Chiplab

Enables AI agents to directly inspect and debug embedded hardware: reading/writing serial ports and bus devices, auto-detecting baud rates, parsing Modbus/NMEA/custom binary frames, plus diagnosing failures through a 45-rule fault-pattern library, error-code tables, and compile/flash error translation. Also provides pin-conflict checking, register and Cortex-M fault decoding, pre-validated driver and component selection libraries, board knowledge, and guarded flashing with post-flash byte-level verification.

Provides AI assistants with a complete FPGA toolchain for HDL linting, simulation, synthesis, and place-and-route across various hardware targets. It features a GitHub-backed IP core registry that enables users to search for and import MIT-licensed cores directly through their chat interface.

Enables AI agents to build, flash, and monitor ESP-IDF projects on real hardware, including serial sessions and pytest-embedded hardware tests.

An AI engineering team for embedded firmware, lab automation, and hardware drivers. It uses 53 specialized experts and a datasheet-to-release pipeline to produce auditable, hardware-aware code.