@suraj_sharma14: 12 real projects that helped builders get into top AI fellowships & residencies. Project 1: Open-Source LLM Evaluation …

12 real projects that helped builders get into top AI fellowships & residencies. Project 1: Open-Source LLM Evaluation Framework Built a testing suite that catches hallucinations before production. 500+ GitHub stars. Stack: DeepEval + Pytest + GitHub Actions + LangSmith >> Got into: a16z AI Camp, Greylock AI Fellowship Why it worked: Solved a real pain point + open-source adoption Project 2: Multi-Agent Research Assistant 3 agents that research, write, and fact-check academic papers. Deployed to 200+ researchers. Stack: LangGraph + CrewAI + Supabase + Vercel >> Got into: Sequoia AI Ascent, YC W24 Why it worked: Real users + clear product-market fit signal Project 3: RAG System for Legal Documents Chunking + hybrid search + citation grounding for contract analysis. 94% accuracy on evals. Stack: LlamaIndex + Pinecone + FastAPI + Docker >> Got into: NEA AI Residency, Stanford AI100 Why it worked: Domain expertise + measurable quality metrics Project 4: Cost-Optimized LLM Router Auto-routes queries to cheapest model that meets quality thresholds. Cut costs by 67%. Stack: LiteLLM + Prometheus + Custom routing logic + Grafana >> Got into: Lightspeed AI Fellowship, a16z AI Camp Why it worked: Hard metrics + infra expertise + money saved Project 5: AI Agent for Open-Source Issue Triage Automatically labels, prioritizes, and assigns GitHub issues. Used by 15+ repos. Stack: GitHub Actions + LangChain + GPT-4 + Redis Got into: Greylock AI Fellowship, Microsoft AI Residency Why it worked: Dogfooding + real adoption + ecosystem impact Project 6: Production Guardrails Gateway Middleware that blocks prompt injection, PII leaks, and malicious outputs. 100% block rate. Stack: Guardrails AI + FastAPI + Redis + OWASP rules >> Got into: Sequoia AI Ascent, YC S24 Why it worked: Security focus + production-ready + compliance angle Project 7: Fine-Tuning Pipeline for Domain-Specific LLMs LoRA/QLoRA fine-tuning on medical/legal/financial data with eval harness. Stack: Unsloth + Hugging Face + MLflow + Weights & Biases >> Got into: NEA AI Residency, Google AI Residency Why it worked: Technical depth + domain specialization + reproducibility Project 8: Real-Time Observability Dashboard for AI Agents Traces, spans, token costs, latency, drift detection. Used by 50+ teams. Stack: LangFuse + PostgreSQL + Grafana + OpenTelemetry >> Got into: Lightspeed AI Fellowship, a16z AI Camp Why it worked: Solves debugging pain + open-source + community adoption Project 9: Multi-Tenant AI SaaS with Usage-Based Billing Stripe integration, tenant isolation, rate limiting, cost attribution per user. Stack: Supabase + Stripe + FastAPI + Next.js + Docker >> Got into: YC W24, Sequoia AI Ascent Why it worked: Full-stack + monetization + production architecture Project 10: Automated Eval Suite for RAG Systems Golden datasets, regression tests, citation quality scoring, grounding metrics. Stack: RAGAS + DeepEval + Pytest + GitHub Actions >> Got into: Greylock AI Fellowship, Stanford AI100 Why it worked: Quality focus + measurable outcomes + open-source contribution Project 11: AI-Powered Developer Tool with 1000+ Users Code generation, refactoring or debugging tool. Real adoption, real feedback. Stack: Tree-sitter + LSP + VS Code Extension + Ollama/vLLM >> Got into: NEA AI Residency, Microsoft AI Residency Why it worked: Developer empathy + usage metrics + ecosystem fit Project 12: End-to-End AI Agent with Human-in-the-Loop Handles complex workflows, pauses for approval, audit trails, rollback logic. Stack: LangGraph + Temporal + PostgreSQL + React + FastAPI >> Got into: a16z AI Camp, YC S24, Lightspeed AI Fellowship Why it worked: Production complexity + reliability + real-world applicability @suraj_sharma14 #AIFellowship #AIResidency #CareerGrowth #OpenSource #GenAI