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1070 points dondraper36 | 2 comments | | HN request time: 0.001s | source
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codingwagie ◴[] No.45069135[source]
I think this works in simple domains. After working in big tech for a while, I am still shocked by the required complexity. Even the simplest business problem may take a year to solve, and constantly break due to the astounding number of edge cases and scale.

Anyone proclaiming simplicity just hasnt worked at scale. Even rewrites that have a decade old code base to be inspired from, often fail due to the sheer amount of things to consider.

A classic, Chesterton's Fence:

"There exists in such a case a certain institution or law; let us say, for the sake of simplicity, a fence or gate erected across a road. The more modern type of reformer goes gaily up to it and says, “I don’t see the use of this; let us clear it away.” To which the more intelligent type of reformer will do well to answer: “If you don’t see the use of it, I certainly won’t let you clear it away. Go away and think. Then, when you can come back and tell me that you do see the use of it, I may allow you to destroy it.”"

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1. analog31 ◴[] No.45070480[source]
If it's a legacy system, then it lives at the edges. The edges are everything.

I wish I could remember or find the proof, but in a multi-dimensional space, as the number of dimensions rise, the highest probability is for points to be located near the edges of the system -- with the limit being that they can be treated as if they all live at the edges. This is true for real systems too -- the users have found all of the limits but avoid working past them.

The system that optimally accommodates all of the edges at once is the old system.

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2. CuriouslyC ◴[] No.45070523[source]
You don't need a complicated proof, just assume a distribution in some very high number of dimensions, with samples from that distribution having randomly generated values from the distribution for each dimension. If you have if you have ~300 dimensions then statistically at least one dimension will be ~3SD from the mean, i.e. "on the edge," and as long as any one dimension is close to an edge, we define a point as being "near the edge."

It's not really meaningful though, at high dimensions you want to consider centrality metrics.