AI & Machine Learning (AI/ML) Knowledge Graph
This is a system-level skill tree dynamically generated based on your cloud Memory. By mastering these knowledge nodes, you will build a solid bridge to roles like AS, MLE, and RS.
Target Company Interview Guides
Field guides for top-tier companies and core specialized roles. Subscribed premium users can query local/cloud agents with verified prompts to initiate tailored preparation loops.
Guides & Materials Log
Updated 'Google MLE System Design Guide': Added communication cost formulas for multi-GPU training and FSDP distributed memory optimization schemes.
Updated 'OpenAI Interview Guide': Added offline derivation analysis of Generalized Advantage Estimation (GAE) inside the RLHF PPO loop.
Added 'Meta Recommender Systems in Production': Explores loss balancing formulas for MMOE multi-task networks under extreme positive feedback sparsity.
Updated 'Short Video Rec Retrieval Guide': Added caching and real-time sync strategies for two-tower vector recall under trillion-parameter sparse embeddings.
Updated 'Multimodal Vision-Language Alignment': Added latest VLM fine-tuning and performance balance case study.
Local Knowledge Base Architecture Guide (Local Template Modules)
Coupled with the cloud skill tree above, when you initialize the knowledge base locally using MCP, the following 6 core modules will be generated automatically. You can use these to build your exclusive local knowledge engine. Click on the cards to expand domain-specific insights.
Target Companies
Used for organizing background checks, core businesses, tech stacks, and past interview experiences of your target companies. E.g., categorize OpenAI or Google's interview styles.
Role Analysis
Displays Job Descriptions, capability models, and common question distributions for target roles (e.g., AS, MLE, DS).
Interview Records
Track and record practical post-mortems of every real interview. Includes interviewer backgrounds, difficult questions, and feedback summaries for self-improvement.
Core Projects
Deeply analyze your personal projects using the STAR principle, document system architecture trade-offs, and prepare for resume deep dives.
Resumes
Specifically used to track and update your different resume versions (e.g., General, Algorithm-specific) and record Bullet Points highlight materials daily.
Tech Fundamentals
Contains your overall knowledge framework. From data structures and high-concurrency system design to AI frontier knowledge (like LLM, RAG), accumulate systematic notes here.