> Hired CEOS take the power of the companies they run for granted, whereas founders remember the days when the company was so weak that it had to delight users to survive.

I've always instinctively followed the generosity path, and have experienced that when customers are delighted and that when frictions are removed (or not added in the first place), good things will follow.

However, I have not always been able to convince others to trust in and follow this path. Can anyone offer any advice or experiences on talking people out of trying to prematurely squeeze out every last penny?

The Doom project is mostly a bit of fun that demonstrates how quickly someone new to the field can see real results with our platform. Our data scientists believe that biological learning was demonstrated in this case.

We focus on using real neurons, I'm not aware of a software based equivalent. But users can `pip install cl-sdk` to get started with our API. The SDK is still early but supports playing back a recording of real data so applications can be built with a realistic spike frequency. (We'll be releasing a set of recordings for this)

When the neurons didn't get stimulated by the application, performance did not improve. The only explanation our data science people has is that the neurons began to learn and perform the desired (highly abstracted) task of 'playing Doom'. This was not a surprise as we've shown this before with a version of Pong using a different platform. We built the CL1 and the CL API to enable rapid iteration on this sort of work.

One benefit to this is that when you have a measurable learning effect, you can measure this before and after exposure to an experimental drug or other molecule. It becomes possible to test the impact on neuron function, not just survival.

Users are provided with an easy to use Python API that allows realtime observation of the raw signals and of detected action potentials (spikes). They can also execute precisely controlled stimulation sequences, which allows information to be presented to the neurons. Then there are a lot of higher level application APIs to handle things like recording data, real-time visualisation, and experiment config management.

Why: it appears to be possible to teach neurons to perform tasks (we taught some to play pong several years ago). Since then our focus has been on building a system that enables rapid iteration in this sort of research. We're selling the system to organisations that have the appropriate biology skills, and renting access to hosted devices where we provide and take care of the cells.

Coding work spans FPGA (SystemVerilog), Linux kernel C, userspace C, Python, and yes, some web services and Browser JavaScript also. I also work on the network engineering of the cloud service and on the Linux OS image.

Easily the most fun I’ve had as a developer and I’ve worked on lots of different types of commercial software projects before. Not all the world is web apps, embedded work can be very satisfying if you’ve not considered it.