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arahangua merged 1 commit into from
Aug 15, 2025
Merged

fixed/updated: model search function, removed outdated update-status method #24

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8 changes: 3 additions & 5 deletions QUICKSTART.md
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ OPENROUTER_MODEL=your_model
#### Option B: Ollama (Local)
```bash
ollama serve
ollama pull model_alias
ollama pull model_alias # or you can just configure using the recent Ollama gui
# Edit .env:
LLM_PROVIDER=local
LOCAL_LLM_TYPE=ollama
Expand Down Expand Up @@ -79,7 +79,6 @@ scapo scrape all --dry-run # Preview what will be processed
- `targeted --service NAME` - Extract tips for one service
- `batch --category TYPE` - Process multiple services (limited)
- `all --priority LEVEL` - Process ALL services one by one
- `update-status` - See what needs updating

## 📚 Approach 2: Legacy Sources

Expand Down Expand Up @@ -189,9 +188,8 @@ NOT generic advice like (but sometimes we get them... sadly):
## 🚀 Next Steps

1. **Explore extracted tips**: `scapo tui`
2. **Update regularly**: `scapo scrape update-status`
3. **Track changes**: `python scripts/git_update.py --status`
4. **Contribute**: Share your findings via PR!
2. **Track changes**: `python scripts/git_update.py --status`
3. **Contribute**: Share your findings via PR!

## Need Help?

Expand Down
61 changes: 25 additions & 36 deletions README.md
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Expand Up @@ -16,7 +16,7 @@
[![PRs Welcome](https://img.shields.io/badge/PRs-Welcome-brightgreen.svg)](CONTRIBUTING.md)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

### 🎯 Real optimization tips from real users for AI services
### 🎯 Real usage tips from real users for AI services

If you find **SCAPO** useful, please consider giving it a star on GitHub!
Your support helps the project grow and reach more people.
Expand All @@ -29,54 +29,51 @@ Your support helps the project grow and reach more people.

**Keywords**: AI cost optimization, prompt engineering, LLM tips, OpenAI, Claude, Anthropic, Midjourney, Stable Diffusion, ElevenLabs, GitHub Copilot, reduce AI costs, AI service best practices, Reddit scraper, community knowledge base

Ever burned through credits in minutes? Searching Reddit for that one optimization tip? Getting generic advice when you need specific settings?
Ever burned through credits in minutes? Searching Reddit for one peculiar problem that you were having? Seach results telling you just generic advice when you need specific info?

![Scapo Intro](assets/intro.gif)

**SCAPO** extracts **specific, actionable optimization techniques** from Reddit about AI services - not generic "write better prompts" advice, but real discussions.
**SCAPO** extracts **specific usage tips and discussion** from Reddit about AI services - not generic "write better prompts" advice, but real discussions. So, can be sometimes wrong (i.e., crowd wisdom) but for sure will lift your eyebrows often "huh? ok, didn't know that..."

## ✨ Two Approaches

SCAPO offers two distinct workflows:

### 1. 🎯 **Service Discovery Mode** (NEW - Recommended)

Automatically discovers AI services and extracts specific optimization tips:

![Scapo Discover](assets/scrape-discovery.gif)

Discover services from GitHub Awesome lists
### 1. 🎯 **Batch Processing via Service Discovery (recommended)**

Discovers existing AI services and cache them for reference and downstream usage (see below):
```bash
scapo scrape discover --update
```

![Scapo Discover](assets/scrape-targeted.gif)

![Scapo Discover](assets/scrape-discovery.gif)


Extract optimization tips for specific services

```bash
scapo scrape targeted --service "Eleven Labs" --limit 20
```
![Scapo Discover](assets/scrape-targeted.gif)

![Scapo Discover](assets/scrape-batch.gif)

Batch process multiple priority services
Batch process multiple priority services (Recommended)

```bash
scapo scrape batch --max-services 3 --category audio
```

### 2. 📚 **Legacy Sources Mode**

![Scapo Batch](assets/legacy.gif)
![Scapo Discover](assets/scrape-batch.gif)


### 2. 📚 **Legacy Sources Mode**
Traditional approach using predefined sources from `sources.yaml`:
```bash
# Scrape from configured sources
scapo scrape run --sources reddit:LocalLLaMA --limit 10
```
![Scapo Batch](assets/legacy.gif)


## 🏃‍♂️ Quick Start (2 Minutes)

Expand All @@ -102,6 +99,8 @@ cp .env.example .env
```

Get your API key from [openrouter.ai](https://openrouter.ai/)
* you can also use local LLMs (Ollama, LMstudio). Check [QUICKSTART.md](./QUICKSTART.md)


### 3. Start Extracting Optimization Tips

Expand All @@ -122,7 +121,7 @@ scapo scrape batch --category video --limit 15
scapo scrape all --priority ultra --limit 20
```

#### Option B: Legacy Sources
#### Option B: Legacy method: using sources.yaml file

```bash
# Use predefined sources from sources.yaml
Expand Down Expand Up @@ -155,13 +154,6 @@ cat models/video/heygen/pitfalls.md
❌ **Generic**: "Try different settings"
✅ **Specific**: "Use 720p instead of 1080p in HeyGen to save 40% credits"

## 📊 Real Results

From actual extractions:
- **Eleven Labs**: Found 15+ specific optimization techniques from 75 Reddit posts
- **GitHub Copilot**: Discovered exact limits and configuration tips
- **Character.AI**: Found 32,000 character limit and mobile workarounds
- **HeyGen**: Credit optimization techniques and API alternatives

## 🛠️ How It Works

Expand All @@ -174,10 +166,10 @@ From actual extractions:
### Intelligent Extraction
- **Specific search patterns**: "config settings", "API key", "rate limit daily", "parameters"
- **Aggressive filtering**: Ignores generic advice like "be patient"
- **Batch processing**: Processes 50+ posts at once for efficiency
- **Context awareness**: Uses full 128k token windows when available
- **Batch processing**: Can process 50+ posts at once for efficiency (we recommend minimum of 15 posts per query)
- **Context awareness**: Uses full token windows of your chosen LLM when available (for local LLM, you need to set your context window in .env)

### Smart Organization
### Output Organization
```
models/
├── audio/
Expand All @@ -202,7 +194,7 @@ scapo scrape discover --show-all # List all services

# Target specific services
scapo scrape targeted \
--service "Eleven Labs" \ # Service name (handles variations)
--service "Eleven Labs" \ # Service name (handles variations, you can put whatever --> if we don't get hit in services.json, then it will be created under 'general' folder)
--limit 20 \ # Posts per search (15-20 recommended)
--max-queries 10 # Number of searches

Expand All @@ -212,9 +204,6 @@ scapo scrape batch \
--max-services 3 \ # Services to process
--limit 15 # Posts per search

# Check update status
scapo scrape update-status # See what needs updating
```

### Legacy Sources Mode
```bash
Expand All @@ -232,7 +221,7 @@ scapo scrape run \
# CLI commands
scapo models list # List all models
scapo models search "copilot" # Search models
scapo models info github-copilot --category coding
scapo models info github-copilot --category code
```

## ⚙️ Configuration
Expand All @@ -252,7 +241,7 @@ LOCAL_LLM_OPTIMAL_CHUNK=2048 # Optimal batch size (typically 1/4 of m
LOCAL_LLM_TIMEOUT_SECONDS=600 # 10 minutes for slower local models
LLM_TIMEOUT_SECONDS=120 # 2 minutes for cloud models

# Extraction Quality
# Extraction Quality (depends on your chosen LLM's discretion)
LLM_QUALITY_THRESHOLD=0.6 # Min quality (0.0-1.0)

# Scraping
Expand All @@ -264,7 +253,7 @@ MAX_POSTS_PER_SCRAPE=100 # Limit per source
```bash
--limit 5 # ❌ Often finds nothing (too few samples)
--limit 15 # ✅ Good baseline (finds common issues)
--limit 25 # 🎯 Optimal (uncovers hidden gems & edge cases)
--limit 25 # 🎯 Will find something (as long as there is active discussion on it)
```
so, hand-wavy breakdown: With 5 posts, extraction success ~20%. With 20+ posts, success jumps to ~80%.

Expand All @@ -283,7 +272,7 @@ Navigate extracted tips with:

## 🔄 Git-Friendly Updates tracking AI services in the Models folder

SCAPO is designed for version control:
SCAPO is designed for version control (this is only for tracking the models folder):
```bash
# Check what changed
uv run scripts/git_update.py --status
Expand Down
42 changes: 3 additions & 39 deletions src/cli.py
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Expand Up @@ -746,43 +746,6 @@ async def _batch():
asyncio.run(_batch())


@scrape.command(name="update-status")
def update_status():
"""Show which services need updating."""
show_banner()

from src.services.update_manager import UpdateManager
manager = UpdateManager()
status = manager.get_update_status()

# Display update status
console.print(Panel(
f"[bold]Update Status[/bold]\n\n"
f"Total services tracked: [cyan]{status['total_services']}[/cyan]\n"
f"Last update: [yellow]{status.get('last_update', 'Never')}[/yellow]\n"
f"Update frequency: {status.get('update_frequency', 'N/A')}\n",
border_style="blue",
title="SCAPO Update Tracker"
))

if status['recent_updates']:
console.print("\n[green]Recently Updated:[/green]")
for service in status['recent_updates'][:10]:
console.print(f" ✓ {service}")

if status['stale_services']:
console.print("\n[yellow]Needs Update (>30 days old):[/yellow]")
for service in status['stale_services'][:10]:
console.print(f" ⚠ {service}")

if len(status['stale_services']) > 10:
console.print(f" ... and {len(status['stale_services']) - 10} more")

# Suggest next action
if status['stale_services']:
console.print(f"\n[dim]Tip: Run 'scapo scrape batch --max-services {min(3, len(status['stale_services']))}' to update stale services[/dim]")


@scrape.command(name="all")
@click.option('-l', '--limit', default=20, help='Max posts per search (default: 20)')
@click.option('-c', '--category', help='Filter by category (video, audio, code, etc)')
Expand Down Expand Up @@ -1167,8 +1130,9 @@ def search_models(query, limit):
console.print("[yellow]No models directory found. Run 'sota scrape run' first.[/yellow]")
return

# Search through all categories and models
for category in ["text", "image", "video", "audio", "multimodal"]:
# Search through all categories and models dynamically
categories = [d for d in os.listdir(models_dir) if os.path.isdir(os.path.join(models_dir, d))]
for category in categories:
cat_dir = os.path.join(models_dir, category)
if os.path.exists(cat_dir):
for model in os.listdir(cat_dir):
Expand Down