The Lesson to Unlearn

When I see a company conducting these kinds of tests during interviews, it's a signal to me that they are probably not a very forward-thinking company, and probably not a good fit for someone like me who tends to think outside the box. If it's another cog in their machine that they want, these kinds of interview tests are probably pretty good. But if they're an innovative company trying to change the world, they'd be much better off with a different, more pragmatic approach designed to attract the independent thinkers.

Beyond that, in my opinion, nearly anyone with average programming experience can hack a typical programming/whiteboard interview, so these kinds of interviews are definitely not the best way to find the best fit for particular roles. It's a good way to filter out the inexperienced, for sure, but it's a pretty low bar to set, especially if it's a company looking for top tier engineers, people with the ability to see the bigger picture.

It's also likely that a great engineer would pass the test with ease, but because their skillset and abilities have been inaccurately assessed due to the poor interview process, the company could have initially assigned them a much more effective role where both the company and the employee could have benefited to a much greater extent, but didn't. Overall, relatively speaking, the "hackable test" approach wastes time and hurts everyone in the long run, both employers and employees. A little investment up front with more personalized, specific interviews can go a long way.

I think we should start refusing these impractical interview processes, or at the very least, from an interviewee standpoint, turn down jobs (if you can) and let the potential employer know that their interview process is the reason.

It's a trust issue. It comes from grades being used to evaluate people outside the particular schools they attend.

We could give more autonomy to individual schools or teachers in terms of designing tests, but then we'd be facing two problems. One, the grades given by different schools/teachers would not be directly comparable. An A in one school could be awarded only for exceptional circumstances, and in another just for above-local-average. Two, once you start using those grades to judge people in a competitive situation like the job market, you'll have schools selling grades, teachers inflating grades to help their pupils compete against those from schools that sell grades, etc. and the whole system will collapse to uselessness.

Standardized testing mitigates these problems by forcing everyone to grade by the same criteria, but in order to do that, the grading must be done on things that are extremely easy to measure - which removes both individual judgement of the teachers and any relation between testing and understanding of the material.

The more I think about it, the more I feel it all boils down to scale. The grading system needs to be scaled up with the size of the market people compete on, otherwise it'll be either trivially gamed, or deeply unfair to unlucky people.

Is anyone else not seeing the footnotes inline? I'm only seeing the notes section at the end.

The managerial class will keep on embarrassing themselves sweating bullets over facile things like tests, college admissions, and school districts until they stop feeling precarious and downwardly mobile.

There's no point looking at one of their idiosyncrasies in isolation.

But then Paul Graham plays himself because all the startups are looking to get or softbanked. Absorbed into the monopolies or put in permanent luxery lift support. What a joke. Playing the VCs is more important than playing the increasingly poorer consumer hoards of the decreasing yield suburban consumer farms. We're years deep into the startup fad, and blinded test takers appealing to their blinded test taker money holders is all that's left.

Add air resistance into the mix, and denser materials do fall faster. Coins vs. napkins. Plums vs. crumpled balls of paper

I'd actually take the lesson the other way. It's hilariously tough for model-makers to acknowledge real-world noise that prevents their theoretically precise models from delivering reliable results.