Every option in one area, in one place, screened against the same criteria.
An AI use-case portfolio is the system that collects, prioritizes and balances scattered AI ideas. Use-case cards, value–feasibility scoring, portfolio balance and review cadence.
Observability is now a production prerequisite. Tracing vs evaluation, hallucination detection, LangSmith/Langfuse/MLflow, and self-hosting options for KVKK.
Free Turkish resources for learning AI: resource categories, a beginner-to-advanced learning path, project-based learning, communities, and free courses in one guide.
What determines AI engineer salaries? Seniority, specialization, company type, location, currency; the logic of salary ranges, benefits, freelance, and the global gap.
From flat vector RAG to agentic RAG and context engineering. In 2026 winning teams invest in the knowledge source, not the model.
MMLU, HumanEval, SWE-bench Verified/Pro, ARC-AGI-2, GPQA Diamond, AIME, LiveCodeBench v6, Terminal-Bench 2.0, OSWorld, HLE, plus Turkish benchmarks (TR-MMLU, TUMLU) — what each one measures, the frontier thresholds, contamination and cherry-picking risks, and practical meaning for CTOs, investors, and engineers. 32+ references.
AI portfolio strategy for Turkish university students (CS, EE, Industrial Engineering, Math, Statistics) from zero to graduation: 4-year year-by-year plan, 15+ recommended project types, Trendyol/Getir/Hepsiburada/Turkcell internship application process, AI opportunities at Turkish universities (AGU/Bogazici/METU/Bilkent/Hacettepe), Erasmus + European internship opportunities, Google STEP / Microsoft Explore / Meta University programs, US university masters application, GitHub + LinkedIn + personal website setup, hackathons + Teknofest + ACM ICPC, academic research + paper publication, open source contributions, Kaggle tier targets, first salary ₺40-70K (intern) → ₺60-100K (junior), Turkey-US-Europe career comparison, 10 success stories.
Grouped by format, newest first within each group.
Situations genuinely encountered in consulting and training engagements — where theory cracks in practice.
An answer to “which one should I pick?” with a criteria table and an explicit decision rule.
Understanding a term from zero, with examples — from definition to application.
A step-by-step, executable path — with prerequisites, outputs and typical pitfalls.
The data behind a development and what it means operationally — consequences, not headlines.
Beyond the overview: architectural decisions, production patterns and measured results.
A subject treated in depth, without a fixed template.