PCB CAD, Rebuilt for Touch

Professional PCB design is one of the last serious engineering disciplines with no credible touch-native tool. The iPad is a precise, pressure-sensitive, direct-manipulation instrument that professionals happily use for illustration, 3D sculpting, and music production — but for board layout, the assumption has always been: real CAD means a desk, a mouse, and thirty years of accumulated desktop interface.

We wanted to know whether that assumption is a law of physics or just a habit.

Why isn’t this already easy? Because PCB CAD is deceptively deep. Under the drawing surface sits a stack of hard problems: exact geometry with manufacturing tolerances, design-rule checking, footprint and symbol libraries covering hundreds of thousands of real parts, netlist semantics, multi-layer routing, and export formats fabs will actually accept.

Incumbent tools carry decades of that accumulated knowledge. A touch-first challenger doesn’t get to skip any of it — a beautiful sketching app that can’t produce fabricatable Gerbers is a toy. Historically, rebuilding that stack was a multi-year job for a funded team, which is exactly why nobody did it for a tablet.

Start from interaction, not from porting. Touch and Pencil are treated as the primary instruments — components are placed and nudged directly, traces are drawn as strokes that snap to deterministic, rule-clean geometry, and the interface is built around what fingers and a pencil are actually good at rather than around menu trees.

Everything under the surface stays rigorously boring: exact-arithmetic geometry, a real rule engine, real netlists. The novelty budget is spent entirely on the interaction layer.

The equation changed on three fronts at once:

• Open EDA data. Open-source footprint and symbol libraries, vendor CAD models, and openly documented formats mean the parts-library problem — once a moat measured in staff-years — can be mined, converted, validated, and continuously ingested by automated pipelines instead of hand-built.
• AI-assisted engineering. Models read datasheets, extract pin tables and land patterns, propose placements, and assist routing — work that used to be pure manual labor at library scale.
• Modern client hardware. Current tablets have desktop-class silicon. The compute for real-time rule checking and interactive routing is already in the user’s hands.

None of these alone makes touch-native CAD viable. Together they collapse the team size the problem used to require.

A working touch-native layout environment, backed by an automated ingestion system that continuously harvests, converts, and quality-checks component libraries from open sources — footprints, symbols, land patterns, datasheet-derived pin data — plus a placement and routing engine developed against a growing corpus of real, manufactured board designs rather than synthetic examples.

The library pipeline runs as its own small fleet of machines, unattended, feeding the corpus around the clock. That pipeline is itself a demonstration of the thesis: it does work that used to be a librarian department.

Plenty, and they were informative:

• Naive touch translation fails immediately. A finger is roughly forty pixels wide; a trace is three. Every interaction had to be redesigned around snapping, gestural intent, and magnification rather than pointer precision.
• Automated library conversion is a swamp of edge cases. Real-world source libraries are inconsistent, subtly wrong, and occasionally lie about their own units. A large fraction of the pipeline is validation and quarantine, not conversion.
• AI-suggested routing without a deterministic geometry layer underneath produces plausible-looking boards that violate manufacturing rules. The lesson stuck: AI proposes, exact geometry disposes.

Active and unfinished, but past the existence-proof stage: boards can be laid out with a pencil, against real parts, with real rules, on a device that fits in a bag. The ingestion fleet keeps growing the component corpus without human attention. The remaining work is depth — routing quality, rule coverage, fab-export breadth — not feasibility.

If one small lab can stand up a credible attack on professional EDA — a category owned by companies with thousand-person engineering organizations — the moat around every legacy professional tool category is thinner than its owners believe. The moats were mostly accumulated manual labor: libraries, format handling, edge cases. That labor is exactly what AI and automation now compress.

NotebookThe interesting boundary isn’t “can a tablet run CAD.” It’s “how much of a legacy software empire was actually just headcount.”