🌳 Java devs: ever feel like AI in Java is a wild forest?
Guess what—tools are finally landing: meet the Java AI toolbox with SpringAI & LangChain4j Ready to wield? Let’s go! #JavaAI #SpringBoot #LangChain4j
🔧 Spring AI feels like your familiar Spring Boot buddy—smooth, seamless, and zero friction. Meanwhile, LangChain4j is the Swiss Army knife: chainable prompts, chat memory, RAG—you name it. Choose wisely. #AIinJava #ChatMemory
🧠 Memory is everything.
No memory = total Dory mode.
With it = conversational coherence.
Thanks, LangChain4j and SpringAI, for not letting my AI go full “What was I doing again?” 🐠 #LLM #SpringAI
💾 Local models > cloud (sometimes).
Spun up Llama 3 via Ollama on my laptop—private, fast, $0 API bill. Turns out, sometimes “AI in the cloud” = “AI in my living room.” #Ollama #Llama3 #OnPremAI
🤯 Models are evolving at light speed.
Take Qwen3: “thinking mode” for deep reasoning or “non-thinking mode” for speed. Want it confident—or fast? Choose your AI mood. #Qwen3 #LLMModes
☕ Java’s AI ecosystem has grown up.
What felt clunky a year ago is now slick. With tools like Spring AI and LangChain4j, building AI in Java is no longer experimental—it’s production-ready. #JavaDev #AI
✨ TL;DR — What I learned:
AI in Java isn’t a hairy hack anymore. It’s polished, pragmatic, and polished. Now, the bigger challenge: Spring AI or LangChain4j—what’s your pick, Twitter fam? 🤔 #JavaAI #ChooseYourTool
Check out my next live session if you also want to know more about persisting said AI memory 📺👉 https://youtube.com/live/iyey7G_Ax0g
#LiveCoding #IndieGame
Before you continue to YouTube