AI or API? | Chatbot cuckoos are bloating tech

🌈 Abstract

The article discusses the use of external APIs, including third-party AI, in building products, and the challenges and considerations around relying on these external services. It examines the trend of startups that are essentially "ChatGPT wrappers" and the importance of proper user research, problem framing, and API design when incorporating AI features into products.

🙋 Q&A

[01] The use of external APIs and third-party AI

1. Questions related to the content of the section?

• What are the benefits and challenges of using external APIs and third-party AI in product development?
• How can teams ensure a good user experience when integrating external AI services into their products?
• What are the potential issues with treating third-party APIs like native functions in the app's codebase?

Answers:

• Using external APIs and third-party AI can allow teams to quickly integrate new features and capabilities into their products, but it also comes with challenges:
  • Relying on constant, poorly designed API calls can slow down a product and make it less scalable.
  • Ignoring API throttling or rate limits can lead to a poor user experience.
  • The high computing power required by AI models can have environmental impacts.
• To ensure a good user experience, teams should:
  • Pay attention to API behavior and design, not just the front-end.
  • Conduct proper user research and problem framing to ensure the right solution is implemented.
  • Follow best practices for API design and integration, such as those outlined in the article's checklist.

[02] The importance of developer experience (DevEx) when using external AI services

1. Questions related to the content of the section?

• Why is developer experience (DevEx) important when building products that incorporate external AI services?
• What are some key aspects of DevEx that teams should focus on?

Answers:

• As large language models (LLMs) and other AI services become more mainstream, they will be increasingly used as third-party SDKs, plugins, and APIs.
• Developer experience (DevEx) is crucial in this context, as it goes beyond just having an AI copilot or LLM assistant.
• Key aspects of DevEx that teams should focus on include:
  • Well-structured, well-named schemas
  • Flexible functions
  • Up-to-date documentation
  • Sufficient testing data
• Paying attention to DevEx will become even more important as AI technologies become more widely adopted and integrated into products.

[03] Innovative applications of AI beyond chatbots

1. Questions related to the content of the section?

• What are some examples of innovative applications of AI beyond chatbots?
• How can teams combine hardware, data, and interactions to create unique AI-powered experiences?

Answers:

• The article suggests some innovative applications of AI beyond chatbots, such as:
  • A smart mirror that can detect early signs of migraines
  • An earbud that can alert law enforcement officers when a colleague's cortisol levels are dangerous
  • A bracelet that can record subtle daily triggers for Cognitive Behavioral Therapy
• The key is to combine hardware, data, and interactions in unique ways to create AI-powered experiences that go beyond simple chatbots or AI wrappers.
• This is where the "fun" is, according to the article, as teams can explore the art of bringing these elements together in novel ways.