Vibecode Colossyan
track this build5 steps, step by step0%NOT REALLY · don't bother
price $27/moyou'd save $324/yrbuild time closest consolation build: one sittingcategory 🔊 voice aireplaced by 0 people
A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Colossyan, create labeled training videos from user-owned slides and licensed local narration. The hard boundary is proprietary avatars, enterprise learning workflow, translation, and rendering, plus models, compute, rights, and safety operations.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Colossyan. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Colossyan indie build ## Goal Build the smallest trustworthy replacement for the core Colossyan workflow for one developer or a tiny team. ## Scope Create clearly labeled training videos from user-owned slides and licensed narration using a local model, and retain provenance for every output. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - proprietary avatars, enterprise learning workflow, translation, and rendering - frontier voice or avatar model - licensed voice catalog - real-time rendering fleet - moderation, consent verification, and enterprise rights If those capabilities are essential, use Piper instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a closest honest personal substitute for Colossyan in an empty repository. Use Python 3.12, FastAPI, SQLite, ffmpeg, and a user-owned local TTS model; do not offer alternative stacks. The core loop is: create clearly labeled training videos from user-owned slides and licensed narration using a local model, and retain provenance for every output. 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. Require a project-level rights and consent acknowledgement before generating audio. Ship with no celebrity, public-figure, or scraped voice assets and accept only explicitly licensed models. Generate speech from text with voice, speed, pause, pronunciation, and segment controls. Create a timeline for audio, captions, uploaded visuals, and simple transitions. Embed project metadata and a visible synthetic-media disclosure in exported assets. Store prompts, model identifiers, consent notes, and output hashes in a local provenance log. 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 cloning a voice without clear consent. Deliberately leave out impersonation or deceptive unlabeled media. Deliberately leave out a frontier avatar model, public hosting, or enterprise rights clearance. Finish by running the tests and listing the exact commands used. ## Required capabilities - local TTS model - ffmpeg - GPU recommended - voices the user has rights and consent to use ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Colossyan. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Colossyan indie build ## Goal Build the smallest trustworthy replacement for the core Colossyan workflow for one developer or a tiny team. ## Scope Create clearly labeled training videos from user-owned slides and licensed narration using a local model, and retain provenance for every output. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - proprietary avatars, enterprise learning workflow, translation, and rendering - frontier voice or avatar model - licensed voice catalog - real-time rendering fleet - moderation, consent verification, and enterprise rights If those capabilities are essential, use Piper instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a closest honest personal substitute for Colossyan in an empty repository. Use Python 3.12, FastAPI, SQLite, ffmpeg, and a user-owned local TTS model; do not offer alternative stacks. The core loop is: create clearly labeled training videos from user-owned slides and licensed narration using a local model, and retain provenance for every output. 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. Require a project-level rights and consent acknowledgement before generating audio. Ship with no celebrity, public-figure, or scraped voice assets and accept only explicitly licensed models. Generate speech from text with voice, speed, pause, pronunciation, and segment controls. Create a timeline for audio, captions, uploaded visuals, and simple transitions. Embed project metadata and a visible synthetic-media disclosure in exported assets. Store prompts, model identifiers, consent notes, and output hashes in a local provenance log. 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 cloning a voice without clear consent. Deliberately leave out impersonation or deceptive unlabeled media. Deliberately leave out a frontier avatar model, public hosting, or enterprise rights clearance. Finish by running the tests and listing the exact commands used. ## Required capabilities - local TTS model - ffmpeg - GPU recommended - voices the user has rights and consent to use ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Colossyan. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Colossyan product brief ## Problem A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Colossyan, create labeled training videos from user-owned slides and licensed local narration. The hard boundary is proprietary avatars, enterprise learning workflow, translation, and rendering, plus models, compute, rights, and safety operations. ## Product outcome Create clearly labeled training videos from user-owned slides and licensed narration using a local model, and retain provenance for every output. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - local TTS model - ffmpeg - GPU recommended - voices the user has rights and consent to use ## Explicit non-goals for v1 - proprietary avatars, enterprise learning workflow, translation, and rendering - frontier voice or avatar model - licensed voice catalog - real-time rendering fleet - moderation, consent verification, and enterprise rights ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build a closest honest personal substitute for Colossyan in an empty repository. Use Python 3.12, FastAPI, SQLite, ffmpeg, and a user-owned local TTS model; do not offer alternative stacks. The core loop is: create clearly labeled training videos from user-owned slides and licensed narration using a local model, and retain provenance for every output. 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. Require a project-level rights and consent acknowledgement before generating audio. Ship with no celebrity, public-figure, or scraped voice assets and accept only explicitly licensed models. Generate speech from text with voice, speed, pause, pronunciation, and segment controls. Create a timeline for audio, captions, uploaded visuals, and simple transitions. Embed project metadata and a visible synthetic-media disclosure in exported assets. Store prompts, model identifiers, consent notes, and output hashes in a local provenance log. 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 cloning a voice without clear consent. Deliberately leave out impersonation or deceptive unlabeled media. Deliberately leave out a frontier avatar model, public hosting, or enterprise rights clearance. Finish by running the tests and listing the exact commands used. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Colossyan capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Colossyan indie build ## Goal Build the smallest trustworthy replacement for the core Colossyan workflow for one developer or a tiny team. ## Scope Create clearly labeled training videos from user-owned slides and licensed narration using a local model, and retain provenance for every output. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - proprietary avatars, enterprise learning workflow, translation, and rendering - frontier voice or avatar model - licensed voice catalog - real-time rendering fleet - moderation, consent verification, and enterprise rights If those capabilities are essential, use Piper instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build a closest honest personal substitute for Colossyan in an empty repository. Use Python 3.12, FastAPI, SQLite, ffmpeg, and a user-owned local TTS model; do not offer alternative stacks. The core loop is: create clearly labeled training videos from user-owned slides and licensed narration using a local model, and retain provenance for every output. 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. Require a project-level rights and consent acknowledgement before generating audio. Ship with no celebrity, public-figure, or scraped voice assets and accept only explicitly licensed models. Generate speech from text with voice, speed, pause, pronunciation, and segment controls. Create a timeline for audio, captions, uploaded visuals, and simple transitions. Embed project metadata and a visible synthetic-media disclosure in exported assets. Store prompts, model identifiers, consent notes, and output hashes in a local provenance log. 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 cloning a voice without clear consent. Deliberately leave out impersonation or deceptive unlabeled media. Deliberately leave out a frontier avatar model, public hosting, or enterprise rights clearance. Finish by running the tests and listing the exact commands used. ## Required capabilities - local TTS model - ffmpeg - GPU recommended - voices the user has rights and consent to use ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Colossyan product brief ## Problem A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Colossyan, create labeled training videos from user-owned slides and licensed local narration. The hard boundary is proprietary avatars, enterprise learning workflow, translation, and rendering, plus models, compute, rights, and safety operations. ## Product outcome Create clearly labeled training videos from user-owned slides and licensed narration using a local model, and retain provenance for every output. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - local TTS model - ffmpeg - GPU recommended - voices the user has rights and consent to use ## Explicit non-goals for v1 - proprietary avatars, enterprise learning workflow, translation, and rendering - frontier voice or avatar model - licensed voice catalog - real-time rendering fleet - moderation, consent verification, and enterprise rights ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build a closest honest personal substitute for Colossyan in an empty repository. Use Python 3.12, FastAPI, SQLite, ffmpeg, and a user-owned local TTS model; do not offer alternative stacks. The core loop is: create clearly labeled training videos from user-owned slides and licensed narration using a local model, and retain provenance for every output. 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. Require a project-level rights and consent acknowledgement before generating audio. Ship with no celebrity, public-figure, or scraped voice assets and accept only explicitly licensed models. Generate speech from text with voice, speed, pause, pronunciation, and segment controls. Create a timeline for audio, captions, uploaded visuals, and simple transitions. Embed project metadata and a visible synthetic-media disclosure in exported assets. Store prompts, model identifiers, consent notes, and output hashes in a local provenance log. 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 cloning a voice without clear consent. Deliberately leave out impersonation or deceptive unlabeled media. Deliberately leave out a frontier avatar model, public hosting, or enterprise rights clearance. Finish by running the tests and listing the exact commands used. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Colossyan capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
People still pay for Colossyan because customers pay for output quality, production speed, licensed voices, consent workflows, and a provider that carries the operational risk. The recurring cost buys model licensing, consent records, impersonation risk, watermarking, GPU queues, media storage, abuse response, and rapid model changes, not just the visible interface.
xproprietary avatars, enterprise learning workflow, translation, and rendering
xfrontier voice or avatar model
xlicensed voice catalog
xreal-time rendering fleet
xmoderation, consent verification, and enterprise rights
share on X ↗"I just replaced Colossyan ($27/mo) with one prompt"
Vibecode Colossyan
Not really. Colossyan's value is not the code: Recheck price before merge. See the honest breakdown above.
How much does Colossyan cost?
Colossyan costs about $27/month (Starter, checked 2026-07-31), which is $324 per year.
What do I lose by replacing Colossyan?
Honestly: proprietary avatars, enterprise learning workflow, translation, and rendering; frontier voice or avatar model; licensed voice catalog; real-time rendering fleet; moderation, consent verification, and enterprise rights. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Colossyan?
Yes: Piper (Active community continuation of the fast local Piper text-to-speech engine.). Using prior art is also vibecoding; the prompt is for when you want it exactly your way.