AI Interview
AI mock interviews and resume analysis with real, role-specific feedback.
// Highlights
- End-to-end AI mock interview: define the role → answer question by question → auto-generated scored report
- Resume management and a question bank to target your weak spots
- WeChat QR login, subscription plans and an order system
- Backed by a microservice architecture (gateway + account / AI / business services + MCP Server)
// Related posts
All posts →- AI Interview vs. Human Mock Interview: When Each Actually Helps Compares AI mock interview tools vs human mock interviews across feedback quality, availability, cost, realism, and scalability. Argues neither replaces the other—AI excels at repetition/consistency for early prep, humans excel at adaptive, high-pressure realism for final rounds. Recommends blending both, sequenced by interview stage.
- Hiring Machine Learning Engineers This Fall: What's Changed in the Talent Market A practical hiring guide for technical leaders recruiting ML engineers this fall. It argues the ML talent market has structurally shifted — top candidates now evaluate infrastructure maturity and ownership scope over compensation. Key takeaways: define the production surface before posting, match evaluation rubrics to the correct ML archetype (research, applied, or platform), and build your technical culture narrative before recruiting begins.
- The Engineering Interview Process for AI Roles: What Actually Predicts Success Argues that AI engineering interviews should prioritize system design fluency, data literacy, evaluation judgment, and communication under ambiguity over algorithmic trivia. Offers practical guidance for both candidates and hiring teams, concluding that scenario-based interviews better predict real job performance than traditional coding-only tests.
- Evaluating Full-Stack Candidates for Leadership-Track Roles: A Founder's Checklist A practical checklist for founders hiring full-stack engineers for leadership roles, covering 8 key signals: systems thinking, communication, ownership, technical depth, shipping track record, legibility, ambiguity tolerance, and stage fit. Targets early/growth-stage founders. Core takeaway: leadership failures stem from poor judgment and communication, not lack of technical skill.
