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什么是 AskSim?
- 以 AI 为先的条件搜索
- 开源模型编排(系统使用多种模型 - Llama、Qwen、DeepSeek 等)
- 并行渐进处理
AI 助手在 200 毫秒内开始回答,逐步增强,并仅在需要时获取实时数据。

7月15日 06:33
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
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