# LinkedIn Connection Request to Sarah Wooders

## Professional Connection Request (Under 300 Characters)

**Option 1 - Systems Engineering Focus:**
Hi Sarah! Impressed by your Skyplane → MemGPT → Letta journey, especially the OS-inspired memory management approach. Just created the first production MCP server for Letta.ai - bridging Claude users to stateful agents directly. Would love to connect and discuss cloud systems + agent infrastructure.

**Character Count: 295**

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**Option 2 - UC Berkeley Research Angle:**
Hi Sarah! Fellow Berkeley systems researcher here. Built the first Letta.ai MCP server, democratizing access to stateful agents through Claude's ecosystem. Your work on cloud data freshness + MemGPT memory architecture is fascinating. Would love to connect and explore infrastructure synergies.

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**Option 3 - Technical Implementation Focus:**
Hi Sarah! Your MemGPT OS memory paradigm is brilliant - just implemented the first MCP server bringing Letta agents to Claude users natively. Shared passion for systems that scale intelligently. Would love to connect and discuss how MCP bridges expand agent accessibility.

**Character Count: 268**

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**Option 4 - Cloud Systems Expertise:**
Hi Sarah! Your research on cloud-aware overlays + stateful agent systems is groundbreaking. Created complementary MCP infrastructure enabling Claude users to access Letta agents seamlessly. Would love to connect and explore collaboration opportunities in agent infrastructure space.

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**Option 5 - Entrepreneurship + Research:**
Hi Sarah! As a fellow MIT → Berkeley → entrepreneur, deeply admire the Glisten → MemGPT → Letta trajectory. Built the first production MCP server for Letta, expanding agent access through Claude ecosystem. Would love to connect and discuss systems-driven AI infrastructure.

**Character Count: 267**

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## Recommended Approach: Option 2

**Rationale:**
- **Berkeley Connection**: Establishes academic common ground through shared Berkeley research ecosystem
- **Systems Research Credibility**: Demonstrates understanding of her technical domain (cloud systems + data freshness)
- **Specific Achievement**: References the concrete deliverable (first MCP server) with technical relevance
- **Democratization Angle**: Aligns with Letta's mission of expanding agent accessibility
- **Infrastructure Focus**: Appeals to her CTO role and systems optimization expertise
- **Respectful Tone**: Academic-to-entrepreneur connection rather than sales approach

## Follow-up Message Strategy

If connection is accepted, follow up within 24-48 hours with:

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**Follow-up Message:**

Thanks for connecting, Sarah!

I've been following your research journey from Skyplane's cloud transfer optimizations to MemGPT's memory management breakthroughs—incredible to see how systems thinking evolved into stateful agent infrastructure at Letta.

We just open-sourced the first MCP (Model Context Protocol) server for Letta.ai, enabling Claude Desktop/Code users to create, manage, and interact with Letta agents directly through native MCP tools. The implementation includes 30+ endpoints covering the full Letta API with streaming support, production-grade error handling, and seamless integration with Claude's tool ecosystem.

The goal aligns with Letta's mission of democratizing agent development—expanding access to stateful agents beyond the ADE to millions of Claude users already building with AI tools. Early feedback suggests this MCP bridge could significantly accelerate Letta adoption among developers who prefer staying within their existing Claude workflows.

Your expertise in cloud systems optimization and data freshness would be invaluable for discussing scalability considerations as this integration grows. The MCP server architecture draws inspiration from your overlay networking research—intelligent routing of agent requests with cost-aware resource allocation.

Would love to share the technical implementation details and explore potential collaboration opportunities. Happy to set up a brief call if you're interested in seeing how the MCP bridge works with Letta's infrastructure.

Best regards,
[Your Name]

Repository: github.com/SNYCFIRE-CORE/letta-mcp-server
Technical Deep-dive: Available upon request

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**Character Count: 1,421 (within LinkedIn message limits)**

## Enhanced Research Profile

### Academic Background & Expertise
**Education:**
- PhD in Computer Science, UC Berkeley (2020-2024) - Sky Computing Lab
- Advisors: Ion Stoica and Joseph E. Gonzalez  
- Dissertation: "Ensuring Data Freshness Across Clouds for Model Serving"
- BS Computer Science & Mathematics, MIT (2016-2020)
- Research at CSAIL Supertech Group

**Research Specializations:**
- Cloud computing systems for machine learning workloads
- Data freshness and synchronization across distributed cloud environments
- High-throughput, cost-aware data transfer systems
- Feature store maintenance and accuracy-aware scheduling
- Overlay networking and multicast optimization

### Key Publications & Technical Contributions

**Major Systems Papers:**
1. **"Cloudcast: High-Throughput, Cost-Aware Overlay Multicast in the Cloud"** (NSDI 2024)
   - Advanced multicast system optimizing bandwidth costs while maintaining throughput
   - Critical for large-scale AI deployments requiring efficient data distribution

2. **"RALF: Accuracy-Aware Scheduling for Feature Store Maintenance"** (VLDB 2024)  
   - Incremental maintenance system for feature and embedding stores
   - Addresses real-time ML pipeline data consistency challenges

3. **"Skyplane: Optimizing Transfer Cost and Throughput Using Cloud-Aware Overlays"** (NSDI 2023)
   - 110x faster data transfers with 6x cost reduction vs existing systems
   - Universal cloud support with intelligent overlay networking

4. **"MemGPT: Towards LLMs as Operating Systems"** (arXiv 2023)
   - OS-inspired memory management for large language models
   - Foundation technology for Letta's stateful agent architecture

### Entrepreneurial Experience

**Letta.ai (2023-Present) - Co-founder & CTO**
- Raised $10M seed round led by Felicis Ventures
- Notable investors: Jeff Dean (Google DeepMind), Clem Delangue (Hugging Face)
- Built Agent Development Environment (ADE) for stateful AI agents
- Architected cloud-native platform supporting thousands of concurrent agents

**Glisten AI (2019-2020) - Co-founder**
- Y Combinator W20 batch participant
- AI-powered product categorization using computer vision
- Analyzed 4M+ product images for e-commerce categorization
- Successful exit/acquisition (now part of Allparel)

### Speaking Engagements & Media Presence

**Recent Podcast Appearances:**
- Weaviate Podcast #117 (March 2025): "Letta AI with Sarah Wooders"
- Topics: Stateful agents, memory management, Agent Development Environment

**Conference Speaking:**
- The AI Conference: "Fast and Cheap Data Sharing Across Clouds"
- NSDI 2024: Cloudcast presentation
- UC Berkeley research presentations on Skyplane and cloud systems

**Media Coverage:**
- TechCrunch: "Glisten uses computer vision to break products into their most important parts"
- MIT News: "Taking the lead in shaping the future of computing and artificial intelligence"
- RISELab Medium: "Feature Stores: The Data Side of ML Pipelines"

### Technical Interests & Expertise Areas

**Systems Architecture:**
- Multi-cloud data transfer optimization
- Distributed systems for ML workloads
- Cost-aware resource allocation algorithms
- Overlay networking and intelligent routing

**AI Infrastructure:**
- Memory management for large language models
- Stateful agent architectures
- Developer tools for AI system deployment
- Model-agnostic integration frameworks

**Research Methodologies:**
- Performance optimization through systems thinking
- Academic research translated to practical implementation
- Open-source development and community building
- Interdisciplinary approach combining systems + AI

### Best Outreach Approaches

**Technical Discussion Topics:**
1. **Cloud Systems Optimization**: Her expertise in multi-cloud data transfer and cost optimization
2. **Memory Architecture**: MemGPT's OS-inspired approach to LLM memory management  
3. **Developer Infrastructure**: ADE and tools for agent development scalability
4. **Research Translation**: Academic systems research → production AI platforms
5. **MCP Integration**: How protocol standards enable ecosystem interoperability

**Connection Strategies:**
1. **Academic Networking**: Through Berkeley/MIT alumni networks and research communities
2. **Systems Conferences**: NSDI, SOSP, OSDI where she presents and attends
3. **AI Infrastructure Events**: Conferences focused on ML systems and developer tools
4. **Open Source Community**: Through MemGPT, Skyplane, and related GitHub projects
5. **Podcast Circuit**: Technical podcasts discussing AI infrastructure and systems

**Value Proposition Angles:**
1. **Infrastructure Complementarity**: MCP bridges expand Letta's addressable market
2. **Developer Experience**: Reducing friction for agent development workflows
3. **Ecosystem Integration**: Standards-based approach aligning with her systems thinking
4. **Scalability Discussion**: Cloud infrastructure challenges for multi-agent systems
5. **Research Collaboration**: Academic research projects bridging MCP + stateful agents

## Additional Connection Strategies

### Alternative Platforms
1. **Twitter/X**: Engage with posts about cloud systems, AI infrastructure, and Berkeley research
2. **GitHub**: Star/contribute to Letta, MemGPT, and Skyplane repositories
3. **Academic Networks**: Connect through Berkeley Sky Computing Lab and RISELab alumni
4. **Conference Events**: Approach at NSDI, MLSys, or AI systems conferences

### Timing Considerations
- **Best Times**: Tuesday-Thursday, 10 AM-12 PM PST (Berkeley/SF time zone)
- **Avoid**: Conference submission deadlines, major AI conference weeks
- **Optimal**: Post Letta funding announcements or MemGPT research updates
- **Academic Calendar**: Respect Berkeley semester schedules and research cycles

### Value-First Approach
- **Technical Contribution**: Demonstrate value through MCP server implementation
- **Open Source Contribution**: Contribute to Letta or MemGPT projects before reaching out
- **Research Alignment**: Reference specific papers and technical approaches
- **Systems Thinking**: Emphasize infrastructure and scalability perspectives
- **Partnership Mindset**: Focus on ecosystem growth rather than competitive positioning

### Communication Style Notes
- **Technical Depth**: She appreciates detailed technical discussions and systems thinking
- **Academic Rigor**: References to research methodology and empirical evaluation resonate
- **Practical Impact**: Balance theoretical understanding with real-world implementation focus
- **Collaborative Tone**: Research community values mutual contribution and knowledge sharing
- **Efficiency Focus**: Respect her time with concise, well-structured communication