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In their spare time, with what energy remains after putting most of their energy into work and other obligations.

Of course it is unfortunate to lose the ability to get paid for something they enjoyed.

Thank you for these references, I really appreciate it.

Agreed. They can probably afford to invest time into delighting those early customers i.e. doing things that don’t scale.

I'm nearly always building early stage companies, where even the dev and operational overhead of charging for some added extra is not guaranteed to be paid for by the money collected.

> Being generous makes you more powerful. As Tim O'Reilly said, you should create more value than you capture. Many hard-headed business types would write this off as idealistic hippy stuff, but in fact this is the route to becoming really rich.

> 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?

But they are less violent.

Perfectly for me I guess I should have said. Nonetheless, this md file packaging mistake suggests that at least once they’ve fallen into not reviewing changes properly, and I wonder if that lack of discipline is more widespread. I hope not, otherwise their ability to deliver on their privacy promises will be compromised.

It worked perfectly for a long before Claude and friends existed, so I don't think it follows that not using these tools has led to the problem now.

I’ve been wondering if vibe coding was responsible for the recent introduction of acoustic echo cancellation (AEC) bugs in FaceTime (muting and unmuting the microphone appears to temporarily fix it). Apple has always had excellent AEC in my experience, it’s sad to see them breaking a fundamental phone function.

This particular revolver is an opt-in service for users that want Cloudflare to block anything that Cloudflare designates as malware.

We'll likely leave the answering of your equivalence question to our users. While we do plenty of internal research, the primary goal of our cloud platform is to dramatically expand access to this field. There are no doubt many low-hanging fruits to be discovered.

It's not the case at all - years ago we tested Pong with human neurons vs mouse neurons, and human neurons performed better. More importantly, they allow the study of the effect of external interventions (such as trial medicines) on the learned function of human neurons. So we spent years building a machine and software platform to facilitate this sort of research.

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.

Thanks!

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)

Cortical Labs CTO here. My focus is on the system itself rather than applications, but for what it's worth ..

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.

The neurons live on an electrode array kept inside a machine which keeps them alive (body temperature, sugar, gas, etc). The embedded system continually measures the voltage at each electrode, and can electrically stimulate the neurons via the same.

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.