Philosophy Mondays: Human-AI Collaboration
Today's Philosophy Monday is an important interlude. I want to reveal that I have not been writing the posts in this series entirely by myself. Instead I have been working with Claude, not just for the graphic illustrations, but also for the text. My method has been to write a rough draft and then ask Claude for improvement suggestions. I will expand this collaboration to other intelligences going forward, including open source models such as Llama and DeepSeek. I will also explore other moda...

Intent-based Collaboration Environments
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Web3/Crypto: Why Bother?
One thing that keeps surprising me is how quite a few people see absolutely nothing redeeming in web3 (née crypto). Maybe this is their genuine belief. Maybe it is a reaction to the extreme boosterism of some proponents who present web3 as bringing about a libertarian nirvana. From early on I have tried to provide a more rounded perspective, pointing to both the good and the bad that can come from it as in my talks at the Blockstack Summits. Today, however, I want to attempt to provide a coge...
Philosophy Mondays: Human-AI Collaboration
Today's Philosophy Monday is an important interlude. I want to reveal that I have not been writing the posts in this series entirely by myself. Instead I have been working with Claude, not just for the graphic illustrations, but also for the text. My method has been to write a rough draft and then ask Claude for improvement suggestions. I will expand this collaboration to other intelligences going forward, including open source models such as Llama and DeepSeek. I will also explore other moda...

Intent-based Collaboration Environments
AI Native IDEs for Code, Engineering, Science
Web3/Crypto: Why Bother?
One thing that keeps surprising me is how quite a few people see absolutely nothing redeeming in web3 (née crypto). Maybe this is their genuine belief. Maybe it is a reaction to the extreme boosterism of some proponents who present web3 as bringing about a libertarian nirvana. From early on I have tried to provide a more rounded perspective, pointing to both the good and the bad that can come from it as in my talks at the Blockstack Summits. Today, however, I want to attempt to provide a coge...
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In manufacturing, when stuff goes wrong, there tends to be physical evidence, such as a part with holes in the wrong place. It is therefore often easy to find the immediate cause of the problem, which might be to say that the wholes were drilled in the wrong place and go yell at the person who does the drilling. But in Kaizen, the immediate cause of a problem is only the beginning of the analysis, not the end. The team is supposed to start with the problem and ask “Why?” seven times to determine the root cause of the problem. Why was the hole in the wrong place? Because it was drilled in the wrong place. Why was it drilled in the wrong place? Because it was marked in the wrong place. Why was it marked in the wrong place? Because the person marking it measured from the wrong location. Why did they measure from the wrong location? Because the measurement directions were unclear. And so on.
There are several wonderful things about root cause analysis. First, it immediately changes the dynamic from finger pointing to learning. The immediate cause of any error is only the last element in a chain of causes and the goal is to understand that chain. Second, by working towards the root cause a simple problem may be found to have a root cause that is in fact responsible for many other problems. This is why it is important not to ignore little problems, which in turn ties back to the first post in this series. If you set seemingly outlandish quality goals, then every little problem is worthy of analysis. That analysis – if done right – will help uncover root causes that were either already – or would in the future be – responsible for many more problems.
In software development we tend not to have any physical artifacts to go on. If something is not working on a site or service, there are a myriad of possible different causes. So often identifying the immediate cause is already difficult. That results in an added tendency when a bug is finally found to just fix it – often with a hack – and move on (possibly after some yelling at the directly responsible person). In the Kaizen approach that represents a lot of lost opportunity for learning and lasting improvement. Using the “seven whys” takes patience but is great discipline. In fact so much so, that I recommend it not just for manufacturing or software errors, but anything you encounter that’s not going the way it should.
![Reblog this post [with Zemanta]](https://img.paragraph.com/cdn-cgi/image/format=auto,width=3840,quality=85/http://img.zemanta.com/reblog_e.png?x-id=80b40d98-34ee-47fd-8760-bb87048eabac)
In manufacturing, when stuff goes wrong, there tends to be physical evidence, such as a part with holes in the wrong place. It is therefore often easy to find the immediate cause of the problem, which might be to say that the wholes were drilled in the wrong place and go yell at the person who does the drilling. But in Kaizen, the immediate cause of a problem is only the beginning of the analysis, not the end. The team is supposed to start with the problem and ask “Why?” seven times to determine the root cause of the problem. Why was the hole in the wrong place? Because it was drilled in the wrong place. Why was it drilled in the wrong place? Because it was marked in the wrong place. Why was it marked in the wrong place? Because the person marking it measured from the wrong location. Why did they measure from the wrong location? Because the measurement directions were unclear. And so on.
There are several wonderful things about root cause analysis. First, it immediately changes the dynamic from finger pointing to learning. The immediate cause of any error is only the last element in a chain of causes and the goal is to understand that chain. Second, by working towards the root cause a simple problem may be found to have a root cause that is in fact responsible for many other problems. This is why it is important not to ignore little problems, which in turn ties back to the first post in this series. If you set seemingly outlandish quality goals, then every little problem is worthy of analysis. That analysis – if done right – will help uncover root causes that were either already – or would in the future be – responsible for many more problems.
In software development we tend not to have any physical artifacts to go on. If something is not working on a site or service, there are a myriad of possible different causes. So often identifying the immediate cause is already difficult. That results in an added tendency when a bug is finally found to just fix it – often with a hack – and move on (possibly after some yelling at the directly responsible person). In the Kaizen approach that represents a lot of lost opportunity for learning and lasting improvement. Using the “seven whys” takes patience but is great discipline. In fact so much so, that I recommend it not just for manufacturing or software errors, but anything you encounter that’s not going the way it should.
![Reblog this post [with Zemanta]](https://img.paragraph.com/cdn-cgi/image/format=auto,width=3840,quality=85/http://img.zemanta.com/reblog_e.png?x-id=80b40d98-34ee-47fd-8760-bb87048eabac)
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