuildInPublic
```

**Platform-specific:**
- LinkedIn: `#SoftwareEngineering #TechStartup #Developer #AITools`
- Twitter: `#buildinpublic #indiedev #python #opensource`
- Reddit: No hashtags (use flair)

---

## Posting Schedule (Suggestion)

| Day | Platform | Asset | Caption Type |
|-----|----------|-------|-------------|
| Mon | LinkedIn | Node Anatomy (square) | Technical deep dive (Post 3) |
| Tue | Twitter | Thread | Punchy thread (4 tweets) |
| Wed | Reddit | Text post | r/Python, r/programming launch |
| Thu | Instagram | Carousel (4 slides) | Query Journey series |
| Fri | Facebook | Hook post | Question format (Post 2) |
| Sat | LinkedIn Story | Query Journey (3 frames) | Swipeable story |
| Sun | Twitter | Single image | Node anatomy + one-liner |
