// CASE_STUDY_05

NETBOT: DIGITAL TWIN FRAMEWORK

ROLE
Creator / AI Engineer
CORE STACK
Python / Agno / Playwright / Supabase Vector
STATUS
OPEN SOURCE (R&D)
// 01. THE_FRICTION

THE "GHOST DEVELOPER" PARADOX

"Senior developers face a dilemma: to build authority, you need to be present online. To be a good engineer, you need deep focus (and zero social media distractions)."

The status quo of automation tools is embarrassing: generic "Great post! 🔥" bots that destroy your reputation. I didn't want a spam script; I needed a Digital Twin. I needed an agent capable of looking at a screenshot of code, understanding the bug, and commenting with a technical solution—while I sleep. The challenge was building an autonomous entity that respects my persona, understands visual context, and navigates the "Anti-Bot" minefield of modern platforms.

// 02. THE_ARCHITECTURE

BRAIN-BODY DECOUPLING

[ Orchestrator ]
      |
      v
[ Discovery Strategy ] --> [ Multimodal Analysis (GPT-4o) ]
                                    |
                                    v
                            [ Image + Caption ]
                                    |
                                    v
[ Supabase Vector (RAG) ] --(Persona Context)--> [ Agno Agent (Brain) ]
                                                        |
                                                        v
                                                [ Action Decision ]
                                                        |
                                                        v
                                            [ Browser Client (Body) ]
                                                |           |
                                                v           v
                                    [ Scroll / Read ]   [ Human-like Typing ]

I strictly separated the "Brain" (AI Logic) from the "Body" (Platform Clients).

The Brain: Uses Agno and Supabase (pgvector) to decide what to say based on my past writings.

The Body: Uses Playwright to execute actions. It doesn't use official APIs (which are limited); it launches a headless browser and physically clicks buttons, making it platform-agnostic.

// 03. R&D_INTELLIGENCE

MULTIMODAL VISION & PERSONA RAG

The core innovation here is Contextual Vision. Most bots are blind—they only read captions. NetBot "sees".

1
VISUAL PARSING

Agent captures the post image. If it's a diagram, it analyzes architecture. If a meme, it detects humor.

2
PERSONA INJECTION

Queries Knowledge Base (RAG): "How would Guilherme react to a bad microservices architecture?"

3
SAFETY GUARDRAILS

Secondary LLM step evaluates if comment is toxic, generic, or off-brand before posting.

# Simplified logic from core/agent.py
async function analyze_and_engage(post_screenshot, caption):
    # 1. Vision Analysis
    visual_context = await vision_model.analyze(post_screenshot)
    
    # 2. Retrieve Persona Context
    memories = await vector_db.search(query=caption + visual_context)
    
    # 3. Decision
    if visual_context.is_relevant_tech_content:
        return await agent.generate_comment(
            context=memories, 
            tone="Senior Engineer, slightly ironic"
        )
    return Action.SKIP
// 04. UX_LEADERSHIP

THE "UNCANNY VALLEY" OF AUTOMATION

In this project, "UX" isn't about a user interface; it's about Human Simulation. Platforms detect bots by analyzing latency and perfection. To bypass this, I engineered "Human Imperfection".

  • VARIABLE LATENCY

    The bot pauses to "read" the post.

  • TYPOS & CORRECTIONS

    The typing engine occasionally makes a mistake and backspaces it, simulating real keystrokes.

  • RANDOM SCROLLING

    It doesn't just go to the target; it scrolls past, "looks" at other things, and then engages. It behaves like a bored human.

PLACEHOLDER: AGENT_TERMINAL_LOGS
95%
ACCURACY

Vision AI - Successfully differentiated between "Code Snippets" and "Lifestyle Photos" in 95% of tests.

0
BANS

Resilience - By using Playwright with "Humanizing" patterns, the agent survived strict rate-limiting filters.

OSS
COMMUNITY

Released as an educational framework for studying Agentic Workflows and RAG-based assistants.