Choose your AI setup

Compare hosted AI, APIs and local models without treating one setup as right for everyone.

What this step is for.

Hosted chat tools are easiest to start with. APIs are useful when software must call a model programmatically. Local model runners keep inference on your machine. A hybrid setup can use each where it is strongest. Model quality, context length, speed, privacy and cost are separate trade-offs.

Keep the scope small enough that you can inspect the result yourself. AI can accelerate the work, but it should not erase the distinction between a suggestion, a changed file, a successful build and a verified product.

Take one concrete action.

Choose one primary assistant and one execution path. Beginners can start with a hosted assistant; local-first users can pair a local model runner with a coding tool. Record which data you are comfortable sending to hosted services.

Turn the idea into evidence.

Do the action in a disposable or backed-up workspace first. Write down what you expected to happen, what actually happened and what you changed when the result differed. This small habit becomes increasingly important as your AI tools gain access to more files and commands.

Do not move on until this is true.

You know where your prompts and project files are processed and what happens if the internet connection disappears.

Why we use this principle.

Nyfir Studios treats generated output and verified output as different states. Development work on local AI, Android software and bookkeeping workflows has repeatedly shown that recoverable state, explicit tests and clear product status are more useful than simply producing more output.