Describe your ML problem in plain English. Neurarch designs the architecture, catches issues before training, and exports production-ready code — in under 3 minutes.
Your sketch has no tensor shapes, no validation, no link to running code. By Monday you can't read it either.
print(model) isn't an architectureA 200-line tree dump won't show data flow, won't catch the wiring bug, won't fit in a Slack message.
Figure 2 is pretty. The actual layer order, init, and shape contracts are buried in 40 pages of methods.
+RandAugment. Generated vit_cifar.py + training script.
Every step above is a real action in the app — not a script. Try it yourself →
Vision model for catching manufacturing defects from camera feeds. Lightweight for edge inference.
Classify and route customer emails by urgency and department. Transformer-based, exports fine-tuning code.
Detect equipment failures early from sensor readings. LSTM/GRU tuned for time-series patterns.
Paste an arXiv URL and get the architecture on the canvas. Diff it against your baseline. Export and train.
Two-tower neural collaborative filtering. User and item embedding model for product recommendations.
Lightweight architecture for mobile inference. Quantization preview, ONNX export, hardware fit analysis built in.
Tell the agent what you need in plain English. It asks follow-up questions if needed. No ML jargon required.
Claude picks the right layers, wires connections, propagates tensor shapes. 14 lint rules run automatically.
Run training. AI reads the loss curves, diagnoses overfitting or underfitting, and applies targeted fixes.
PyTorch, Keras, ONNX, Jupyter notebooks. Clean, readable code you own — no vendor lock-in.
142 layer types + 70 macro blocks. Tensor shapes propagate automatically. Dimension mismatches caught before you train.
Claude understands your selected layers. Edits, explains, compares alternatives — in your architecture's context.
Catches vanishing gradients, missing normalization, overfitting risk, ordering errors before you waste compute.
Paste a paper URL. The agent parses the architecture and builds it on the canvas. Diff against your version.
Any HF model ID → visual architecture instantly. Understand ResNet, BERT, GPT-2, Whisper, LLaMA in minutes.
PyTorch, Keras, ONNX, Jupyter notebooks, PDF reports. Full training script with optimizer and scheduler.
Real-time loss/accuracy curves. AI reads the results and applies targeted architecture fixes — not generic advice.
Save architecture checkpoints. Diff any two versions visually. Roll back in one click.
Live cursors, shared canvas, team model library. Built-in WebSocket collab — no third-party service needed.
| Capability | Neurarch | HF AutoTrain | Google AutoML | Write code |
|---|---|---|---|---|
| Natural language → architecture | ✓ | — | — | — |
| Visual canvas + tensor shape trace | ✓ | — | — | — |
| Architecture lint (gradient / overfit / order) | ✓ | — | — | — |
| Import arXiv paper → canvas | ✓ | — | — | — |
| Import any HuggingFace model | ✓ | ✓ | — | manual |
| Import .onnx / .safetensors → editable canvas | ✓ | — | — | — |
| PyTorch + ONNX code export (you own it) | ✓ | — | — | ✓ |
| AI reads training curves + targeted fixes | ✓ | — | limited | — |
| Real-time team collaboration | Pro+ | — | — | — |
| Free to start | ✓ | ✓ | trial | ✓ |
AutoML tools train a model for you — but you can't see inside it, can't customize it, and can't learn from it. When it fails, you're stuck. Neurarch keeps you in control: visual, explainable, and the code is yours.
print(model)
| Capability | Whiteboard / Excalidraw | print(model) |
Netron | Neurarch |
|---|---|---|---|---|
| See full layer graph | manual | tree only | ✓ | ✓ |
| Tensor shapes verified end-to-end | — | if forward runs | ✓ | ✓ |
| Edit graph + re-export code | — | — | read-only | ✓ |
| Architecture lint (gradient/overfit/order) | — | — | — | ✓ |
| Build from prompt or arXiv paper | — | — | — | ✓ |
| AI reads training curves + applies fixes | — | — | — | ✓ |
| Shareable URL of your architecture | screenshot | — | file only | ✓ |
.onnx or .safetensors file onto Neurarch — we reconstruct the full graph as editable canvas nodes.
Modify layers, fix architecture issues, run training, and export clean PyTorch code.
Netron shows you what a model is. Neurarch lets you change it.
Beta pricing — rates lock in for life when you subscribe during early access.
Team plan ($49/user/mo) with SSO and audit log — contact us →
No accounts required for the canvas, agent, or export. When you BYOK, the request goes browser → provider — we're not in the path.
Architecture, prompts, and pasted code live in browser memory only. Refresh the tab to wipe them.
sessionStorageYour Anthropic / Gemini key never touches our servers and is cleared automatically when the tab closes.
Browser → Anthropic / Google. We're not a man-in-the-middle. Open the network tab and verify it yourself.
Snapshots, team workspaces, and cloud saves are explicit Pro+ actions. One-click delete from the dashboard at any time.
Drag-and-drop architecture builder with automatic tensor shape propagation and 14-rule lint checker.
Describe your problem in plain English. Claude designs the architecture, catches errors, and applies fixes.
Paste any arXiv URL or HF model ID and the architecture appears on your canvas instantly.
Production-ready code, full training scripts, Jupyter notebooks — you own the output.
One-click training on A10G GPUs with real loss curves, early stopping, and AI diagnosis of results.
Live cursors, shared canvas, team model library. Pro Plus feature launching with Stripe billing.
Upload a CSV or HuggingFace dataset URL. Neurarch handles tokenization, training loop, evaluation.
Export your trained model to HuggingFace Hub or generate a production-ready Docker container.
Full canvas + AI agent + code export on the free plan.
Drop your email to get Pro updates and early access pricing.
No spam. Or just open the app now →
Add this badge to your README, notebook, or paper to show the architecture was built with Neurarch.
Click to copy Markdown
Bug reports, feature requests, partnerships, design partners, investor intros — fastest way to reach us is email or GitHub.