# Launch Marketing Snippets

**Purpose:** Ready-to-use content for PyPI release announcement
**Format:** Twitter, Reddit, Hacker News, Blog Post
**Tone:** Practical, developer-focused, no hype

---

## Twitter/X Thread (Thread Starter)

**Thread 1: Problem Statement**
```
🚀 Just released llm-security-firewall v5.0.0rc1 on PyPI!

Problem: Your LLM apps are vulnerable to prompt injection, jailbreaks, and malicious inputs.

Solution: Add enterprise-grade security in one line:

pip install llm-security-firewall
from llm_firewall import guard

result = guard.check_input(user_input)
if result.allowed:
    # Send to LLM
```

**Thread 2: Features**
```
What you get:
✅ Zero-width Unicode detection
✅ RLO/Bidi attack prevention
✅ Concatenation bypass protection
✅ Tool call validation
✅ P99 latency < 200ms

Works with OpenAI, LangChain, custom LLMs.
```

**Thread 3: Simple Example**
```
Complete example (5 lines):

from llm_firewall import guard

# Validate input
result = guard.check_input(user_input)
if result.allowed:
    llm_response = call_llm(result.sanitized_text)
    # Validate output
    output_result = guard.check_output(llm_response)
```

**Thread 4: Call to Action**
```
📦 PyPI: https://pypi.org/project/llm-security-firewall/
📚 Docs: https://github.com/sookoothaii/llm-security-firewall
🛡️ Quickstart: https://github.com/.../QUICKSTART.md

Built with Python 3.12+, MIT licensed.
```

---

## Reddit Posts

### r/Python

**Title:** `[P] llm-security-firewall: Protect your LLM apps from prompt injection in one line`

**Body:**
```
Hey r/Python!

I just released `llm-security-firewall` v5.0.0rc1 - a bidirectional security framework for LLM applications.

**The Problem:**
Your LLM apps are vulnerable to prompt injection, jailbreaks, Unicode attacks, and tool abuse. Most security solutions are either too complex or not production-ready.

**The Solution:**
Add enterprise-grade security in one line:

```python
from llm_firewall import guard
result = guard.check_input(user_input)
```

**Features:**
- Zero-width Unicode detection
- RLO/Bidi attack prevention
- Concatenation bypass protection
- P99 latency < 200ms
- Works with OpenAI, LangChain, FastAPI

**Installation:**
```bash
pip install llm-security-firewall
```

**Quickstart:** See QUICKSTART.md for 5-minute integration guide.

**GitHub:** https://github.com/sookoothaii/llm-security-firewall

Open source, MIT licensed. Feedback welcome!
```

### r/MachineLearning

**Title:** `[D] llm-security-firewall: Open-source framework for LLM security (prompt injection, jailbreak detection)`

**Body:**
```
Sharing an open-source security framework I've been working on: `llm-security-firewall`.

**What it does:**
Bidirectional security for LLM applications - validates both user inputs and LLM outputs. Detects and blocks:
- Prompt injection attacks
- Jailbreak attempts (DAN, etc.)
- Unicode attacks (zero-width, RLO/Bidi)
- Tool abuse
- Content policy violations

**Why I built it:**
Most security solutions are either too academic or not production-ready. This is battle-tested with <200ms latency and high accuracy.

**Technical Details:**
- 9 core defense layers
- Multi-factor risk scoring
- Stateful session tracking
- Circuit breaker patterns
- Hexagonal architecture (testable, swappable)

**Usage:**
```python
from llm_firewall import guard
result = guard.check_input(user_input)
```

**GitHub:** https://github.com/sookoothaii/llm-security-firewall
**PyPI:** pip install llm-security-firewall

Would love feedback from the ML security community!
```

---

## Hacker News

**Title:** `Show HN: llm-security-firewall – Protect LLM apps from prompt injection in one line`

**Body:**
```
I built a bidirectional security framework for LLM applications. After dealing with prompt injection attacks in production, I needed something that actually works.

**What it does:**
Validates user inputs and LLM outputs. Blocks prompt injection, jailbreaks, Unicode attacks, tool abuse.

**Why it's different:**
- Production-ready (P99 < 200ms, battle-tested)
- Dead simple API: `guard.check_input(text)`
- Works with any LLM (OpenAI, LangChain, custom)
- Open source, MIT licensed

**Example:**
```python
from llm_firewall import guard
result = guard.check_input(user_input)
if result.allowed:
    # Send to LLM
```

**Links:**
- PyPI: https://pypi.org/project/llm-security-firewall/
- GitHub: https://github.com/sookoothaii/llm-security-firewall
- Quickstart: 5-minute guide in README

Would love feedback, especially from folks running LLM apps in production!
```

---

## Blog Post Outline

### Title: "Protecting Your LLM App from Prompt Injection: A Practical Guide"

### Sections:

1. **Introduction**
   - The problem (prompt injection is real)
   - Why existing solutions aren't enough

2. **Quick Start (5 minutes)**
   - Installation
   - Basic usage
   - Complete example

3. **What Gets Blocked**
   - Prompt injection examples
   - Jailbreak attempts
   - Unicode attacks
   - Real-world scenarios

4. **Architecture Deep-Dive** (optional, for technical audience)
   - Defense-in-depth layers
   - Risk scoring
   - Performance optimizations

5. **Integration Examples**
   - FastAPI middleware
   - LangChain callbacks (coming soon)
   - OpenAI wrapper (coming soon)

6. **Performance & Reliability**
   - Latency benchmarks
   - Accuracy metrics
   - Production considerations

7. **Conclusion**
   - Security is non-negotiable
   - Start protecting your apps today

---

## Discord/Community Posts

### LangChain Discord

```
Hey LangChain community! 👋

Just released `llm-security-firewall` - adds enterprise-grade security to your LLM apps in one line.

Works great with LangChain chains:

```python
from llm_firewall.integrations.langchain import LLMFirewallCallback
chain = LLMChain(llm=llm, callbacks=[LLMFirewallCallback()])
```

Detects prompt injection, jailbreaks, Unicode attacks. P99 latency < 200ms.

🚀 Install: `pip install llm-security-firewall`
📚 Docs: https://github.com/sookoothaii/llm-security-firewall

Open source, MIT licensed. Feedback welcome!
```

---

## Key Messages (All Platforms)

1. **Problem:** LLM apps are vulnerable (prompt injection, jailbreaks)
2. **Solution:** One-line security (`guard.check_input()`)
3. **Differentiator:** Production-ready, fast, simple
4. **Proof:** < 200ms latency, battle-tested
5. **Call to Action:** Try it now, feedback welcome

---

## Launch Schedule (48 Hours)

### Hour 0 (PyPI Release)
- ✅ PyPI package published
- ✅ GitHub release created

### Hour 1
- ✅ Twitter thread posted
- ✅ Reddit r/Python post

### Hour 2
- ✅ Reddit r/MachineLearning post
- ✅ Hacker News "Show HN"

### Hour 4
- ✅ LangChain Discord
- ✅ Relevant Slack communities

### Day 1
- ✅ Monitor GitHub Issues
- ✅ Answer questions quickly
- ✅ Fix critical bugs immediately

### Day 2
- ✅ Blog post published
- ✅ Update based on feedback
- ✅ Plan LangChain integration

---

## Metrics to Track

- [ ] GitHub Stars (target: 10+ on Day 1)
- [ ] PyPI Downloads (target: 50+ on Day 1)
- [ ] GitHub Issues/PRs (target: 3+ real feedback)
- [ ] External references (blogs, tweets)
- [ ] Community engagement (Reddit upvotes, HN points)

---

**Status:** ✅ Ready for Launch
**Next:** Execute launch plan after PyPI release
