Can Speak be vibe coded?
Build scripted language roleplay using a user-supplied model and clear lesson objectives
A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Speak, build scripted language roleplay using a user-supplied model and clear lesson objectives. The hard boundary is proprietary speech tutor models, curriculum, mobile experience, and learning data, plus content, pedagogy, and network.
Jump to the build brief ↓Checked Jul 2026
What you pay today, before any DIY hosting
high editorial confidence
Buildability by layer
Screens, forms, and focused interactions
The repeatable job the product performs
Availability and legality of required data
Uptime, queues, support, and maintenance
Security, compliance, and user confidence
The achievable core
- Run scripted language roleplay with clear lesson objectives using a user-supplied model, schedule retrieval practice, and show progress without claiming accredited instruction.
- Build a focused single-user workflow with real persistence, search, and export.
- A responsive interface with real empty, loading, success, and error states.
The parts a prompt cannot buy
- proprietary speech tutor models, curriculum, mobile experience, and learning data
- licensed course catalog
- expert curriculum
- human feedback
- Model quality and inference operations are part of the product.
- Licensed content and distribution rights are not reproducible with an LLM.
Why people still pay
People still pay for Speak because the learning loop is buildable, while trusted curriculum, instructors, feedback, credentials, and community are not. The recurring cost buys curriculum quality, assessment validity, content rights, spaced-repetition tuning, speech models, sync, moderation, and support, not just the visible interface.
Model quality and inference operations are part of the product.
Licensed content and distribution rights are not reproducible with an LLM.
The value comes from the people already using it.
The brief
Context, requirements, acceptance criteria, non-goals, and the full production standard — as Markdown, ready for any coding agent.
Build brief — a focused alternative to Speak
Context
**Speak** — Build scripted language roleplay using a user-supplied model and clear lesson objectives. It currently costs $19.99/mo.
A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Speak, build scripted language roleplay using a user-supplied model and clear lesson objectives. The hard boundary is proprietary speech tutor models, curriculum, mobile experience, and learning data, plus content, pedagogy, and network.
This brief describes a focused, single-operator replacement for the part of Speak that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.
What you are building
Run scripted language roleplay with clear lesson objectives using a user-supplied model, schedule retrieval practice, and show progress without claiming accredited instruction.
Build a focused single-user workflow with real persistence, search, and export.
A responsive interface with real empty, loading, success, and error states.
Requirements
Functional
Modern browser.
User-supplied study material.
Data and integrations
Optional speech or language-model API key.
Each of these needs a real account, credential, or quota. Set them up before writing feature code.
Non-functional
Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
Security: server-side secrets, validated input, and no credentials in the client bundle.
Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
Portability: the operator can export their data and leave without losing it.
Implementation brief
Build a closest honest personal substitute for Speak in an empty repository.
Use Next.js 15, TypeScript, SQLite, Drizzle ORM, and a service worker for offline use; do not offer alternative stacks.
The core loop is: run scripted language roleplay with clear lesson objectives using a user-supplied model, schedule retrieval practice, and show progress without claiming accredited instruction.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Import Markdown, text, or user-authored notes and split them into editable learning units.
Create flashcards and short quizzes only after showing the generated items for approval.
Implement a transparent spaced-repetition schedule with due, again, hard, good, and easy actions.
Add goals, study sessions, streak-free progress charts, notes, and weak-topic review.
For language practice, record the learner and compare timing or pronunciation without presenting medical claims.
Support offline use, full export, reset, backup, and deletion of all learning data.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out licensed premium courses.
Deliberately leave out accredited certificates and grading.
Deliberately leave out live tutors, large classrooms, and a learner marketplace.
Finish by running the tests and listing the exact commands used.
Delivery standard
Inspect the repository first, then write a short implementation plan before writing code.
Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
Add structured logs around every external call and return actionable errors without leaking sensitive details.
Write unit tests for the core logic and one automated test of the main user journey.
Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.
Acceptance criteria
A clean install starts the app using only the README and .env.example.
The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
Tests, type checking, linting, and a production build all pass with no ignored failures.
No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.
Non-goals
Do not build these, and do not claim to have replaced them:
Proprietary speech tutor models, curriculum, mobile experience, and learning data.
Licensed course catalog.
Expert curriculum.
Human feedback.
Model quality and inference operations are part of the product.
Licensed content and distribution rights are not reproducible with an LLM.
What you still own after launch
Secure credentials, rotate secrets, and handle provider rate limits.
Run migrations, backups, restores, and dependency updates.
Test the critical journey after every model, API, or hosting change.
Monitor failures and fix the edge cases a first prompt will miss.
Risk
**Manageable.** A personal version is realistic if you test the critical journey and keep reliable backups.
Editorial confidence in this assessment: high. No independent one-shot implementation is linked yet.
Prior art
Working open-source software you can read, fork, or borrow from before starting:
[Moodle](https://github.com/moodle/moodle) — Long-running open-source learning platform with courses, assessments, and progress tracking
Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/speak-language
You still own the product
- Secure credentials, rotate secrets, and handle provider rate limits.
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.
Open-source prior art
Before you start
Can Speak be vibe coded?
Not faithfully. A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Speak, build scripted language roleplay using a user-supplied model and clear lesson objectives. The hard boundary is proprietary speech tutor models, curriculum, mobile experience, and learning data, plus content, pedagogy, and network.
What can an AI coding agent reproduce from Speak?
Run scripted language roleplay with clear lesson objectives using a user-supplied model, schedule retrieval practice, and show progress without claiming accredited instruction. Build a focused single-user workflow with real persistence, search, and export. A responsive interface with real empty, loading, success, and error states.
What will a DIY Speak replacement still be missing?
proprietary speech tutor models, curriculum, mobile experience, and learning data; licensed course catalog; expert curriculum; human feedback; Model quality and inference operations are part of the product.; Licensed content and distribution rights are not reproducible with an LLM.
What do I still own after building a Speak alternative?
Secure credentials, rotate secrets, and handle provider rate limits. Run migrations, backups, restores, and dependency updates. Test the critical journey after every model, API, or hosting change. Monitor failures and fix the edge cases a first prompt will miss.