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Recruiters spend countless hours manually reviewing resumes, comparing candidate qualifications, and managing hiring pipelines.
Considering this problem I developed Developed HireIQ, an AI-powered recruitment platform that streamlines the entire hiring process by combining resume analysis, AI candidate scoring, and workflow automation into a single dashboard.
Key Features:
š„ AI-powered resume parsing and analysis
š„ Automated candidate scoring and ranking
š„ Resume-to-job matching
š„ Multi-step recruitment dashboard
š„ Candidate management system
š„ AI-generated hiring insights and recommendations
š„ Recruiter analytics dashboard
š„ Secure authentication and role-based access
š„ Modern responsive UI with dark theme
Impact it created:
š Reduced manual resume screening time by 90%
š Accelerated candidate shortlisting by 75%
š Improved recruiter productivity by 3Ć
š Reduced hiring workflow steps by 60%
š Enabled recruiters to evaluate 100+ resumes in minutes instead of hours
š Increased consistency in candidate evaluation through AI-driven scoring
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
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This is my submission or the RivexContra Character Challenge!
AI Agents are on the rise, and I always found it to be the best opportunity for interactive character experiences - So I built just that - A fully functional Avatar Builder and Selector with 5 fully designed, animated and interactive Characters embedded in a realistic AI Agent!
UI DesignAI Agent DesignerRive AnimationRivecharacterchallengecharacteranimationinteractivedesignMascot Design
Amazing!
Would you let an AI write SQL against your production database?
I wouldn't either. So when I built Quanta ā a workspace where you ask questions in plain English and get back a live dashboard ā the model never touches your data directly.
Every query it generates is SELECT-only and runs read-only. If one fails, the system feeds the exact database error back to the model and lets it correct itself once before anything reaches you. Credentials stay encrypted with AES-256-GCM and never leave the vault.
Connect Postgres or MySQL, switch between Gemini, Claude, and GPT, and build dashboards from 12 chart types. It's multi-tenant now too. Orgs, roles, and per-workspace AI spend caps.
Curious how other devs handle the "LLM + real database" problem. What guardrail would you add?
ā¢ Jul 5
The self-correction loop is such a smart touch. Only thing I'd add is a statement_timeout + forced LIMIT so a valid-but-heavy query can't tank prod. Ideally run against a read replica. Great build.
Hi everyone! š
Iāve just updated my portfolio on Contra with my latest projects, including AI SEO Copilot and custom UI tools. I focus on building high-performance, AI-native SaaS products from scratch.
Iām currently open to new collaborations and challenges. If you need help with your project or want to discuss modern web development, feel free to reach out!
Just updated my portfolio with new AI projects. Open to collaborations!
good work eduard
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