# RaySurfer: Before & After

## The Problem

Every time an AI agent encounters a common task, it regenerates code from scratch:

```
User: "Fetch my GitHub profile"

🐌 WITHOUT RAYSURFER:
├── Agent thinks... (2s)
├── Claude generates code... (5s)
├── Agent writes file... (0.5s)
├── Agent runs code... (1s)
└── Total: ~8.5 seconds + 1,500 tokens ($0.05)
```

## The Solution

RaySurfer caches proven code and retrieves it instantly:

```
User: "Fetch my GitHub profile"

⚡ WITH RAYSURFER:
├── Semantic search for matching code... (100ms)
├── Return cached, tested code... (50ms)
├── Agent runs code... (1s)
└── Total: ~1.2 seconds + 0 tokens ($0.0001)
```

---

## Code Comparison

### ❌ Before: Manual Agent Code

```python
from anthropic import Anthropic

client = Anthropic()

def run_agent(task: str):
    """Agent regenerates code every single time."""
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        messages=[{"role": "user", "content": task}],
        # Agent generates fresh code each time
        # Even for tasks it's done 100 times before
    )
    return response.content[0].text

# Every call = new tokens, new latency, inconsistent output
result = run_agent("fetch github user octocat")
```

**Problems:**
- 3-10 seconds per generation
- 500-2000 tokens consumed each time
- Code quality varies run-to-run
- No learning from past successes

---

### ✅ After: RaySurfer-Powered Agent

```python
from raysurfer import RaySurfer

rs = RaySurfer()

def run_agent(task: str):
    """Agent retrieves proven code instantly."""
    # Check cache first - 50ms
    result = rs.retrieve_best(task)

    if result.best_match and result.retrieval_confidence > 0.7:
        # Cache hit! Return proven code instantly
        return result.best_match.code_block.source

    # Cache miss - generate new code and store it
    code = generate_with_llm(task)
    rs.store_code_block(
        name=task[:50],
        source=code,
        entrypoint="main",
        language="python"
    )
    return code

# First call: generates + caches
# All future calls: instant retrieval
result = run_agent("fetch github user octocat")
```

**Benefits:**
- 50-200ms retrieval (30x faster)
- Zero tokens for cache hits
- Consistent, proven code quality
- Learns which code works via verdicts

---

## Integration: 3 Lines of Code

```python
# Before
response = claude.generate(task)

# After
from raysurfer import RaySurfer
rs = RaySurfer()
cached = rs.retrieve_best(task)
response = cached.best_match.code_block.source if cached.best_match else claude.generate(task)
```

---

## Real Numbers

| Metric | Without RaySurfer | With RaySurfer | Improvement |
|--------|-------------------|----------------|-------------|
| Latency | 5,000ms | 150ms | **33x faster** |
| Tokens | 1,200 | 0 | **100% saved** |
| Cost | $0.04 | $0.0001 | **400x cheaper** |
| Consistency | Variable | Proven | **Reliable** |

---

## Try It Now

```bash
pip install raysurfer
python -c "from raysurfer import RaySurfer; print('Ready!')"
```
