MianShiYa (Interview Duck)
A free, clean interview-prep quiz site with 200+ question banks and 9000+ high-frequency questions, synced across web, mini-program, and IDE plugin.
- Source repo
- liyupi/mianshiya
- Stars
- ★ 6k
- Last updated
- 8mo ago
- License
- MIT
- Primary language
- TypeScript
- FA score
- 25/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specific
- Cost
- Free, no paid service needed
- Setup effort
- High · needs real infrastructure
- You'll need
- Typical use
- Job seekers: Before an interview, filter questions by direction (e.g., Java, frontend) and quickly memorize answers to improve pass rates.
- Not a fit if
- Teams that want a turnkey app without running MongoDB, Redis and Elasticsearch
- Teams needing fully offline or self-hosted deployment without Tencent Cloud services
- Job seekers who just want the hosted question bank and won't deploy the stack
- Source review
- 25/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
MianShiYa is an open-source full-stack interview preparation platform. The open-source version is a React + CloudBase/Node.js project that includes both the public frontend and an admin backend. It provides multi-dimensional filtering and sorting of questions, collapsible answers, favoriting, collaborative editing, text-to-speech for questions, and a question basket for one-click paper generation and download with visibility controls. The tech stack spans React, Umi, Ant Design Pro, TypeScript; backend Node.js (Express) or Tencent CloudBase; MongoDB database; Redis and Elasticsearch middleware; and Tencent Cloud COS storage. Designed for job seekers, interviewers, students, and teachers, it emphasizes being clean, free, comprehensive, community-driven, and up-to-date. The live site is available at mianshiya.com.
MianShiYa enables online interview question practice. Users can browse, search, filter (by difficulty, tag, type) and sort (by popularity, favorites, frequency) questions, view collapsible answers, and favorite them. It supports collaborative editing of answers, uploading questions, reporting, hot searches, and search history. Users can answer questions, like answers, and follow tags. The paper feature includes a question basket, one-click paper generation, download, visibility control (public/private), and text-to-speech. Admins manage questions, answers, papers, and users. Frontend uses React and Ant Design; backend provides Express or cloud function APIs; data is stored in MongoDB with Redis and Elasticsearch for optimization.
- Job seekers: Before an interview, filter questions by direction (e.g., Java, frontend) and quickly memorize answers to improve pass rates.
- Interviewers: Use the one-click paper generation to quickly create assessment papers from the question basket.
- Students: Understand key enterprise exam points, clarify learning direction, and build a personal mini question bank.
- Teachers: Align teaching with real enterprise questions and interview experiences to help students get hired.
How do you install or deploy this agent?
Frontend: Requires Node.js >= 14. In the project root, run yarn to install dependencies, then yarn start to launch the dev server. For production, run yarn build to get the dist directory, which can be deployed to a web server or packaged with Docker. Backend (Node.js) code is in the server directory; cloud functions are in cloudfunctions. Detailed steps are in their respective README files.
How do you use this agent?
After starting the frontend, visit the local dev server URL to start practicing. You can register, browse questions, favorite them, and generate papers. For custom deployment, configure MongoDB, Redis, Elasticsearch, and Tencent Cloud COS. See server/README.md or cloudfunctions/README.md for backend usage.
What are this agent's strengths and limitations?
- Broad coverage: 200+ question banks, 9000+ high-frequency questions across Java, frontend, AI, OS, etc., continuously updated.
- Rich features: Multi-dimensional filtering, sorting, favoriting, collaborative editing, one-click paper generation, download, TTS, etc.
- Cross-terminal sync: Web, mini-program, and IDE plugin data sync for anytime practice.
- Heavy dependency stack: Requires Node.js, MongoDB, Redis, Elasticsearch, etc., making deployment complex.
- Platform tie-in: Optional Tencent CloudBase backend may lock you into Tencent's ecosystem.
- Sparse deployment docs: Backend setup is only referenced via external README links, not detailed in the main README.
How does this agent compare with similar options?
The source does not mention any direct competitors.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| MianShiYa (Interview Duck) This agent | 25 · Major gaps | Web appFree | ★ 6k | 8mo ago | TypeScript | — |
| zero2Agent: Zero-to-Production Agent Engineering Tutorials | 61 · Some gaps | Web appFree + model costs | ★ 842 | 1d ago | Python | — |
| AI Dev Tools Zoomcamp: AI-Native Software Engineering | 32 · Major gaps | Web appFree | ★ 1.7k | today | Python | — |
| Free Programming Resources | 24 · Major gaps | Hosted serviceFreemium | ★ 3.8k | 7mo ago | HTML | — |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows a conventional web application with no documentation or implementation of Agent-related permission management, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. Therefore all trust criteria score 0.
Self-consistency: README and package.json are largely consistent, but version number (5.0.0) does not match the 2022 version mentioned in README, and no clear version correspondence, so score 1. Dependency availability: package.json lists many dependencies but no lock file or version pinning strategy, risking dependency drift, score 1. Failure messages: no documentation of error handling or user-visible failure messages, score 0.
Audience and scenarios: README clearly lists audiences (job seekers, interviewers, students, teachers) and describes multiple usage scenarios, score 2. Capability boundaries: feature list is detailed but does not clearly mark experimental or future features, boundaries are vague, score 1. Trigger precision: no Agent trigger mechanism, score 0. Environment fit: provides deployment instructions for frontend and backend but not detailed configuration for different environments, score 1.
Information architecture: README is well-structured with project background, features, tech stack, quick start, etc., score 2. Install notes: provides installation steps for frontend and backend, but backend relies on reference docs, score 2. Naming stability: project name and version are clear in package.json, but README does not mention version history, score 1. Examples and FAQ: has project screenshots but no FAQ, score 1. Known limitations: none mentioned, score 0. License: MIT license is clear, score 2. Versioning and changelog: package.json has version but no CHANGELOG, score 1. Maintenance responsibility: README lists contributors but does not clearly define maintenance responsibility, score 2.
Output usability: As a web application, output is web pages, but no API or data export format provided, score 1. Marginal value: provides a large number of interview questions and features, offering unique value compared to similar projects, score 2. Cost-benefit: free and open source, but deployment and operation require some technical skill, score 1.
Claim traceability: Feature claims in README are not backed by code or test evidence, score 1. Cross-source corroboration: no external sources to verify, score 0. Fact-inference separation: README distinguishes facts and future plans but not explicitly, score 1.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- This repository is a conventional web application, not an Agent product, so Agent-related criteria are not applicable.
- Dependencies are not pinned, posing supply chain risks.
- Feature claims in README lack code or test evidence.