Agents choose the next physical test and run solver tests within rules a hardware team sets once. Bench tests go to a qualified person. Works with any agent. Physical tests come with a procedure and a forecast. An engineer decides whether to spend the bench time, and an operator enters the result.
I built Translucent because AI agents can now propose hardware designs faster than any lab can test them, and right now the bottleneck is the test bench. Hardware teams want agents in their workflow, but they can't hand over the bench to something whose decisions they can't trace.
So Translucent does one job: it chooses the next physical test. The agent picks the few tests that settle a design and writes down a forecast for each before the result comes in. It runs solver tests on its own inside cost, time and run-count limits your team sets once. Bench tests always go to your engineer with a procedure attached. When the data can't support a design, the agent asks for the measurement instead of guessing. Every step goes into a record anyone can re-check offline.
Batteries are the first system. In a simulated fast-charge race with 8 experiments, Translucent found a 33.8-minute charge (10 to 80%), compared with 42.6 for Bayesian optimisation and 60 for the cell's standard charge. It stayed inside the anode-plating and 45 °C temperature limits. These results are all simulated; no physical cell has been tested yet. Next we're working on data-center hardware, robotics and power electronics.
Feedback that would help most:
1. If you run hardware tests: which tests take up the most bench time without settling anything?
2. Would your quality team accept solver runs approved by standing rules, or does every run need a sign-off?
3. What instruments and file formats should we support next?
Translucent AI launched on Product Hunt on November 6th, 2026 and earned 56 upvotes and 2 comments, placing #54 on the daily leaderboard. Agents choose the next physical test and run solver tests within rules a hardware team sets once. Bench tests go to a qualified person. Works with any agent. Physical tests come with a procedure and a forecast. An engineer decides whether to spend the bench time, and an operator enters the result.
On the analytics side, Translucent AI competes within Developer Tools, Artificial Intelligence, GitHub and Tech — topics that collectively have 1.7M followers on Product Hunt. The dashboard above tracks how Translucent AI performed against the three products that launched closest to it on the same day.
Who hunted Translucent AI?
Translucent AI was hunted by Yashas Gunderia. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Translucent AI including community comment highlights and product details, visit the product overview.
Yashas Gunderia
Hey Product Hunt!
I built Translucent because AI agents can now propose hardware designs faster than any lab can test them, and right now the bottleneck is the test bench. Hardware teams want agents in their workflow, but they can't hand over the bench to something whose decisions they can't trace.
So Translucent does one job: it chooses the next physical test. The agent picks the few tests that settle a design and writes down a forecast for each before the result comes in. It runs solver tests on its own inside cost, time and run-count limits your team sets once. Bench tests always go to your engineer with a procedure attached. When the data can't support a design, the agent asks for the measurement instead of guessing. Every step goes into a record anyone can re-check offline.
Batteries are the first system. In a simulated fast-charge race with 8 experiments, Translucent found a 33.8-minute charge (10 to 80%), compared with 42.6 for Bayesian optimisation and 60 for the cell's standard charge. It stayed inside the anode-plating and 45 °C temperature limits. These results are all simulated; no physical cell has been tested yet. Next we're working on data-center hardware, robotics and power electronics.
Feedback that would help most:
1. If you run hardware tests: which tests take up the most bench time without settling anything?
2. Would your quality team accept solver runs approved by standing rules, or does every run need a sign-off?
3. What instruments and file formats should we support next?
Try the live demo: https://translucent.axcelner.com/demo/
Code and self-hosting: https://github.com/ychampion/translucent
I'll be here all day answering questions.