Blog Posts
Synthetic Data: When Fake Data Beats Real Data
Generative models as data factories — for privacy, rare events, and languages the internet forgot.
AI Governance in Practice: KVKK, GDPR, and the EU AI Act
What Turkish companies deploying ML actually need to do about the new regulatory landscape.
Real-Time Computer Vision at the Edge: A Practical Playbook
From YOLO on Jetson to fleet-wide OTA updates — running vision where the cameras are.
Prompt Engineering Is Dead. Long Live Context Engineering.
The craft moved from magic words to information architecture: what you put in the window matters more than how you phrase it.
An Honest MLOps Maturity Model
Five levels from notebook chaos to continuous training — and where most companies actually are.
Multimodal Foundation Models: Seeing Is the New Reading
Vision-language models moved document AI, quality control, and accessibility from research to routine.
Small Language Models: The Contrarian Bet That Won
Quantization, distillation, and why your next AI feature might run on a phone.
Hybrid Search and Reranking: The Unreasonable Effectiveness of BM25
Why the best retrieval systems still keep a 30-year-old algorithm in the loop.
From RNNs to Transformers: A Field Guide for the Curious
How sequence modeling evolved from recurrent networks to attention — and what the old ideas still teach us.
OCR for Ottoman Turkish: From PhD Research to Production Pipelines
What historical document recognition teaches us about modern transformer-based OCR.
Data Transformation Is Still 80% of the Work
LLMs changed the models, not the ratio: why data engineering dominates every ML project.
Agentic AI in Production: Lessons from the Trenches
Tool-calling, MCP, multi-agent orchestration — and why evaluation is 80% of the work.
From MNIST to Production: What Toy Datasets Don't Teach You
The gap between a 99% accurate notebook and a model your business can rely on.
RAG vs. Fine-Tuning: How to Choose
A decision framework for grounding LLMs in your data — retrieval, fine-tuning, or both.
Why you don't need a specialist Vector Database
Postgres implements everything a vector database does and a lot more
Implementing Authorization with Postgres Row Level Security (RLS)
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A Comprehensive Guide to Building Successful Large Language Model (LLM) Applications
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Content Marketing for Developers
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