Hardware Deserves Better Tools

Software engineering was transformed by intelligent tooling over the past decade. From autocomplete to AI pair programming, developers now have powerful assistants that handle boilerplate, catch bugs, and accelerate iteration. Hardware design is still stuck in the past.

The Current State of Hardware Design

Today's hardware engineers spend weeks doing work that should take hours. They context-switch between dozens of tools, manually search through datasheets, and perform tedious layout work that follows clear patterns. Meanwhile, their software counterparts ship features in days with AI-powered tools that handle the grunt work.

The gap is widening. Software teams iterate in hours. Hardware teams iterate in weeks. This isn't because hardware is inherently harder — it's because the tooling hasn't kept up.

Why Now?

PCB design is fundamentally a combinatorial optimization problem — exactly the kind of problem AI excels at. Recent advances in reasoning models, combined with domain-specific training on thousands of real schematics, make true automation possible for the first time.

We've reached an inflection point where AI can understand design intent, navigate complex constraint spaces, and produce manufacturable results. The same revolution that gave software developers GitHub Copilot is coming to hardware.

Our Vision

At WishEDA, we're building the future of hardware design. Not AI bolted onto legacy tools, but AI-native from the ground up. We gave AI a native format — text-based for reasoning, visual for humans — so it can optimize across your entire project.

Trace, our first product, handles the entire PCB workflow from idea to manufactured board. Describe what you want to build, and AI agents handle schematic capture, component selection, layout, routing, and export. For experienced engineers, we accelerate. For newcomers, we make it possible to get started at all.

The Road Ahead

PCBs are just the beginning. The same principles that make AI-native PCB design possible apply to FPGA prototyping and ASIC workflows. We're building the platform layer for hardware's AI transformation.

Hardware teams deserve better tools. We're building them.