Tag: Deliver Results

It Is And It Is

It Is And It Is

Last night ChatGPT had a bug. But not your run-of-the-mill problem like increased latency or complete unavailability. No, it went completely off-the-rails: spouting gibberish, repeating itself ad infinitum, and other nonsensical behavior.

Hilarious though some of the outputs were, it was a powerful reminder that AI technologies are still new and mysterious, and definitely require human oversight. While this incident ended up with random output, I can now imagine a whole class of bugs where language model outputs are wrong in all manner of specifically bad ways. Humorous now, but perhaps less so once we give them agency to act on our behalf.

I anticipate the day coming when I ask my personal Scarlett Johansson to book a family vacation to Fiji and it instead sends an email to my mom lambasting her for wearing white after Labor Day and then sells my living room furniture on eBay.

The future’s going to be something else, of that we can be sure.

Forth Eorlingas

Forth Eorlingas

I once had a customer call me an “idea hamster” because of how easily I went down rabbit trails of ideation when discussing the project we were working on. We had a good laugh about that turn of phrase, and I do see the value in idea generation to some degree, but ideas are easy. I’m not impressed by someone who can come up with many of them (least of all myself). What impresses me is people and organizations that can execute on their ideas.

There might be “second half of life” factors at play as well in my desire to get better at finishing. In the last week I’ve added 10 draft ideas to my blog backlog, and this will be only the first one I’ve published. At the rate I’m going I’ll never get done, which perhaps is a good thing, but still, I want to get a few of these ideas out in the wild, and that means I need to power through the writing part.

True Grit

True Grit

Speaking of persisting, there’s two books I’d recommend on the topic:

While targeted to traditional artists like writers and musicians, both have much to say about broader creative activity, within which technological work definitely falls.

P.S. If you’re short on time, you can find a selection of extracts from the latter book in this article: How to Fall in Love with Hard Work.

P.P.S. I almost didn’t write this post because both these books have considerably mixed reviews on Goodreads. But I decided to take their advice, post anyways, and let the reader decide. How meta!

Not Forgotten

Not Forgotten

Generally speaking, people want to know they’ve made a difference in the world that will outlast themselves. Few occupations have the intense immediacy of potentially giving one’s life for the future of human flourishing than that of the solider. It’s an admirable profession that is worthy of respect, especially for those for whom this potential became reality. However, not many are suited for such work, not to mention ideally the need for soldiers will shrink as societies mature.

Thankfully there are myriad other ways in which a person can approach a career with the future in mind. Last week I read through a number of articles from 80,000 hours, and I have the book of the same name queued up as well. The premise is that our jobs take up a considerable fraction of our lives, likely more than any other activity, and thus it behooves us to think deeply of how to spend that time. That’s hardly controversial, but applying logical principles and data-backed guidance to maximize the future impact can be (similar to discussions around Effective Altruism).

Personally, I find the arguments compelling. It’s why I’ll spend the rest of my time as a technologist dedicated to work in the public sector, and especially in the space of tech and politics. When it comes to maximizing human flourishing, good public policy is critical, and with a shrinking world, the risk of bad policy existential.

Many have died to give those who remain the chance to build this better world. Honored to be among the latter group; don’t intend to let it go to waste.

Slow And Steady

Slow And Steady

As of today I have a backlog of 49 draft posts going back to 2017. A good chunk of this backlog, especially recently, centers around themes of maintenance, longevity, sustainability, and generally making sure that work outlasts oneself in some form or another. It’s obviously something on my mind.

So when I sit down to write, why do I most often start with something new instead of picking up an old draft? I suspect it has to do with a fresh take being more motivating. For today, that motivation was my completion of the La Jolla Half Marathon. It’s a race I’ve run before, though it’s been six years.

What’s the relevance to the theme that I mentioned earlier? It’s not only that I am still able to run it despite being in my mid-40s, but that I beat my prior time by over 10 minutes (today’s result was 1:56:05). Which means my years of running has created the kind of habits that enable successful long runs with little to no additional preparation (I only signed up for this race three weeks ago, and thus didn’t have much margin to do a ramp-up beyond my normal weekly miles).

Similarly, while I don’t code every single day (nor should I, it’s not the focus of my current role), I aim to do enough programming regularly so that I’m ready to do more if a situation calls for it.

There’s probably also something to learn here about the long-term benefits of keeping systems running that are doing their jobs as designed and continue to provide value, versus continually looking to replatform and rebuild.

In short: maintenance matters.

It’s Never Five Minutes

It’s Never Five Minutes

There’s no magic method to software estimation that produces perfect results. Nor is it a skill that’s easily taught. For the most part, you just have to start doing it, make a lot of mistakes, and gradually you’ll get better over time.

That being said, here are a couple articles on the topic worth reading:

My own two (or more) cents is that individual task estimates will always have considerable uncertainty, but that can be mitigated by quantity. As long as there’s no systemic bias, the sum of estimates across a set of tasks will become more accurate as the set of tasks itself grows. And any bias that might exist (a tendency to underestimate is most common) can be compensated for by a final multiplicative factor (1.25 is a useful starting point, but can be refined over time).

Further, a diversity of estimates per task also increases accuracy, and directly proportional to the diversity of perspectives brought to it. Have front-end folks contribute to back-end estimates and vice versa; get estimates from both senior and junior level engineers; include people that cut across multiple projects. If nothing else it will promote collaborative conversations.

Here’s a process I use to achieve the above diversifications. It’s most applicable when estimating a project, but could also be used for estimating large features, though it’d be overkill for a single task. It can be done either synchronously or (mostly) asynchronously, and doesn’t take more than a couple hours.

  1. Put together a team of three estimators, ideally with a variety of skills and experience relative to the project being scoped.
  2. Each person reads through whatever project materials have been assembled so far (descriptions of the work, details on the customer and industry, overall objectives, etc).
  3. On their own, each person independently writes down in a few sentences their understanding of the objective of the project. This description should be non-technical and describe business outcomes, not implementation details.
  4. The group comes together and synthesizes their separate descriptions into a unified paragraph they all agree accurately captures the project objectives.
  5. Each person independently puts together a list of tasks to be completed based on that description. These should include everything needed to get to done, including not only implementation but design time, testing, bug fixing, deployments, documentation, and so on.
  6. The group reassembles and synthesizes each list into a unified task list, removing duplicates as needed, and discussing any items that aren’t broadly understood, until everyone feels good that the list is as complete and detailed as possible.
  7. The scopers separate once again, and on their own each person comes up with an estimate for each task on the unified list. Scopers should not get hung up too long on each task; give it a few minutes thought, make a best effort (err on the high side), and capture any assumptions or unknowns.
  8. Once everyone has independently estimated, come together again, and compare estimates task by task (as well as any assumptions anyone captured for that task). If there’s little variance across the estimates on a task, use the average as a final value. If there is disagreement on the estimate, discuss until a common understanding is reached. Reword the task, or split it into multiple tasks if needed, and then estimate those subtasks. If a consensus final estimate cannot be reached after a reasonable amount of time, the largest original estimate for the task should be used.
  9. Sum the final estimates for each task to come up with a total estimate for the project. Each person gets a chance to share their feelings on this total: does it seem right in aggregate compared to the original description of work determined earlier? If so, excellent. If not, re-review the task list and iterate on estimates again, either as a group, or even going back separately.
  10. Collate the list of assumptions and unknowns and discuss. Based on what is identified, decide on a final uncertainty multiplier. As a general guidance, add 10% if the list is short and/or simple, 25% for “standard” assumptions, and up to 50% if there are large unknowns or significant anticipated complexities. There’s no hard rule here; apply professional judgment.
  11. Apply this multiplier to the task estimate sum to get a final estimate for the project. Share it with stakeholders along with the project description and list of assumptions. Be prepared to defend the value, but also be open to questions and challenges.

I haven’t met anyone who truly loves software estimation, but it’s critically important if you want to be successful beyond hobbyist level. Put in the effort as a team and reap the benefits.

Can’t Fight This Feeling

Can’t Fight This Feeling

Yesterday I closed the book on my job with Amazon Web Services. It was a truly great gig; I was able to help numerous public sector customers advance their missions, grow both my technical and business skills, and work with some great people. But on Monday I start a new adventure as the Chief Technology Officer of Research Improving People’s Lives.

Officially, “RIPL is a tech-for-social-impact nonprofit that works with governments to help them use data, science, and technology to improve policy and lives.” But if you want a better sense of what I’ll be up to, these two opinion pieces by my colleagues are a great place to start:

I’ll have a lot more to say in the coming days about what led me to a new role, the emotions involved in transitioning from a job you love, and how to end your time at a company well. But since the cat’s out of the bag given my updated LinkedIn page, I wanted to get an announcement made here also.

Spirit And Truth

Spirit And Truth

As I was reading Things they didn’t teach you about Software Engineering, I found myself frequently nodding along. There’s a fountain of wisdom here. Seriously, go read it, or at least go read the headers, which are helpfully summarized along the side of the page.

One worth a deeper dive is this: domain knowledge is more important than your coding skills. I cannot agree more. If you don’t know your customer, it’s unlikely anything you build will add value, no matter how perfectly it’s constructed. And given that the typical end-user is rarely going to be a developer themselves, this knowledge won’t come automatically. It needs to be pursued intentionally.

I’ll go even further, though: knowledge isn’t enough. When the going gets tough (and it will, because programming is hard), a team needs more than information about their customers to keep them moving forward. They need an emotion powerful enough to overcome diverse roadblocks. They need empathy.

The best engineers don’t just seek to know about the problems they’re solving. They internalize those problems, engaging their emotions alongside their intellect. When a customer has a challenge, they feel it in their bones (it’s not by accident that we use the metaphor of a “pain point” when describing problems). When a customer is angry at a missed deadline or poor quality outcome, they lean in, acknowledging failure. And when a customer is overjoyed at the success of a new product launch, they get to share in that happiness.

Empathy is the key that unlocks the willpower to achieve great things. Regardless of technical prowess, show me an empathetic developer, and I’ll show you a good one.

On The Turning Away

On The Turning Away

I write this blog post sitting in my favorite coffee shop having begun yesterday a nearly three week micro-sabbatical. Not since I was laid off in early 2019 have I taken more than a few days of time off that didn’t involve travel or other busy-ness. I’m looking forward to spending some time relaxing, some time away from technology, and some time purposefully pursuing activities I haven’t had time to otherwise accomplish.

What sorts of activities? Well, for one I want to publish a new CDK construct, which I’ll talk about here once it’s published. Another is recording a podcast, which I’m happy to announce now has a brief trailer. Mostly it’s going to be similar to material covered on this blog, but perhaps with some conversations also. I’ve no idea if I’ll be able to keep it up, but I’m starting nonetheless.

I’ll also be reading quite a bit. Finished a book this morning, and started a second, the appropriately titled How To Do Nothing.

Finally, I’ll be drinking copious flat whites with the above view from my corner table. Life is good.

Whoever Will Lose Their Life

Whoever Will Lose Their Life

Last month, my post on layoffs got 113 views in one day. I suppose that’s what passes as “going viral” around these parts. While I certainly wouldn’t mind more readers, I’m okay with my current reach.

How does that square with the principle of thinking big? The common way of considering scale is quantity; it’s certainly the easiest to measure. But one can have an outsized impact in quality without needing large numbers. Think of it like big in depth vs big in breadth. When it comes to technology I embrace breadth, but when it comes to impact, I value depth quite a bit more. And sometimes to get that result requires thinking small.

On my desk is the following photograph by Eric Peters. It captures my objective well.

Speaking of things on my desk, perhaps I’ll document my setup in a future post. That could be fun.