Meruzhan Danielyan

Lessons from YC: Culture Isn’t a Slide Deck YC didn’t just change how we built products. It changed how we worked together. Before YC, “culture” was something we’d figure out later. We thought it meant perks, team dinners, or a list of values on the wall. But inside YC, culture meant one thing: how fast your team can move without breaking. That’s when it clicked. Culture isn’t what you say. It’s what you tolerate. If someone cuts corners and you let it slide, that’s your culture. If someone owns their mistake and you celebrate it, that’s your culture. We started treating culture like product — build, test, iterate. That's when we realized small habits create the real DNA: how you communicate under pressure, how you make decisions, how you handle bad days. YC pushed us to grow up as a team, not just as founders. Because a product might attract customers but culture is what keeps them.

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$35M Series A led by CRV, announced September 17. Raindrop builds reliability infrastructure for AI agents, and the round came with a product launch: Simulations, which tests agent changes before they reach production. The founding story is the product story. Ben Hylak spent four years at Apple, moving from engineering into the Human Interface design group. Zubin Singh Koticha and Alexis Gauba built Opyn, a financial software company, together at UC Berkeley; Coinbase acquired it. Then the three built Sidekick, a coding agent and VS Code extension, through Y Combinator's Winter 2024 batch. They had paying users but no effective way to understand why their own agent failed after deployment. So they walked away from the application and built the infrastructure they had needed: first production monitoring that catches how agents break in the wild, now pre-release testing that catches the break before customers see it. Simulations runs the agent's real code against synthetic versions of the services it touches: databases, communication tools, payment systems, external APIs. It replays relevant production traces on every pull request and posts a verdict before engineers merge: regressions, cost changes, output drift, tool errors. Handwritten tests only catch the failures you already expect. This targets the other kind: the unanticipated sequence of tool calls, the stale context, the model substitution that changes behavior five steps into a task. CRV led the round. The $15M seed came from Lightspeed in December 2025, which puts total funding at $50M. Researchers and executives from Anthropic, OpenAI and Thinking Machines backed the round. Raindrop says it processes billions of traces a month, with teams including Vercel, Speak, Clay, Framer, AngelList and Tolan on the platform. Making every production failure becomes a future pre-release test is just brilliant because eval sets your users wrote are the ones harder to game. Congrats Ben, Zubin, Alexis and the entire Raindrop team. Up and to the right my friends! 💥🚀💪

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CRV Security: Request for Startups I never know if this actually works for our friends over at YC but figured we'd try. Here's what we want to fund in 2026! 1. Golden Artifacts: Think Chainguard but more broad. Artifact attestation exists for open source. Almost nothing exists for internal software — especially the vibe-coded tooling now running in production. We want the company building cryptographic proof of secure software delivered from secure artifacts: who built it, how, and whether it was reviewed. If more things are being yeeted into the world via Claude Code (myself included), this feels like an issue. 2. MCP & Agentic Security: Agents are getting real credentials and taking real actions. The security posture of most orgs around this is basically zero. That changes fast. You'd never give an employee hardcoded API keys or write access to your email without supervision/trust. Why give it to agents? 3. AI Governance: Boards are asking CISOs to account for AI risk. CISOs have no good answer other than "Palo has a module" 4. Next-Gen Endpoint: CrowdStrike was built for a world of static binaries and human operators. AI workloads, cloud-native infra, and AI-assisted attackers need a new architecture. The category is ready to be reinvented. 5. Networking in the AI Era: Zero trust was designed for humans. What does network security look like when the entity requesting access is an agent? Nobody's really solved this. 6. Email Security + Next-Gen Phishing: LLMs have made spear phishing infinitely scalable. I've never truly understood why Abnormal and KnowBe4 aren't one company. Maybe this time it's different. 7. Frontier Security Lab: We'd back a credible, well-staffed lab focused entirely on red-teaming models and setting the evidentiary standard the industry needs as LLM built apps become the norm. 8. Dependency Security: That Actually Remediates Malicious and vulnerable dependencies are a top attack vector. The tooling is mostly noise — scanners that don't close the loop. The winner here ships fixes, not just alerts. 9. Critical Infrastructure Cyber: Data centers, satellites, power grids, undersea cables. The physical backbone of the internet is increasingly exposed and wildly under-defended. We have data centers in space, for God's sake. Surely we need better cyber for critical infrastructure? 10. PAM for the Modern Era Legacy: PAM was built for static roles, human users, on-prem directories. Cyberark was founded in 1999.....Agents, ephemeral workloads, and cloud-native infra have broken all of those assumptions. Is anyone rebuilding this from scratch? If you're building in any of these areas — or something we haven't thought of — reach out. james@crv.com

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AI has entered its systems phase. Outcomes are no longer driven by model size alone, but by how well models are integrated into context, tools, routing, and feedback loops. A small set of primitives is defining this shift: - Tool use turns models into operators, not just assistants. - Dynamic context (e.g. MCPs) makes context portable, composable, and governed at runtime. - Intelligent routing allocates the right model to the right task under real-world constraints. - Telemetry closes the loop, turning production behavior into learning. On top of these primitives, new alignment strategies – synthetic data, verifiable rewards, and multi-turn objectives – are allowing systems to learn from real outcomes rather than static labels. The implication: As capabilities commoditize, differentiation moves up the stack to system design, domain grounding, and alignment with measurable value. Pricing follows the same path, shifting from labor proxies to outcomes, and in some cases even risk-sharing. For builders: think more about control planes that govern routing, context, and accountability. For buyers: evaluate AI as an operator, one that can be audited, improved, and ultimately trusted with responsibility 🔗 More here: https://lnkd.in/gYfF7cWu

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