Tweaking the system

Back in January, I laid out some pretty detailed plans for myself.  I have mostly stayed focused on those.  They also prompted me to have a hard conversation with my family about the house stuff.  We defined what it really means to clean up after dinner and who is really responsible for what.  As I type this, my kitchen is fairly clean.  There are no dishes on the counters or in the sink.  And that’s progress for us.  I’ve also tried to create some new, very simple, habits that will help keep the chaos in check.  For example, I take the time to unload the dishwasher in the mornings, something that bogged things down for those responsible for cleaning up.  Also, I’ve been putting clothes away more regularly at night, but when they don’t get put away daily, I tackle this specifically on the weekends.

Aside from habits, I’ve tried to clean out spaces that had become too cluttered. I’m kind of midway through that, leaving the living room in a bit of chaos, but a whole room has been cleared out.  I’m trying to set aside a time every week or so to purge.

I also broke down and hired a housekeeping service to come once a month.  I just have them here for a couple of hours, but that’s helped keep some of the clutter at bay. Doesn’t help with the issue below, but it’s a boon to me, at least.

The money front has been harder, but I have created a good habit of checking on things regularly.  It helps having an app that makes it easy to check in, so that when I think of it, I can look.  Mr. Geeky and I have cut out some things,  as we’ve seen subscriptions and other things pop up that we realize we’re not using, including finally cutting the cable.  That will help in the longer term.  I’ve also made some progress on debt, and made myself a clearer plan to pay it down. A struggle on this front has been that extra money has gone toward college savings instead, but I’ve rearranged the savings plan as well to manage both of these things.  Mostly, I think we need to cut some spending somewhere–mainly in things like food and eating out.  I’m starting to keep a closer eye on patterns, and find myself doing math in my head about saving x amount per week by, say, bringing lunch.

The algorithms book has helped a little on both these fronts.  Just choosing a place to start–either randomly or using a system–has been helpful.  My closet and drawers are now organized according to a LRU system.  Not only is it helpful in finding things, but will be helpful when it comes time to decide what to get rid of.  Whatever’s in the back is a candidate for going.

I usually set goals for the school year, but I think I’ll be sticking with these for a while.  There’s enough there that I can break them down into parts, focusing on one area at a time for a month or so. I’m looking forward to feeling more significant success in both these areas.

Coding Bootcamps (and perhaps coding itself) on the Decline?

Audrey Watters writes about a couple of coding bootcamps closing up shop and ponders whether this is a trend.  Alfred Thompson references this same trend and wonders if it means there’s a more full-on CS bubble.  We’ve seen this script before.  I’m not so sure it’s the same script.  The landscape is much more complex than it was almost 20 years ago.  It is possible there’s a market correction in store, but maybe not a full on bubble burst.

Coding bootcamps have always bothered me.  I can’t find the reference, but I’ve written about bootcamps aimed at school-aged kids before.  What I think some of them do is let schools off the hook for teaching computer science.  Schools think, we don’t need to offer this because there are three bootcamps around the corner.  Never mind that some of those cost hundreds of dollars for a week’s worth of lessons and so, equity is out the window.

As Audrey points out, companies have complained about those bootcamps, saying that they don’t really prepare people for the work they need done in their industry.  A CS degree or extensive experience on the job is much better.  As I say to students and parents, it’s not about learning x language or y framework, it’s about learning a way to think, about logic, critical thinking, and problem solving.  You can Google the specific code you need, but designing the algorithm or the interface, that takes some deeper thought.

So, in some ways, reducing the number of these that are out there might be a good thing.  It might mean that schools finally say, oh, hey, our students don’t have access to these anymore; maybe we should offer that.  And as people aren’t able to get jobs with their limited training, they might work their way through a CS degree, starting perhaps at a community college, where the tuition is reasonable and federal aid is available.

Does any of this mean that CS will disappear or the bubble will burst? Hard to know.  While the number of jobs requiring coding may in fact be inflated, lots of jobs benefit from having not just the thinking skills one acquires from Computer Science, but also the actual technical skills.  Interestingly, responses from those in the field tend to fall along the spectrum of real coding is so specialized that only certain people can do it to AI is going to be doing all the coding anyway.  When there are extremes like that, it’s hard to know where the truth is.

Summer Reading: Algorithms to Live By

I’ve been giving this book, Algorithms to Live By: The Computer Science of Human Decisions, to my students for the last couple of years.  I hadn’t yet made it through the whole thing until this year.  I’m so glad I did.  There’s nothing quite so exciting to me as having actual scientific data to guide my choices and improve how I do things.  Each chapter takes a different CS algorithm, explains in mostly laymen’s terms the theory behind the algorithm, and then shows how that algorithm can work in real life for non-computer-related activities.

Some chapters were better than others at making the connection between an algorithm and human decisions or activities.  I found the first five chapters particularly compelling and relevant.  It was hit or miss with the last 6.  I still enjoyed the theory, but I didn’t always see the application, or it just wasn’t as salient for me as it might be for others.

I took the most notes in the scheduling chapter, which covered a lot of ground related to to-do lists, prioritizing tasks, etc.  The key to the scheduling chapter had to do with goals.  Depending on your goal, you would use a different strategy for deciding what to do when.  Goals include things like getting things done on time or getting the most important thing done on time or getting everything done within a certain amount of time.  In each of these three, you  have to consider different factors.  In the first, you’re just looking at due dates: which thing needs to get done first.  In the second, you’re looking at due dates and “weightiness”: what needs to get done first and which of these is most important.  In the last, you’re adding up how long everything might take and just going through at random.

The chapter also addresses the issue of the cost of context switching and how to decide when to switch or keep working on what you’re working on.  And, it also addresses human thrashing, when you’ve stalled and don’t seem to be able to work on anything or can’t decide where to start.  Randomness is your friend in this case.  Facing a pile of email.  Just respond in random order.  It may not be optimal, but it’s better than not doing anything.

My other favorite and most applicable chapter was about caching, which is really just about organizing information so that it’s quickly retrievable.  The short-term memory on your computer is a way of organizing information so that, for example, typing an email isn’t a slow, one-letter-at-a-time process.  Computers have to figure out how accessible to make information and how to clear memory in order to store more information.  Computers have to predict what you might use next.  There are many ways of organizing cache, but one way that’s pretty efficient is called the Last Recently Used algorithm.  The idea is in the name.  Whatever you most recently used, you’re likely to use again.  And the opposite is also true.  The thing that you haven’t touched in a long time can probably be bumped to make room for other things. Applying this idea to humans and the physical world is kind of cool.

They use a closet and filing as examples.  Some people have their closets organized by clothing type and then by color or by color then clothing type.  Whichever, the idea is that you have a system of organization that in theory helps you find something to wear.  However, it turns out, mathematically, this system is likely not any faster than if you put the items you just wore at the front of the closet (for me, this is on the left side) and sometimes had to search through everything to find that one pair of pants.  Ditto for piles of paper.  Yes, occasionally, you have to go through the whole pile to find something, but the things you need and use most are likely on the top.

Another lesson from this chapter has to do with forgetting things–or seeming to forget things–as we age.  As anyone who’s prone to opening 50 tabs on their browser knows, the more information you’re trying to hold in memory, the slower the retrieval of that information gets.  Going back to a tab you haven’t looked at in an hour may take a while.  A similar process seems to be happening as you age.  It’s not that your brain is starting to deteriorate (though there is some of that), the main reason it’s sometimes hard to remember names or even words is that you have to sift through a lot of information to find those things, especially things that are not on top of the pile.  As you accumulate more information and knowledge (and store it somewhere), it becomes computationally harder to retrieve it.  Kind of cool.

There are lots of other good tidbits in here about sorting, randomness, game theory and more.  They’re well worth reading about.  The authors end the book writing about computational kindness, a concept I can get behind.  The basic idea, which is really the central idea of the book, is that algorithms are created to reduce computational load.  Computing, whether on a machine or in a brain, is work and takes energy.  We would do well to reduce that workload when we can.  An example they give is about making your preferences for say, which restaurant you want to go to with your friends, explicit rather than doing the polite thing of saying, “Oh, wherever we go is fine with me.” Usually it’s not and you’re companions know that it’s not and so they have to do the computational work of considering what you’ve left unsaid.  In the end, we think of computers as completely rational in the sense that they can grind through every possible option, and come out with the “right” decision by doing so. But that’s only for easy problems. Most things we confront as humans, and frankly, for computers, too, are really hard problems.  And there’s only so much time in a day, so computers, and humans, use algorithms that

make assumptions, show a bias toward simpler solutions, trade off the costs of error against the costs of delay, and take chances. These aren’t the concessions we make when we can’t be rational.

They’re what being rational means.

So go be more computer-like.  You’ll be doing yourself and the rest of humanity a pretty big favor.


Computer Science as Vocational Training

Larry Cuban has written a three-part series about how teaching CS is the new vocational training (one, two, three).  His argument comes from a place of watching a range of top-down mandates (think No Child Left Behind) create crappy outcomes for kids. I get that. Many districts and schools shove various reforms down teachers’ throats, without buy-in, without conversation.  And maybe there are places out there that are doing that with Computer Science.  But that’s not been my experience.  I may teach in an independent school where teachers have a lot of freedom to develop curriculum but through my various CSTA (Computer Science Teachers Association) connections, I know a lot of public school CS teachers.  And many of them are fighting to get their schools or districts to accept CS–often as just an elective much less as a requirement.

And I understand Cuban’s queasiness about industry seemingly dictating what to teach.  I read the Times article, too. Idaho, what are you thinking?  But Computer Science, the field, not just coding, underlies so much of how our world runs.  To discount its importance in public school, to denigrate it as merely vocational, seems to me to miss the point.

Part of the issue here is the coding (or programming) is the easiest way to explain what Computer Science is to most people.  Programming is also a good tool to use to understand a range of concepts related to Computer Science.  And there are programmers out there who may have learned the concepts of CS through their many programming classes, but now never use them nor need to use them.  Just as there are people doing math in their jobs who have forgotten their mathematical proofs.  But we would never call math simply vocational, because it could lead to other, bigger, things.  So can CS, so I don’t understand why we continue to think of it as limiting.

I would also contend that even if one wants to think of CS as primarily vocational, the careers CS supports are not just software engineering careers. Cuban cites, for example, business services, as a bigger growth area than technology careers.  Business services involves a lot of CS.  Ad targeting, shipping logistics, sales analyses–all part of business services–all need computing.

I would argue, too, that increasing underrepresented groups in Computer Science depends on introducing CS at an early age in public schools.  Black, hispanic, latina/o, and female students often arrive at college to find that their white and Asian (mostly male) counterparts have had much more exposure to CS (either through school or extracurriculars) and feel discouraged and unable to catch up. They need a foundation under them that will give them confidence to continue or even try the field at this advanced level.

Students get exposed to Biology in elementary school and just as few careers in biology appear on the BLS “most growth” career list (nurses and medical assistants) as for CS (software engineers and systems analysts). Oh sure, you can argue we need to know about our bodies.  Well, we need to know about the machines we use everyday and hold in our hands and that are running our refrigerators and light bulbs.  How is okay not to understand that stuff?  If, as Cuban argues, part of schooling is about creating informed citizens, then learning CS fits right in with that goal.

It’s important to know that Facebook and Google use algorithms to present information and those algorithms can be exploited.  It’s important to understand what Net Neutrality is and why that changes the Internet as we know it.  It’s important to understand that hacking takes place at the intersection of technology and a keen understanding of human vulnerability.  It’s important to know that some things really do not compute, but we can get close with a few tweaks (i.e. we still sometimes need human intervention and ingenuity).  And Computer Science, even just coding, can help one develop the habit of breaking down a problem into smaller parts.  Most problems worth solving are not small. And there are many more things that Computer Science teaches us that help us be better citizens.  And that’s why students should learn Computer Science.

Reading Interlude: Summer Life

So my next book is taking me a bit longer.  It’s really good, though, so just hang on.  Also, I have been back at work and traveling for things where there hasn’t been time for reading.  I’ve read a little every day or so, but it hasn’t been enough to get through the book.  I’ll get there!

Work has been both slower and busier.  In the summer, our hours are 9-3, which is nice, but it also goes by in a blink.  There aren’t pressing deadlines (yet), so I don’t feel the need to come in early or stay late.  By August, that will change, I’m sure.  Mostly, I’m still in the process of hiring for a handful of positions.  This happens every year for a variety of reasons, but this year has been particularly busy on the hiring front.  Hiring takes time.  There’s reading resumes, scheduling phone interviews (often with several other people), doing the phone interviews, and then, scheduling face-to-face, and then doing the face-to-face.  And finally, there’s making the decision, which can be quick or take a while, depending on our pool.  Other projects have taken a back seat, as hiring is one of the most important things we do.

Summer is going by very fast! I can’t believe it’s mid-July already.  A month from now, we’ll be headed to California to drop off Geeky Girl. I’ve booked an Airbnb and flights for our trip.  Everyone keeps asking me if I’m ready.  I’m ready.  I don’t get too sentimental about these things.  I’ll miss Geeky Girl, but she’ll be in touch.  Technology has changed the going off to college dynamic.  As I was planning the trip, I had planned to arrive the day before she needs to be there.  Mr. Geeky suggested going a day earlier than that so that we could all be together for a day.  I argued that we’d have plenty of time together and that Geeky Girl probably didn’t want us to be there any longer than necessary. So we asked her, and guess who was right.  Yep, me.  She’s ready, and that makes me ready.

Yesterday became family cleaning day, weirdly, which made me quite happy.  I went to work for a half day, came home, ate lunch and started tackling some projects around the house.  I’ve been cleaning out a “junk room” and I’ve been purging clothes (and getting Mr. Geeky to do so as well!).  As I started on my projects, Mr. Geeky randomly joined in, and then Geeky Boy decided it was time to tackle his room–he’s been talking about it forever.  Geeky Girl was out, but she’d already started on her room earlier this week.  And then, the maid showed up (yes, I finally hired cleaning help).  So while we were reorganizing upstairs, she tackled the downstairs.  It was great! And yes, that makes me sound a little nerdy.

Other summer goings on include a family reunion on Mr. Geeky’s side of the family, which involved staying at an old train station that was very cool. On the way we listened to Sh*t Town, the podcast from the makers of Serial.  Well worth listening to.  We also listened to some shorter ones: Reveal, Freakonomics, Invisibilia.  Thank goodness for podcasts.

This weekend, we’re headed out camping in the Niagara Falls area.  I’m looking forward to being away from civilization (sort of; it is car camping).  We camped in May in a cabin and that was fun, but this will be our first trip this summer.  We usually manage two, but I don’t think that’s going to happen this year.

All that’s to say that summer has been busy in a good way.  I’m not yet looking forward to the school year, but I will be soon.

Summer Reading: Theft by Finding

Book three is on the lighter side, David Sedaris’s Theft by Finding.  This is a collection of diary entries from 1977-2002.  It reads differently from his other work in some ways, but as a long-time fan, I could see where the ideas for much of his work came from, and by the end, could hear his distinctive voice.

The book begins in media res, so to speak, unlike a memoir, which might cover childhood, etc.  Instead, we’re thrown into a time when David is hitchhiking and unless you do the math, you’re not sure how old he is.  The early entries are filled with interesting observations of people and places, but also tales of his own harassment by others, his drinking, doing drugs and being broke frequently.  I was glad I knew how things turned out for him because if I didn’t, I’d have been worried.

Though Sedaris has always downplayed his ambitions, you can see glimpses of it even in what looks like pretty desperate moments.  He knows how much he needs to save to get to New York.  There are brief mentions of writing he’s working on.  You can tell he wants to get somewhere, and that’s why the story doesn’t end in tragedy.

As always, there are some really funny moments where I found myself laughing out loud.  His thoughts are so weird and yet, somehow, not that different from our own weird thoughts. I found myself thinking simultaneously, wow, that’s odd and oh, yeah, that’s exactly how I’d feel.

I always find myself thinking after reading books like this, that detail how people live their lives, that I should do more.  I think I should travel more, write more, go out more, etc.  If a memoir/diary has ended up in print, generally the person’s life isn’t boring–or at least the slices we’re shown aren’t boring–and so then I think my own is boring by comparison.  Of course, I’m writing this from the porch of a beach house, so my life isn’t that bad.

Summer reading: Everybody Lies

I heard about Everybody Lies from listening to the Freakonomics podcast where Seth Stephens-Davidowitz was interviewed about the book.  I absolutely love data.  I’m not necessarily good at evaluating it as I don’t have the same toolkit as many modern data scientists, but I do often turn to data to answer questions I have in my life.  This book didn’t disappoint in answering some really interesting questions–about racism, sexism, poverty, sex, and more.

The main points of the book, I’d say, is that our intuition about things is often wrong and that we have enough data at our fingertips and the tools to dig into (in the form of computing power) to answer some really big and important questions that might make life better for lots of people.

Stephens-Davidowitz is also a really good writer, so while the book is about datasets and regression analyses, it’s not at all dry.  And the insights the book reveals about human nature are also compelling.  Here are a few of my favorites:

  • While we talk all the time about implicit bias when it comes to race, search data reveals that racism is not as implicit as we think it is.  It’s really explicit. People just hide it well.  They’re not unaware that they’re racist, as implicit bias would have us believe.  They just don’t share their racism with others.  But they share it with Google.
  • Parents display a lot of bias against their daughters. They assume she’s not smart, that looks are more important, and that ugliness is a very undesirable characteristic to have in daughters but not necessarily sons.  (I found this nugget particularly interesting given my interest in girls education).
  • The Internet is not as segregated as one might think.  Most of us bump into people whose opinions are very different from our own very regularly.
  • People say they’re going to do one thing — like watch a documentary and not the chick flick — but they do something else entirely.  Which is why Netflix and Amazon and other Internet sellers pay more attention to what you actually do (watch the chick flick) and not what you are projecting you’ll do (because you added that documentary to your queue).
  • Sometimes data doesn’t give you the whole picture, so you need human intervention. Test scores, for example, don’t tell you everything you might need to know about how effective a teacher is in creating student success. Test scores, student surveys, and teacher observations (the last two qualitative data from humans) taken all together give you a really solid picture.
  • Also, the size of a horse’s left ventricle is a big indicator of whether that horse will win a lot of races.

And those are just a few of the cool things I learned.  But the other cool thing about the book is that it’s also a story of data itself, of how much we have (even us regular people), of what kinds of things scientists are investigating and discovering from all this data, and the untapped potential that’s there.  I actually think I’ll be applying some of what I learned from this book pretty immediately.  And that’s cool.

Summer reading: Pitch Perfect

I finished book two of my summer reading project.  Pitch Perfect was about how to speak more effectively, in many different situations, from formal presentations to conversations at cocktail parties.  A couple of years ago, I read The Well-Spoken Woman, in preparation for a TED-style talk I had to give.  I think both books are helpful.  Communication is one of the most important things we do, and we are constantly sending messages with what we say and how we say it.  It’s an area I’m working on all the time.

The message from the book that I found most helpful is that you should always be prepared, no matter how often you do public speaking or how confident you feel.  Speaking well under any circumstances takes preparation and practice.  That’s comforting to think that everyone needs to prepare.  So I don’t feel stupid for going over things in my head before I say them or thinking through what I might say in a meeting, even if I’m not the one running the meeting.

The book is broken down into seven basic principles, which I’m going to paraphrase for my own sake: Get to the point, tell stories, keep it short, slow down, convey confidence, be curious, change/control the conversation.  Many of these you’ve likely heard before, but McGowan’s specific stories and examples drive these points home, giving you some very specific places to start.

I’m looking forward to putting some his tips into practice, both for myself and for my students.


Feedback via observation

This year I’ve spent a lot of time thinking about classroom observations.  One of my roles is to oversee the evaluation process for faculty and classroom observations are a part of that.  I’ve felt ambivalent about our observations.  Sometimes they work well, sometimes not.  Typically, teacher and observer arrange a time to observe a particular class.  The observer writes up the observation and then the teacher and observer have a conversation about what went well, what didn’t with the idea that the teacher improves in areas that she feels she needs to.

Most of the time, that’s precisely what happens, and sometimes the teacher invites the observer back in to see how things are better or the observer sets up a revisit and there’s further conversation and things are good.  But that’s best case scenario.  Often there’s not a revisit for another year by which time whatever was discussed before has been forgotten.  This is especially true when the teachers are already pretty good.

I’ve been in conversation over the past year, and especially the last few months with our division directors and department chairs (who do most of the actual visits) about shifting the focus of our observation process from something we do as part of the evaluation process to something we do to improve teaching.  In order for that to happen, we need two things.  One, we need more frequent observations.  Once a year is not enough.  And two, we need them to not be planned.  Not everyone plans the. perfect. lesson. for observation day, but many do, and then you may or may not be getting a clear picture of what’s happening regularly in the classroom.

We’re still working out what this will look like for us.  There are a number of models out there, but we want to find the one that works for us.  Interestingly, as happens when you’ve learned a new word or something new, suddenly that word or concept is everywhere.  This morning Matt Reed writes about observations at the college level, highlighting an article by a former colleague of mine.  And last week, on Connected Principals, Sam LeDeaux writes about successful completing a challenge to visit 500 classrooms during the school year.

All mention the trickiness of separating the feedback from observations, which can be very valuable, from evaluations.  I think that’s easier at the higher ed level than at the secondary level.  We can’t employ our students to observe our classes as Cook-Sather, in the article linked above suggests.  Seniors may be ready for it, but I don’t think most faculty would be open to having a 14 year old provide feedback.  I could be wrong about that. But it seems like a real challenge.

Another route to go would be peer, not supervisor, observations. We want to implement peer observations and many faculty already do this informally.  In my mind, I’m thinking requiring a couple of peer observations that are shared only between the teacher and observer would be valuable.  The only requirement is that they get done, and we just have to trust that the feedback is productive.  The problem there is time.  Unlike professors, our teachers’ time is much more compressed.  They’re in class, generally, most of the day, and when they’re not, they’re grading, in meetings, working with students, or prepping for class.   It’s why observations tend to fall to administrators.  While they’re busy, they’re not tied to a class schedule, so can more easily get into classrooms.

There are other ways to separate, potentially.  One thought we have is to try to observe each teacher at least 10 times.  We could say that only 5 of those count–maybe two from early in the year and three from later (when presumably if improvement is needed, that improvement has happened).  The basic idea is to get faculty to trust that yes, observations are connected to the evaluation process.  They are, after all, evidence for how well a teacher is doing their job.  It can’t all be self reporting or a single data point.  But they need to trust that our main goal is not to find ways to ding them, but to help them grow, to celebrate their hard work.

I recognize that I’m a glutton for feedback–positive or negative–and not everyone else enjoys feedback the way I do.  And they don’t trust that the feedback is not just in the school’s best interests, but theirs as well.

Managing Humans

Cover of "Managing Humans: Biting and Hum...
Cover via Amazon

First book of the summer down.  I started with Managing Humans by Michael Lopp.  It promised to be funny and yet meaningful, and connected two of my favorite areas of study together, geekery/software and management/leadership.  It did prove to be an interesting read, although I started to skip a chapter here and there toward the end that were more specific to software engineering contexts than anything else.  But most of the book is applicable no matter what your industry.

The basic premise is for managers to not think of their employees (or themselves) as cogs in a machine, but to understand our own humanity, and how we tick so that we can all do our jobs better (and feel happy!).  Lopp, in fact, begins the book by saying that a manager’s most important job is to understand the needs of the people that work for him/her and to meet those needs. It’s harder than writing software.

There were lots of good tidbits in here, but one of my favorites was the Rands Test.  It’s a series of questions to determine the health of your team (or whole company).  The questions include things like “Can you say ‘no’ to your boss?” and “Do you have time to be strategic?” Or my favorite “Are you actively killing the Grapevine?”  As Lopp puts it, “In the absence of information, people make shit up. Worse, if they at all feel threatened, they make shit up that amplifies their worst fears.”  This is why, he explains, those who fear losing their jobs come up with conspiracy theories that confirm that fear.  Often leaving you the manager to try to explain that no, you’re not going to lose your job, and that theory is quite a doozy.

Lopp also explained the conflict between the Old Guard and the New Guard in a couple of different places, which I found interesting and enlightening.  And while Lopp is talking about the Old Guard being the company founders and the New Guard being those who come along after, every place I’ve ever worked has this dynamic.  Those who’ve been around the longest lord it over those who have been around for less time.  The New Guard often wants to clear processes in place while the Old Guard doesn’t see the need to.  Lopp explains that the Old Guard essentially embodies the culture of the company, but doesn’t articulate it effectively to the New Guard.  Instead, they just argue with each other.  The Old Guard needs the New Guard because there’s no growth without them.  It’s an interesting dynamic, and made sense to me in a variety of contexts.

Finally, toward the end of the book, he discusses how to leave and what to do about employees who leave.  Some of this I’ve always known.  When an employee gives notice, they’ve often been thinking about leaving for a while.  It didn’t start in the two weeks before.  It might have been a few months before.  The idea of leaving often begins with a small thing that bothers them, something said in a meeting that went against their values or what they believed to be company values, a lack of follow up somewhere, a desire for growth that isn’t cultivated.  Whatever it is, managers must try to prevent them.  It is far less costly to work to keep someone happy and productive than it is to hire bring another person up to speed.  I say this out loud pretty often.

So this book didn’t knock my socks of, but there were some good tidbits.  I would recommend it to anyone working in the tech industry, for sure.  A lot of the details are directly applicable there.  I would also recommend it to anyone who is not a manager, but is frustrated by their management.  It’s a straightforward account of how management typically thinks, and the many things they’re juggling.  So if you want that insight, and to maybe have a better relationship with your boss, pick this book up.