# Reddit Post - Daslearnsai

## Target Subreddits
- r/LocalLLaMA
- r/SideProject
- r/programming
- r/MachineLearning

## Post Title Options
1. "I built an LLM router that beats GPT-5 at 1/213th the cost — #1 on RouterArena"
2. "A3M Router: 70.32 score, $0.047/1K, open-source"

## Post Body

```
I built A3M Router — an open-source LLM routing proxy that ranks #1 on RouterArena (arXiv:2510.00202).

**The Numbers:**
- RouterArena Score: 70.32 (#1 of 19 routers)
- Cost: $0.047 per 1K queries
- vs GPT-5: 213x cheaper with better accuracy
- vs RouteLLM: 59% higher score at 5.7x lower cost

**How it works:**
Instead of sending every query to expensive models, A3M routes queries to the cheapest capable provider using 12 keyword signals.

Simple query (hi, thanks) → free tier (Groq llama)
Complex query (explain quantum entanglement) → premium (GPT-4o)

**Features:**
- Parallel multi-LLM execution (fire multiple, pick best)
- 47+ providers: OpenAI, Anthropic, Groq, Cerebras, DeepSeek, Gemini, Mistral...
- Memory across sessions
- Semantic cache (30%+ hit rate)
- Budget enforcement
- Circuit breaker with auto-failover

**Quick start:**
```bash
npx a3m-router serve
```

Then use it like OpenAI:
```python
from openai import OpenAI
client = OpenAI(
    api_key="your-key",
    base_url="http://localhost:8787/v1"  # A3M proxy
)
response = client.chat.completions.create(
    model="auto",  # A3M routes automatically
    messages=[{"role": "user", "content": "Your query"}]
)
```

GitHub: https://github.com/Das-rebel/a3m-router
npm: https://www.npmjs.com/package/adaptive-memory-multi-model-router

Demo: [asciinema.org/a/RpqOZM9tFMALYWvs]

AMA!
```

## Posting Strategy
1. Post to r/LocalLLaMA first (most receptive)
2. 24h later: r/SideProject, r/programming
3. Track engagement
