Showing 433–456 of 540 articles, newest first.
We benchmarked GPT-5.5, Claude Opus 4.7 and Gemini 3.1 Pro on Turkish workloads end to end: TR-MMLU and TUMLU benchmark numbers, a 50-prompt real-world test across legal, finance, code, creative writing and Q&A, an A/B in a Turkish enterprise, TL-based cost analysis and a decision matrix for picking the right model for each Turkish task. 35+ references.
An enterprise decision matrix between self-hosted LLM and API: ~500M tokens/day break-even, H100/H200/B200 GPU cost, quantization impact, KVKK + BDDK + ITAR/EAR constraints, AI sovereignty strategy, and three anonymized Turkish sector cases (banking, healthcare, SMB) on hybrid architecture. 2026 reference guide for Turkish enterprises.
A 2026 snapshot of the Turkish open-source LLM ecosystem: Trendyol-LLM, Cosmos-Llama, KanarYa, Kumru AI, the TÜBİTAK BİLGEM domestic model, and the T3 AI Baykar defense model. Detailed decision guide covering MMLU-TR and TUMLU benchmarks, licensing, tokenization gap, VRAM requirements, self-hosting needs, and which model to pick for which use case.
Prompt engineering is dead, context engineering is alive. Anthropic's 90% cost-cutting prompt caching, GPT-5.5's 272K input threshold, Claude Opus 4.7's 1M context, and agent runtime state management are rewriting AI engineering in 2026. Turkish token efficiency, KVKK-compliant state stores, the 'Don't Break the Cache' principle.
Naive RAG's six fatal weaknesses are fully solved in 2026 by agentic RAG. A production-grade RAG with plan/reflect/verify loops, hybrid retrieval, and claim-verification built on the LangGraph v0.4 state-graph used by Klarna, LinkedIn, and Uber — plus a KVKK-compliant Turkish bank case study and cost-latency tradeoffs.
30 ChatGPT prompts for Turkish lawyers - monopoly-depth prompt library. 10 categories covering legal drafting, contract analysis, case research, client preparation, KVKK compliance, employment law, family law, criminal law. Each prompt: Turkish legal framework (TBK, TCK, IK), real article references, limitations, when lawyer verification mandatory, KVKK confidentiality warnings, model recommendations.
Most comprehensive Turkish technical guide for Tree of Thoughts (ToT): academic foundation (Yao et al. 2023 NeurIPS paper), CoT vs ToT vs GoT comparison, search algorithms (BFS, DFS, Beam Search, A*), 4 ToT components, classic benchmark results, 25+ Turkish practical examples, LangGraph implementation, cost analysis, Graph of Thoughts evolution, agentic systems integration.
Most comprehensive Turkish technical guide for ReAct Pattern (Reasoning + Acting): academic foundation (Yao et al. 2022 ICLR paper), CoT vs ReAct difference, Thought-Action-Observation loop, 5 ReAct variants (Vanilla, MRKL, Self-Ask, ReWOO, Plan-and-Execute), LangChain + LangGraph + LlamaIndex implementations, agentic tool use integration, 25+ Turkish practical examples, error handling, production deployment, observability, cost optimization, model comparison.
Most comprehensive Turkish technical guide for Few-Shot Learning prompt optimization: academic origins (Brown et al. 2020 GPT-3 paper, in-context learning discovery), 8 example selection strategies (random, similarity-based KATE, diversity, semantic, active learning), optimum example count analysis (1 vs 3 vs 5 vs 10 vs 32), ordering effects (Lu et al. 2022 'lost in middle'), delimiter and formatting best practices, Anthropic XML tags pattern, Few-Shot + CoT combination, recency + primacy bias, dynamic few-shot retrieval, prompt versioning, A/B test framework, 25+ Turkish practical examples, evaluation framework, production deployment.
Most comprehensive Turkish technical guide for Chain-of-Thought (CoT) prompting: academic foundations (Wei et al. 2022 NeurIPS paper, Kojima et al. 'Let's think step by step'), 6 CoT variants (Zero-shot CoT, Few-shot CoT, Self-Consistency, Tree-of-Thoughts, Graph-of-Thoughts, Auto-CoT), benchmark performance (GSM8K 18% → 78%), 35+ Turkish practical examples, model-specific CoT behavior, when NOT to use, hallucination control, multi-step task design, agentic system integration, Turkish-specific pitfalls, cost impact.
100 ready-to-use ChatGPT prompts for Turkish professionals — categorized (Management 15, Marketing 15, Content 15, Education 15, Software 10, E-commerce 10, HR 5, Customer Service 5, Finance 5, Legal 5, Health 3, Personal Productivity 2). Each prompt: use case, full prompt text, expected output example, variation tips, best-fit model (GPT-5/Claude/Gemini), KVKK warnings on sensitive topics. Prompt anatomy (Role, Context, Task, Format, Constraints — RBGFK framework), Turkish prompt engineering tips, official best practices from Anthropic + OpenAI, prompt iteration strategies, versioning.
Detailed math guide for AI/ML engineering: 5 main areas (Linear Algebra, Calculus, Probability + Statistics, Optimization, Information Theory), per area depth required by job type (AI Engineer / ML Engineer / Research Scientist differ), 50+ concepts (vector/matrix/derivative/gradient/eigenvalue/SVD/lambda/expected value/MLE/MAP/Adam/Lagrange/KL divergence), Turkish + English learning resources (3Blue1Brown / Gilbert Strang / Khan Academy / BTK Akademi), Andrew Ng vs Andrej Karpathy approach difference, 6-month math learning plan, which formulas to memorize vs intuition only, practical vs theoretical math, math interview questions, course order for beginners, sequential book recommendations.
Deep comparison of 3 main learning paths for AI/ML engineering candidates + students: LeetCode (algorithm focus, Big Tech interview), Kaggle (competition + ML algorithm + Notebooks tier), Real Project (end-to-end, GitHub portfolio, production experience). Each path strengths, time investment, job-finding contribution, position-priority matching, Turkish company vs US Big Tech vs European differences, hybrid strategy recommendations, Junior vs Senior focus difference, 12-month recommended mix, time-investment ROI calculation, 8 success stories, common mistakes, post-interview feedback distribution.
Detailed AI/ML interview preparation guide: candidate side (5-stage process, 50+ technical questions with answers, ML system design, behavioral STAR, salary negotiation, Turkish company patterns), employer side (effective technical interviewing, what NOT to ask, bias-free evaluation, junior vs senior question difference), role-specific questions (Data Scientist, ML Engineer, AI Engineer, Research Scientist), Trendyol/Getir/Turkish bank interview formats, AI-assisted interview prep with GPT-5/Claude, mock interview platforms (Pramp, interviewing.io), AI cheat detection methods, live coding rules, real salary negotiation scenarios (Turkey + US remote).
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.
Comprehensive Turkish guide for learning data science with Kaggle from zero to Master: platform structure (Notebooks, Competitions, Datasets, Models, Discussions), 5 progression tiers (Novice → Contributor → Expert → Master → Grandmaster), per-tier requirements + process, 20+ free Kaggle Learn courses, 6-month plan from first competition to first medal, ensemble + stacking + blending techniques, GPU/TPU notebook strategies, tabular vs CV vs NLP competition differences, Turkish Kaggle masters success stories, team formation tactics, code competitions, Notebooks tier separate path, dataset/discussion medal strategy, optimizing Kaggle profile for job hunting, 10 practical tips.
Deep technical guide to AI certifications from three main cloud providers: AWS (AI Practitioner, ML Engineer Associate, ML Specialty, Generative AI Specialty), Microsoft Azure (AI-900, AI-102, DP-100, AI-3001), Google Cloud (Cloud Digital Leader, ML Engineer, Generative AI Leader). Per certification: exam details (duration, price, passing score, question count), prep time, recommended resources, Turkey market value, which companies expect which, value hierarchy, ordering recommendation, pass strategy, real experience tips. Turkish company examples (Trendyol AWS, Turkish banks Azure, Big Tech GCP).
Detailed 12-month roadmap to become an AI engineer from zero: Month 1-2 Python + math foundation, Month 3-4 classic ML, Month 5-6 deep learning + PyTorch, Month 7-8 LLM + RAG + agentic, Month 9-10 MLOps + production, Month 11-12 specialized + job search. Each month with specific courses (Coursera, fast.ai, DeepLearning.AI), books, milestone projects, Turkish resources (BTK Akademi, Coursera Turkish subtitles), daily study plan, portfolio requirements (5-10 GitHub projects), Kaggle strategy, certifications, job application tactics. SMB/freelance/abroad options.
Deep technical + career comparison of AI Engineer, ML Engineer, Data Scientist roles: historical origins (2010 Data Scientist → 2015 ML Engineer → 2023 AI Engineer), day-to-day work, tech stack (PyTorch/TF/scikit-learn vs LangChain/MCP/vector DB), Turkey salary ranges 2026 (₺55K-300K), global comparison (US $130K-500K), two main career paths (academia vs industry), 7 main differences, which role suits you, transition strategies, interview questions, seniority levels, Turkish company examples (Trendyol, Getir, Turkcell, BiTaksi), 6 Turkish specialized niches.
Deep technical comparison of three main AI video generation platforms: OpenAI Sora 2 (ChatGPT Pro integrated, 20s HD), Runway Gen-4 (commercial standard, agency favorite), Kuaishou Kling AI 2.0 (Chinese leader, photoreal human). Plus alternatives: Luma Dream Machine, Pika 2.0, MiniMax Hailuo, Lightricks LTXVideo, Open-Sora, Veo 3 (Google), Mochi (Genmo). Architectures, duration, resolution, motion quality, prompt understanding, image-to-video, video-to-video, lip sync, character consistency, pricing, commercial rights, KVKK + Turkish copyright, 15 use cases, video prompt engineering, troubleshooting.
Deep technical guide for Stable Diffusion local installation: GPU selection (NVIDIA, AMD, Apple M-series, Intel Arc), Windows/macOS/Linux platform-specific steps, 3 main UI comparison (Automatic1111 mature, ComfyUI professional, Forge optimized, Fooocus simple, SD.Next advanced), model files (SD 1.5, SDXL, SD 3.5, FLUX, Pony Diffusion), VAE setup, sampler (Euler a, DPM++ 2M Karras, UniPC) deep explanation, CFG scale + denoising strength, LoRA + ControlNet + IP-Adapter setup, inpainting/outpainting workflow, CivitAI ecosystem, Dreambooth/LoRA fine-tune, OOM/CUDA/Black image troubleshooting, performance optimization (xFormers, FlashAttention, torch.compile), KVKK self-host security, 25+ practical tips.
Deep technical guide for Black Forest Labs' FLUX.1 image generation model: founding team story (ex-Stability AI Robin Rombach team), Rectified Flow Transformer architecture (DiT + flow matching), 4 variants (Schnell Apache 2.0, Dev non-commercial, Pro API, 1.1 Pro Ultra), training methodology, benchmarks (human face, hands, text), ComfyUI + Diffusers + Forge installation step-by-step, ControlNet + LoRA + IP-Adapter for Flux, prompt engineering specifics, T5 vs CLIP text encoder differences, GGUF quantization (8-bit, 4-bit, NF4), Mistral Le Chat integration, 20+ Turkish use cases, troubleshooting (OOM, NaN, slow), KVKK self-host.
Comprehensive Turkish guide to Midjourney V7 from zero to professional: signup, Discord + web UI, prompt fundamentals, parameters (--ar --s --c --weird --niji), Style Reference, Character Reference, Image Prompt, Vary (Region), Pan, Zoom, Upscale, Custom Presets, pricing + commercial rights, KVKK, Turkish prompt strategies, 25+ practical prompt examples, 10 professional use-cases.
Detailed head-to-head of four main AI image-gen models: Midjourney V7 (aesthetic champion), OpenAI DALL-E 3 / GPT-Image (ChatGPT integrated), Stable Diffusion 3.5 / SDXL (open-source), Black Forest Labs FLUX (newest photoreal). Quality, pricing, commercial rights, KVKK + Turkish law, speed, Turkish prompt fluency, ControlNet/LoRA advanced features, 12-scenario selection guide.