What if I told you the voice at the start of this video wasn’t actually me speaking live? In this video, I test Fish Audio’s brand new S2 Pro model and show you how to generate realistic AI voices, add emotion tags, create multi-character conversations, clone your own voice, and then plug everything into a real Next.js app using the Fish Audio API. We’ll go from testing voices inside the Fish Audio dashboard to building a full developer demo with text-to-speech, speech-to-text, voice search, voice cloning, and real-time streaming audio. We’ll cover: ✅ Testing Fish Audio’s new S2 Pro voice model ✅ Adding emotion tags like laughing, aggressive, cheerful, and more ✅ Creating multi-speaker AI conversations ✅ Cloning your own voice from a short audio sample ✅ Keeping cloned voices private ✅ Creating a Fish Audio API key ✅ Building a Next.js demo app with the JavaScript SDK ✅ Text-to-speech, speech-to-text, and voice search ✅ Real-time streaming audio with low latency ✅ How to keep API keys safely on the server side If you’re building AI agents, voice assistants, creator tools, customer support bots, or any app that needs realistic AI voice, this is one of the coolest tools to try right now.

Hi everyone I recently completed my current projects and I’m now open to new opportunities. I’m interested in connecting with developers building in backend, SaaS, AI, or automation, and I’m always open to exchanging ideas with people working in similar areas. If you’re interested in a technical session, mentorship, collaboration, or discussing a project, feel free to reach out. Happy to connect.

Set your reminder here 👉 https://youtube.com/live/jDTuQTR8gU0 Episode 4 of our new Series 'Code with AI the Right Way' is here! — and this time, we're vibe coding a Quickbooks / Xero Clone LIVE from scratch! This is a LIVE build — mistakes, debugging, and all. That's the point. You learn more watching someone solve real problems in real-time than from a polished, pre-recorded tutorial.

I have previously conducted lectures on LLM orchestration, RAG pipeline, multi-modal models, and multi-agent architecture. I am going to explain how to implement chatbot functionality by utilizing the previous lecture. A chatbot MVP is essentially: A system that takes a user message → understands it → optionally looks things up → generates a response → returns it You can express this as a simple loop: The 5 Core Components of a Chatbot MVP Break the system into 5 understandable parts: ① User Interface (UI) Chat screen (web, app, Slack, etc.) Where users type messages ② Backend Controller (Orchestrator) The “brain” that decides what to do next Routes requests between components Connect to your previous lectures: This is where **LLM orchestration logic** lives. ③ Large Language Model (LLM) Generates responses Understands natural language ④ Knowledge / Data Layer (Optional but critical for MVP+) Documents, database, APIs Used in **RAG (Retrieval-Augmented Generation)** ⑤ Memory (Optional but powerful) Conversation history User preferences User ↓ UI ↓ Orchestrator ├── LLM └── Knowledge Base (RAG) ↓ Response contact information: telegram:@kingsudo7 whatsapp:+81 80-2650-2313

Today I’m starting a lecture on Multi-Agent Architecture, focusing on how modern AI systems move beyond single LLM prompts and into coordinated agent ecosystems. In real-world AI products, the challenge isn’t generating text — it’s orchestrating multiple agents that can plan, reason, and execute tasks reliably. In this session we’ll break down: • Core architecture patterns for multi-agent systems • Agent orchestration, routing, and task decomposition • Tool usage and memory management • Building reliable pipelines instead of fragile prompt chains • Real production use cases from modern AI systems The goal is simple: move from demos to production-grade AI architectures. If you're building with LLMs, AI agents, or automation pipelines, understanding multi-agent design patterns will be one of the most important skills going forward. More details and implementation walkthrough coming in the lecture. Let’s build systems that actually scale. ⚙️