AI Models

Search Console MCP: practical guide with definitions, implementation checklist, security notes, and internal links across the agentic stack.

Open-weight vs closed AI models in 2027—privacy, cost, quality, ops burden, and a framework to choose per workload.

AI chips in 2027—why NVIDIA dominates, what alternatives matter for cloud and edge, and how builders should plan capacity and cost.

Edge AI in 2027—on-device models for privacy, latency, and offline use, plus hardware constraints and an architecture checklist.

Synthetic data for AI in 2027—when to generate training/eval data, privacy benefits, leakage risks, and a practical quality playbook.

Vector databases for AI in 2027—when you need one for RAG, how to choose, hybrid search, metadata filters, and ops pitfalls.

ChatGPT vs Claude vs Gemini in 2027—strengths for coding, research, enterprise, and pricing mindset so teams pick the right default.

Why small language models (SLMs) matter in 2027—lower cost, on-device AI, routing architectures, and when to skip frontier LLMs.

RAG vs fine-tuning vs agents in 2027—when to retrieve, when to train, and when to let agents take actions across tools.

What multimodal AI means in 2027—models that see, hear, and read together—and how startups use vision+voice+docs in real products.