Translucent AI, presented by Yashas Gunderia (Melete Maker), is a system that proposes which physical test should run next in a hardware development workflow and records the decision trail for later review. The project aims to reduce time spent on bench testing by letting agents handle pre-qualified solver runs while sending any bench procedures and final measurements to human engineers.
How the system works
According to Yashas Gunderia, the agent selects a small set of tests intended to settle a design question and publishes a forecast for each test before results arrive. Agents can run solver tests autonomously within cost, time and run-count limits defined by the hardware team. Bench tests, including a procedure, are routed to a qualified engineer for execution and data entry. When data are insufficient to support a decision, the agent requests a measurement rather than guessing. Every step is recorded so records can be re-checked offline.
Battery simulation and scope
The first system targeted at batteries was evaluated in simulation. In a simulated fast-charge experiment with eight trials, Translucent reported finding a 33.8-minute charge time from 10% to 80%, compared with 42.6 minutes for Bayesian optimisation and 60 minutes for the cell’s standard charge. The agent’s solutions stayed within stated anode-plating and 45 °C temperature limits. Yashas Gunderia noted these results are simulated and that no physical cell has been tested yet.
The team plans to extend the approach to data-center hardware, robotics and power electronics. Translucent AI is described as compatible with any agent and is offered with public code and documentation. Research materials and evaluation reports are available publicly, and a live demo has been posted by the developers for testing and feedback.
Original source: Product Hunt