Startup Promotions and Equity Grants

  • The problem: Employees who stay with your company deserve to be rewarded with more salary and equity. How do you design a system that is fair, easy to understand, and easy to implement?
  • Our process: We asked friends in the industry to explain how promotion process works in their company and researched online to understand the status-quo for the tech industry. Based on our findings, we identified parts of other companies’ processes that we liked. Finally, we assembled a policy that felt the most in-line with the company culture.

Our first step was to ask our friends. Here’s what they had to say (I highlight the parts interesting to me):


It all depends on performance, very heavily weighted. The most obvious thing they do is to have a set of responsibilities for each level and see quantitative proof that the person meets the expectations of the next level. They do this by discussing among the next to next level guys. The most realistic estimate is 1-1.5 years at lower levels. Hike is subjective, again, by performance. They have a committee that decides the hike. it’s a combination of max pay per level, how good this person is, and how much he gets. As a common trend, they give more bonuses and fewer hikes. Because bonus is a one-time thing and hike is a commitment. I don’t know the qualitative number for hike, but bonus is around 10-12% for normal performance


Compensation is reevaluated every year according to market standards. Promotions are totally merit-based, performance-based, so it depends on the level you are operating on, consistently, for at least 6 months. We want people to be consistently performing at the next level for at least 6 months before promotion. Every 6 months, we have a review cycle. Managers get peer feedback on what the engineer accomplished, and managers from different orgs will review the anonymized engineer, and compare engineers at the same rank to calibrate for impact across orgs.


The employee who thinks he/she is eligible for promotion applies to their manager. Along with this application he/she gets recommendation from other engineers they worked with during the last 6 months. These peer employees write a single paragraph describing what they worked on how that employee did a good job. Once the application is in, the manger sits with the promotion committee and determines the case. A promotion committee is usually 2-3 top senior engineers who evaluate the work and give their opinion. Ultimately its the manager’s decision to promote that employee.


At Salesforce, my title transitions were: AMTS -> MTS: 13 months, 10% increase MTS -> SMTS: 15 months, 8.77% increase Here’s what I think is standard: AMTS -> MTS: 11-18 months MTS -> SMTS: 24 months SMTS -> LMTS: 24-36 months LMTS -> PMTS: 36+ months.


Based on the above accounts and other research we did, we picked out the patterns that stood out to us:

  • Performance Reviews - Everybody does them. People hate them. It seems that they are imperfect in many ways, but are often the best that can be done in tackling the impossible challenge of gauging how an employee has performed. Performance reviews are inherently challenging because:
    • Performance is subjective: People can easily have a different idea of how an individual is performing based on how much they like that person, their visibility of that role, and other non-objective factors. This is especially the case in startups where employees where multiple hats and are continuously redefining their role.
    • Criteria is subjective: It is difficult to set an expectation on what behavior results in a promotion when . Especially in a startup that does not have other employees to serve as data points in a compensation function.
    • Power Dynamics: It leads to a power dynamic where those conducting the performance review have power over the one being evaluated. Which is especially challenging for a startup which values collaboration over competition.
  • Stack Ranking - Also known as a vitality curve, this is the management technique of grading employees based on their individual productivity. Many large organizations such as Yahoo, Amazon, and Microsoft have implemented stack ranking with mixed results.
  • Peter Principle and The Law of Crappy People - The Law of Crappy People (TLCP) states: “For any title level in a large organization, the talent on that level will eventually converge to the crappiest person with the title.” Ben Horowitz has a good segment on mitigating TLCP that boils down to being very disciplined about defining skills at each level.


After absorbing the above research, we finally came up with a rough guideline to handle promotions: Promotions are done every quarter and generally the expectation for promotions are as follows, broken down by the frequency of promotions depending on performance. These frequencies are relative to start date or date of last promotion. Keep in mind that these are guidelines, not hard rules, and does not mean that circumstances outside of the guidelines will not be considered: 3 months - a promotion can be given in 3 months after an employee’s start date or after any promotion if evaluations show that the company would be in a more fair state if the employee was set at a higher multiplier. Promotions do not necessarily mean that your next promotion is delayed by 3 months. 6 months - an exceptional promotion given very rarely, to employees whose performance greatly exceeds expectations 9 months - a great promotion, given infrequently to employees whose performance exceeds expectations 12 months - a good promotion given regularly to employees whose performance meets expectations


Role Differences: The compensation vs. time (assuming constant performance) curves for sales and engineering are different when considering market rate and as such the guidelines may be applied differently depending on the role. See graphic below from Seniority: The seniority of positions affects how frequently promotions are given. It is common for promotion frequency to decrease as the position becomes higher, and this will affect how the guidelines will be applied. Expectations Work smarter, not harder (aka work more efficiently, not more hours). There is no expectation for work outside of regular office hours, and you will not be held back for promotion because you didn’t work outside office hours.


Our system isn’t perfect. After having implemented it and running it for the past two quarters, we have identified challenges that we will want to solve as we grow:

  • No crisp definitions of skill levels at different skill multipliers. This has left us vulnerable to The Law of Crappy People. As the roles in our organization become more specialized, this issue will become easier to resolve.
  • No crisp definitions of responsibilities at different skill multipliers. This has caused employees to feel that there is no clear long-term path for career advancement in the company.
  • High overhead process: Currently the way we decide who gets promoted involves a lengthy representative nomination process with multiple handoffs and a committee meeting that needs to be scheduled around everyone’s busy schedules. This happens every quarter, and takes up a lot of time to organize.
  • Responsibility bottleneck: Right now the cofounders are the ones ultimately making the decision on promotions. This is a problem because as the team is growing, the cofounders are becoming more and more distant from the day-to-day tasks of everyone and less qualified to make judgements on compensation. In addition, our time just does not scale.


If you have any questions or feedback for us on what you’ve read or our promotion guidelines, feel free to tweet me @ryochiba and @tint . Would love to hear your ideas and share what we have learned!


Good Pair Bad Pair

  • A good partner understands pairing is a difficult skill that takes time and effort to cultivate. A bad partner expects others to be great pairs right off the bat.
  • A good partner seeks to understand their own bad pairing habits. A bad pair fails to think about their own pairing habits and only blames others.
  • A good partner brings up mistakes that they make and that others make in a way that turns a mistake into an improvement opportunity. A bad partner bottles up issues they have while pairing and allows the issues to harm relationships and their quality of life at work.
  • A good partner frequently syncs up on schedule and goals and is not afraid to adjust the schedule to make it more realistic. A bad partner does not talk about schedule or goals and avoids adjusting the schedule until the last minute, causing confusion and mismatched expectations.
  • A good partner sets small achievable goals and celebrates achieving them. A bad partner sets one big (likely unrealistic) goal and stresses out about achieving it.
  • A good partner clearly communicates availability. A bad partner disappears for unpredictable periods of time with no notice leaving the other partner to work alone and confused.
  • A good partner uses 2 input devices and 1 computer and avoids using 2 computers unless necessary. A bad partner defaults to using 2 computers and gets distracted / unpaired.
  • A good partner balances give and take in decision making. A bad partner will not be aware of the decision making balance and either be a bunny or Alpha Male.
  • A good partner is aware of skill differences and works to rebalance them by making the effort to be a mentor or a student.
  • A good partner will continue to help with an epic all the way to the finish line and after with deployment tasks and bug fixes. A bad partner will stop contributing to the epic once the feature is accepted and not help their pair get the feature stable and in production.

A good partner focuses on cultivating the following pairing roles

  • Mentor - explains concepts and reasoning with minimal judgement
  • Moleskine - maintains state and checks for edge cases and unexpected side effects
  • Captain - cultivates their own soft skills necessary in effective pairing and cultivating their partners as well

And a bad partner falls into the following bad pairing roles

  • Alpha Male - mainly takes in a give and take discussion
  • Superman - grabs keyboard and starts coding quickly and silently
  • Bunny - mainly gives in a give and take discussion
  • Rodolfo Valentino - never disagrees with their partner’s decision
  • Monk - does not acknowledge the validity of other’s ideas
  • Puppet Master - uses their influence to control the driver’s coding
  • Backseat Driver - uses their voice to control the driver’s coding
  • Auditor - nitpicks small, trivial decisions at the expense of larger and more important decisions

About Drivers:

  • A good driver frequently asks for agreement on ambiguous choices. A bad driver makes difficult choices silently and does not ask the navigator for input.
  • A good driver makes easy decisions fast. A bad driver constantly asks for input on decisions that do not require input.
  • A good driver is aware of the navigator’s focus and will ask for input or suggest trading off if they notice the navigator is distracted.
  • A good driver thinks out loud and communicates intentions and reasons.

About Navigators:

  • A good navigator is continuously sanity checking code. A bad navigator is checking their phone for text messages.
  • A good navigator anticipates next steps and maintains the overall state of the system, reminding the driver about necessary changes that may have been overlooked. A bad navigator is working on a bug while simultaneously attempting to pair, but not contributing to the pair.
  • A good navigator is aware of their own focus, and suggests switching roles if they notice that their focus is slipping. A bad navigator allows their focus to deteriorate and is afraid of suggesting to switch roles.


  • A good pair has built the rapport between them to share a few laughs while pairing and have fun.


Fair Startup Salary Compensation

Fair Startup Salary Compensation

I was having lunch with my friend in South Park. Earlier that week, he texted me about how we calculate our team’s salaries at TINT. He wasn’t sure if he was getting paid fairly at his startup and wanted to use TINT’s formula as a data point to figure it out. His questions sounded familiar because they were the same questions we asked ourselves when we set up our salary formula! The most important question being: “What is fair compensation for working at my startup?” As cofounders of TINT who’ve created a transparent salary structure that is now implemented for more than 20 employees, Tim, Nik, and I have collectively spent hundreds of hours discussing the answer. Here are my thoughts:


Before we start, we should address the question that was the first question my friend asked: “Is it fair to even ask?” e.g. Is it immoral to be that guy, the squeaky wheel that gets the grease? At TINT, we’ve solved this problem by being transparent, but my friend made me realize that not everyone has that luxury. From stories I’ve heard from other friends, it’s important to ask the question, especially if you don’t have transparency.

Another friend who works at a large undisclosed company in San Francisco told me about how her manager secretly offered an intern a higher salary than hers, even though she’s been working with the company for the past 2 years. Remember that asking the difficult questions about compensation may be the only way to move up. Don’t feel guilty about negotiating.


Fair compensation depends on 3 main factors, which are:

  1. Is it fair in relation to other people on your immediate team?
  2. Is it close to market rate?
  3. Is it fair to YOU?

Is it fair in relation to other people on your immediate team?

At companies with closed salaries, the answer is usually off the table, and even asking the question can feel inappropriate. However, if you can find a way to ask your colleagues in a socially appropriate manner, you should learn more about how others are being compensated. Afraid of repercussions? In the US, the National Labor Relations Act makes it illegal to fire anyone because they shared their compensation within the organization.

Managers have long used salary information asymmetry to try to keep salaries as low as possible, hoping that employees will keep their heads in the sand. But salaries never stay secret forever. It’s a liability for organizations that hide salaries from employees, which makes it even more mysterious why the practice is mainstream. If you are still feeling weird about asking, read these two comment threads on Hacker News here and here.

Is it close to market rate?

Luckily, answering the second part of the fairness equation is easier than the first. There are a variety of resources to give you a general estimate and range.

The challenge in figuring out your market rate is that every company is slightly different, and every role varies as well. Your own market rate will likely be based on:

  • Location / cost of living - Wolfram Alpha Cost of Living Calculator is a great resource for this
  • Company size/risk - Riskier businesses will usually involve sacrificing salary for equity. But you’ll have to judge whether the potential equity value, increased ownership and other perks of working on a smaller team will outweigh the sacrifice.
  • The role’s growth opportunity - If the role could be a valuable stepping stone in your career, it could possibly be worth a sacrifice in salary.
  • Job perks - Free lunch, 401k matching, mandatory vacation policy, and other perks should be included in your calculations regarding the total compensation package.

Is it fair to YOU?

The third part of the fairness equation is the most interesting because it is both the most personal and also the most important component of the equation. It encapsulates your current situation as well as intangible factors that can make the biggest impact in your day-to-day work life:

  • “The Happiness Factor” - A totally subjective value that depends on you. It represents how happy you would be working on this team because of the company’s mission, the people on the team, or the flexibility it’s structure allows. For many people, this is the most important factor.
  • Your potential growth opportunity - If you could grow into a role at an accelerated pace, management may be willing to pay more.
  • Your skills - Do you bring anything special to the table that few others could? List them out, and don’t be shy about bringing them up. An angel dies every time a talented but quiet employee gets screwed by their management.

How do I figure it out?

The resources below will give you a range and you will tweak that range yourself based on the conditions above.

Wealthfront startup salary chart

The strong point of this chart is in how it visualizes the ranges of compensation and the ways it allows you to filter down into its dataset via role and company size. Just keep in mind that sometimes the data available is small, and that many other factors such as the Happiness Factor are not included in the compensation.

Angellist Salary Chart

Although it’s filters aren’t as good, Angellist has a larger and more timely dataset than Wealthfront, and is another great place to begin exploring compensation ranges.

First Round Capital: Startup Compensation

This is a great article if you’re wanting to convince management to adopt a more transparent approach to compensation. It reveals that many large companies do attempt to make some effort to make their compensation transparent, and that even the biggest names in the startup world recognize the fact that good compensation structures are founded on openness and formula.

Buffer Salary/Equity Formula

This formula, from Buffer, that Tint used as the basis for our formula, and serves as a great example of one startup’s salary structure.

Wolfram Alpha Cost of Living

Did you know that Wolfram Alpha can give you a cost of living index between two locations and visualize it? Pretty handy to figure out how location influences your salary range. Sometimes it can make a huge difference!

Quora Startup Salary

Quora has an active community discussing startup salaries and the plethora of questions surrounding them. A great place to browse and explore, there are well-written answers to questions ranging from “Is it worth working at a small startup for no salary but for equity?” to “What technology skill set pays more in Silicon Valley: front-end, back-end, mobile or user experience?”.

Now what?

The worst thing you can do to yourself is to voluntarily remain ignorant about how fair your salary is. I guarantee that if you remain ignorant in a closed-salary organization, your salary will end up being unfair, maybe even less than the intern’s. Unfortunately, most organizations would prefer you to stay ignorant. Educate yourself and fight back against information asymmetry. Or even better, work for a company like TINT that prides itself on fair compensation and implements innovative compensation structures such as monthly company-wide bonuses. Your most valuable asset is yourself, so don’t let people have it for less than you deserve.

Move Fast and (actually) Break Things


“Move fast and Break things” (MF&BT) The company mantra is as common in the Bay Area startup scene as a Chrome backpack. Why? It captures the essence of why startups run by 20-somethings in hoodies can make headway into markets monopolized by Fortune 500 behemoths. More generally, it describes why the Silicon Valley tech industry has thrived in the last half century. If you are involved in tech, it defines the culture here: Take risks. However, the phrase has become so common that seeing a startup poster with MF&BT has become the equivalent of a boat of rowers paddling above “TEAMWORK”. Plenty an engineer has worked with a team that espoused the notion without success. The thing is, it’s not enough to tell employees to MF&BT and expect a faster MVP in the same way that it is meaningless and silly to tell people to increase their “TEAMWORK”. how excited would you be to see this poster at work? These phrases become vapid in the mouths of management who have forgotten the hard work involved in creating human and computer systems that naturally embody these ideals. The mistake is in thinking that these behaviors are the root of why some startups are successful and some are not. It is a mistake to aspire to these ideals without understanding that teams MF&BT not because of a poster in the break room, or because management told them so, but because that is what naturally comes to them. Some teams just naturally MF&BT, others don’t. But why? Teams that MF&BT:

  1. Are not afraid of repercussions when they break things
  2. Quickly learn lessons when things break
  3. Can “unbreak” their systems with minimal effort

To make this happen, they are usually good at creating policies and cultivating workplace habits that focus on feedback, learning from failure, and fault tolerance. Let’s examine these in more detail: dramatic business people wearing black and white

1. Feedback

Employee feedback systems are boring, unappreciated, and hard. Creating a culture that encourages everyone to give feedback everyday is even harder. But doing so ensures that everyone can feel safe when they break things and know that it is sanctioned within the group.

  • When someone has a great idea, let them know! Feedback doesn’t just mean negative feedback. Positive feedback increases morale and helps communicate what everyone wants to see more of.
  • Cultivate your ability to give constructive criticism. This is a difficult skill that can take a lifetime to perfect, so start today! When having to give criticism, approach it as a challenge to improve your skills instead of an uncomfortable task to get over with quickly. There is nothing that you can read that will improve your skill here. Instead, find people who are known for giving good feedback and watch them. Then, find every opportunity you can to practice what they preach. I’ve found that people who are good at constructive criticism are great listeners, empathizers, and instinctively know how to walk the fine line between being realistic and demoralizing.
  • Make feedback a regular process. At Tint, we’ve instituted monthly feedbacks where every employee requests feedback from at least 3 others in the company, and we take company time to introspect and write thoughtful feedback about what we like and what we want to see improved professionally.

Fail wall at Spotify

2. Learning from failure

Creating policies that allow people to examine failures without blame.

  • After every failure whether it’s a missed sales quota or system downtime, gather the team and figure out what went wrong, without blame, without shame. Etsy has a great blog post on how they conduct their blameless postmortems. The most important thing is to NOT ignore failure and to NOT point fingers.
  • Write down what went wrong, why it went wrong, and what was learned, and email it out to everyone on the team. It helps everyone understand that failures are okay as long as we learn from them and helps spread knowledge.
  • Write blog posts about your most notable failures. That way the community at large can see how you fixed the problem. You’re probably not the only one that failed!
  • Create a fail wall at the office! You can see from the photo above the Fail Wall created by Spotify engineers. They have a great video series on their engineering culture that describes how they promote failure.

XKCD: Move fast and break things

3. Fault Tolerance

Being able to easily tell when things go wrong and being okay with changing direction quickly is easier said than done. For some occupations, it’s not even possible. Luckily, it’s probably possible for you.

  • Aspire to having great continuous integration - This means having high test coverage, fast build times, and as much automation as possible. Obviously, this takes a lot of work. But the faster it is to test code, the faster it is to write code.
  • If your team is large enough, aspire to have innovative continuous integration. Github’s ChatOps is a great example of how far operations can be taken.
  • Be disciplined about ROI. At TINT, we used to not formally measure the impact of going to social media conferences. However, we realized that without measuring impact, we can’t discern whether it was worth it or not.
  • Leadership should encourage experimentation. If someone has an idea that they think could improve the process, but involves some risk, they should be given the freedom to try it out! Teams that experiment naturally fail more (in a good way), and failing more will make a team naturally more fault tolerant.

None of these techniques make for a sexy inspiration poster, but iterating on HR surveys for employee feedback are just as important, if not more important, as how many features get shipped every month. Ironically, MF&BT requires the opposite of recklessness. It requires attention to detail and more importantly, discipline.

2014 Financial Year in Review

I did an analysis of my transaction history for 2014 to try to dig up answers to a question that has been in the back of my mind all year: How am I spending my money?

In the spirit of radical transparency, here are the numbers.

Dataset: Transactions

  • Paychecks: $75225
  • Savings: $39713
  • Savings Rate: 52.8%
  • Rent: $9600/yr
  • Average Rideshare Spend: $121/mo
  • Average Total Transportation Spend: $252/mo
  • Big costs of 2014:
    • Equity: $7096.61
    • Personal Debts: $2300
    • Trip to Chicago: $635 + $608 flight for 2 = $1243
    • Trip to Denver: $595 trip + $262 flight = $857
    • Trip to KC: $514 trip + $345 flight = $857
  • Total 2014 Subway Spend: $172
  • Average Subway Meal: $6.10
  • Total 2014 Lee’s Deli Spend: $389
  • Average Lee’s Meal: $8.84
  • Approx. Calculated Food/Discretionary: $876/mo -> $29.2/day


Image of Transportation 2015

Conclusions and Goals

After taking income and subtracting savings, travel, transportation, and unexpected large costs, the remainder comes out to about $876/mo, which is $29.2 a day in food and discretionary spending. If I were to reduce this by 30% and save it, this would come to around three thousand dollars, which would increase my savings rate by only 4%, and given that a 30% cut in discretionary income would involve changes in my lifestyle, I would say that I did a good job this year of managing my money appropriately. What this analysis says is essentially that my food and discretionary (not including travel) spend comes to use 13% of my annual income, which I am comfortable with.

Total travel spend this year comes down to approximately $3.5k, which is about 5% of my income, which I see as too little. In 2015 I want to travel more, and aim to increase that percentage to 7% which would correspond with a large trip every quarter.

As for savings goals, my goal is to continue to avoid lifestyle inflation and exceed a savings rate of 55% for the next year. If no large unexpected big costs occur, then both my travel and savings goals should be attainable. If some unexpected costs come up as they always do, I will probably cut into my savings rate rather than travel just because I may only have the next decade to travel with true freedom. Another goal for this year is to find a way to put more time into volunteering and philanthropy as I know many people don’t have the opportunity to pursue financial independence.

How I reduced my reddit consumption

My monthly self improvement challenge this month was to reduce low quality info consumption. More specifically, reduce the time I spend on Hacker News, Reddit, and other news sites. Why?

  • Value: Although entertaining, the content is ultimately offers little value after being read.
  • Time: Too much time spent passively reading, easy to “veg out”
  • Opportunity Cost: There are many higher value things to read that I would personally feel more gratification reading.

How did I go about doing it?



October Productivity

December (reducing bad media)

December Productivity

Some interesting numbers:

  • reddit, HN, medium, vanity fair, chow, sfgate, nytimes: Top distracting sites in October
  • 10 hours -> 4 hours: 60% reduction in time spent on distracting websites from October to November
  • 63 hours -> 64 hours: negligible change in time spent doing software development
  • So over the course of the month, I added 6 more hours to my life by just reducing the time I vegetate!


The hardest thing was to stop the habit of opening a new reddit or hacker news tab during any downtime. It was practically muscle memory! I found myself opening and immediately closing tabs many times a day for the first week. However, the knowledge that my performance was being monitored by RescueTime helped keep me going.

One thing I anticipated was that I would be less up-to-date both in local and technical news. However, I was surprised to find the funniest or most important content being shared in the company chatroom anyway, filtered and curated by my friends. This didn’t end up being as large of a problem as I originally anticipated.

In the end, I felt more focused and able to direct my energy toward more difficult media consumption goals. I got halfway through a book that I would not have picked up if I didn’t do this challenge. I believe that it’s worth continuing this challenge into the future and hopefully I’ll have the discipline to do so.


  • Write down a list of alternate content to consume in advance.
  • Download books that you’ve been meaning to read and on your phone so you’re not languishing in the
  • Track yourself using RescueTime both as a motivation tool and to measure your performance change.

The Mythical Man Month

In 1975, Frederick Brooks wrote a book on software engineering that is still applicable today in 2014! That book is called The Mythical Man Month and I found myself relating to many of the software-related scheduling and planning issues Brooks encountered almost half a century ago. Below are some of the key ideas I found the most compelling:

Programmers are naturally optimistic and programming is a tasks that lends itself to optimism.

Brooks argues “All programmers are optimists… Perhaps the hundreds of nitty frustrations drive away all but those who habitually focus on the end goal.” which leads to a false assumption that engineering tasks will take only as long as it ‘ought’ to take. However, any software development effort usually consists of many tasks chained end-to-end. The probability that every single one of them will go well is almost zero considering the perfection necessary as a programmer. Considering the volume of nitty frustrations I encounter everyday I definitely relate to being an optimist and have observed others and myself misjudge how long things will take due to this optimism.

1/3 planning 1/6 coding, 1/4 component tests, 1/4 integration tests

From my anecdotal evidence, this breakdown for how software time is spent is spot-on. Almost all of the engineering tasks that have been underestimated at our company have not taken into account the amount of time needed to properly test and integrate the system before it is production ready. Much of the reason is because we (the engineering team) are transitioning from “startup-mode” where we didn’t need as adequate testing because we had fewer customers. More customers find more bugs, so our acceptable threshold for stability has increased. And with it, the effort spend testing. I’ve just recently started to integrate testing into my estimates and so far both quality of product is higher, and target completion dates are more accurate.

**The Mythical Man Month: More people doesn’t equate to faster completion. **

Consider the following 2 graphs:

communication overhead illustrated

time vs number of workers

The first one shows just how difficult it is to maintain communication among more than a few people. The second illustrates how many months a project will take given the number of people on a team. Organizing work around a complex task is difficult with more people involved. But just how much more difficult did not dawn on me until I saw it visually. This bolsters my belief that features should be owned by two people or less who serve as a hub for collecting the knowledge necessary.

The Second System Effect: The second system you build will tend to be overengineered due to pent up desires

We are in the beginning stages of building parts of a second system, so we have not witnessed this yet. But after reading this chapter I will be more vigilant to make sure every part of a spec has solid business value, and to watch out for costly unimportant components.

Better to extend the schedule than release a half baked product

Brooks uses the analogy of an omelette as a delayed software project. You can either spend more time cooking it properly, turn off the heat and serve it raw, or turn up the heat and burn it. However, from my experience it is actually more effective to use less egg from the start. I actually disagreed with this point because you can better hit a target by removing non-essential functionality from a feature early on in the planning stages, which is a better alternative than extending the schedule or releasing a bad product.

Conceptual integrity is the most important consideration in system design

“It is better to have a system omit certain anomalous features and improvements, but to reflect one set of design ideas, than to have one that contains many good but independent and uncoordinated ideas”, Brooks comments, “[the] ratio of function to conceptual complexity is the ultimate test of system design”. This definition is great because it describes what distinguishes good code from bad. It also helps in clarifying the objective of certain processes we have at the office, like code reviews and pair programming. By collaborating and reviewing each other’s code, we can hold each other up to high standards and maintain conceptual integrity.

An interesting example they brought up of conceptual integrity was the WIMP (windows, icons, menus, and pointing) interface of the modern GUI. I never much thought about it until now, but on further inspection, it is incredible how much can be done on a modern OS with such a simple concept (compared to typing commands in a terminal, as was computing before the GUI).

Documentation is an essential tool that can be the difference between catastrophe and success

A couple of months ago we started instituting a process where features are planned out using spec documents. It was a process modeled after what we were already doing informally: putting together a rough outline of how we were going to build things out so that we could get feedback on it. Over time, we’ve seen these documents come in handy, but only if the document has an owner, and only if effort is put into it to make it the canonical source of truth for anything related to the feature. This requires careful effort in not just making sure the spec covers all the details, but also in writing it such that it is easy to digest. I think the ability to write organized prose is undervalued among technical people, as this is essential in making sure a spec document delivers value.

People don’t set targets or write specs if they feel the organization will not see them as tentative.

I liked this note because it makes sure we understand that specs are living documents and never to expect them not to change over time.

Members of the team need to strive to be flexible because change is the only thing that’s guaranteed.

“Structuring an organization for change is much harder than designing a system for change. Each man must be assigned to jobs that broaden him, so that the whole force is technically flexible. On a large project the manager needs to keep two or three top programmers as a technical cavalry that can gallop to the rescue wherever the battle is thickest.”

I truly believe that the last sentence applies to each member of the current engineering team and our aspiration is to make sure every member can be part of the “cavalry”. It helps define who we are looking for technically as well, since we expect every member of the team to respond fast to changes in requirements, fast in both communication, understanding, and implementation.

Program maintenance is 40% more than cost of development

Cumulatively, I’ve spent a few months out of this year working purely on regressions and bug fixes. We need to always remember that maintenance is far larger than development. It is especially important as we build out features and make choices about technical debt and how much effort we’re going to spend time on testing. Because a couple days spent on testing can save us weeks of time fixing bugs. And it results in happier customers!

Bugs will naturally scale with time and customers

Bugs found per month vs months since installation

The more time customers spend with a product, the more bugs they’ll find as they bump into edge cases. I’ve experienced this firsthand as well.

Tooling - make effort to share and find tools. Unified toolsets can boost productivity.

We definitely embody this both on the engineering and customer happiness teams at our company. Although, as we’ve grown it’s become more difficult to get tool usage to be adopted company-wide. Finding and adopting great tools is something our company culture promotes.

Disastrous schedule slippage happens one day at a time.

The takeaway for this point is that it is essential to recognize slippage faster and communicate it clearly. One thing that I have found works is setting more granular targets that allow for more segmented estimation. Targets that are 1-2 weeks are less likely to be totally derailed than targets that span multiple months.

Milestones need to be concrete and defined with ‘knife edge’ sharpness. On the flip side, fuzzy milestones are actually millstones that grind down morale

I have seen this first hand, but wasn’t able to pinpoint exactly what was causing the problem. I am glad to see the idea expressed in a way that presents the root of the problem clearly. Milestones need to be concrete. This is where having a test suite comes in handy, because tests either run green, or they don’t.

No silver bullet, software is inherently complex and no management or process changes can improve the inherent complexity.


Closest thing to silver bullet is to buy not build

We’ve been lucky to have the budget to buy and not build, and personally my preference swings towards buying instead of building for components just because maintaining your own system is expensive! I found it fascinating to hear someone from 40 years ago, before the existence of SAAS, say the same thing.


In conclusion, I think the book had a plethora of wisdom, a good amount of truisms, but allowed me to better form a framework that our existing engineering processes can call on for justification. For example, pair programming doesn’t just make our code vaguely better, it establishes a consistent conceptual integrity. Why do we set concrete targets for ourselves? Because disastrous schedule slippage happens one day at a time. And how about removing that feature from Tint 2.0? Because of the Second System Effect! The reason why this book is timeless is because it’s about people, not software, and as long as writing software is complex, people, not computers, will be the ones dealing with the complexity.