How AskSim System Works - AI Research Assistant Architecture Overview User Query → Progressive Response Orchestrator ├── Phase 1: Instant Response (200-300ms) │ └── Fast models (Llama-3.1-8B-fast) ├── Phase 2: Enhanced Response (parallel) │ └── Powerful models (Llama-3.3-70B), DeepSeek └── Phase 3: Search Enhancement (conditional) └── Serper/Exa API → Synthesis with citations In this particular example: 🔧 Progressive Enhancement Explained: Phase 1: Llama-3.1-8B-Instruct-fast - 8 billion parameters - Optimized for speed - 200ms response time - Covers 80% of answer quality Phase 2: Llama-3.3-70B-Instruct - 70 billion parameters - 8.75x larger model - Adds nuance, examples, depth - Completes the remaining 20% Result: 100% quality, 10x better UX. It's like having a quick assistant who answers immediately, while a professor prepares a detailed lecture in the background. Special Features 1. Lightning-Fast Progressive Responses - 200ms to first token - Users see responses instantly, not after 3+ seconds - Parallel execution of phases - enhanced and search run simultaneously - Progressive enhancement (instant → enhanced → search) 2. Intelligent Search Integration - Automatic detection of time-sensitive queries - Dual search providers (Serper + Exa) 3. Cost-Optimized Multi-Model System - tier-based model selection @nebiusaistudio - Quality tiers: instant → enhanced → premium - Payments using x402 by @CoinbaseDev @yugacohler and @Sagaxyz__ @solana $CLSTR $DND