AI Tools & Platforms

The company behind GPT-4, ChatGPT, and DALL-E

OpenAI builds the most widely used large language models in the world. If your product has AI features, there's a decent chance it's powered by OpenAI under the hood. Their API is basically the default starting point for any team building AI into their product.

The framework for building LLM-powered applications

LangChain is a framework that makes it easier to build applications on top of LLMs. Think of it as the plumbing that connects your AI model to your data, your tools, and your users. It's become the most popular framework for building AI apps, for better or worse (it's gotten some criticism for being over-engineered).

The vector database for AI applications

Pinecone is a managed vector database. If that sentence means nothing to you: it's a special database that stores data in a way that lets AI find similar things really fast. It's the backbone of RAG systems, recommendation engines, and semantic search.

The toolkit for building AI-powered web applications

Vercel's AI SDK makes it easy to build streaming AI interfaces in web apps. If your product needs a ChatGPT-like streaming response UI (and it might), this is one of the easiest ways to build it. It handles the annoying parts: token streaming, state management, model switching.

The data cloud that powers analytics and AI

Snowflake is a cloud data warehouse — which is enterprise-speak for "the place where companies put all their data to analyze it." It's become a central player in the AI space because, it turns out, you need a lot of well-organized data to build AI features. Who knew.

Product analytics for understanding what users actually do

Amplitude is a product analytics platform that tracks user behavior in your product. Click this, visit that, complete this flow, churn after 3 days. It's essential for measuring whether your AI features are actually working or just looking cool in demos.