I'm currently available for freelance projects, contract work, and full time software engineering roles. Over the past few years, I've worked with startups and companies building AI powered products, web platforms, mobile applications, automation systems, chatbots, and voice AI solutions. My experience spans the entire product lifecycle from planning and architecture to development, deployment, and scaling. Some of the areas I've worked in include: • AI agents and multi-agent systems using LangGraph, AutoGen, CrewAI, and ReAct • Generative AI applications powered by OpenAI, Claude, DeepSeek, and Hugging Face • RAG systems, AI assistants, and custom chatbot solutions • Voice AI and IVR platforms using Vapi AI, Retell AI, and Twilio • Workflow automation with n8n, Zapier, Make.com, and custom API integrations • Full-stack web development with Next.js, React, and Vue.js • Cross-platform mobile development with React Native and Expo • Computer vision, OCR, AI avatars, and media-generation applications Tech Stack: Python, TypeScript, JavaScript, Next.js, React, Vue.js, React Native, LangChain, LangGraph, OpenAI, Claude, DeepSeek, Hugging Face, n8n, Zapier, Make.com, Twilio, Vapi AI, and Retell AI. I've had the opportunity to work on projects for companies across a variety of industries and collaborate with distributed teams in different parts of the world. I'm comfortable working across time zones and can provide overlapping hours with teams in North America, Europe, and Asia. If you're looking for an engineer who can help build, launch, or scale AI products, web applications, mobile apps, or automation solutions, I'd be happy to connect. Feel free to send me a message I'd love to learn more about your project or team.

Tim Bratton generously offered the 3XF community one free Patent Geyser credit so folks can kick the tires and see the platform in action. If you want access, fill out the request form: https://forms.gle/9XC4yQEbBZeABm569 Before you apply, I highly recommend watching Tim's session on Dailysigh so you have all the context. 👉 Recording link here IMPORTANT: Submissions close Friday, May 15 at 11:59 PM MST ❗ Once the deadline passes, the form closes and no additional requests will be accepted.

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.

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. ⚙️