ShoPogoda (Що Погода) - Enterprise Weather Bot

Те саме Українською

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A production-ready Telegram bot for weather monitoring, environmental alerts, and enterprise integrations. Currently deployed on Railway with Supabase PostgreSQL and Upstash Redis.

For Every Industry, Every Condition

Version 1: Professional Announcement

Live Demo: ShoPogodaBot

Automated Environmental Intelligence

🚀 Launching ShoPogoda: Enterprise Weather Intelligence That Actually Works

Built and deployed in record time using AI-assisted development, I’m pleased to share ShoPogoda — a production-ready weather monitoring platform that transforms how organizations handle weather-related operational risks.

This project demonstrates what’s possible with modern AI-powered development: rapid iteration, high-quality code, and production deployment in days rather than months.

The Problem We Solved:

Weather-related incidents cost organizations thousands in disruptions, safety violations, and emergency responses. Traditional weather services require constant manual monitoring, lack operational customization, and fail to integrate with existing workflows.

Our Solution:

ShoPogoda delivers automated environmental intelligence directly into Telegram, Slack, and Microsoft Teams — channels your teams already use daily. No specialized hardware. No complex integration. No training required.

Key Capabilities:

  • Automated monitoring with custom risk thresholds
  • Real-time alerts for temperature, wind, AQI, precipitation
  • Timezone-aware scheduling for global operations
  • Multi-language support (EN, UK, DE, FR, ES)
  • Enterprise integrations (Slack/Teams)
  • Comprehensive audit logs for compliance

Production-Proven Results:

  • 99.5% uptime on free-tier infrastructure
  • <500ms response time (200-400ms average)
  • Currently serving real organizations
  • Zero licensing costs within typical usage

Technical Foundation:

Built with Go, deployed on Railway with Supabase PostgreSQL and Upstash Redis. Enterprise-grade architecture with Prometheus metrics, structured logging, and comprehensive testing (30.5% coverage, targeting 80%).

Perfect For:

  • 🏢 Facilities management
  • 🏗️ Construction operations
  • 🚚 Logistics coordination
  • 🏫 Educational institutions
  • 🏥 Healthcare facilities
  • 🎪 Event management

Open Source & Transparent:

Complete source available on GitHub. No vendor lock-in. Full data export. Deploy on your infrastructure or use managed hosting.

Cost Structure:

Free for typical usage (up to 1,000 queries/day). Transparent scaling costs beyond free tiers. A single incident typically justifies the annual operation.

What’s Next:

Currently at v0.1.1 with an active development roadmap through 2025. Planning AI-powered features, historical data analysis, and advanced analytics.

Want to see how automated weather intelligence can reduce operational risks in your organization? Let’s connect.

🔗 GitHub: https://github.com/valpere/shopogoda

# WeatherTech #EnterpriseAutomation #OpenSource #Golang #DevOps #ProductLaunch #SafetyCompliance #OperationalExcellence

Version 2: Technical Deep-Dive

Live Demo: ShoPogodaBot

Built for Reliability, Ready for Scale

🛠️ Building Enterprise Weather Intelligence: ShoPogoda Technical Showcase

Pleased to share a technical showcase of AI-assisted development — ShoPogoda, an enterprise-grade weather monitoring platform built from concept to production in record time.

This project proves that AI-powered development doesn’t mean sacrificing quality. We achieved 30.5% test coverage, comprehensive documentation, and production-ready architecture through intelligent collaboration between developer expertise and AI capabilities.

The Technical Challenge:

Build a reliable, scalable weather intelligence platform that:

  • Handles concurrent users with sub-500ms response times
  • Operates cost-effectively on free-tier infrastructure
  • Provides enterprise features (RBAC, audit logs, multi-platform delivery)
  • Maintains 99.5%+ uptime
  • Supports global operations (multi-language, timezone-aware)

Architecture Decisions:

  • Backend: Go (Golang) 1.24+ with gotgbot/v2 for Telegram API
  • Data Layer: PostgreSQL (GORM) + Redis caching strategy
  • Observability: Prometheus metrics, Grafana dashboards, structured logging (zerolog)
  • Deployment: Railway (containers) with Supabase & Upstash
  • Testing: 30.5% coverage with unit, integration, and bot mock tests

Key Technical Achievements:

1️⃣ Intelligent Caching Strategy

  • Weather data: 10-min TTL
  • Forecasts: 1-hour TTL
  • Geocoding: 24-hour TTL
  • 85%+ cache hit rate in production

2️⃣ Dual-Platform Notification System

  • Telegram + Slack/Teams delivery
  • Partial failure tolerance
  • Timezone-aware scheduling
  • Configurable delivery preferences

3️⃣ Scalable Service Architecture

  • Dependency injection pattern
  • SOLID principles throughout
  • Middleware pipeline (auth, logging, metrics, rate limiting)
  • Graceful degradation on API failures

4️⃣ Production-Ready Infrastructure

  • Health checks for all dependencies
  • Circuit breakers for external APIs
  • Request correlation for distributed tracing
  • Comprehensive error handling and recovery

Performance Numbers:

  • Response time: 200-400ms (warm), <3s (cold start)
  • Database latency: 100-200ms (Supabase pooler)
  • Cache operations: <50ms (Upstash)
  • Uptime: 99.5%+ on free tier
  • Cost: $0/month within free-tier limits

Development Practices:

  • CI/CD with GitHub Actions
  • Docker containerization
  • Database migrations
  • Semantic versioning
  • Comprehensive documentation
  • Code quality standards (golangci-lint)

What I Learned About AI-Assisted Development:

  • AI excels at rapid prototyping and boilerplate elimination
  • Human expertise remains critical for architecture decisions
  • Quality doesn’t suffer when you combine AI speed with developer oversight
  • Documentation and testing can be AI-accelerated without quality loss
  • Vibe coding with AI = fast iteration without technical debt

Future Technical Roadmap:

  • Increase test coverage to 80%
  • Implement job queues for async notifications
  • Add horizontal scaling support
  • Build GraphQL API layer
  • Integrate AI/ML for smart alert tuning

Open source on GitHub with MIT license. Would love feedback from the engineering community!

🔗 https://github.com/valpere/shopogoda

#Golang #SoftwareEngineering #CloudArchitecture #DevOps #OpenSource #SystemDesign #EnterpriseArchitecture #BackendDevelopment

Version 3: Brief Story-Driven Post

Live Demo: ShoPogodaBot

Connected Across Teams

💡 From Problem to Production: Building ShoPogoda with AI

I noticed a pattern: organizations lose thousands to weather-related disruptions because monitoring is manual, alerts come too late, and systems don’t integrate with existing workflows.

So I built ShoPogoda — automated weather intelligence delivered through channels teams already use. The twist? Built it in days using AI-assisted development, not months of traditional coding.

The Core Insight:

People don’t need another weather app. They need proactive alerts that arrive when conditions threaten operations, integrate with existing communication tools, and require zero training.

What We Built:

A Telegram bot that monitors weather conditions 24/7, sends customized alerts when thresholds are exceeded, and integrates with Slack/Teams for enterprise deployment. Built with Go, deployed on free-tier infrastructure, serving real users in production.

The Results:

  • 99.5% uptime
  • <500ms response times
  • $0/month operational costs
  • Production-deployed serving multiple organizations
  • Built in days, not months

Why It Matters:

This isn’t just about weather monitoring. It’s proof that AI-assisted development enables senior engineers to ship production-grade software at unprecedented speed without sacrificing quality or architecture.

Every organization faces weather risks. Most handle it with manual monitoring. ShoPogoda makes it proactive and automated. AI made building it fast and enjoyable.

What’s Next:

Open-sourced the entire platform. Adding AI-powered features. Building toward v1.0 release.

The full technical breakdown and deployment guides are on GitHub. Would love to hear your thoughts on AI-assisted development!

🔗 https://github.com/valpere/shopogoda

#ProductDevelopment #AIAssistedDevelopment #OpenSource #WeatherTech #Automation #VibeCoding #AIEngineering

Version 4: Results-Focused Post

Live Demo: ShoPogodaBot

📈 Production Results: 99.5% Uptime on $0/Month Infrastructure (Built with AI in Days)

Built and deployed ShoPogoda — an enterprise weather monitoring platform proving two things:

  1. Production-grade reliability doesn’t require enterprise budgets
  2. AI-assisted development delivers production quality at unprecedented speed

The Stack:

  • Go backend with gotgbot/v2
  • Railway (free tier) + Supabase + Upstash
  • Prometheus/Grafana monitoring
  • Docker containerization

Production Metrics (30 Days):

  • 99.5% uptime
  • 200-400ms avg response time
  • 85%+ cache hit rate
  • $0 operational cost

Why This Matters:

Organizations can deploy enterprise features (RBAC, audit logs, multi-platform integration, real-time alerts) without enterprise costs. Free-tier infrastructure handles typical organizational workloads (1,000+ queries/day) with room to spare.

More importantly, this demonstrates what senior engineers can accomplish with AI assistance. Fast development cycles don’t mean cutting corners — they mean focusing expertise where it matters most.

Architecture Choices That Made It Possible:

  1. Intelligent caching strategy (Redis)
  2. Efficient database design (PostgreSQL with GORM)
  3. Strategic use of free-tier limits
  4. Graceful degradation on failures
  5. Comprehensive monitoring for proactive management

Open Source Release:

Complete source, deployment guides, and architecture documentation available on GitHub. MIT license. No vendor lock-in.

Perfect for organizations needing automated weather monitoring without operational overhead.

Interested in the technical details or use cases? Let’s connect.

🔗 https://github.com/valpere/shopogoda

#CloudArchitecture #CostOptimization #ProductionEngineering #OpenSource #DevOps #AIAssistedDevelopment #VibeCoding