Infinityay

Infinityay / FaceTomato

Public

聚焦海量面经检索、简历分析与模拟面试的 AI 求职准备平台

face-tomato.me
46
2
100% credibility
Found Mar 26, 2026 at 46 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

FaceTomato is an AI-powered web app that parses resumes, matches them to job descriptions, generates optimization suggestions, simulates technical interviews, and provides performance reviews.

How It Works

1
🌐 Discover FaceTomato

You find this friendly AI career coach online while preparing for job interviews.

2
📄 Upload your resume

Simply drag your PDF or image resume – it reads everything clearly in seconds.

3
Add job details?
✅
Yes, add job

Tailored matching and suggestions for that exact role

➡️
No, general

Broad optimization for any tech job

4
✨ Unlock insights

Watch as it reveals strengths, gaps, and smart tweaks to make your resume shine.

5
🎤 Practice interviews

Chat with an AI interviewer that feels real, speak naturally with voice support.

6
📊 Review progress

Get honest feedback on answers, export notes, and track your improvement.

🚀 Interview ready!

With polished resume and practice, you walk into interviews feeling confident.

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Star Growth

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AI-Generated Review

What is FaceTomato?

FaceTomato is a Python-based AI platform for job prep, letting you upload resumes (PDFs, images) for instant parsing into structured data, extract job descriptions from text, and get tailored optimizations to match specific roles. It searches a massive bank of real interview experiences (面经) by company, category like backend or AI, and runs simulated interviews via chat with speech-to-text support, pulling relevant questions via RAG for realism. Developers get a full-stack web app via Docker Compose, hitting ports for backend API and frontend UI.

Why is it gaining traction?

It stands out with end-to-end job hunt tools in one deployable package—resume tweaks, JD matching scores, mock sessions that adapt like real ByteDance/阿里 interviews, and post-mock reviews scoring your answers. The RAG-powered question retrieval from real面经 makes practice feel authentic, not generic, and multi-LLM support (OpenAI, Anthropic) keeps costs flexible. Easy local setup beats scattered scripts or paid services.

Who should use this?

Tech job hunters targeting Chinese big tech (后端开发, 大模型应用), especially mid-level devs or new grads prepping社招/校招 mocks. Resume writers needing quick JD alignments, or teams building internal interview trainers without starting from scratch.

Verdict

Grab it if you're in the Chinese job market—solid foundation for personal use, despite 46 stars and 1.0% credibility signaling early maturity and thin docs. Fork and extend for production; test mocks locally first.

(198 words)

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