Vibecode Meshy
track this build5 steps, step by step0%NOT REALLY · don't bother
The UI here is a thin shell over the actual product: generative 3D models trained on large mesh corpora, plus the GPU fleet that runs them in under a minute. You can absolutely wire up a local pipeline with open weights like Hunyuan3D or TRELLIS, and for hobby props it will get you surprisingly far. What you will not reproduce in a session is the topology cleanliness, the PBR texture quality, the remesh/retopology passes, or the auto-rigging that makes output usable without a cleanup artist. Also, one decent generation on your own hardware needs 16 to 24GB of VRAM and patience, which is exactly the cost the subscription is hiding from you. Build the local version if you enjoy the process, not because it is cheaper.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Meshy.
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 =====
# Meshy indie build
## Goal
Build the smallest trustworthy replacement for the core Meshy workflow for one developer or a tiny team.
## Scope
A local web app that takes a prompt or a reference image, runs an open-weights image-to-3D model on your own GPU, and hands back a viewable, downloadable GLB.
## 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:
- Clean quad topology and low-poly remeshing; open pipelines give you dense, messy triangles
- PBR material maps and texture upscaling that hold up under a real light rig
- Auto-rigging and animation of humanoid output
- Sub-minute generation, plus the ability to fire off ten variations without your machine seizing up
- Any commercial-use clarity around the model weights you are running
If those capabilities are essential, use Meshy 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 local single-user web app called "meshlab" that generates 3D meshes from a prompt or an image on my own GPU.
Stack, no substitutions:
- Python 3.11, FastAPI backend, plain HTML + vanilla JS frontend served by FastAPI
- SQLite via sqlite3 for the job table, no ORM
- three.js loaded from a CDN for the model viewer
- A single background worker thread with an in-process job queue. No Celery, no Redis.
Generation pipeline:
- Image to 3D is the primary path. Use an open-weights image-to-3D model from Hugging Face (Hunyuan3D-2 or TRELLIS, pick one and commit to it in the code and README). Load it lazily on first job and keep it resident.
- Text to 3D is a two stage path: call a local Stable Diffusion checkpoint via diffusers to make a single reference image, then feed that image into the same 3D pipeline. If no SD checkpoint is present, disable the text path in the UI with a clear message instead of failing.
- Export GLB. Also write the raw OBJ if the pipeline produces one.
UI, one page:
- Left: a text prompt box, an image upload dropzone, a "generate" button, and a seed field.
- Right: a job list with status (queued, running, done, failed), newest first, polling every 2 seconds.
- Clicking a done job loads its GLB into a three.js canvas with orbit controls, a grid floor, and one directional plus one ambient light. Add a wireframe toggle and a "download GLB" link.
- Show generation time and peak VRAM per job.
In scope: local file storage under ./outputs/{job_id}/, a jobs table, graceful failure with the traceback saved to the job row, a .env for MODEL_ID and SD_MODEL_PATH, a README that states the VRAM requirement bluntly.
Out of scope, do not build: user accounts, cloud storage, telemetry, retopology, UV unwrapping, PBR texture baking, rigging, animation, multi-GPU, payment, sharing links.
The README must include a short "limitations" section stating that output is dense triangle soup with baked vertex colors or a single texture, that it needs cleanup in Blender before engine use, and that model weight licenses must be checked before any commercial use.
## Required capabilities
- An NVIDIA GPU with 16GB+ VRAM, ideally 24GB
- CUDA toolkit and a working PyTorch install
- Tens of GB of disk for model weights
- Tolerance for dependency hell in the 3D generation ecosystem
- Optional: an image model or API key if you want text-to-image as the first stage
## 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 Meshy.
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 =====
# Meshy indie build
## Goal
Build the smallest trustworthy replacement for the core Meshy workflow for one developer or a tiny team.
## Scope
A local web app that takes a prompt or a reference image, runs an open-weights image-to-3D model on your own GPU, and hands back a viewable, downloadable GLB.
## 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:
- Clean quad topology and low-poly remeshing; open pipelines give you dense, messy triangles
- PBR material maps and texture upscaling that hold up under a real light rig
- Auto-rigging and animation of humanoid output
- Sub-minute generation, plus the ability to fire off ten variations without your machine seizing up
- Any commercial-use clarity around the model weights you are running
If those capabilities are essential, use Meshy 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 local single-user web app called "meshlab" that generates 3D meshes from a prompt or an image on my own GPU.
Stack, no substitutions:
- Python 3.11, FastAPI backend, plain HTML + vanilla JS frontend served by FastAPI
- SQLite via sqlite3 for the job table, no ORM
- three.js loaded from a CDN for the model viewer
- A single background worker thread with an in-process job queue. No Celery, no Redis.
Generation pipeline:
- Image to 3D is the primary path. Use an open-weights image-to-3D model from Hugging Face (Hunyuan3D-2 or TRELLIS, pick one and commit to it in the code and README). Load it lazily on first job and keep it resident.
- Text to 3D is a two stage path: call a local Stable Diffusion checkpoint via diffusers to make a single reference image, then feed that image into the same 3D pipeline. If no SD checkpoint is present, disable the text path in the UI with a clear message instead of failing.
- Export GLB. Also write the raw OBJ if the pipeline produces one.
UI, one page:
- Left: a text prompt box, an image upload dropzone, a "generate" button, and a seed field.
- Right: a job list with status (queued, running, done, failed), newest first, polling every 2 seconds.
- Clicking a done job loads its GLB into a three.js canvas with orbit controls, a grid floor, and one directional plus one ambient light. Add a wireframe toggle and a "download GLB" link.
- Show generation time and peak VRAM per job.
In scope: local file storage under ./outputs/{job_id}/, a jobs table, graceful failure with the traceback saved to the job row, a .env for MODEL_ID and SD_MODEL_PATH, a README that states the VRAM requirement bluntly.
Out of scope, do not build: user accounts, cloud storage, telemetry, retopology, UV unwrapping, PBR texture baking, rigging, animation, multi-GPU, payment, sharing links.
The README must include a short "limitations" section stating that output is dense triangle soup with baked vertex colors or a single texture, that it needs cleanup in Blender before engine use, and that model weight licenses must be checked before any commercial use.
## Required capabilities
- An NVIDIA GPU with 16GB+ VRAM, ideally 24GB
- CUDA toolkit and a working PyTorch install
- Tens of GB of disk for model weights
- Tolerance for dependency hell in the 3D generation ecosystem
- Optional: an image model or API key if you want text-to-image as the first stage
## 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 Meshy.
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 =====
# Meshy product brief
## Problem
The UI here is a thin shell over the actual product: generative 3D models trained on large mesh corpora, plus the GPU fleet that runs them in under a minute. You can absolutely wire up a local pipeline with open weights like Hunyuan3D or TRELLIS, and for hobby props it will get you surprisingly far. What you will not reproduce in a session is the topology cleanliness, the PBR texture quality, the remesh/retopology passes, or the auto-rigging that makes output usable without a cleanup artist. Also, one decent generation on your own hardware needs 16 to 24GB of VRAM and patience, which is exactly the cost the subscription is hiding from you. Build the local version if you enjoy the process, not because it is cheaper.
## Product outcome
A local web app that takes a prompt or a reference image, runs an open-weights image-to-3D model on your own GPU, and hands back a viewable, downloadable GLB.
## Target user
A serious builder who needs a maintainable product foundation rather than a one-off demo.
## Required capabilities
- An NVIDIA GPU with 16GB+ VRAM, ideally 24GB
- CUDA toolkit and a working PyTorch install
- Tens of GB of disk for model weights
- Tolerance for dependency hell in the 3D generation ecosystem
- Optional: an image model or API key if you want text-to-image as the first stage
## Explicit non-goals for v1
- Clean quad topology and low-poly remeshing; open pipelines give you dense, messy triangles
- PBR material maps and texture upscaling that hold up under a real light rig
- Auto-rigging and animation of humanoid output
- Sub-minute generation, plus the ability to fire off ten variations without your machine seizing up
- Any commercial-use clarity around the model weights you are running
## 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 local single-user web app called "meshlab" that generates 3D meshes from a prompt or an image on my own GPU.
Stack, no substitutions:
- Python 3.11, FastAPI backend, plain HTML + vanilla JS frontend served by FastAPI
- SQLite via sqlite3 for the job table, no ORM
- three.js loaded from a CDN for the model viewer
- A single background worker thread with an in-process job queue. No Celery, no Redis.
Generation pipeline:
- Image to 3D is the primary path. Use an open-weights image-to-3D model from Hugging Face (Hunyuan3D-2 or TRELLIS, pick one and commit to it in the code and README). Load it lazily on first job and keep it resident.
- Text to 3D is a two stage path: call a local Stable Diffusion checkpoint via diffusers to make a single reference image, then feed that image into the same 3D pipeline. If no SD checkpoint is present, disable the text path in the UI with a clear message instead of failing.
- Export GLB. Also write the raw OBJ if the pipeline produces one.
UI, one page:
- Left: a text prompt box, an image upload dropzone, a "generate" button, and a seed field.
- Right: a job list with status (queued, running, done, failed), newest first, polling every 2 seconds.
- Clicking a done job loads its GLB into a three.js canvas with orbit controls, a grid floor, and one directional plus one ambient light. Add a wireframe toggle and a "download GLB" link.
- Show generation time and peak VRAM per job.
In scope: local file storage under ./outputs/{job_id}/, a jobs table, graceful failure with the traceback saved to the job row, a .env for MODEL_ID and SD_MODEL_PATH, a README that states the VRAM requirement bluntly.
Out of scope, do not build: user accounts, cloud storage, telemetry, retopology, UV unwrapping, PBR texture baking, rigging, animation, multi-GPU, payment, sharing links.
The README must include a short "limitations" section stating that output is dense triangle soup with baked vertex colors or a single texture, that it needs cleanup in Blender before engine use, and that model weight licenses must be checked before any commercial use.
## 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 Meshy capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.README.md
# Meshy indie build ## Goal Build the smallest trustworthy replacement for the core Meshy workflow for one developer or a tiny team. ## Scope A local web app that takes a prompt or a reference image, runs an open-weights image-to-3D model on your own GPU, and hands back a viewable, downloadable GLB. ## 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: - Clean quad topology and low-poly remeshing; open pipelines give you dense, messy triangles - PBR material maps and texture upscaling that hold up under a real light rig - Auto-rigging and animation of humanoid output - Sub-minute generation, plus the ability to fire off ten variations without your machine seizing up - Any commercial-use clarity around the model weights you are running If those capabilities are essential, use Meshy 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 local single-user web app called "meshlab" that generates 3D meshes from a prompt or an image on my own GPU.
Stack, no substitutions:
- Python 3.11, FastAPI backend, plain HTML + vanilla JS frontend served by FastAPI
- SQLite via sqlite3 for the job table, no ORM
- three.js loaded from a CDN for the model viewer
- A single background worker thread with an in-process job queue. No Celery, no Redis.
Generation pipeline:
- Image to 3D is the primary path. Use an open-weights image-to-3D model from Hugging Face (Hunyuan3D-2 or TRELLIS, pick one and commit to it in the code and README). Load it lazily on first job and keep it resident.
- Text to 3D is a two stage path: call a local Stable Diffusion checkpoint via diffusers to make a single reference image, then feed that image into the same 3D pipeline. If no SD checkpoint is present, disable the text path in the UI with a clear message instead of failing.
- Export GLB. Also write the raw OBJ if the pipeline produces one.
UI, one page:
- Left: a text prompt box, an image upload dropzone, a "generate" button, and a seed field.
- Right: a job list with status (queued, running, done, failed), newest first, polling every 2 seconds.
- Clicking a done job loads its GLB into a three.js canvas with orbit controls, a grid floor, and one directional plus one ambient light. Add a wireframe toggle and a "download GLB" link.
- Show generation time and peak VRAM per job.
In scope: local file storage under ./outputs/{job_id}/, a jobs table, graceful failure with the traceback saved to the job row, a .env for MODEL_ID and SD_MODEL_PATH, a README that states the VRAM requirement bluntly.
Out of scope, do not build: user accounts, cloud storage, telemetry, retopology, UV unwrapping, PBR texture baking, rigging, animation, multi-GPU, payment, sharing links.
The README must include a short "limitations" section stating that output is dense triangle soup with baked vertex colors or a single texture, that it needs cleanup in Blender before engine use, and that model weight licenses must be checked before any commercial use.
## Required capabilities
- An NVIDIA GPU with 16GB+ VRAM, ideally 24GB
- CUDA toolkit and a working PyTorch install
- Tens of GB of disk for model weights
- Tolerance for dependency hell in the 3D generation ecosystem
- Optional: an image model or API key if you want text-to-image as the first stage
## 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
# Meshy product brief ## Problem The UI here is a thin shell over the actual product: generative 3D models trained on large mesh corpora, plus the GPU fleet that runs them in under a minute. You can absolutely wire up a local pipeline with open weights like Hunyuan3D or TRELLIS, and for hobby props it will get you surprisingly far. What you will not reproduce in a session is the topology cleanliness, the PBR texture quality, the remesh/retopology passes, or the auto-rigging that makes output usable without a cleanup artist. Also, one decent generation on your own hardware needs 16 to 24GB of VRAM and patience, which is exactly the cost the subscription is hiding from you. Build the local version if you enjoy the process, not because it is cheaper. ## Product outcome A local web app that takes a prompt or a reference image, runs an open-weights image-to-3D model on your own GPU, and hands back a viewable, downloadable GLB. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - An NVIDIA GPU with 16GB+ VRAM, ideally 24GB - CUDA toolkit and a working PyTorch install - Tens of GB of disk for model weights - Tolerance for dependency hell in the 3D generation ecosystem - Optional: an image model or API key if you want text-to-image as the first stage ## Explicit non-goals for v1 - Clean quad topology and low-poly remeshing; open pipelines give you dense, messy triangles - PBR material maps and texture upscaling that hold up under a real light rig - Auto-rigging and animation of humanoid output - Sub-minute generation, plus the ability to fire off ten variations without your machine seizing up - Any commercial-use clarity around the model weights you are running ## 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 local single-user web app called "meshlab" that generates 3D meshes from a prompt or an image on my own GPU.
Stack, no substitutions:
- Python 3.11, FastAPI backend, plain HTML + vanilla JS frontend served by FastAPI
- SQLite via sqlite3 for the job table, no ORM
- three.js loaded from a CDN for the model viewer
- A single background worker thread with an in-process job queue. No Celery, no Redis.
Generation pipeline:
- Image to 3D is the primary path. Use an open-weights image-to-3D model from Hugging Face (Hunyuan3D-2 or TRELLIS, pick one and commit to it in the code and README). Load it lazily on first job and keep it resident.
- Text to 3D is a two stage path: call a local Stable Diffusion checkpoint via diffusers to make a single reference image, then feed that image into the same 3D pipeline. If no SD checkpoint is present, disable the text path in the UI with a clear message instead of failing.
- Export GLB. Also write the raw OBJ if the pipeline produces one.
UI, one page:
- Left: a text prompt box, an image upload dropzone, a "generate" button, and a seed field.
- Right: a job list with status (queued, running, done, failed), newest first, polling every 2 seconds.
- Clicking a done job loads its GLB into a three.js canvas with orbit controls, a grid floor, and one directional plus one ambient light. Add a wireframe toggle and a "download GLB" link.
- Show generation time and peak VRAM per job.
In scope: local file storage under ./outputs/{job_id}/, a jobs table, graceful failure with the traceback saved to the job row, a .env for MODEL_ID and SD_MODEL_PATH, a README that states the VRAM requirement bluntly.
Out of scope, do not build: user accounts, cloud storage, telemetry, retopology, UV unwrapping, PBR texture baking, rigging, animation, multi-GPU, payment, sharing links.
The README must include a short "limitations" section stating that output is dense triangle soup with baked vertex colors or a single texture, that it needs cleanup in Blender before engine use, and that model weight licenses must be checked before any commercial use.
## 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 Meshy 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
Because the alternative is either a 3D artist or a week of your own time per asset, and neither is cheap. People paying for this are usually indie game devs or product folks who need a prop, a placeholder, or a printable model right now, and the credit cost is trivially less than the labor it replaces. The self-hosted route is real but it is a hobby, not a substitution: you inherit the CUDA errors, the VRAM ceiling, and the cleanup work the paid pipeline quietly absorbs.
xClean quad topology and low-poly remeshing; open pipelines give you dense, messy triangles
xPBR material maps and texture upscaling that hold up under a real light rig
xAuto-rigging and animation of humanoid output
xSub-minute generation, plus the ability to fire off ten variations without your machine seizing up
xAny commercial-use clarity around the model weights you are running
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Meshy ($20/mo) with one prompt"
Vibecode Meshy
Not really. Meshy's value is not the code: The moat is not the shape, it is the retopology, texture baking, and rigging that make the shape usable, plus a GPU fleet that does it in forty seconds. See the honest breakdown above.
How much does Meshy cost?
Meshy costs about $20/month (Pro, checked 2026-08-18), which is $240 per year.
What do I lose by replacing Meshy?
Honestly: Clean quad topology and low-poly remeshing; open pipelines give you dense, messy triangles; PBR material maps and texture upscaling that hold up under a real light rig; Auto-rigging and animation of humanoid output; Sub-minute generation, plus the ability to fire off ten variations without your machine seizing up; Any commercial-use clarity around the model weights you are running. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Meshy?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.