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OFEKTIVE is a modern fitness studio website built with Next.js 14 and TypeScript, designed to showcase personal training services in Tirat Carmel, Israel. The site features a hero section with studio imagery, client gallery, and comprehensive contact footer with multiple communication channels (phone, location maps, Instagram, WhatsApp). The implementation demonstrates strong performance optimization with Lighthouse scores and PWA support. The technical stack includes Tailwind CSS for responsive design, Sharp for image optimization, and full Hebrew RTL language support using Noto Sans Hebrew typography.
Initial Next.js app setup with TypeScript configuration, Tailwind CSS, and project structure. Established foundation with Next.js best practices and build configuration.
Completed full mobile-responsive design implementation. Built core components including hero section, client gallery, and footer with proper mobile-first responsive breakpoints.
Restructured project by moving components to dedicated folder. Added comprehensive favicon configurations for web, iOS, and Android platforms.
Fixed footer font rendering issues, corrected contact information and links. Implemented SEO-friendly footer with proper phone, location, and social media integration for Google Maps, Waze, Instagram, and WhatsApp.
Enhanced Progressive Web App capabilities with manifest configuration. Installed Sharp for advanced image optimization, improving Lighthouse scores and SEO.
Integrated official OFEKTIVE logo and refined overall layout sizing for professional appearance. Optimized client images to square format and fixed flex layout issues for consistent visual presentation across devices.
Enterprise-grade AI-powered recruitment platform with voice interviews, resume screening, multi-channel communication, and seamless ATS/HRIS integrations. Automates hiring workflows for Quick Service Restaurants, healthcare, and construction industries.
8 MCP tools, 335K+ indexed chunks, hybrid semantic+keyword search, knowledge graph with entity resolution, local LLM enrichment via Groq/MLX/Ollama. pip install brainlayer.
A production-ready computer vision model that detects and classifies hands, arms, and non-hand objects in real-time with 96% accuracy.
2 MCP tools (voice_speak + voice_ask), 5 voice modes, whisper.cpp STT (~300ms), edge-tts, macOS Voice Bar widget, session booking. 236 tests. bunx voicelayer-mcp.