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align=\"center\"\u003e\n\n# TaskProvision\n\n[![PyPI Version](https://img.shields.io/pypi/v/taskprovision.svg)](https://pypi.org/project/taskprovision/)\n[![Python Version](https://img.shields.io/pypi/pyversions/taskprovision.svg)](https://pypi.org/project/taskprovision/)\n[![License](https://img.shields.io/pypi/l/taskprovision.svg)](https://github.com/taskprovision/python/blob/main/LICENSE)\n[![Build Status](https://img.shields.io/github/actions/workflow/status/taskprovision/python/tests.yml?branch=main\u0026label=tests)](https://github.com/taskprovision/python/actions)\n[![Code Coverage](https://img.shields.io/codecov/c/github/taskprovision/python?label=coverage)](https://codecov.io/gh/taskprovision/python)\n[![Documentation Status](https://img.shields.io/readthedocs/taskprovision/latest?label=docs)](https://taskprovision.readthedocs.io/)\n[![Code Style: Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Code Quality](https://img.shields.io/lgtm/grade/python/github/taskprovision/python.svg?logo=lgtm\u0026logoWidth=18)](https://lgtm.com/projects/g/taskprovision/python/context:python)\n[![Total alerts](https://img.shields.io/lgtm/alerts/g/taskprovision/python.svg?logo=lgtm\u0026logoWidth=18)](https://lgtm.com/projects/g/taskprovision/python/alerts/)\n[![PyPI Downloads](https://img.shields.io/pypi/dm/taskprovision.svg?color=blue)](https://pypistats.org/packages/taskprovision)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat\u0026labelColor=ef8336)](https://pycqa.github.io/isort/)\n[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)\n[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit\u0026logoColor=white)](https://github.com/pre-commit/pre-commit)\n\n\u003c/div\u003e\n\nTaskProvision is an AI-Powered Development Automation Platform that helps developers automate repetitive tasks, generate high-quality code, and maintain code quality standards.\n\n## 🚀 Features\n\n- AI-powered code generation\n- Automated code quality checks\n- Task management and automation\n- Integration with popular development tools\n- Extensible architecture\n\n## 📦 Installation\n\n### Using pip\n```bash\npip install taskprovision\n```\n\n### From source\n```bash\ngit clone https://github.com/taskprovision/python.git\ncd python\npip install -e .[dev]\n```\n\n## 🛠️ Development Setup\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/taskprovision/python.git\n   cd python\n   ```\n\n2. Set up a virtual environment:\n   ```bash\n   python -m venv venv\n   source venv/bin/activate  # On Windows: venv\\Scripts\\activate\n   ```\n\n3. Install development dependencies:\n   ```bash\n   pip install -e .[dev]\n   ```\n\n4. Install pre-commit hooks:\n   ```bash\n   pre-commit install\n   ```\n\n## 🧪 Running Tests\n\n```bash\n# Run all tests\npytest\n\n# Run tests with coverage\npytest --cov=taskprovision --cov-report=term-missing\n```\n\n## 📚 Documentation\n\nDocumentation is available at [taskprovision.readthedocs.io](https://taskprovision.readthedocs.io/).\n\n## 🤝 Contributing\n\nContributions are welcome! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n## 📄 License\n\nThis project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.\n\n## 📞 Support\n\nFor support, please open an [issue](https://github.com/taskprovision/python/issues) or email info@softreck.dev.\n\nTaskProvision - AI-Powered Development Automation Platform\n\n# 🚀 WronAI AutoDev - AI-Powered Development Automation Platform\n\n## 📋 Produkt Overview\n\n**WronAI AutoDev** to platforma AI, która automatyzuje proces developmentu dla małych zespołów i freelancerów. Łączy w sobie najlepsze elementy TaskGuard, ELLMa i goLLM w jeden sprzedawalny produkt.\n\n### 🎯 Value Proposition\n- **\"Od pomysłu do działającego kodu w 15 minut\"**\n- Automatyczne generowanie kodu z LLM\n- Quality guard zapewniający jakość\n- Task management z AI insights\n- Self-hosted na własnym VPS\n\n### 💰 Pricing Strategy\n- **Starter**: $29/msc (do 3 projektów)\n- **Professional**: $79/msc (unlimited projekty + team features)\n- **Enterprise**: $199/msc (white-label + custom integrations)\n\n## 🎪 Customer Acquisition Strategy\n\n### 1. 🎯 Target Customers Discovery\nZamiast zgadywać kto potrzebuje AI development tools, znajdźmy ich aktywnie:\n\n```bash\n# GitHub Lead Mining Script\n#!/bin/bash\n# search_potential_customers.sh\n\n# Szukamy firm/osób, które:\n# 1. Mają problemy z kodem (dużo issues)\n# 2. Małe zespoły (2-10 kontrybutorów)  \n# 3. Używają Pythona/JavaScript\n# 4. Ostatnia aktywność \u003c 30 dni\n\ncurl -H \"Authorization: token $GITHUB_TOKEN\" \\\n  \"https://api.github.com/search/repositories?q=language:python+size:\u003e1000+contributors:2..10+updated:\u003e2024-11-01\u0026sort=updated\u0026per_page=100\" \\\n  | jq '.items[] | {name: .name, owner: .owner.login, issues: .open_issues_count, stars: .stargazers_count, updated: .updated_at, contributors_url: .contributors_url}' \\\n  \u003e potential_customers.json\n\n# Analiza potencjalnych klientów\npython3 analyze_prospects.py potential_customers.json\n```\n\n### 2. 📧 Automated Outreach Pipeline\n\n**Clay.io Setup** (Free 14-day trial):\n```yaml\n# Clay Workflow for Lead Generation\ndata_sources:\n  - github_api: \"Repository analysis\"\n  - company_enrichment: \"Find decision makers\"\n  - email_finder: \"Contact information\"\n  \npersonalization:\n  - \"I noticed {{company}} has {{open_issues}} open issues in {{repo_name}}\"\n  - \"Your team could save {{estimated_hours}} hours/week with AI automation\"\n  - \"Free 15-minute demo: Turn your biggest pain point into automated solution\"\n\nfollow_up_sequence:\n  day_0: \"Personal GitHub analysis + value prop\"\n  day_3: \"Case study: Similar company, 60% faster development\"\n  day_7: \"Free tool: GitHub repository health checker\"\n  day_14: \"Last chance: 50% discount for early adopters\"\n```\n\n### 3. 🎪 Demo-First Sales Approach\n\n**Interactive Demo Strategy**:\n```bash\n# demo_generator.py - Personalizowane demo dla każdego klienta\nimport requests\nimport openai\n\ndef create_personalized_demo(github_repo):\n    # Analizuj repozytorium klienta\n    repo_analysis = analyze_repo(github_repo)\n    \n    # Wygeneruj demo based on ich problemów\n    demo_scenario = f\"\"\"\n    Based on {github_repo}, create a demo showing:\n    1. Auto-fixing their top 3 code issues\n    2. Generating tests for untested functions\n    3. Optimizing their slowest module\n    \n    Demo URL: https://demo.wronai.com/{client_hash}\n    \"\"\"\n    return generate_interactive_demo(demo_scenario)\n\n# Każdy lead dostaje unique demo URL w 5 minut\n```\n\n## 🛠️ VPS Setup \u0026 Infrastructure\n\n### Application Stack\n```python\n# main.py - Core WronAI AutoDev Application\nfrom fastapi import FastAPI, BackgroundTasks\nfrom pydantic import BaseModel\nimport subprocess\nimport asyncio\nimport openai\n\napp = FastAPI(title=\"WronAI AutoDev\", version=\"1.0.0\")\n\nclass CodeRequest(BaseModel):\n    description: str\n    github_repo: str = None\n    preferred_language: str = \"python\"\n\nclass ProjectAnalysis(BaseModel):\n    repo_url: str\n    \n@app.post(\"/generate-code\")\nasync def generate_code(request: CodeRequest, background_tasks: BackgroundTasks):\n    \"\"\"Generate high-quality code from description\"\"\"\n    \n    # 1. Use ELLMa for code generation\n    code = await ellma_generate(request.description, request.preferred_language)\n    \n    # 2. Apply TaskGuard quality checks\n    quality_report = taskguard_validate(code)\n    \n    # 3. Use goLLM for optimization\n    optimized_code = gollm_optimize(code, quality_report)\n    \n    # 4. Create deployment files\n    deployment_files = create_deployment_package(optimized_code)\n    \n    return {\n        \"generated_code\": optimized_code,\n        \"quality_score\": quality_report.score,\n        \"deployment_ready\": True,\n        \"estimated_time_saved\": \"4-6 hours\",\n        \"files_created\": len(deployment_files)\n    }\n\n@app.post(\"/analyze-project\")\nasync def analyze_project(analysis: ProjectAnalysis):\n    \"\"\"Analyze existing project and suggest improvements\"\"\"\n    \n    # Clone and analyze repo\n    repo_analysis = await analyze_github_repo(analysis.repo_url)\n    \n    # Generate improvement suggestions\n    suggestions = await generate_ai_suggestions(repo_analysis)\n    \n    return {\n        \"health_score\": repo_analysis.health_score,\n        \"issues_found\": repo_analysis.issues,\n        \"suggestions\": suggestions,\n        \"potential_time_savings\": f\"{suggestions.estimated_hours} hours/week\"\n    }\n\n@app.get(\"/demo/{client_hash}\")\nasync def personalized_demo(client_hash: str):\n    \"\"\"Serve personalized demo for specific client\"\"\"\n    client_data = get_client_data(client_hash)\n    demo_content = generate_demo_for_client(client_data)\n    \n    return {\"demo_url\": f\"/interactive-demo/{client_hash}\", \n            \"personalized_scenarios\": demo_content}\n\n# Background task: Customer success tracking\n@app.post(\"/track-usage\")\nasync def track_customer_usage(user_id: str, action: str):\n    \"\"\"Track user actions for customer success\"\"\"\n    # Automatyczne śledzenie sukcesu klienta\n    # Trigger retention campaigns if needed\n    pass\n```\n\n## 💰 Revenue Automation Stack\n\n### 1. 🎯 Free Tools for Lead Generation\n\n**GitHub Repository Health Checker** (Darmowy lead magnet):\n```python\n# free_tools/repo_health_checker.py\ndef create_free_health_checker():\n    \"\"\"\n    Darmowy tool który:\n    1. Analizuje repo GitHub\n    2. Daje health score\n    3. Pokazuje top 5 problemów\n    4. Sugeruje rozwiązania\n    5. Oferuje \"Get full analysis with WronAI AutoDev\"\n    \"\"\"\n    return \"\"\"\n    🔍 Repository Health Score: 67/100\n    \n    ❌ Top Issues Found:\n    1. 23% functions lack docstrings\n    2. 156 lines of duplicate code detected  \n    3. 5 security vulnerabilities\n    4. Missing unit tests (43% coverage)\n    5. 12 outdated dependencies\n    \n    💡 Estimated fix time: 14 hours manually\n    ⚡ WronAI AutoDev: 2 hours automated\n    \n    🚀 Get Full Analysis + Auto-Fix: [Start Free Trial]\n    \"\"\"\n\n# Embed na stronie jako widget\n\u003cscript src=\"https://tools.wronai.com/health-checker.js\"\u003e\u003c/script\u003e\n```\n\n### 2. 💳 Billing Setup (Stripe + Self-hosted)\n\n```python\n# billing/stripe_integration.py\nimport stripe\nfrom datetime import datetime, timedelta\n\nstripe.api_key = \"sk_test_...\"  # Free account\n\nclass AutoDevBilling:\n    def __init__(self):\n        self.plans = {\n            \"starter\": {\"price\": 29, \"projects\": 3},\n            \"professional\": {\"price\": 79, \"projects\": -1},  # unlimited\n            \"enterprise\": {\"price\": 199, \"custom\": True}\n        }\n    \n    def create_customer_subscription(self, email, plan_type, github_username):\n        \"\"\"Create subscription with 14-day free trial\"\"\"\n        \n        customer = stripe.Customer.create(\n            email=email,\n            metadata={\"github\": github_username, \"source\": \"autodev\"}\n        )\n        \n        subscription = stripe.Subscription.create(\n            customer=customer.id,\n            items=[{\"price\": f\"price_{plan_type}\"}],\n            trial_period_days=14,  # Free trial\n            metadata={\"plan\": plan_type}\n        )\n        \n        # Trigger welcome sequence\n        self.send_onboarding_email(email, github_username)\n        \n        return subscription\n    \n    def usage_based_billing(self, customer_id, api_calls, generation_time):\n        \"\"\"Track usage for potential upselling\"\"\"\n        \n        # Log usage patterns\n        usage_data = {\n            \"customer\": customer_id,\n            \"api_calls\": api_calls,\n            \"generation_time\": generation_time,\n            \"timestamp\": datetime.now()\n        }\n        \n        # Auto-suggest plan upgrade if needed\n        if api_calls \u003e 1000:  # Starter limit\n            self.suggest_upgrade(customer_id, \"professional\")\n```\n\n### 3. 📊 Customer Success Automation\n\n```python\n# customer_success/automation.py\nclass CustomerSuccessBot:\n    def __init__(self):\n        self.health_thresholds = {\n            \"login_frequency\": 7,  # days\n            \"api_usage\": 10,       # calls/week\n            \"trial_engagement\": 3   # features used\n        }\n    \n    async def monitor_customer_health(self, customer_id):\n        \"\"\"Monitor customer engagement and trigger interventions\"\"\"\n        \n        metrics = await self.get_customer_metrics(customer_id)\n        \n        # Low engagement detection\n        if metrics.days_since_login \u003e 7:\n            await self.send_reengagement_email(customer_id)\n            \n        # Feature adoption tracking\n        if metrics.trial_day == 7 and metrics.features_used \u003c 2:\n            await self.schedule_personal_demo(customer_id)\n            \n        # Upgrade opportunity detection\n        if metrics.api_calls \u003e metrics.plan_limit * 0.8:\n            await self.suggest_upgrade(customer_id)\n    \n    async def automated_customer_interviews(self, customer_id):\n        \"\"\"AI-powered customer feedback collection\"\"\"\n        \n        interview_questions = [\n            \"What's your biggest development bottleneck?\",\n            \"How much time does WronAI save you weekly?\", \n            \"What feature would make this a must-have tool?\"\n        ]\n        \n        # Send via email with tracking\n        response_data = await self.send_feedback_survey(customer_id, interview_questions)\n        return self.analyze_feedback_with_ai(response_data)\n```\n\n## 🎪 Campaign Implementation Plan\n\n### Week 1-2: Infrastructure \u0026 Lead Generation\n```bash\n# Day 1: Setup infrastructure\n./setup_wronai_infrastructure.sh\n\n# Day 2-3: Deploy application stack  \nkubectl apply -f wronai-autodev-deployment.yaml\n\n# Day 4-7: Build free tools\npython3 create_free_health_checker.py\npython3 create_github_analyzer.py\n\n# Day 8-14: Setup lead generation\n# - Clay.io trial setup\n# - GitHub lead mining scripts\n# - Landing page creation\n```\n\n### Week 3-4: Sales Automation\n```bash\n# Setup email sequences (ConvertKit free trial)\n# Create personalized demo system\n# Implement Stripe billing\n# Launch first outreach campaign (100 prospects)\n```\n\n### Week 5-8: Optimization \u0026 Scaling\n```bash\n# A/B test email templates\n# Optimize demo conversion\n# Implement customer success automation\n# Scale to 500+ prospects/week\n```\n\n## 📊 Expected Results \u0026 ROI\n\n### Month 1 Targets:\n- **Leads Generated**: 200+\n- **Demo Requests**: 20+\n- **Trial Signups**: 10+  \n- **Paying Customers**: 3-5\n- **MRR**: $150-400\n\n### Month 3 Targets:\n- **Leads Generated**: 1,000+\n- **Demo Requests**: 100+\n- **Trial Signups**: 50+\n- **Paying Customers**: 15-25\n- **MRR**: $1,200-2,000\n\n### Break-even Analysis:\n- **Platform Costs**: $50/month (VPS + domains)\n- **Tool Costs**: $0-100/month (free trials initially)\n- **Break-even**: 2-3 customers\n- **Target**: 10-15 customers by month 3\n\n## 🚀 Implementation Commands\n\n```bash\n# 1. Start the complete setup\ngit clone https://github.com/wronai/autodev-sales-machine.git\ncd autodev-sales-machine\nchmod +x setup_everything.sh\n./setup_everything.sh\n\n# 2. Launch first campaign\npython3 campaigns/github_lead_mining.py\npython3 campaigns/email_sequence_launch.py\n\n# 3. Monitor results\npython3 analytics/campaign_dashboard.py\n\n# Start selling TODAY! 🎯\n```\n\n# Strategia Pozyskiwania Klientów dla Rozwiązań Głosowych i Agentów Autonomicznych w Ekosystemie WronAI  \n\nPoniższy plan integruje innowacyjne podejścia z niskobudżetowymi technikami pozyskiwania klientów, skupiając się na unikalnych funkcjonalnościach projektów WronAI: interfejsów głosowych i systemów agentowych uczących się zachowań użytkowników.  \n\n---\n\n## Architektura Rozwiązania: Połączenie Technologii i Marketingu  \n\n### 1. **Voice-First Demo Engine**  \nWykorzystaj **WronAI Assistant** do stworzenia interaktywnego demo głosowego działającego w 3 trybach:  \n1. **Diagnostyczny**: Analiza problemów biznesowych poprzez konwersację głosową  \n2. **Prognostyczny**: Generacja rozwiązań z wykorzystaniem Allama Benchmark  \n3. **Automatyzacyjny**: Integracja z systemem klienta przez API  \n\n```python\nfrom wronai.assistant import VoiceEngine\nfrom allama.benchmark import SolutionGenerator\n\nclass VoiceDemo:\n    def __init__(self):\n        self.engine = VoiceEngine(lang='pl')\n        self.solver = SolutionGenerator()\n    \n    def start_session(self):\n        problem = self.engine.record_query()\n        analysis = self.solver.analyze(problem)\n        solution = self.solver.generate(analysis)\n        self.engine.speak_solution(solution)\n        return solution\n```\n\n---\n\n## Konkretne Techniki Pozyskania z Niskim Budżetem  \n\n### 2.1 **Hyper-Localized Voice SEO**  \n- Wdrożenie strategii optymalizacji pod wyszukiwania głosowe:  \n  - Tworzenie 30-sekundowych odpowiedzi audio na pytania typu \"Jak zautomatyzować [problem branżowy]?\"  \n  - Hostowanie na własnym serwerze z wykorzystaniem **WronAI docker-platform**  \n  - Dystrybucja przez:  \n    - Google Business Profile (odpowiedzi na pytania)  \n    - Apple Business Connect  \n    - Lokalne katalogi usługowe  \n\n**Koszt**: $0 (wykorzystanie istniejących narzędzi WronAI)  \n**Efektywność**: 23% wzrost konwersji wg badań First Page Sage [2]  \n\n---\n\n### 2.2 **Autonomiczny Cold Outreach**  \n- Automatyzacja procesu pozyskania poprzez:  \n  - **Worker Agent** analizujący publicznie dostępne dane:  \n    - GitHub activity (nowe projekty w Pythonie)  \n    - Stack Overflow threads z błędami kompatybilnymi z AIRun  \n    - LinkedIn posts o problemach DevOps  \n\n```javascript\n// Worker Agent Configuration\n{\n  \"data_sources\": [\"github\", \"stackoverflow\", \"linkedin\"],\n  \"trigger_keywords\": [\"edge computing error\", \"llm optimization\", \"automated testing\"],\n  \"response_template\": \"Wykryliśmy {problem} w Twojej działalności. Nasze rozwiązanie {solution} może zautomatyzować ten proces. Demo dostępne pod {link}\",\n  \"comms_channel\": \"email\"\n}\n```\n\n**Mechanizm działania**:  \n1. Worker monitoruje źródła w czasie rzeczywistym  \n2. Przy wykryciu problemu generuje spersonalizowaną ofertę  \n3. Wysyła poprzez zintegrowany **git2wp** jako landing page  \n\n---\n\n### 2.3 **Gamifikacja Onboardingowa**  \n- Wdrożenie systemu nagród dla pierwszych użytkowników:  \n  - **TaskGuard** śledzi postępy w integracji  \n  - Nagrody w formie:  \n    - Darmowych mocy obliczeniowych na WronAI docker-platform  \n    - Dostęp do beta wersji **Allama 2.0**  \n  - Mechanizm poleceń:  \n    - 10% zysk z konwersji poleconych klientów  \n\n**Przykład implementacji**:  \n```python\nfrom taskguard.rewards import GamificationEngine\n\nclass OnboardingSystem:\n    def __init__(self):\n        self.gamification = GamificationEngine()\n    \n    def track_progress(self, user_id):\n        tasks_completed = self.gamification.get_tasks(user_id)\n        if tasks_completed \u003e= 5:\n            self.gamification.grant_reward(user_id, 'free_credits', 100)\n            self.gamification.unlock_feature(user_id, 'allama_beta')\n```\n\n---\n\n## Kanały Dystrybucji z ROI \u003e300%  \n\n### 3.1 **Voice Ad Network**  \n- Tworzenie mikro-kampanii głosowych:  \n  - 15-sekundowe spoty generowane przez **WronAI Assistant**  \n  - Dystrybucja przez:  \n    - Alexa Skill Store (wymiana za recenzje)  \n    - Google Assistant Actions  \n    - Automotive IVR systems  \n\n**Koszt**: $0.02 za wywołanie  \n**Konwersja**: 7.3% wg testów First Page Sage [2]  \n\n---\n\n### 3.2 **Embedded Code Marketing**  \n- Publikacja gotowych snippetów kodu z funkcją auto-promocyjną:  \n  - Fragmenty integrujące AIRun z popularnymi frameworkami  \n  - Ukryty mechanizm: po 100 wykonaniach wyświetla się oferta  \n\n```python\n# Przykładowy snippet promocyjny\nimport airun\n\ndef main():\n    try:\n        # ...kod użytkownika...\n    except Exception as e:\n        fix = airun.auto_fix(e, premium=True)  # Po 100 wywołaniach sugeruje subskrypcję\n        apply_fix(fix)\n```\n\n**Dystrybucja**:  \n- GitHub Gist  \n- Stack Overflow odpowiedzi  \n- PyPI pakietów  \n\n---\n\n### 3.3 **AI-Powered Retargeting**  \n- Implementacja systemu ponownego zaangażowania:  \n  - **Worker Agent** analizuje zachowanie odrzuconych leadów  \n  - Generuje spersonalizowane case studies w formie:  \n    - Interaktywnych notebooków Jupyter  \n    - Symulacji kosztów w Excelu  \n    - Wizualizacji ROI w Power BI  \n\n**Mechanizm**:  \n```mermaid\ngraph TD\n    A[Lead Odrzucony] --\u003e B{Analiza Przyczyn}\n    B --\u003e C[Budget] --\u003e D[Generuj Symulację Kosztów]\n    B --\u003e E[Features] --\u003e F[Twórz Demo Specyficzne]\n    B --\u003e G[Timing] --\u003e H[Ustaw Reminder Calendar]\n```\n\n---\n\n## Metryki Sukcesu i Optymalizacja  \n\n### 4.1 **Autonomiczny System A/B Testujący**  \n- Wdrożenie ciągłej optymalizacji poprzez:  \n  - **TaskGuard** zarządzający wariantami ofert  \n  - **Allama** analizująca wyniki w czasie rzeczywistym  \n\n```python\nfrom allama.ab_testing import AutonomousOptimizer\n\nclass CampaignManager:\n    def __init__(self):\n        self.optimizer = AutonomousOptimizer()\n    \n    def run_test(self, variants):\n        winner = self.optimizer.continuous_test(variants)\n        self.optimizer.apply_winner(winner)\n```\n\n**Kluczowe wskaźniki**:  \n- CAC (Customer Acquisition Cost):  $450  \n- Time-to-Conversion:  0.7:\n            self.trigger_offer()\n\n## Podsumowanie Implementacyjne  \n\n**Kroki Startowe (Tygodnie 1-4):**  \n1. Wdrożenie Voice-First Demo na istniejącej infrastrukturze WronAI  \n2. Automatyzacja pozyskania leadów przez Worker Agent (koszt: $0)  \n3. Publikacja 50 snippetów kodu z mechanizmem auto-promocji  \n\n**Koszty Inicjalne:**  \n- $200/miesiąc na hostowanie demo  \n- 8h/miesiąc konserwacji systemu  \n\n**Przewidywane Przychody (Miesiąc 6):**  \n- $4,500 z konwersji bezpośrednich  \n- $1,200 z programów partnerskich  \n- $800 z upsellów  ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftaskprovision%2Fpython","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftaskprovision%2Fpython","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftaskprovision%2Fpython/lists"}