The trend of LLMs and AI is creating numerous new dev tools, but entering this market requires a clear differentiation strategy to stand out among many competitors.

AI tools are improving, but they still require clear instructions and descriptions of tasks. The ability to articulate what AI should do remains crucial.

Before writing code, collaborate with the LLM to create a comprehensive project plan, which you can refer back to throughout the development process. After creating the first draft of your plan, remove features you don't like and mark certain features as 'won't do' if they're too complicated, while keeping a section of ideas for later to inform the LLM that they are out of scope for now.

LLMs aren't just for coding; they can assist with non-coding tasks as well. For example, I used AI to configure DNS servers and set up hosting, significantly speeding up my progress.

Spend ample time using LLMs to define the scope and architecture of your project before transitioning to coding tools like Cursor.

Work with the LLM to implement your plan section by section, explicitly stating which section to work on at a time, and check that it works before moving on.

You still need to provide AI with clear instructions on what tasks to perform, as AI tools can only execute a specific set of instructions based on the data they have been trained on.

To effectively use AI, users must clearly define the tasks and expectations for the AI, similar to how one would instruct an intern on their job responsibilities.

The introduction of large language models (LLMs) provides a language interface for robots, allowing for easier task descriptions and enabling robots to perform generic tasks more effectively.