Have you measured this assertion in anyway? I'm not making a claim one way or the other, but what I have found is that people's perceptions about what is indicative of good candidates is rarely what is actually indicative and data goes a long way to dispel/confirm myths.
It's taken me from working in an obscure company on an obscure project into being at least somewhat heard of in the specific field. People are using this in lots of projects and it's been ported to many other languages. It's opened up networking with others in the field and in interviews I've had people state "yeah we looked at your code, it's really clean and efficient".
In my own experience, folks with blogs or public projects are great to interview because it makes it a lot faster to skip past the basic bozo filter of whether or not someone can code at all (fizzbuzz, etc.) That's fantastic because that's one of the suckiest parts of interviewing in general, but so often necessary. But that rarely tells you how talented someone is.
I agree that the negative's are a very difficult case, but in my experience is that most people don't cover the easier case of candidates they can evaluate. Further, evaluation techniques themselves are pretty error prone. But without collecting data and experimenting with it, how do we improve the problem?
But that means that you don't actually know if this extra signal of quality (or at least, notability) has any value at all. That tells me that you shouldn't be including it in your analysis of a candidate if you don't know what it actually represents.
Comments
Have you measured this assertion in anyway? I'm not making a claim one way or the other, but what I have found is that people's perceptions about what is indicative of good candidates is rarely what is actually indicative and data goes a long way to dispel/confirm myths.
Will you accept an anecdote?
The following very simple project has helped me immensely.
http://www.reddit.com/r/programming/comments/281msj/fast_off...
It's taken me from working in an obscure company on an obscure project into being at least somewhat heard of in the specific field. People are using this in lots of projects and it's been ported to many other languages. It's opened up networking with others in the field and in interviews I've had people state "yeah we looked at your code, it's really clean and efficient".
Yes, but the article is about companies hiring talented people, not about being hired.
Also, having developers with a minimum competence, a good business ambiance will make the difference, more than hiring a supposed coder-rock-star.
In my own experience, folks with blogs or public projects are great to interview because it makes it a lot faster to skip past the basic bozo filter of whether or not someone can code at all (fizzbuzz, etc.) That's fantastic because that's one of the suckiest parts of interviewing in general, but so often necessary. But that rarely tells you how talented someone is.
The problem is we don't have data on the candidates we don't hire.
I agree that the negative's are a very difficult case, but in my experience is that most people don't cover the easier case of candidates they can evaluate. Further, evaluation techniques themselves are pretty error prone. But without collecting data and experimenting with it, how do we improve the problem?
But that means that you don't actually know if this extra signal of quality (or at least, notability) has any value at all. That tells me that you shouldn't be including it in your analysis of a candidate if you don't know what it actually represents.