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> often needs to be optimized for scale or refactored for reusability It's so easy to make subtle assumptions when you redo their code that completely invalida
by jvans 4y ago
> often needs to be optimized for scale or refactored for reusability
It's so easy to make subtle assumptions when you redo their code that completely invalidates the work they've done. ML completely collapses on extremely small errors. Handing something off to someone else to refactor is a dangerous step in the process that risks everyone wasting their time
- Tade0 4y ago> It's so easy to make subtle assumptions when you redo their code that completely invalidates the work they've done. It's equally easy to test whether those assumptions actually break anything. Especially when minor errors can be catastrophic. I'm part of a team that was tasked with producing a web app from something that was originally a piece of Matlab code. We considered just running the Matlab code in a container but ultimately IEEE 754 is IEEE 754 regardless of platform/language, so creating a 1:1 implementation in C++ proved possible.
- jvans 4y agoThe catastrophic errors aren't the problem, the problem is when things are degraded by 20% which results in a small loss when it could have been a big win. You just assume it didn't work when in reality it wasn't implemented correctly