Looking for Your Valuable Feedback & Suggestions
Hey everyone,
I’ve been working on Career Compass AI for the Agent Demo Track 3 — it’s a hybrid-agent (agent + native LLM) tool that helps you:
- Search for jobs smartly
- Match them with your resume
- And generate cover letters based on context
I’d really love your honest feedback — anything that can help me make it better!
Some things I’d love your thoughts on:
- Did the job/resume matching feel accurate or helpful?
- Was the cover letter generator actually useful?
- Any bugs, slowdowns, or UI quirks that stood out?
- Did the basic vs. advanced search make sense?
Feature-wise:
- Anything you wish it could do that it currently doesn’t?
- Ideas for extra tools, better analysis, or integrations?
Feel free to be as honest or detailed as you want!
Thanks a ton, and good luck on all your builds too...
— Manan
Hi 👋,
Great idea and a clean implementation — I especially like the solid documentation and architecture! 👏
Just a heads-up: it looks like your secret key (eyJhbGciO...) might be exposed in the frontend. You’re using value=os.environ.get("NEBIUS_API_KEY", ""), but double-check that it isn’t being passed to the client unintentionally. Secrets should stay server-side to avoid potential security risks 🔐
Also, are you leveraging Google Jobs in the backend? That part caught my curiosity — would love to hear more about the stack you're using.
Keep it up — really promising work! 🚀
Cheers,
Chris
Hey Chris,
Thanks so much for the detailed feedback — I really appreciate it!
And good catch on the secret key. I did set it as a private secret on HF so users wouldn’t have to enter it (since we had free Nebius credits), but I didn’t realize it might still be exposed on the frontend. I’ll definitely dig into that. Do you have any tips on ensuring it's fully server-side?
Also, great point about the job search via SerpAPI. I actually tried a few approaches before that — using DuckDuckGo with a browsing tool worked for finding jobs, but the agent often hallucinated the job apply links. I also tried scraping (even with Crawl4AI), but many job sites blocked it. With limited time, SerpAPI turned out to be the most reliable option for accurate job links.
I have attached all the details along with demo links and short video here: https://www.linkedin.com/posts/manan-j-shah_ai-hackathon-jobsearch-activity-7339341523957821441-6N_B?utm_source=share&utm_medium=member_desktop&rcm=ACoAACxJaEoBCCmQ-l2XSrWEHKhy3p45-5-LpQg
Thanks again — this kind of feedback is super helpful!
Hi
And good catch on the secret key. I did set it as a private secret on HF so users wouldn’t have to enter it (since we had free Nebius credits), but I didn’t realize it might still be exposed on the frontend. I’ll definitely dig into that. Do you have any tips on ensuring it's fully server-side?
Instead of putting os.environ.get("NEBIUS_API_KEY", "") in gr.Textbox
serp_api_key = gr.Textbox(
label="SerpAPI Key",
placeholder="Enter your SerpAPI key here...",
type="password",
value=os.environ.get("SERP_API_KEY", ""),
info="Required for all job searches",
elem_classes=["api-input"]
)
nebius_api_key = gr.Textbox(
label="Nebius API Key",
placeholder="Enter your Nebius API key here...",
type="password",
value=os.environ.get("NEBIUS_API_KEY", ""),
info="Required for AI-enhanced searches",
elem_classes=["api-input"]
)
rather put it in a function that is called later or in a config file.
Chris