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