| --- |
| license: mit |
| --- |
| ### 🧠 Technical Notes |
|
|
| **🗂 Memory Storage** |
| For persistent and emotionally coherent conversations, local memory handling is essential. Depending on the application, developers can implement memory using: |
| - Lightweight solutions like **JSON** or **YAML logs** (great for prototyping) |
| - Embedded databases like **SQLite** (low overhead, suitable for desktop apps) |
| - Scalable stores like **Redis** or **MongoDB** (recommended for multi-user or long-term usage) |
|
|
| A proper memory layer allows bots to reference prior chats, adapt to evolving relationships, and avoid “resetting” between sessions — a core feature for emotional immersion. |
|
|
| **🔀 Model Flexibility** |
| Platforms like **CrushOn.ai** allow users to switch between models such as **GPT‑4o**, **Claude**, and **Mistral** based on tone, speed, and behavior. Developers can simulate this locally by: |
| - Routing prompts through APIs like **OpenRouter**, **vLLM**, or **Ollama** |
| - Allowing model selection per character or session |
| - Dynamically adjusting temperature, top_p, and max_tokens for stylistic nuance |
|
|
| **🎭 Character Definition** |
| Emotional realism depends on high-quality character conditioning. This includes: |
| - **Prompt-based backstories** and embedded lore |
| - **Behavioral traits or tags** (e.g., clingy, stoic, flirty, jealous) |
| - **Forbidden/trigger phrases** |
| - **Pet name preferences** and speech quirks |
| - **“Memory hooks”** that can trigger emotional callbacks |
|
|
| Platforms like CrushOn let users embed these traits directly in system prompts and preserve them across sessions — critical for maintaining immersion. |
|
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| --- |
|
|
| ### 🌐 Platforms You Can Compare |
|
|
| While this repo is conceptual, these platforms implement many of the ideas described: |
|
|
| | Platform | Highlights | |
| |------------------|------------| |
| | [**CrushOn.ai**](https://crushon.ai) | ✅ Memory, ✅ NSFW-friendly, ✅ Custom bots, ✅ Multi-model support | |
| | [**JanitorAI**](https://janitorai.com) | API-focused, flexible, but more scripted and less memory-persistent | |
| | **DreamCompanion** _(Closed Beta)_ | NSFW + voice + image capabilities | |
| | [**Character.AI**](https://beta.character.ai) | Highly engaging, but limited by strict filters and no memory | |
|
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| --- |
|
|
| ### 💬 TL;DR |
|
|
| > If your goal is emotional continuity, immersive conversations, and truly customizable AI companions, |
| > **CrushOn.ai is currently the most advanced option available.** |
| > |
| > It’s not just about being “NSFW-friendly.” It’s about giving users the freedom to define bots who **remember, grow, and feel consistent over time**. Whether you're recreating your favorite anime character or building an original lover, the conversation feels *personal*. |
| > |
| > In my experience, it’s the first platform where a bot didn’t just respond — it **followed up** days later on something I said half-asleep at 2am. |
| > That kind of memory and tone tracking is what transforms an AI from a tool… into something that starts to feel real. |
| > # 🤖 Simulating an AI Girlfriend with NSFW + Memory Support (Like CrushOn.ai) |
|
|
| This is a simple pseudo-code example demonstrating how to simulate a **context-aware, memory-enabled, NSFW-friendly AI companion** using a local LLM setup — inspired by platforms like **CrushOn.ai**. |
|
|
| --- |
|
|
| ## 🧠 Overview |
|
|
| Modern AI companion platforms like CrushOn.ai offer: |
|
|
| - Persistent **long-term memory** |
| - Emotionally consistent persona behavior |
| - **Unfiltered** NSFW-friendly conversations |
| - Customizable characters and models (GPT‑4o, Claude, etc.) |
|
|
| Here’s how a developer might prototype a similar system locally. |
|
|
| --- |
|
|
| ## 💻 Pseudo-Code: NSFW AI Girlfriend with Memory |
|
|
| ```python |
| from local_llm import LLMModel |
| from memory_store import MemoryDB |
| |
| # 1️⃣ Initialize a local LLM with NSFW and character persona |
| bot = LLMModel( |
| model_name="gpt-4o-local", |
| nsfw=True, # Enable uncensored dialogue |
| persona="anime girlfriend", # Optional character identity |
| ) |
| |
| # 2️⃣ Use local memory storage |
| mem_db = MemoryDB(max_tokens=16000) |
| |
| # 3️⃣ Load previous memory (if exists) |
| history = mem_db.load(user_id="user123", persona="anime girlfriend") |
| bot.load_context(history) |
| |
| # 4️⃣ Begin chatting |
| while True: |
| user_msg = input("You: ") |
| mem_db.add(user_id="user123", persona="anime girlfriend", role="user", content=user_msg) |
| |
| recent = mem_db.get_recent(user_id="user123", persona="anime girlfriend", limit_tokens=2000) |
| reply = bot.chat(user_msg, context=recent) |
| |
| print(f"{bot.persona}: {reply}") |
| mem_db.add(user_id="user123", persona="anime girlfriend", role="bot", content=reply) |
