Hi everyone,
I’m currently working on a custom chatbot inspired by Candy AI — focusing on emotionally aware, character-based conversations. I initially built a prototype using OpenAI’s API, but I’m planning to switch to DeepSeek API to overcome moderation challenges and improve performance. Before diving into migration, I want to finalize the most efficient and scalable tech stack.

My Current Tech Stack (OpenAI-Based)

• Frontend: Next.js + Tailwind CSS (React-based SPA)
• Backend: Node.js (Express) with Firebase for auth & storage
• AI: OpenAI GPT-4 API for chat, Stable Diffusion (via Replicate) for image gen
• Voice: ElevenLabs for audio response per character
• Hosting: Vercel (frontend), Render.com (backend APIs)
• Database: Firestore for user sessions + Redis for short-term memory

While this works well in testing, I’ve run into roadblocks with content moderation, context loss in long chats, and lack of memory features.

What I Want to Build with DeepSeek

I’m planning to rebuild my Candy AI Clone using DeepSeek API to overcome the limitations I faced with OpenAI, especially around heavy content moderation and inconsistent persona memory. My vision is to create a scalable, emotionally intelligent chatbot that can simulate real-time conversations with distinct, customizable characters—just like Candy AI or DreamGF. But with more flexibility and less content restriction.

With DeepSeek, I want to:

• Build a multi-turn, character-driven AI chatbot that stays emotionally consistent across sessions
• Support long-form, NSFW or mature conversations without constant moderation interruptions
• Enable memory simulation using either prompt injection or a connected memory module (Redis or vector DB)
• Deploy the project as a modular Candy AI Clone—so others can fork or extend it
• Use DeepSeek’s large language models for unfiltered generation and persona-based customization
• Integrate third-party tools like Stable Diffusion for image replies and ElevenLabs for realistic voice outputs

My goal is to make the chatbot capable of handling both emotional roleplay and adult-themed use cases, while maintaining high-quality responses, low latency, and cost-efficiency.

If DeepSeek allows private deployment, minimal API constraints, or local inference, that would make it the ideal backend for this type of Candy AI Clone project.

Questions for the Community

1. What backend stack pairs best with DeepSeek for real-time chat applications?
2. Does DeepSeek support custom training, memory modules, or prompt caching techniques?
3. Should I continue using Node.js, or is Python more reliable with DeepSeek’s APIs?
4. Any suggestions on rate limits, latency, or hosting environments (e.g., GPU-enabled backend)?
5. Has anyone integrated DeepSeek with Stable Diffusion or ElevenLabs for similar use cases?

I’m open to suggestions from developers or startups who’ve used DeepSeek in production chatbots. Whether it’s architectural advice, prompt engineering tricks, or a full-stack sample repo—any help would be appreciated.

Thanks in advance to the DeepSeek team and community!