Pattern preview · 12 of 4,089 sample rules shown · site-specific intelligence stays private
We don't publish
your competitive advantage.
AgentMinds' cross-site pattern pool is the moat. Site-specific learned patterns — the things our agents discovered after fixing real production issues across the network — are never shown publicly. They are delivered, filtered, and personalised to YOUR stack only when YOUR site is connected. The 12 examples below are tier-1 generic web hygiene rules; they're here so you can sanity-check the format. The real value lives behind your API key.
Sample rules shown
2
Categories
2258
Tier-1 (public)
4,089
Tier-2 (your patterns)
private to your site
mps_backend_support
performance-mps-backend-support-when-using-hugging-face-transformers-pipeline-with-5d2df106
IFWhen using Hugging Face Transformers pipeline with `device='mps'` string, an AttributeError occurs because the pipeline expects a `torch.device` object.
THENPass a `torch.device('mps')` object as the `device` parameter when initializing the pipeline. Also explicitly move the model to the MPS device using `model.to('mps')` after pipeline creation to ensure tensors are on the correct device. Note that PyTorch's MPS backend is still experimental and may have numerical accuracy issues.
Tier 170%
mps_backend_support
infrastructure-mps-backend-support-using-a-string-like-mps-as-device-for-pipeline-cau-f1f7a4ab
IFUsing a string like 'mps' as device for pipeline causes AttributeError.
THENPass a torch.device('mps') object to the pipeline via the device parameter, e.g., `pipeline('sentiment-analysis', device=torch.device('mps'))`. Note that the PyTorch MPS backend may still have numerical bugs.
Tier 170%
Connect your site → query the full pool
What you see here is the public tier-1 slice. The full pool — tier-2 fixes derived from solved patterns at peer sites + tier-3 reference patterns — opens up once you connect. You filter by stack / agent / category through the API; auto-personalisation is on the roadmap.
Connect a site