[eckesicle]

My wider point is not that the way companies are run is perfect and that we should stop the “innovators” (to quote the sibling comment). Each of these examples speak of corporate dysfunction, but we never give any weight to the constraints that force them in place. Leetcode is bad, but it’s bad in the sense that it errs too heavily on filtering out false negatives - the cheaper of the two errors. The alternative is worse.

Giving Tim the benefit of the doubt in this story, it still holds true that for every extraordinary and invisible superstar like Tim there are 99 under-performers who are indistinguishable from him.

We need to empathise with our managers and the processes in our organisations to understand their purpose and how they came to be.

We, software engineers, keep picking out singular data points of evidence to point at a flawed and unfair world, that go against our self inflated egos.

The brew guy inverting the binary tree and Tim being great, does not invalidate the practices of whiteboards and story points as a general practice.

To your final point, the best organisations that I’ve worked with used metrics in a very effective way (mostly in start ups). The worst did too. Just because some do it poorly, does not mean that it’s bad across the board.

What is tiring, is the unfair, and low expectation of the quality of evidence demanded of the anti-establishment notions in software development, before they are taken as gospel by this community.

And, in my experience, the people who are the strongest proponents of sidestepping or dismantling these processes overlap strongly with those who also do not deliver value to their teams.

[Kamq]

> Leetcode is bad, but it’s bad in the sense that it errs too heavily on filtering out false negatives

But, it doesn't. It filters for something orthogonal to development, which is ability to obsess over clever algorithmic solutions. Ok, well my company does HackerRank instead of LeetCode, maybe LeetCode is magically better, but I'm not seeing anything that tells me someone who grinds LeetCode is actually going to be useful on my team.

Look, you want an idiot check to make sure someone is actually able to code, fine. That's probably a good idea. But the number of stories on here about people being turned away because they hadn't run into a particular tricky algorithm problem is concerning.

> Giving Tim the benefit of the doubt in this story, it still holds true that for every extraordinary and invisible superstar like Tim there are 99 under-performers who are indistinguishable from him.

But they aren't indistinguishable. The author of the blog post was perfectly able to distinguish them. That's my point. There are plenty of ways to be able to distinguish them, this metric just isn't one of them. Because it's a bad metric.

It may not be legible to the higher ups, but good lower level managers have no problem distinguishing good unconventional people, and under-performers.

> We need to empathise with our managers and the processes in our organisations to understand their purpose and how they came to be.

I do empathize with the managers, at least the lower level ones. That's why I advocated for putting them in complete control and giving them unilateral firing privileges and increasing their pay.

> the best organisations that I’ve worked with used metrics in a very effective way (mostly in start ups). The worst did too.

You're really making it sound like metrics (at least as traditionally practiced in software) are orthogonal to being a good organization. If that's true, we might want to re-think how much time we spend on them and how much money we spend capturing them.

Now, if you want to use profit, adoption, or user satisfaction as metrics, I'd love to talk about that, but I've seen nothing in my experience in the corporate world that tells me that the way we're currently using them is even net positive value.

[eckesicle]

It only appears that HackerRank/Leetcode isn’t good at filtering because you’re viewing it from your perspective, and not the perspective of the entire population that is tested. To you, the predictive power at the top tail end of the distribution is low, because you’re thinking of two strong developers Alice and Bob. Alice happens to know algorithm X and would pass the test, whereas Bob does not. But that’s not the population we’re testing. Think more along the lines of Alice and Bob and your grandmother were the test population. It’s absolutely fantastic at filtering the lower 95% of applicants because they will _never_ be able to pass. Yes, inadvertently 2.5% of “good developers” are filtered too, but that doesn’t matter to the outcome of your company. They just want someone competent, and they don’t care if it’s Alice or Bob.

The same logic sort of applies to Tim and his performance. The bias of having an imperfect metric is probably much better than the bias of letting an army of middle managers go with their cut. Besides, it doesn’t have to be a hard filtering function at this stage, but a metric to indicate that we need to look a little closer at Tim