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Why we look for operators who have carried real products to production, not just prototypes.

Trust comes from legible reasoning and tight context. Notes from building tools for specialists.

Your vector database is bleeding money. Pinecone starts at $70/month and scales to $3,000+. Self-hosted Milvus costs $3,000/month in DevOps time. TurboPuffer does the same job for $64/month with zero operational overhead. Here's how.

LangChain has 100K GitHub stars and everyone uses it. It's also abstraction hell that makes production debugging impossible. Here's why we chose Mirascope's simplicity—and saved 298 hours of maintenance over 3 years.

While US tech leaders debate AGI timelines, China is building the infrastructure, models, and cultural influence to dominate AI's next decade. Here's what American companies need to understand about the new global AI landscape.

We switched from Next.js to TanStack Start and our velocity increased 40%. Here's why consistency beats novelty, and how our unified tech stack philosophy compounds value across portfolio companies.

We've released three major open source projects for AI engineering. Here's why giving away our tools makes business sense.

Building a full-time AI team sounds logical—until you realize you're paying top talent to sit idle 60% of the time. Here's a better model.

After two years of building AI products through our venture studio model, here's what actually works and what's just VC fairy tales.

While Silicon Valley chases the next social app, the real AI revolution is happening in manufacturing plants, farms, and construction sites across the heartland. Here's why we're betting on the industries coastal VCs ignore.

Most AI products stagnate after launch. The ones that dominate? They build data flywheels from day 1. Here's how we turn every user interaction into model improvements across Grey Haven portfolio companies.

Tired of AI predictions from people who've never deployed a model? Here's what will actually matter in 2025 from engineers in the trenches.

Think throwing OCR and ChatGPT at your documents is a strategy? Your document processing pipeline is probably held together with duct tape and delusion.

Your vector database is not a SQL database with extra math. It's time to stop pretending otherwise before your infrastructure collapses.

Your RAG pipeline is hemorrhaging money faster than a venture-backed startup in 2021. Here's why your retrieval augmented generation is actually just expensive random generation.

While everyone else is writing Medium articles about theoretical AI architectures, we're in the trenches building systems that actually work.

Finally, enterprise AI that actually works. No more duct tape and optimistic thinking—just solid engineering that ships to production.