# Build brief — a focused alternative to Meshy > **Verdict:** Not faithfully · **Buildability:** 17/100 · **Category:** Generative Media > **Source:** https://www.canitbevibecoded.com/meshy > Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from Meshy. Verify current pricing and capabilities before acting. ## Context **Meshy** — Turns text prompts or reference images into textured 3D meshes you can drop into a game engine. It currently costs $20/mo. 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. This brief describes a focused, single-operator replacement for the part of Meshy 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 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. - Chain media models behind fixed prompts, presets, and review steps. - A responsive interface with real empty, loading, success, and error states. ## Requirements ### Functional - 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. ### Data and integrations - Optional: an image model or API key if you want text-to-image as the first stage. 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 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. ## 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: - 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. ## 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 **Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost. Editorial confidence in this assessment: medium. No reviewed project implementation is linked yet. --- Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/meshy