A language-practice app with an AI teacher
Build a native Tauri application where an AI teacher can run useful language practice for the person. Build it extremely well. Choose a situation they genuinely want to handle better, such as an interview, explaining their work, or talking with relatives. Follow the shared launch.
Find out the languages involved, the person's present comfort, and what success in that situation would feel like. Prepare a complete first practice session around that purpose, with material they can understand and something worth saying.
Give the teacher a lesson it can operate
Make the existing coding agent a first-class participant through tools that expose the current activity, learning material, and useful session history. Reuse its installation and account for the capabilities it already supplies. Let it introduce an activity, present a prompt, review an attempt, replay an exchange, and save material for another session through the application's actual controls.
Give the teacher room to respond to what the learner means. If the learner is practicing explaining a delayed delivery, the conversation should develop according to their explanation and the other person's reasonable concerns. A successful response can be expressed in many ways.
Let practice move naturally between conversation and focused work. The teacher might revisit a misunderstood question, demonstrate a clearer response, or give the learner another attempt in a changed situation. Keep the current task and the reason for practicing it apparent.
Make speech and listening part of the experience
Support speaking, hearing the teacher, replaying useful moments, and inspecting language when the learner wants help. Give the person comfortable controls for taking time, pausing, interrupting, or switching to text. Give a hesitant speaker time to finish and a clear way to indicate they are ready for a response.
Discover the available models and connections that can actually hear audio and provide suitable speech. Where additional access is needed, explain that choice and its costs as part of setup. Choose for the languages, voices, listening, and teaching this project needs.
Keep the original recording available when useful for feedback. Ground comments about pronunciation, rhythm, or delivery in the audio the reviewing model actually heard. Use transcripts for the work they support, such as discussing word choice or finding an earlier phrase.
Pay attention to the conversational experience: how quickly a reply arrives, whether the person knows the app is listening, and whether a correction interrupts a worthwhile attempt. Make feedback selective enough that the learner can use it on the next try.
Remember what will help next time
Save useful expressions, selected attempts, feedback, and the situations practiced. Let the learner revisit them and choose a new direction. Show progress through recognizable work: a difficult exchange they can now complete, or an explanation that has become clearer.
Keep teacher judgments attached to the attempts that informed them and open to revision. The learner can correct a misunderstanding or explain that a suggested phrase does not fit their life. Make their recordings and learning material exportable and recoverable through the shared launch's ownership guidance.
Deliver a practice session worth returning to
Complete the installed app, agent operation, speech connections, and first session. Use the actual microphone and speakers, work through a conversation, revisit a difficult moment, and resume from saved learning on another visit.
Judge whether the practice helps this person communicate. Controls and successful model replies establish only part of that. Adjust the teaching from the learner's experience while preserving useful material already created.
Teach the person how model choice, application tools, and continuing context change what an AI teacher can do. Let them shape the next session by describing what they want to become able to say.