docs: add LinkedIn technical post about DeepResearch architecture #2
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Add comprehensive technical breakdown explaining: - The reasoning loop state machine (SEARCH, READ, REFLECT, ANSWER) - 7 cognitive personas for query expansion - Multi-dimensional evaluation system - Semantic deduplication via embeddings - Performance analysis comparing TypeScript vs Rust - Architectural lessons and design patterns
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Comprehensive technical document covering: - State machine implementation with exhaustive enums - Cognitive personas system with traits and parallelism - Multi-dimensional evaluation pipeline - SIMD optimizations for cosine similarity (AVX2) - Zero-copy string handling with Cow and arena allocation - Async runtime comparisons (Tokio vs Node.js) - Streaming HTML parsing with lol_html - Performance benchmarks: TypeScript vs Rust Estimated gains: 10-20x throughput, 80-90% less infrastructure cost
- Add Cargo.toml with all dependencies (tokio, rayon, serde, etc.) - Add agent module with state machine, actions, context, permissions - Add personas module with 7 cognitive personas and orchestrator - Add evaluation module with fail-fast pipeline - Add performance module with SIMD (AVX2) optimizations - Add LLM and Search client traits with mock implementations - Add utils module with token tracker and text processing - Add CLI binary entry point (main.rs) - Add complete conversation documentation (CONVERSATION.md) This implementation demonstrates: - Type-safe state machines with enums - Trait-based extensibility for personas - Real parallelism with Rayon - SIMD optimizations for cosine similarity - Zero-copy patterns where possible
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Add comprehensive technical breakdown explaining: