4 ms·
Is this still relevant?
by thelastidiot 8y ago
Is this still relevant?
- joker3 8y agoIt's more relevant every year.
- glup 8y agonah, thankfully technical debt has been solved.
- setquk 8y agoGnome solved it. Just burn the universe every five years!
- paulie_a 8y agoThat should honestly be a more frequently employed tactic in codebases.
- favorited 8y agoI'm definitely a believer in this. I read Carmack refer to it as "grooming" at one point. Codebases where components and subsystems are periodically rewritten naturally end up being easier to change, which is an undervalued metric.
- setquk 8y agoDefinitely agree. However it mostly depends on your product being well isolated little chunks of functionality which is rarely the case. So what happens is you end up with a perpetually half baked Cthulhu.
- bkor 8y ago2.0 came out around Sep 2002 (a bit less than 16 years ago). 3.0 came out in Apr 2011 (7 years ago). Most of the components were the same between the last 2.32 release and 3.0, except for the very visible gnome-panel to gnome-shell switch. Gnome isn't a good example.
- setquk 8y agoSorry I forgot the /s on my original post.
- Xorlev 8y agoI work at Google on a product driven by ML doing ranking and regression tasks. Can confirm, very relevant. That said, ML is usually superior to the rules and heuristics systems we've been able to come up with, so we take on the debt once we stop being able to improve our heuristics, but only once we've tried really hard at the heuristics such that we have a baseline quality bar to beat. That justifies the effort, but it's still a lot of work to be vigilant and keep an eye on shifts in signals, unintended dependencies, good metrics that mean something, etc..
- jacquesm 8y agoWhat really bugged me for a while is how unbelievably easy it was to beat a very large amount of hand tuned code using ML. Going from 92.x% accuracy to 97% accuracy even without any tweaking at all feels like cheating.
- brlewis 8y agoAre you at liberty to share, accuracy of what?
- Radim 8y agoCan't speak for OP, but such accuracy numbers often hide a 20/20 hindsight bias. After having built and run a rule-based system for a while, you always get tremendous subject matter expertise, a feel for what works. Any rewrite of the system at that point will lead to much improved accuracy. The clarity is reflected in a better choice of the input signals, features, data preprocessing, metrics, workflows… A "magic ML" (without domain understanding) beating well-tuned SME rules is a dangerous fantasy, in any non-trivial endeavour. In other words, without that clarity, you're better off gaining it first through simple iterations of rules, figuring out what matters.
- jacquesm 8y agoLego parts recognition.
- heavenlyblue 8y ago