Spinning Code

Solve Interesting Problems

During a recent department event my wife introduced me to the sister of one of her students. The event was an award ceremony for some of the history and political science majors – the student’s sister was along to support her brother (and as a smart college student get a free meal outside the dinning hall).

As a CS major, this student is trying to understand her options for what kind of programming she might be interested in. As a good professor my wife introduced us so the CS major wouldn’t have to pretend to be as excited about history as everyone else present. Listening to me talk about what I’ve done in my work she commented that maybe she should be a web developer – my reply was that she should do the work with problems she finds interesting.

Dodge the Gate Keepers

Like many people who are going through a CS education she has been surrounded by people who are confused about the difference between IT and CS. She talked about going to a conference and running into a bunch of guys who belittled her because she wasn’t into computer hardware. Apparently one even criticized her for misstating the directionality of a Lightning adapter (I can’t remember the last time I cared about cable directionality in a digital connector). My suggestion that was old, familiar, and involved one finger. I also pointed out the guy was probably just wrong.

That kind of adolescent gate keeping out of other college students isn’t surprising, but it is annoying. I work in a field that’s short handed, and we need smart people interesting creating great systems. We were short handed before the whole U.S. economy started to run short on workers.

From a short conversation I could tell she was smart, capable, and friendly – exactly the kind of person any employer will be lucky to have some day soon. But she also felt discouraged, as if she was weak in some important part of the field. Assembling her own PC hadn’t been fun for her (I have no shame in admitting that I’ve never built a PC from scratch); fussing with hardware just doesn’t excite her like coding does right now.

I really love having a good IT team to support my work. And having great hardware at my disposal is critical to good work. But I have minimal interest in working on that part of the technology stack myself. Sure, I’ve done my time installing RAM chips onto mother boards, and re-seating PCI cards, but I never really wanted to care about the details of those components.

I always wants to create tools that solved interested problems.

What Problems are Interesting

We are all attracted to ideas and projects that sound exciting. This student became interested in the kind of work I do because I can talk about it with excitement and confidence. I enjoy the problems I get to solve on a day-to-day basis and that shows. I have no idea if she’d enjoy them. Being a Salesforce or web developer might bore her to tears.

A problem is not intrinsically interesting. We find problems interesting for our own reasons. That interest makes us intrinsically motivated to solve them.

I like writing middle-ware and creating related tools. Filling gaps left between other tools is interesting to me. I know people who love to create great UIs because it makes people love the product. Other friends love to work on security problems because it keeps systems secure (and gives them excuses to break into systems they should access). Some of my friends work on creating software to advance science. Still others love to create high performance solutions to handle big data problems. And others who help create games. I could go on, byt you get the picture.

My point is all the problems are interesting – to someone. None of the problems are interesting to all of us.

If you want to have or want a career creating software, look for jobs that solve problems you think are interesting. It doesn’t matter if I think your work is exciting. If you are excited about it, I’ll be excited to hear what you’re doing.

Salesforce Developer Podcast Episode 119

This week’s Salesforce Developer Podcast featured an interview I did with the host, Josh Birk, the end of last year. As much as I still don’t like the sound of my voice on recordings it was a fun interview and I am really excited to see it come out.

We talk about Snowfakery, Salesforce Open Source Commons, the evolution of PHP, Drupal, my career in general, and even a bit about spinning. I’d love to hear what you think.

Supportive Open Source Projects

For a little over two years I’ve had the privilege of helping lead the Data Generation Toolkit Project for Salesforce. In general, Open Source projects are not known for their inclusive and supportive communities. I believe it is fair to say our project demonstrates that building a supportive community can yield great results.

Started by a question I wrote on a piece of paper in 2019 and posted to a wall, the project grows more every time I look around. That first meeting inspired the creation of Snowfakery and the recent training for Snowfakery has attracted more than 300 registrants. Contributors created documentation and presentations to help Salesforce admins learn to seed sandboxes. We are building a recipe library to help people starting out on Snowfakery. We launched two faker data providers. This year we reviewing and documenting other tools to generate and move Salesforce data.

More importantly we’ve built a project that is useful to the community, and supports new contributors to open source projects.

We did not get here accidentally. The project leadership wants to support the community members as much as create new tools. From the beginning we chose to encourage people unfamiliar with open source projects, contributions, and technologies.

Leading an Supportive Open Source Project

To be a supportive project starts with the leaders.

The project currently has seven identified leaders: Alisa Edwards, Allison Letts, Jung Mun, Paul Prescod, Samantha Shain, Cassie Supilowski, and myself. For those who like diversity statistics: that can be seen as 5 women, 2 men, 3 countries of residence, 3 counties of origin (not the same 3), 3 developers, 4 Salesforce admins, and at least 3 racial identities. No sub-group perfectly reflects its community, but that’s not a bad start in my opinion.

We have agreed that some of us lead specific sub-projects, while others tend to the health of the community. Everyone has a role.

When we started out with just 4 leaders either Paul (as creator and maintainer of Snowfakery) or I (as the person who started the project) could have dominated the project claiming founder status. Certainly men leading other open source projects have used that status to control the project direction. But Salesforce Open Source Commons projects are designed to discourage that behavior, and Paul and I embraced that design. Cori O’Brien created a wonderful space for our project to grow within.

Any open source project should focus on supporting its users. Our goal as a project is to support the Salesforce community – particularly nonprofit and education users. To do that we need the insights that only come from being open to outside ideas.

Our project’s leadership also established a pattern of self-review and reflection. Each year we will gather to discuss if the leadership group is the right size, and if anyone needs to step down. That creates a space for us to routinely reflect on what we’re doing and if we’re doing it well. The invitation to leave frees people from responsibilities they have to the project without frustration or burn out.

Recipes for Open Source Projects

Beyond our leadership team structure we also are intentional about how we encourage all contributors.

The project as a whole has space for all kinds of contributions. If someone wants to write documentation we will support them. When someone wants to learn to write recipes, we will work in pairs to get through the first one. People who want to write Python code for our faker providers get the chance to do that too.

We try to be kind to all contributors. We thank everyone when they open pull-requests or issues. Even I get thanked when I open a PR. Even if someone needs to make significant changes to a contribution before we commit it, we make sure to praise their effort.

These aren’t shit sandwiches. We genuinely appreciate all contributions and divorce that from any corrections that we request. As a long time open source contributor I am surprised at how much I appreciate the messages.

Maintaining Supportive Discussion Channels

Most open source maintainers know we have to maintain good ways for contributors to ask questions. Over time projects have used a wide variety of tools: IRC, News Groups, Email, issue trackers, and now Slack. In most projects those are the spaces that tend to become ugly. You see RTFM-style answers, personal criticisms, identity-based attacks, and other ugliness. The Slack channels for our project are a key piece of how we engage with each other, and support new contributors. We work hard to make sure people get timely answers, clear directions, and steady encouragement.

Our work falls within a community where our reputations matter and so there is a level of decorum absent in some open source projects. But no group is perfect and we will address issues when they arise. The open source common’s DEI Framework project will hopefully help us continue to deepen our understanding of the community. The kinds of attacks open source community spaces frequently allow in the name of good code, are simply unacceptable.

Come Join Us

The next community sprint for our project is coming up May 4th & 5th.

Still a B Student

Now and then something happens to us all that reminds us we’re not perfect. I’ve written about the strengths of B Students before, and well last week I proved that sometimes I still have room to improve.

When I started this blog I set the goal of posting once a month. I have stuck with, or exceeded, that target except for two months since 2016. I missed September 2019 and last month. Last time around I set the goal of four posts in the following month, so that is my established case law.

I did have a piece written that I had planned to release. But it needed another editing pass. Before I got those edits done a project at work took over my life. I will have those edits done for next weekend, and I already know the general topic of the post what will follow it.

Last time the increased pressure to write actually resulted in some descent pieces. I wrote that B Student piece, which I quite like, in part as a response to that failure. Two others from that month are how-to articles that continue to get good readership years later.

Hopefully this will be an equally good month (although this post probably isn’t the best example of that).

Disable a Salesforce Trigger in Production with VS Code

I recently had to disable a Salesforce Trigger from a client’s production environment. Having discovered the need last minute for a project, I needed to react quickly and make the change quickly. Since the official documentation is a bit lacking I decided it was time for blog post of better directions.

The Salesforce CLI directions in the article above make a few annoying assumptions:

That you’re happy to use MDAPI not SFDX.
That all your tests pass in production (which should be true, but let’s be real it;s always).
You enjoy working out CLI commands in a rush.

What you need

Disabling a Trigger Using SFDX in VS Code.

Connect VS Code to your target org. Easiest solution here is to just go to the command palate and authorize an org.

VS Code command pallet wit with SFDX commands shown.

Pull down the trigger from your org. I find it easiest to go to the metadata browser, find the trigger under Apex Triggers and click the download icon on the right.

Screenshot of VS Code org browser showing an Apex Trigger to download.

Update the trigger’s XML file to change the status. All project code files in SFDX are accompanied by an XML file of class metadata. Open the file and change the status to “Inactive”.

Screenshot of the VS Code file browser with the trigger's metadata file highlighted. — Navigate to the file in the file browser on the left in VS Code

Screenshot of code snippet emphasizing the Status XML tag and Inactive value. — In the editor change the status from Active to Inactive, and save the file.

Generate a Manifest file for your trigger. Right click on the trigger code file in VS Code’s file explorer and select Generate Manifest, provide the file a useful name (in this case used DisableTrigger)

A segment of the contextual menu to show Generate Manifest command's location. — Depending on your setup the context menu might be quite long, SFDX additions are generally near the end of the list.

Deploy the change. If all your org’s tests currently pass you can just right click on the new manifest file and instruct VS Code to deploy the source in the manifest to the org.

What if tests fail on trigger deploy?

While all our tests should always pass all the time, Salesforce admins frequently find that’s not actually true in practice. Heck this trigger could be part of that problem in your org. But to deploy a code change we have to run some tests. Since we are disabling this trigger, the trigger code doesn’t need to be in tests we just need to run a working test with enough coverage to get the job done. So go find a test class and then we can deploy.

VS Code doesn’t currently have a setting to set the testing mode on a deployment, so you’ll need to do this last step in VS Code’s terminal (available by hitting control-` if you don’t have it open already). In the terminal run the following command (replacing [good_tests] with the name of your test class).

sfdx force:source:deploy -x manifest/DisableTrigger.xml -l RunSpecifiedTests -R [good_tests]

It should deploy pretty quickly, but you can check the status by going to settings in your org and checking the Deployment Status.

Project Estimates Tool 2.0

A few years ago I wrote a piece about project time estimation and created an estimating tool. My goal was to get project managers to listen to the fact that estimates were inherently a guess not promise. The tool I created took a series of project tasks, the estimated time range, and a level of estimator confidence. It then ran a Monte Carlo simulation with those tasks, and generated a histogram of possible outcomes.

Five years later I still use it for project estimates. But I have grown tired of its interface weaknesses and needed to add cost estimation to keep it useful. So I recently heavily revised the tool and posted an updated version (the old version is still available here).

The interface is still very utilitarian (pull requests welcome), but this version makes it make easier to adjust the tasks. Much more importantly it now also estimates costs, not just time.

XY Scatter plot of cost projections, in a nice bell curve shape

The new version is faster than the previous. And it adjusts the graph type based on the range of possible outcomes.

Each task now includes inputs for min and max time, confidence, and hourly cost of that task. So if different people at different bill rates are part of the project it can still give you useful numbers.

The histograms broke down when faced with too many bars. So I settled on an XY scatter approach to help visualize the broader range that the cost estimator made normal.

Please give it a try, I’m always open to feedback, suggestions, and pull requests.

Costs Estimates

For this version I added the ability to include a unit cost for each task. The first tool worked just fine when you were estimating the tasks for one person who had one billing rate (or where hourly costs aren’t important). In practice teams need to be to able to do an estimate across all work streams, and different roles will have different billing rates.

This version includes a rate for each task and a graph of projected project costs.

Why I Created A Project Estimator

I wrote the original when I was struggling with project managers who would take any estimate you gave them as a range, pick a number, and promise the client (and themselves) we would hit it. To them an estimate was a promise – one that had to be kept. That lead me to badly overestimate projects so that the lowest end of my range would be a safe number – but that’s just a different form of bad estimation.

Histogram of time estimates. — This graph from the original version helped convince PMs that estimates weren’t promises.

I had a good amount of experience providing estimates, and had read a lot on the topic. I knew there were teams that did better and I wanted to help our team improve.

The original tool was loosely inspired by one Joel Spolsky described ten years earlier. He has several important ideas on his process regardless of your project methodology. But his idea of using Monte Carlo simulations had stuck with me since that article had been new. After failing to find a tool that included it, I wrote my own.

Are the Project Estimates Any Good?

Fundamentally the simulations are only as good as the estimates provided. For any project I have been able to compare my simulated project estimates to final hours my work fell within one standard deviation of the median.

The confidence measure helps more than I expected. Originally, I added the measure of confidence because I needed something to determine how often the simulator should assume people are just plain wrong – and by how much. While I could have hard coded a solution I did not know how to pick good values. I knew that my confidence varies by the task. I also knew the less confident I am the more I am likely to be wildly off. So decide to make confidence an estimator provided variable, and use that to pick the size of overruns.

For every 10% you reduce the confidence, the simulation will allow the upper bound of the estimate to increase by the size or the entered upper bound. On a task you estimate at 7-10 hours, a 90% confident estimate will allow overruns up to 20 hours (just in the 10% of times that aren’t in the 7-10 range), and 30 hours for an 80% estimate.

That extra box also immediately helped me feel comfortable with my estimates. Knowing that the simulator would offset optimism bias for me I could stop trying to do that myself. My estimates can use tighter ranges trusting the software to offset expected bias.

A Value of the Graphs in Project Estimates

The graph has turned out to be the most important feature. Initially I included it because I wanted to play with D3 and have something more impressive than numbers to show. What I discovered was a reminder of the importance of data visualizations – even simple ones.

As I said before I created this tool when working with project managers who simplified all estimate ranges to a single number and held everyone to that number. The first time I presented numbers from the simulator those project managers picked the median and complained I made it too hard. The median was better than what we had before, but not enough to treat as a promise.

When I started presenting the graph those same people immediately started to change how they talked about the project. By visualizing the impact of uncertainty over several tasks they could see that the project might run far over my estimate – or far under. The more uncertainty, the longer the tail on the graph.

Suddenly they were comfortable talking about risks from overruns, finding ways to help clients understand the possible risks, and being understanding when a task proved harder than expected.

The graphs tell the story, and empowers the team to have an honest and productive about project estimates.

Estimate your own project timeline.

Announcing Two New Snowfakery Faker Providers

For the last two years I’ve been fortunate to serve as a leader of the Salesforce Open Source Commons Data Generation Toolkit project. That project has produced and inspired a variety of efforts, including Snowfakery and a collection of starter recipes.

This week, along with my colleague Allison Letts, Salesforce’s Paul Prescod (the creator of Snowfakery), and our fellow project contributor Jung Mun, I helped create two new faker providers for Snowfakery:

What These Faker Providers do

The use of Snowfakery is growing. The more we use it, the more we want the data tailored to specific projects. And the more we find places where the Faker project’s providers do not have quite what we want. In particular the project does not (well did not) have providers for nonprofits and education specific data.

The Nonprofit provider currently just provides organization names:

$ snowfakery snowfakery_nonprofit_example.recipe.yml --target-count 10 nonprofit
nonprofit(id=1, nonprofit_name=Eastern Animal Asscociation)
nonprofit(id=2, nonprofit_name=1st Animal Foundation)
nonprofit(id=3, nonprofit_name=Upper Peace Alliance)
nonprofit(id=4, nonprofit_name=Southern Peace Home)
nonprofit(id=5, nonprofit_name=Unity Home)
nonprofit(id=6, nonprofit_name=Western Peace Home)
nonprofit(id=7, nonprofit_name=Upper History Foundation)
nonprofit(id=8, nonprofit_name=Upper Friends Committee)
nonprofit(id=9, nonprofit_name=Eastern Pets Center)
nonprofit(id=10, nonprofit_name=Northern Animal Foundation)

For the Education provider we have a bit more. You can generate college names, departments, and faculty titles.

$ snowfakery snowfakery_edu_example.recipe.yml
Account(id=1, Name=South Carolina University)
Contact(id=1, FirstName=Roberto, LastName=Stanton, Title=Associate Professor of Microbiology & Immunology)
Account(id=2, Name=French)

The providers can of course run as a standard faker community provider. Once you are setup with Faker just add the new providers with pip:

pip install faker-edu faker-nonprofit

Then you can use the libraries in your code:

from faker import Faker
import faker_edu

fake = Faker()
fake.add_provider(faker_edu.Provider)

for _ in range(10):
    print(fake.institution_name())

from faker import Faker
import faker_nonprofit

fake = Faker()
fake.add_provider(faker_nonprofit.Provider)

for _ in range(10):
    print(fake.nonprofit_name())

How we got here

A few months ago I posted on how to extend Faker to create nonprofit organization names. Allison took that as a starting point to create a similar project to generate names of colleges, departments, and academic titles (and a greatly improved phone number generator but that’s not included since it’s more general). Both of these projects were good proof-of-concept but were rough around the edges. So this week, with Paul’s guidance and Jung’s input, we contributed the more polished versions to the community.

During a virtual working session on Wednesday we restructured the projects, cleaned up code, added sample recipes for Snowfakery, and published them to PyPi. By publishing these as Faker providers on PyPi, and not as Snowfakery plugins, they are available to a wider audience. By having them owned by a larger open source community we are expecting them to enjoy long-term support.

Both are still just getting started. For example the nonprofit one still just generates organization names, but would benefit from job titles, program names, and more. The EDU provider right now just handles colleges, but is expected to generate other education related data in the future. We also have a plan for a third provider to help improve the diversity of the names generated by Faker to make our fake data more representative of real communities.

The Data Generation Toolkit project has been a great example of what happens when you bring people from a wide variety of backgrounds together to solve technical problems. Like the larger Data Generation Toolkit team Paul, Allison, Jung, and I all have different backgrounds, skills, and experiences. By coming together we are able to help each other find better solutions than any one of us would have found on our own.

It’s been an exciting week, and I’m looking forward to more to come.

Faith’s Joy

This is Faith. Faith has found joy, happiness, and contentment – except when she needs her toenails trimmed, dinner is late, or she gets woken up for a nap.

Life for Faith wasn’t always easy. She spent a little time on the race track before they closed, and was among the last dogs to ever race in Florida. Her first adoptive home didn’t treat her well, and she bounced back to the rescue group underweight, with a cracked tooth, and uncontrolled multi-drug resistant worms. But those problems are gone (at least we hope, the worm treatment takes several months and we just got our first clean test last month – need three in a row). But Faith found a life she loves in our home.

She loves to cuddle with her brother.

Greyhound in a room covered in shredded paper towel with a toy in her mouth.

She loves paper towels.

Greyhound sleeping on a couch with her feet in the air.

She loves a good inverted nap.

Greyhound curled in a ball, sleeping on a chair.

She even loves hiking with us in the mountains of North Carolina (trust me on this one, she doesn’t hold still again until she’s napping when we are done).

Like many people I aspire to be more like my dog. She has handled life’s ups and down with grace (mostly) and forgiveness (graciously – even after I trim her nails). I hope to learn to be as openly loving as forgiving as her.

Build Cycles of Respect

In consulting we should expected developers to do all the same basic things as everyone else on the team. That includes supporting a culture of mutual respect with other team members.

We should explain our work clearly, be effective in meetings, and take feedback well. In consulting especially, we should engage with clients professionally, effectively, and happily. We should not be isolated off on our own teams working from specs we didn’t write, for clients we haven’t met. Basically, we need to be good team members and we can be expected to be that way. Developers, like most everyone else, do their best when when surrounded by people who treat them with respect.

One of the things I really like about my current job is that we have a respectful culture. It’s not that we don’t have disagreements, at times energetic ones, but we work through those challenges as a team of experts with differing perspectives. We are all expected to be experts who can collaborate with other experts, and to explain our work to clients.

Cycles of Disrespect

But that hasn’t been the consistent norm since I shifted to consulting. I have been on teams, and seen teams, that routinely insult the basic professional behavior of developers and other technologists. In those settings leaders made it clear that the standard of behavior was lower for developers than other team members. Most people I know end up adjusting their behavior to meet the standards set for them. Set a low standard of behavior get bad behavior.

I want to be clear that I have no intention justifying the harassment, bullying, and assault that is too common among developers (and more generally by men in the workplace) – that is not the challenge I’m addressing here (although I’ve addressed those issues before). There are behavioral problems in places that don’t permit and cover up that kind of behavior. Developers and other IT specialists are often still allowed to act as a grumpy trouble maker, or aloof superior jerk. We are told our brains work differently somehow allowing us and our colleagues to ignore healthy social conventions. And by allowing it, stating that we expect it, and taking no actions to correct it, we encourage that behavior.

Signs of Trouble

On more than one occasion, at more than one employer, I’ve been told things like:

“You’re talking techie now, I’m not listening anymore.”
“Developers hate meetings and never contribute productively.”
“You just have a thin skin and act defensive of your work when questioned.”

Often the person will laugh as they say these things like we have some kind of shared inside joke about some basic lack of professionalism. There are lots of reasons why people dismiss developers with those kind of statements, but that doesn’t stop them from being destructive.

When that’s your work environment, it’s tempting for developers to degrade to match expectations. Who takes feedback well when told upfront they have a thin skin? Who likes being in meetings with project managers who explicitly state their contributions aren’t welcome? What expert thinks it’s funny when you stop them and say you are not listening?

This builds a cycle of disrespect between developers and their colleagues, dividing teams and impeding progress. When people are treated with disrespect while still performing critical tasks, I expect to see them act with disrespect is exchange. That is no okay, but it very human.

Breaking the Cycle

Respect is a two way street which makes it important to break the cycle on both sides. Everyone on a project needs to respect the roles we each have and the contribution we each bring. Those may be, and usually should be, vastly different across the team – otherwise there is no point to having a team at all.

As team members who are often in a position of some power – in part because we are hard to replace in the current job market – developers should take it upon ourselves to be the first to build respectful practices with colleagues.

We can, nicely, point out when we hear statements that feel isolating and rude. We need to try to understand why someone acts intimidated by the technical detail and find ways to help them along. Developers should make a point of soliciting feedback from team members and processing ideas with them. Everyone ought to build personal relationships across our teams to help improve everyone’s ability to work through hard problems (there is a bunch of research on this question).

Creating a Cycle of Respect

To build an ongoing cycle of respect take more than just breaking the old patterns. Developers are often in a position of leading by example and so that is a great first step. Asking friends and colleagues to make an effort with you will help build a re-enforcing cycle in a small group, which makes a great second step.

Understand that cultural change of an organization is hard, but usually it is possible on project teams. So it may help to start with just one team and making a real effort to improve communication and collaboration with the team. From there build out and try to draw others into your new patterns.

When teams work together well, understanding the project as a whole, they do better work. That puts everyone in a position to add ideas, raise concerns, validate suggestions, and adjust to changes. When teams allow any member to work in isolation they losing the shared vision and project failure.

Being Nice vs Being Kind

A few years ago during a job interview at a nonprofit organization, the Executive Director asked me about the culture of the job I was leaving. He had noticed that I was stressing the importance of good feedback during my interviews and wanted to know why it was so important to me. Indeed, part of why I was job hunting was the job I had rejected the notion of mistakes – everyone’s work was always good. I told him that, and that I do my best work when I get honest feedback. He responded by drawing a distinction between being nice and being kind. He was sure everyone I worked with was quite nice, but they didn’t sound very kind to him.

Being nice when giving feedback just means saying positive things.
Being kind when giving feedback requires helping another person understand how to improve.

It was one of those moments in life where someone offers you words to explain a struggle you’re having and suddenly things make more sense. I knew I was unhappy in the job, but his insight brought into focus the reasons I wasn’t fitting in. Being told my work was great did not mesh with my understanding that it’s important to admit we all make mistakes and find ways to avoid repeating them. My desire to examine my own work caused conflict, let alone my efforts to introduce peer feedback.

The CEO at that job once told me that if people heard their work wasn’t perfect, they wouldn’t want to come in the next day. He didn’t seem to realize that getting dishonest feedback causes the same thing. And seeing no chance for improvement was even worse. There was no room to revisit problems that lead to near failure and ongoing technical debt for the client. Instead they convinced clients the technical debt was a feature, and used it to sell support contracts.

All the feedback my colleagues and I got was nice to hear (“This is great.”, “The client will be thrilled”). But unrelenting positive feedback didn’t help us improve as individuals or as a team. Our leadership gave team members a false sense of success and held the company back from improving.

The thing about being kind, is that sometimes you have to tell people things they don’t want to hear. That can be hard, and to do it well requires you to care about the other person. If want people to like everything you say, it will be hard to be kind.

Giving nice feedback involves offering a string of hollow platitudes that leaves people with a false sense of achievement. That makes people vulnerable to failure when they move forward without understanding their weak foundation. Giving kind feedback requires you to provide an honest assessment of a person’s work and helping them recognize errors. Kindness requires listening to people when they push back, and finding ways to help them find ways to excel as they move forward.

When all was said and done the opportunity wasn’t the right fit for me. Turning down their job offer was probably the hardest career decision I ever made. But they and I left good enough impressions with one another that I recently started serving as an volunteer advisor to the program I would have been running. That service has allowed me to reaffirm my sense that it was the right decision not to take the role both for me and for the organization. And it has given me a chance to help give them some kind feedback to help – I hope – improve their operations. I try to be as kind to them as an organization as they were to me as an applicant.