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// References

What we've built.

Anonymized case studies from real projects. Each system was implemented from concept to production — complete, functional, in record time.

network.dashboard — 16 Modules // Mesh Topology // Live Stats Uptime 99.9% Nodes Online Latency <1ms // Traffic Flow // Health Matrix

Real-Time Monitoring Dashboard

Case Study #01

Enterprise VPN & Monitoring Platform

Challenge

Distributed multi-cloud infrastructure with multiple nodes required encrypted networking, live monitoring and automatic failover.

Solution

Encrypted mesh network with multi-path routing, WebSocket-based real-time dashboard (16 modules) and eBPF traffic steering.

Result

Sub-1s latency for health updates, 99.9% uptime, full network sovereignty without cloud dependency.

16 dashboard modules Sub-1s health updates 99.9% uptime
Go React WebSocket eBPF Reverse Proxy
community.platform — 12+ Modules Channels 🔊 Voice Lounge 💬 General 📢 Events 🎮 Gaming 🔊 Voice Active — 4 Users Write a message... Members

Community Platform — Voice, Chat & Guild Management

Case Study #02

Real-Time Community Platform

Challenge

A gaming community needed voice/video chat, guild management and live events — fully self-hosted, without external services.

Solution

Custom platform with 12+ modules, SFU-based voice/video, real-time chat and automated content management system.

Result

60+ API endpoints, stable video conferences with multiple participants, own JS framework with 85+ methods.

60+ API endpoints 12+ platform modules 85+ framework methods
PHP JavaScript WebRTC/SFU Socket.IO MySQL
ai.inference — Dynamic Model Routing $ ai-router status ┌─────────────────────────────────────────────┐ │ Model Router — Active Pipelines │ ├─────────────────────────────────────────────┤ │ ● Code-Gen │ 35B │ GPU-0 │ 42 tok/s │ │ ● Chat │ 14B │ GPU-0 │ 68 tok/s │ │ ◐ RAG-Index │ 7B │ CPU │ indexing... │ │ ● Vision │ 12B │ GPU-1 │ ready │ │ ○ Fine-Tune │ -- │ queue │ scheduled │ └─────────────────────────────────────────────┘ $ mcp-agent orchestrate --workflow=code-review → Spawning agent: code-analyzer (35B) → Tool chain: grep → ast-parse → diff → apply ✓ Review complete — 3 fixes applied, 0 regressions

AI Inference Pipeline — Model Routing & Orchestration

Case Study #03

AI Inference & Model Orchestration

Challenge

AI inference with full data sovereignty and maximum flexibility — complete control over models, data and deployment target.

Solution

Dedicated GPU inference pipeline with dynamic model routing (hot-swap), tool integration via MCP and code-RAG.

Result

Multiple models (7B–35B) available on-demand, high inference speed, seamless IDE integration and dynamic routing.

7B–35B model size range Hot-swap model routing Full data sovereignty
Python CUDA Docker MCP LLM Routing
infrastructure.cluster — HA + Storage ☁ Cloud Nodes Node-1 VPS Node-2 VPS Node-3 Bare ⬡ On-Premise HA-Cluster Hypervisor GPU Compute AI Inference ████░░ 67% ⬡ Encrypted Mesh Network — WireGuard Multi-Path 💾 Enterprise Storage ZFS Pool — 73% used NFS Share — healthy Snapshots: hourly | Replication: async 🔧 Self-Hosted Services ● DNS + Mail ● App Platform ● Monitoring ● Auth/SSO

Hybrid Cloud — HA Cluster & Enterprise Storage

Case Study #04

Hybrid Cloud Infrastructure

Challenge

Heterogeneous infrastructure (bare metal, VPS, home lab) needed unified management, high availability and central storage.

Solution

Virtualization cluster with HA failover, NFS-based shared storage, self-hosted app platform and professional mail system with full-stack authentication.

Result

Multi-node HA, central enterprise storage, self-hosted password manager, remote desktop and zero-downtime deployments.

Multi-node HA Enterprise storage Zero-downtime deployments
Hypervisor Cluster ZFS/NFS Postfix/Dovecot Let's Encrypt Ansible
game.server — Management Console Server Instances Survival #1 — Online Creative #2 — Online PvP Arena — Restart Test Server — Idle ▶ Start ■ Stop // Live Console [INFO] Server started on port 25565 [INFO] Loading plugins: 12 found [PLUGIN] CustomLogic v2.1 loaded [PLUGIN] AutoRestart v1.3 loaded [INFO] World generation complete [PERF] TPS: 20.0 | RAM: 4.2GB / 8GB [INFO] Player connected (3/64) Players 3/64 Uptime 47d Plugins 12

Game Server Management Console

Case Study #05

Game Development & Server Hosting

Challenge

Multiplayer games required dedicated server infrastructure with automated deployment, plugin systems and real-time monitoring — without cloud dependency.

Solution

Containerized game servers with automated provisioning, custom extensions/plugins, CI/CD pipelines for updates and WebSocket-based live monitoring.

Result

Stable multiplayer servers with automatic restart, plugin system for custom logic and centralized management of multiple instances.

Automated provisioning Custom plugin system Multi-instance management
Node.js Game Engines Docker Automation Scripts WebSocket
ml.pipeline — Training & Inference // Training Pipeline Dataset 50K imgs → PyTorch CUDA 12 → Model v2.4 Epoch 45/50 — Loss: 0.0023 GPU: 98% | VRAM: 22.1/24GB | ETA: 12min // Live Detection target_01 97% obj_02 84% FPS 60 Latency 16ms // Performance Metrics Accuracy Over Time Inference Throughput Model Versions ● v2.4 — active (97.2%) ○ v2.3 — archived (95.8%) ○ v2.2 — archived (93.1%)

ML Training &amp; Live Detection Pipeline

Case Study #06

Machine Learning & Computer Vision

Challenge

Real-time detection and classification of visual data required GPU-accelerated training, optimized inference pipelines and live processing with minimal latency.

Solution

PyTorch-based bot development with CUDA-accelerated training, custom models for live detection and automated data pipeline for continuous retraining.

Result

Reliable real-time detection with sub-100ms inference, automated training pipeline and productive system running for months.

Sub-100ms inference Months in production Automated retraining
PyTorch CUDA Python Computer Vision Live Detection
automation.hub — Pipeline Control // Deploy Pipeline #247 Build 23s ✓ Test 45s ✓ Stage 18s ✓ Deploy running... Verify pending // Automation Scripts ✓ deploy.sh — zero-downtime rolling ✓ healthcheck.py — self-healing ✓ provision.yml — Ansible playbook ✓ backup.sh — ZFS snapshot + rotate ⟳ monitor.py — running (24/7) 42 scripts | 8 playbooks | 3 cron jobs // Deployment Targets ● Production 3 nodes | healthy Deploy: 1m 26s ● Staging 1 node | deploying Deploy: 0m 47s Last 5 Deploys: ✓ ✓ ✓ ✓ ✓ — 100% success rate Avg: 1m 32s | Rollbacks: 0

CI/CD &amp; Automation Pipeline

Case Study #07

Automation & Scripting

Challenge

Repetitive workflows — deployment, monitoring, maintenance, provisioning — consumed too much manual time and were error-prone.

Solution

Fully automated pipelines with Ansible playbooks, custom Bash/Python scripts, CI/CD integration and plugin development for existing systems.

Result

Deployments in under 2 minutes instead of hours, automatic health monitoring and self-healing for critical services.

Deploy < 2 min Self-healing services Zero-touch provisioning
Bash Python PowerShell Ansible CI/CD
media.pipeline — Processing Engine // Audio Pipeline Import WAV/MP3 → Process FFmpeg → Master Loudness Output: FLAC, MP3, AAC | Bitrate: 320kbps // Video Pipeline Source 4K RAW → Encode H.265 → Multi-Format 1080/720/480 ▶ Preview GPU: NVENC Speed: 8.2x Output: MP4, WebM, HLS | Codec: H.265/VP9 // Batch Processing Queue album_master.wav → FLAC + MP3 + AAC 86% promo_4k.mov → 1080p + 720p + thumb 45% batch_images/ → webp + compressed [queued — 128 files] Queue: 3 jobs | Processed today: 47 | GPU: 92%

Media Processing Pipeline

Case Study #08

Multimedia & Creative Engineering

Challenge

Creative production — music, video, image — required repetitive manual steps. Encoding, mastering and publishing were time-intensive and error-prone.

Solution

Code-based media pipelines with FFmpeg automation, generative audio/visual workflows, batch processing and automated publishing to various formats.

Result

Fully automated production pipeline from raw material to finished medium, GPU-accelerated encoding and consistent quality across all output formats.

Automated pipelines GPU encoding Multi-format output
FFmpeg Python Audio/Video APIs Generative AI Streaming
cluster.mgmt — Virtualization // Cluster Nodes Node-01 — 64GB | 12C Node-02 — 32GB | 8C Node-03 — 16GB | 4C (HA) // Virtual Machines cloud-platform — 8GB | running storage-node — 16GB | running mail-server — 4GB | running password-mgr — 2GB | running remote-desktop — 8GB | running 5 VMs | 38GB allocated | HA enabled // ZFS Storage Pool rpool/data — 4.2TB / 8TB (52%) backup — 1.8TB / 4TB (45%) RAID-Z2 | Snapshots: hourly | Scrub: weekly // LXC Containers ● DNS + DHCP ● Monitoring ● Reverse Proxy ● Auth / SSO

Virtualization Cluster Dashboard

Case Study #09

Virtualized Platforms & Self-Hosting

Challenge

Numerous services — cloud platform, storage, password manager, remote desktop — required isolated, manageable environments with central storage and backup.

Solution

Virtualization cluster with container and VM isolation, ZFS-based enterprise storage, automated snapshots and central management interface.

Result

Each service as independent productive system, central management, automated backups and fast recovery in case of failure.

Container + VM isolation ZFS enterprise storage Automated snapshots
Hypervisor LXC Docker ZFS NFS

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