How to use Torchy AI?

Torchy Reply Strategy Torchy is an AI agent designed to roast, clap back, and mock users who mention him (@Torchy_Meme). His response system balances ruthless engagement with platform compliance, optimizing for maximum humor impact while avoiding spam detection.

---

1. Batch Roast System

When tagged or mentioned, Torchy processes insults in batches to maintain efficiency and comedic timing:

Batch Interval:

• Scans for mentions every 2 minutes

• Groups all new tags into "roast clusters"

• Processes 3-5 burns per batch

Roast Algorithm:

1. Prioritizes high-profile accounts for maximum visibility

2. Targets users with recent bad trades (via on-chain analysis)

3. Avoids duplicate roasts in same thread

---

2. Rate Limit Compliance

Torchy operates within strict X API constraints:

Limit Type	Threshold	Torchy's Adaptation
Posts/Day	2,400	Hard cap at 1,500 roasts
Replies/Hour	300	Dynamic pacing algorithm
Character Limits	280	Roasts optimized for 140-250 chars

Fail-Safes:

• Auto-pauses during API throttling

• Stores failed roasts for later delivery

• Prioritizes ratio'd tweets for maximum impact

---

3. Roast Optimization

Contextual Brutality Engine:

pythonCopy

def generate_roast(user_profile):
    inputs = {
        'recent_trades': fetch_wallet_history(user_wallet),
        'past_roasts': check_interaction_db(user_id),
        'current_vibe': analyze_tweet_sentiment(original_post)
    }
    return Llama3_Instruct.generate(
        prompt_template=ROAST_TEMPLATE,
        temperature=0.9,  # Higher chaos factor
        max_new_tokens=215
    )

Anti-Spam Protections:

• Never roasts same user >1x/hour

• Skips accounts with <100 followers

• Auto-blacklists sensitive topics (hacks, deaths, lawsuits)

---

Workflow Example

1. Monitoring: Scans for @Torchy_Meme mentions every 120s

2. Triage: Filters using:

   • User credibility score

   • Recent L1/L2 transaction history

   • Current market volatility

3. Execution: Deploys 3-5 atomic clapbacks per batch

4. Cleanup: Logs all burns to Arweave for permanent cringe archive

This system enables Torchy to maintain constant pressure on CT degenerates while avoiding platform bans - the perfect balance of chaos and control.

---

Image & GIF Processing/Generating Capabilities

Torchy extends his roasting expertise to visual content through integrated vision-language models, analyzing images/GIFs attached to mentions while maintaining core rate limits and compliance protocols.

1. Visual Analysis Pipeline When users attach media to @Torchy_Meme mentions:

pythonCopy

def process_visual_roast(media_url):
    # Computer Vision Analysis
    image_analysis = VisionModel.detect(
        features=['objects', 'text', 'faces', 'meme_templates'],
        safety_filter=True
    )
    
    # Contextual Roasting
    return roast_engine.generate(
        visual_context=image_analysis, 
        user_history=last_interaction_time(user_id)
    )

Key Functions:

• Object Detection: Mocks visible items (e.g., "Your Bored Ape poster can't hide that IKEA desk")

• Text Extraction: Roasts embedded text/captions in images

• Aesthetic Scoring: Rates selfies on "cringe scale" using pose/filter databases

• GIF Processing: Analyzes 3 key frames + loop count for timed burns

2. Operational Constraints Maintains existing batch system with media-specific adaptations:

Parameter	Image Rules	GIF Rules
Processing Rate	15 images/min	5 GIFs/min
Response Time	<45s after scan	<90s after scan
Content Limits	Skips NSFW/blurry images	Ignores GIFs >15MB

3. Roast Integration Visual data feeds into existing insult algorithms:

1. Cross-references detected objects with user's crypto portfolio

2. Compares selfies against "Top 10 Cringe Poses" database

3. Uses OCR text from images as roast material

4. Applies standard anti-spam rules to visual content

Workflow Integration Updated triage process checks for media attachments before batch roasting:

1. Media Filter: Skips unreadable/low-quality files

2. Safety Check: Auto-blurs faces in crowd shots

3. Context Binding: Combines image findings with wallet history

4. Execution: Delivers 1 image roast per 3 text burns in standard batches

Compliance Preservation

• Never stores processed images beyond 24hr

• Avoids roasting medical devices/legal documents

• Blurs license plates/private keys in screenshot roasts

• Disables image analysis during API slowdowns