Software development is the process of designing, coding, testing, releasing and operating a solution to a need. This section contains hands-on guides with working code on AI, computer vision, IoT, databases and web performance.
Work in this area
Abdulaziz Akyol brings more than 20 years of enterprise IT experience to product engineering at CX Teknoloji. The guides are written around real problems met in production systems: security, scalability, measurement and maintenance.
- End-to-end code from RTSP camera to event with Python and YOLO
- Edge AI acceleration with ONNX, TensorRT and INT8
- Scaling GPU services with Docker and Kubernetes
- SQL Server performance tuning and fast websites with Astro
Frequently asked questions (3)
Which language is used in computer-vision projects?
Python is common for training and prototyping; in production the heavy lifting goes to accelerated runtimes such as C++/CUDA, TensorRT or ONNX Runtime. The integration layer is usually built on events and REST/MQTT interfaces.
How are AI services taken to production?
The model is packaged at a fixed version (container), resource limits and health checks are defined, and latency and accuracy are measured continuously. GPU sharing and the update strategy must be planned from the start.
How do you improve a website's Core Web Vitals?
The biggest gains come from images: correct size and modern formats (WebP/AVIF), early discovery and prioritisation of the LCP image, removing render-blocking font/CSS requests and cutting third-party scripts.
- Accelerating edge AI models: ONNX, TensorRT and INT8 quantizationArtificial intelligenceExport a PyTorch model to ONNX and speed it up with ONNX Runtime and TensorRT: FP16, INT8 calibration, accuracy loss and p50/p95 latency, with working code.
- AI agents and MCP: architecture, security and a first project for companiesArtificial intelligenceAI agents and MCP explained: connecting them safely to ERP and CRM with least privilege, prompt injection defences, approvals, audit logs and a first project.
- Enterprise RAG: how to build an AI assistant that talks to company documentsArtificial intelligenceBuild a RAG assistant that answers from company documents with citations: parsing, chunking, embeddings, pgvector, hybrid search, permissions, evaluation.
- Real-time object detection with Python and YOLO: RTSP camera to eventComputer visionReal-time detection from an RTSP camera with Python, OpenCV and YOLO: ByteTrack tracking, line counts, zone alerts and JSON events to MQTT, full script.
- Scaling GPU video analytics services with Docker and KubernetesSoftware developmentRunning a per-camera GPU analytics service on Docker and Kubernetes: NVIDIA Container Toolkit, GPU Operator, time-slicing/MIG, k3s, monitoring and updates.
- Secure IoT with MQTT: Python, TLS and access controlIoTRunning MQTT securely: QoS, retain, last will and MQTT 5; TLS, client certificates and ACLs in Mosquitto; Python publisher and subscriber with paho-mqtt 2.x.
- SQL Server performance tuning: indexes, statistics and query plansMicrosoft SQL ServerFind slow SQL Server queries by measuring, not guessing: wait statistics, Query Store, execution plans, index design, statistics and parameter sniffing.
Older technical notes (HTML5, PowerShell, Windows administration, Active Directory, HP/Aruba switches) are available in Turkish only. Türkçe →