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...

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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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Following up to yesterday’s post about randomness, Excel provides a very easy tool for modeling probability. Just insert =RAND() into a cell. This will generate a new (pseudo) random number between 0 and 1 each time the spreadsheet is recalculated.
For most business modeling one wants to have a discrete distribution of values with a subjective probability distribution. For instance, in projecting out the cap table of a company one might have three possible valuations for the next round – low, expected and high with probabilities of 30%, 60%, 10%. To model this, one needs to form the cumulative distribution, which is 30%, 90%, 100%, and then use vlookup (or hlookup) with the random number generate by RAND() as the lookup value to find the outcome.
A good model will have a fair number of instances of RAND(). If the model is large it’s usually a good idea to set Calculation to “Manual” in the options while working on the model (just hit F9 to get a new set of random numbers). Finally, to generate an actual distribution of outputs of the model one wants to run the model hundreds or even thousands of times. This can easily be done with a VBA macro that uses CALCULATE to get a new set of random numbers and then copies the outputs to a results table (in fact for speed it’s best to gather the results in an array and then dump the array into an output spreadsheet in one go).
Following up to yesterday’s post about randomness, Excel provides a very easy tool for modeling probability. Just insert =RAND() into a cell. This will generate a new (pseudo) random number between 0 and 1 each time the spreadsheet is recalculated.
For most business modeling one wants to have a discrete distribution of values with a subjective probability distribution. For instance, in projecting out the cap table of a company one might have three possible valuations for the next round – low, expected and high with probabilities of 30%, 60%, 10%. To model this, one needs to form the cumulative distribution, which is 30%, 90%, 100%, and then use vlookup (or hlookup) with the random number generate by RAND() as the lookup value to find the outcome.
A good model will have a fair number of instances of RAND(). If the model is large it’s usually a good idea to set Calculation to “Manual” in the options while working on the model (just hit F9 to get a new set of random numbers). Finally, to generate an actual distribution of outputs of the model one wants to run the model hundreds or even thousands of times. This can easily be done with a VBA macro that uses CALCULATE to get a new set of random numbers and then copies the outputs to a results table (in fact for speed it’s best to gather the results in an array and then dump the array into an output spreadsheet in one go).
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