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:
β Rank 50+ candidates instantly
β Beyond keyword matching vector similarity scoring
β 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:
β Deep ATS score (Skills 40%|Experience 25%|Projects 15%|Education 10%|Formatting 10%)
β Semantic gap analysis - finds what's missing vs the JD
β AI bullet-point rewriting (weak β powerful)
β Explainability layer - tells you *why* you scored what you scored
π Built IRS β AI PDF Q&A system (RAG)
π https://t.co/P7RkrVhqym
Turn PDFs into chatbots π¬π
πΉ Ask questions
πΉ Get accurate answers
πΉ See source citations
πΉ Multi-PDF + memory
βοΈ LangChain + FAISS + LLMs
π» https://t.co/eKyLENoYfh
#AI#LLM#RAG#Python
From running my first LLM β to building a chatbot with LangChain π
βοΈ Multi-turn conversation
βοΈ Chat history handling
βοΈ 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 π
OpenRouter + memory + tools β 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 β optimizing, building, and going deeper into AI systems π
#LangChain#LLM#AIJourney