RAG is when an AI looks up info from your documents first, then answers using that evidence. It helps reduce “made-up” answers because the model is grounded in real sources.Agentic AI is AI that can take actions, not just chat—like clicking, calling tools, or updating systems. It can plan steps and use tools to complete a task end-to-end.Contextual AI means the AI understands your situation (who you are, what you need, and what’s happening right now).It uses the right details at the right time to be more relevant and helpful.Strategic AI is AI that thinks longer-term, not just the next reply. It makes choices based on goals, trade-offs, and outcomes over time.Many real systems combine all four: retrieve (RAG), understand (context), act (agent), and optimize (strategy).The goal is AI that’s not only smart—but also reliable, safe, and useful.

Postgres continues to serve transactional workloads, ClickHouse handles analytics and an ecosystem has developed that brings them closer together. https://thenewstack.io/postgres-clickhouse-the-oss-stack-to-handle-agentic-ai-scale/

https://www.dw.com/en/ai-bubble-about-to-pop-as-returns-on-investment-fall-short-chatgpt-microsoft-nvidia-grok-elon-musk/a-74636881 Warren Buffett hold cash, he is waiting for crash https://www.gobankingrates.com/investing/strategy/why-warren-buffett-holds-more-cash-than-fed-signals-about-market/

LLMs are very large, so instead of changing all their weights, we use parameter-efficient fine-tuning to only train small parts. Methods like LoRA, QLoRA, prefix/prompt tuning, adapters, and BitFit add tiny extra layers or low-rank updates while keeping the main model frozen. This saves memory and compute, lets people train on normal GPUs, and makes it easy to reuse one base model for many different tasks.

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