Showing 169–192 of 540 articles, newest first.
MIT NANDA: 95% of pilots produce no P&L return, 73% have no success definition. A recipe for CTOs/CDOs to escape the traps and build a culture of measurement.
The secret to agentic AI success is discipline, not the model. Four bottlenecks in pilot-to-production, maturing frameworks, and KVKK/EU AI Act governance.
On August 2, 2026 GPAI enforcement powers go live: 3% fines, transparency rules, and the Digital Omnibus deferral. A practical countdown guide for Turkish companies.
Agentic commerce is the third wave: agents research and buy on the user's behalf. Google 'Buy for me', 4x conversion, the trust problem, and KVKK personalization limits.
RAG or fine-tuning? Wrong question. The right sequence: Prompt → RAG → Fine-tune → Distill. A 2026 decision framework covering LoRA/QLoRA, GRPO, small language models, and KVKK.
LLM observability is now a production requirement. Tracing, cost, and quality monitoring with OpenTelemetry GenAI; 90% savings with semantic caching; a KVKK-compliant content-logging guide.
RAG didn't die, it matured. Raise retrieval accuracy with late chunking, contextual retrieval, hybrid search, and reranking. A practical 2026 guide for Turkish and KVKK contexts.
Prompt engineering didn't die, it matured. Build a resilient prompt architecture with structured output, prompt caching, self-consistency, and programmatic tool calling — in Turkish and KVKK contexts.
GPT-5.6 vs Claude Opus 4.8 vs Gemini 3.1 Pro: code, agentic tasks, price, context, and Turkish performance. A guide to choosing the model that fits your job, not the smartest one.
95% of enterprise GenAI pilots fail; the cause is strategy, not the model. A CTO ROI framework with baselines and integration. The successful 5% see a median 188% ROI.
EU AI Act GPAI obligations become enforceable on 2 August 2026. A guide to technical documentation, copyright, data summaries, and KVKK compliance for model providers and fine-tuners.
Agentic AI in e-commerce: conversational product discovery, autonomous customer service, and personalization. A 2026 Turkish field guide to building it all within KVKK limits.
The number-one cause of AI agent failure is memory. A field guide to short/long/graph memory architectures, multi-scope patterns, LangGraph, and KVKK-compliant retention for 2026.
A guide to AI for non-technical roles: becoming an AI champion without coding, the skills you need, no-code tools, finding use cases, change leadership and career impact.
A comparison of AI education paths: bootcamp, certificate, master's, and self-study; which fits you by duration, cost, depth, and career payoff in this comprehensive guide.
Free Turkish resources for learning AI: resource categories, a beginner-to-advanced learning path, project-based learning, communities, and free courses in one guide.
AI transition for software developers: a skill bridge that leverages your existing experience, a step-by-step learning plan, portfolio projects, and a role-change roadmap.
Do AI certifications really add value? The value of cloud, academic, and vendor certifications, which one for which role, their weight in hiring, and a selection framework.
What are the differences between AI Engineer, ML Engineer and Data Scientist? Role definitions, skill sets, responsibilities, transition paths and career path here.
A guide to prompt engineering training: the content it must cover, duration and format options, career value, hands-on projects, the value of certificates, and how to choose a good program.
What determines AI engineer salaries? Seniority, specialization, company type, location, currency; the logic of salary ranges, benefits, freelance, and the global gap.
How to become an AI engineer? Skill set, a step-by-step roadmap, portfolio and production project building, finding a job in Türkiye, and career transitions in one guide.
Choosing a vector database depends on scale, latency and operations. I compare pgvector, Qdrant, Milvus and Pinecone with 2026 benchmarks and a decision framework.
The era of hand-writing prompts is closing. I explain DSPy, meta-prompting and eval-driven loops that let the machine optimize your prompts.