75% of resumes are rejected before a human sees them.
Not because the candidates aren't good.
Because they failed a keyword scan.
I spent weeks building a solution.
Live at - https://t.co/IMzxx6lS3s
Github - https://t.co/uSsnZTXn4f
For recruiters:
โ Rank 50+ candidates instantly
โ Beyond keyword matching vector similarity scoring
โ Full PDF report per candidate
16-module pipeline. Fully async. Production-deployed on Render + Vercel.
Built this because I felt the problem personally.
#AI#BuildInPublic
75% of resumes are rejected before a human sees them.
Not because the candidates aren't good.
Because they failed a keyword scan.
I spent weeks building a solution.
Live at - https://t.co/IMzxx6lS3s
Github - https://t.co/uSsnZTXn4f
For job seekers:
โ Deep ATS score (Skills 40%|Experience 25%|Projects 15%|Education 10%|Formatting 10%)
โ Semantic gap analysis - finds what's missing vs the JD
โ AI bullet-point rewriting (weak โ powerful)
โ Explainability layer - tells you *why* you scored what you scored
๐ Built IRS โ AI PDF Q&A system (RAG)
๐ https://t.co/P7RkrVhqym
Turn PDFs into chatbots ๐ฌ๐
๐น Ask questions
๐น Get accurate answers
๐น See source citations
๐น Multi-PDF + memory
โ๏ธ LangChain + FAISS + LLMs
๐ป https://t.co/eKyLENoYfh
#AI#LLM#RAG#Python
From running my first LLM โ to building a chatbot with LangChain ๐
โ๏ธ Multi-turn conversation
โ๏ธ Chat history handling
โ๏ธ Terminal-based interaction
Small step, but this is how real AI systems start.
Next stop: tools, agents, and smarter workflows
#LangChain#LLM#AIJourney
Built my first AI agent with n8n ๐
OpenRouter + memory + tools โ handling real queries end-to-end
Running locally and actually seeing the pipeline work hits different.
#AI#n8n#LLM#BuildInPublic
Just ran my first LLM locally on my machine
No APIs. No cloud. Just pure local inference.
Started with simple queries, but seeing it work in real-time feels insane.
From here โ optimizing, building, and going deeper into AI systems ๐
#LangChain#LLM#AIJourney