标签：#gai

In the previous parts, we connected Spring AI with cloud-based AI models. But there is one important question: What if you don't want to send your data to an external AI provider? What if you want to: Run an LLM on your own machine Develop AI applications without API costs Work without an internet connection Keep sensitive company data private Experiment with different open-source models Build AI features locally before moving them to production This is where Ollama becomes very useful. In this article, we will learn how to run a local LLM using Ollama and connect it with Spring AI . We will build a simple real-world AI Customer Support Assistant using Java, Spring Boot, Spring AI, and Ollama. What We Are Building Our application will look like this: User | | HTTP Request v +---------------------+ | Spring Boot API | +---------------------+ | v +-------------+ | Spring AI | | ChatClient | +-------------+ | v +--------+ | Ollama | +--------+ | v Local LLM (Llama/Qwen) | v AI Response | v User The important part is that the LLM is running locally . There is no need to send every prompt to OpenAI, Anthropic, or another cloud provider. 1. What Is Ollama? Ollama makes it easy to run open-source LLMs locally. Instead of calling a remote API like: Spring Boot | v OpenAI API | v Cloud LLM we can run: Spring Boot | v Spring AI | v Ollama | v Local LLM Ollama can run models such as: Llama Qwen Gemma Mistral DeepSeek and many other compatible models The exact models available change over time, so always check the Ollama model library before choosing one. 2. Why Run an LLM Locally? Imagine you are building an internal HR application. Employees may send questions such as: What is our maternity leave policy? or: What is the process for requesting annual leave? You may not want internal company information leaving your infrastructure. A local LLM can help: Employee | v Spring Boot | v RAG / Business Logic | v Ollama | v Local LLM This can provide a useful privacy boundary. However

Stop Asking 'Is GAI Here' — Ask 'At What Layer' The GAI debate has a structural problem. Someone says "passing this benchmark means GAI." A model passes it. Then they say "that benchmark wasn't hard enough." The goalpost moves. Someone says "passing the Turing test means GAI." Models pass it. Then they say "the Turing test is too easy." The goalpost moves again. Someone says "inventing new mathematics means GAI." Models do it. Then they say "that's just pattern matching in disguise." Goalpost moves. This isn't bad faith. It's a missing layer definition. We never agreed on what "general" means. Without that, every achievement gets reclassified as "not really general." I've been working on a framework that might fix this. It started as a capability map. Then I realized: this isn't just a map. It's a GAI maturity model. The Five Layers Layer Name Definition L0 Embodied Perceive and operate in the physical world L1 Application Complete single-domain tasks using tools L2 Engineering Build and maintain systems L3 Meta-Domain Abstract and transfer between unrelated domains L4 Meta-Cognition Perceive and control your own thinking process The rule: layers cannot be skipped. It's a maturity sequence, not a checklist. This immediately explains the goalpost problem: some people define GAI as L1. Others define it as L4. They're using different layers for the same word. What About Models Without Bodies? L0 requires embodiment. Text-only models don't have bodies. The cleanest answer: LLMs have no L0. They start at L1 — cognition without embodiment. This isn't a defect. It's an architectural difference. Humans build up from L0 (a baby senses the world before understanding it). LLMs start at L1 (they understand the world directly, skipping physical experience). The result: humans can "feel" when something is wrong — that's L0 feeding signals up to L4. LLMs don't have this channel. The framework forced me to face something uncomfortable: human intelligence cannot exist without a body

Thea Energy's pixel-inspired magnets could give its power plant plans a boost. The fusion startup hopes to get a commercial reactor working by 2034.