Category archive
Images, audio and a device budget change the whole pipeline.
Text pipelines assume input is cheap to read and cheap to store. Documents, audio and images break both assumptions, and a phone breaks them again with a memory ceiling that cannot be scaled out of. These articles work through what survives those constraints.
Articles in Multimodal and Edge
Multimodal AI Pipelines for Images, Audio, and Documents
Engineering preprocessing, temporal and spatial grounding, context budgets, validation, and storage around multimodal models.
Edge LLM Inference: Designing for the Device
Quantization, memory, thermal budgets, runtimes, privacy, and hybrid execution for useful models on constrained hardware.
Apply the category
Put this against a real system.
If a decision in Multimodal and Edge is in front of you right now, the fastest version of this is a call: bring the architecture, the failure you are seeing, and the constraint you cannot move.
Discuss the systemIn the age of AI
The advantage was never the model. It's knowing what to build with it — and having a team that can actually ship it.
That's the part I help with: finding where AI genuinely makes your business faster, deciding what's worth building, and standing behind it once it's live.
Four offices, one very full passport
Every dot on this map is a conversation I still remember.
- Where I've spoken
- Office
