AI Trust Drops to 29%, Usage Climbs to 84%: On What We Don't Trust
I examine the paradox behind the decline in trust in AI technologies despite their increasing usage, from a pragmatic perspective. Why we don't trust...
39 posts found.
I examine the paradox behind the decline in trust in AI technologies despite their increasing usage, from a pragmatic perspective. Why we don't trust...
As AI model token costs rapidly increase, I explain how you can reduce your bill using practical methods I've experienced.
Learn how to build your own AI agent using Python, LangChain, and the OpenAI API. A step-by-step guide to automating tasks.
I ran my own AI agents autonomously for 6 months. In this process, I encountered successes, disappointments, technical details, and my cost analysis…
In the AI-transformed tech world, the meaning of 'senior' is changing. Experience, problem-solving, and workflow mastery are more important than prompt.
I examine how over-reliance on AI tools dulls our professional skills, with examples from my 20 years of field experience. In the long run, this…
With 20 years of experience, I explain how developers should position themselves in the AI era, emphasizing the importance of technical depth and real.
With 20 years of experience, I evaluate how AI will affect the future of developers and what the real risk is.
Exploring the Microservice Communication Protocol (MCP) standard, which solves the incompatibility problem between AI models, using a USB-C analogy and my own.
Is writing code with AI tools blunting our developer skills? I share my own experiences and thoughts on this topic.
Sharing my experience building self-hosted AI automations using n8n. Creating no-code agent flows, RAG, and multi-LLM integration steps.
I examine the cost increases brought by GitHub Copilot's new token-based pricing model and the strategies I've developed to counter it.
Move beyond 'vibe coding' in software development and discover how to become more systematic and AI-friendly with Spec Kit. A detailed guide.
Connecting real-world tools to AI agents fundamentally changes their capabilities. I explain how I set up my own tool server and the challenges I faced.
With 20 years of field experience, I examine the fundamental differences, commonalities, and operational challenges of system architecture and AI solution.
I argue that vibe coding is outdated and has been replaced by Karpathy's 'Agentic Engineering' approach. This new era focuses on AI agents in engineering...
In light of 20 years of experience, I discuss the impact of AI tools on my engineering career, the areas they've accelerated, and the importance of critical.
In 2026, we'll explore the differences, advantages, and disadvantages between AI coding tools like Cursor and Claude Code to help you make the right choice...
Examines technical and behavioral defense mechanisms against AI voice cloning scams, and strategies for distinguishing a real voice from a fake one…
I compare AI's promised acceleration in software development with the actual decrease in productivity observed in the field. Why did we slow down, and how can.
I examine the potential dangers of AI agents in production environments through a real data loss scenario. Why should we be careful?
In this guide, I'll walk you through setting up and running your own Large Language Model (LLM) on your local machine using Ollama. We'll do it in 5 simple.
Mustafa Erbay's pragmatic take on whether using a vector database is truly necessary for your AI projects, exploring trade-offs and alternative approaches.
As artificial intelligence rapidly enters our lives, I discuss the limits of AI and what it has yet to achieve, drawing on my 20 years of experience in system.
Should prompt security strategies always be the same in AI applications? I share my flexible approaches and lessons learned for different scenarios.
An in-depth analysis of AI agent tool-use architecture, its limitations, and costs. Featuring real-world scenarios and concrete data.
I examined the impact of large language models (LLMs) on retrieval quality in Retrieval-Augmented Generation (RAG) systems. Real-world scenarios and concrete.
With the rise of AI in code generation, the most critical question for system architects and developers is: Who is responsible for the errors that occur?
We explore when and why to stretch the tool usage limits of AI agents, with practical examples and technical analyses. We'll delve into trade-offs and...
Learn how to manage the boundaries of AI agents' tool usage in 3 steps to ensure these tools are used safely, efficiently, and in a controlled manner...
I delve into the importance of retrieval quality in Retrieval-Augmented Generation (RAG) systems with concrete examples and in-depth analysis.
How do you control the tool usage of AI agents? Secure agent architecture with schema hardening, isolation, and RBAC.
Fail-over discipline across Gemini, Groq, Cerebras in production AI: quotas deplete invisibly, silent decay degrades quality unnoticed.
Problems I hit, lessons I learned, and the small tweaks behind my AI-driven content pipeline. From VPS to GitHub Actions, real field experience.
The operational challenges I faced while building my own AI-driven blog pipeline, and how I solved them. AI content generation, contrary to popular belief…
AI-powered software development tools and their impact on modern software engineering.
With 20 years of experience, I question how AI tools like GitHub Copilot impact developer productivity and whether they lead to laziness.
With 20 years of system architect experience, I discuss AI's future role and how it will shape us. We won't be unemployed, but we will transform.
A personal experience about the cost of using AI-generated code without questioning it, and the lessons I learned in the process.