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Search your GitHub codebase to power tailored technical mock interviews.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Mock Interview RAG Server" yet — see the docs or source repo.
Use my indexed GitHub repositories to generate 10 mid-to-senior backend interview questions. For each question, cite the relevant repository and code area, explain the skill being assessed, and provide key points for a strong answer.
A tailored interview set grounded in the user's actual code, with code references, evaluation focus, and answer highlights.
Based on the microservices and CI/CD code in my repositories, act like an interviewer and prepare follow-up questions. Start with 5 common main questions, then add 2 deeper follow-ups for each, tailored to how I actually implemented things.
A layered set of interview and follow-up questions tied to the real architecture and workflows in the repositories.
Search across all my repositories for code implementations worth discussing in interviews, especially complex logic, performance optimization, error handling, and architecture decisions. Group the findings by theme and explain what I may be asked about.
A theme-based list of high-value code areas and likely interview discussion points for targeted preparation.
Retrieve and understand code faster with hybrid RAG search across repositories.
Search code and technical docs privately with local-first RAG for developers.
Retrieve relevant context and metadata from Qdrant using natural language queries.
Turn unstructured documents into a searchable knowledge base for AI agents.
Search codebases semantically and find relevant snippets with source locations.
Index documents and retrieve relevant context for better LLM responses.