kinghy949

kinghy949 / joblens

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AI 简历优化工作台 · 多 Agent 协作 · 把简历和 JD 变成 30 秒可执行的优化建议

17
1
85% credibility
Found May 31, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

JobLens is an AI-powered resume optimization tool that analyzes how well your resume matches a specific job posting. You paste a job description, upload your resume, and within about a minute, five AI agents work together to produce a detailed report: a five-dimensional match score shown as a radar chart, a breakdown of which job keywords your resume hits or misses, sentence-by-sentence rewrite suggestions with reasons, and targeted interview questions you'd likely face. A demo mode lets you explore the experience without real AI calls or sharing any personal data. Results can optionally be shared via a temporary link that expires after 24 hours. The project emphasizes privacy: uploaded files are parsed in memory and never stored on disk.

How It Works

1
🔍 You discover JobLens

You hear about an AI tool that can instantly tell you how well your resume matches a job posting, and what to fix.

2
🎬 You try the demo first

Before sharing any real information, you click 'Try Demo' to watch the tool work with sample data — no account needed.

3
📄 You paste the job posting

You copy the job description you're targeting and paste it into the tool — it takes just a few seconds.

4
📎 You upload your resume

You drag and drop your resume (PDF, Markdown, or plain text) and the tool reads it instantly — your file is never saved anywhere.

5
⚡ You watch five AI helpers work together

The screen shows five AI agents analyzing your materials in real-time — one reads the job, one reads your resume, one scores the match, and two suggest improvements.

6
Your results arrive
🔗
Share the link

Optionally, you generate a temporary link that expires in 24 hours so a friend or recruiter can view your report.

🔄
Try another job

You paste a different job posting to see how your same resume matches a different role.

✅ You know exactly what to fix

Instead of guessing, you now have a clear roadmap: which keywords to add, which sentences to rewrite, and what questions to prepare for.

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Star Growth

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AI-Generated Review

What is joblens?

JobLens is an AI-powered resume optimization workspace that analyzes job descriptions and resumes to generate actionable feedback in about 30 seconds. You paste a job posting, upload your resume, and get back a matching radar chart across five dimensions, keyword coverage analysis, bullet-by-bullet rewrite suggestions with STAR framework reasoning, and targeted interview questions the hiring manager might ask. Built with TypeScript and Next.js, it orchestrates five AI agents in a three-phase pipeline that runs in parallel where possible. The entire stack uses SSE streaming so you watch each agent light up in real-time as it works.

Why is it gaining traction?

The multi-agent architecture is the hook--instead of a single LLM call that gives you generic advice, JobLens chains specialized agents where each has a distinct role (JD parser, resume analyst, match scorer, rewriter, interviewer) working from structured outputs. A demo mode with `?demo=1` lets anyone try the full flow instantly without an API key, which lowers the barrier to seeing if it actually works. Privacy is baked in: resume parsing happens in memory, nothing hits disk unless you explicitly generate a shareable link, and those links expire after 24 hours.

Who should use this?

Job seekers preparing for specific roles who want concrete, actionable edits rather than vague "make it better" feedback. Mid-career engineers who suspect their resume bullets are weak but cannot identify exactly why. Technical recruiters building pipelines who want to benchmark candidates against structured criteria. Early-stage developers who have no idea what a strong resume looks like for their target stack.

Verdict

JobLens is a well-architected, privacy-conscious tool with a compelling demo and production-ready Docker deployment, but with only 17 stars it sits firmly in the "early-stage project" category. The credibility score of 0.85% reflects this--the codebase looks solid (comprehensive Zod schemas, eval framework, streaming SSE throughout), but it has not been battle-tested by a community yet. Worth exploring for the technical approach alone, but do not bet your next job application on it without running the eval harness yourself first.

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