Kent Beck comparing baskets of options to product roadmaps and goals:

I’ve come to hate the damage the “product roadmap” metaphor does to the brains of everyone involved in developing a product. When I use an actual map of actual roads, I assume that I know where I’m going and how I’m going to get there. This is never the case when developing a product.

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When you encounter long lead times, you’re hearing option-on-a-basket thinking. “We need to know what features will be in the release in 8 months so Marketing has time to prepare.” What if product development doesn’t go according to plan? The value of the option on a basket falls to zero. What if the launch doesn’t come off? The value of the option on a basket falls to zero.

The man can present.

Gergely Orosz:

Estimates matter because most people and businesses are date-driven.

Estimation is difficult but developing it as a skill is helpful for delivering software.

Charity Majors:

it’s actually an unqualified good for engineers to be interacting with production on a daily basis, observing the code they wrote as it interacts with infrastructure and users in ways they could never have predicted.

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A system’s resilience is not defined by its lack of errors; it’s defined by its ability to survive many, many, many errors. We build systems that are friendlier to humans and users alike not by decreasing our tolerance for errors, but by increasing it. Failure is not to be feared. Failure is to be embraced, practiced, and made your good friend.

Michael Feathers:

When people have divided attention, work suffers. The area of code that you work for months is something that you understand deeply. The framework, off to the side, that you update just to facilitate your work may not seem as important. This is a function of distance: cognitive, temporal, and locational distance. In a way, these are all the same.

Gary P. Pisano:

The cliché “celebrating failure” misses the point—we should be celebrating learning, not failure.

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Without discipline, almost anything can be justified as an experiment. Discipline-oriented cultures select experiments carefully on the basis of their potential learning value, and they design them rigorously to yield as much information as possible relative to the costs.

Kent Beck:

The first step is acknowledging that our relationship is more important than the design of the system. As long as we have a productive working relationship we can move the design in any direction. When our relationship breaks down we don’t get anywhere.

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Okay, so you just want to go implement the next feature and along I come and say no no no this should be designed completely differently. Even if you are right that the new structure will eventually make my behavior changes easier to implement it’s not eventually, it’s today.

First, acknowledge that our incentives diverge in this moment. It doesn’t help to pretend that we agree when we don’t.

Second, as the structure changer I need to acknowledge that I am placing a burden of learning on you. I think it’s worth it, but if I’m asking something of you I better be prepared to offer something to you.

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Software design is a human relationship problem with interesting technical aspects. Geeks relating to geeks requires as much effort as geeks relating to their systems. Maintaining relationships may be hard and confusing and frustrating to geeks (I could be projecting here but yeah no I don’t think I am), but if you want your technical skills to matter you really have no choice but to improving your people skills.

We couldn’t just start replacing old code with new code willy-nilly; without some type of structure to keep the old and new code separate, they’d end up getting hopelessly tangled together and we’d never have our modern codebase. To solve this problem, we introduced a few rules and functions in a concept called legacy-interop:

• old code cannot directly import new code: only new code that has been “exported” for use by the old code is available
• new code cannot directly import old code: only old code that has been “adapted” for use by modern code is available.

The progressive approach to the rebuild is interesting. Especially the rules that enforced how the rewritten parts of the code base could interact with the old code they were ultimately replacing.

Richard Hamming giving advice to researchers in 1995, plenty of which serves as general career advice.

Here’s a selection:

• Work on important problems.
• Luck favours a prepared mind.
• Work on problems you’re committed to.
• Talk to people outside of your field.
• Pursue opportunities when they’re presented.
• Schedule regular time for deep reflections.
• Take a step back to see the larger problem.
• Every defect can be looked at as an asset.

Tiago Forte:

I often say that with knowledge workers, the biggest bottleneck is always getting up in the morning. Knowledge work requires not only our time and effort, but also our engagement and creativity. For that reason, personal motivation is the prime problem that supersedes all other problems.

Phil Calçado:

BFFs are not about the shape of your endpoints, but about giving your client applications autonomy.

Roman Imankulov:

Rough consensus relies on the distinction between two types of objections:

1. “Not the best choice” feedback: “I don’t believe Solution A is the best choice, because XYZ. I believe Solution B would be better, but I accept that Solution A can work too.”

2. Fundamental flaws: “I believe Solution A is unacceptable because XYZ.”

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A chair who asks, “Is everyone OK with choice A?” is going to get objections. But a chair who asks, “Can anyone not live with choice A?” is more likely to only hear from folks who think that choice A is impossible to engineer given some constraints. The objector might convince the rest of the group that the objections are valid and the working group might choose a different path.

John Barton:

In my very first programming role my manager said to me “You can make any mistake you like once. You’ll have my full support the first time you screw anything up. If you’re not making mistakes, you’re not learning, and if you’re repeating mistakes you aren’t either”.

Claire Lew:

A leader doesn’t shape people – a leader shapes an environment.

Liz Fong-Jones:

In order to succeed at production ownership, a team needs a roadmap for developing the necessary skills to run production systems. We don’t just need production ownership; we also need production excellence. Production excellence is a teachable set of skills that teams can use to adapt to changing circumstances with confidence. It requires changes to people, culture, and process rather than only tooling.

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Even a perfect set of SLOs and instrumentation for observability do not necessarily result in a sustainable system. People are required to debug and run systems. Nobody is born knowing how to debug, so every engineer must learn that at some point. As systems and techniques evolve, everyone needs to continually update with new insights.

Will Larson:

Standardizing technology is a powerful way to create leverage: improve tooling a bit and every engineer will get more productive. Adopting superior technology is, in the long run, an even more powerful force, with successes compounding over time. The tradeoffs and timing between standardizing on what works, exploring for superior technology, and supporting adoption of superior technology are at the core of engineering strategy.

An effective approach is to prioritize standardization, while explicitly pursuing a bounded number of explorations that are pre-validated to offer a minimum of an order of magnitude improvement over the current standard.

Dan Slimmon:

Ideas are funny things. It can take hours or days or months of noodling on a concept before you’re even able to start putting your thoughts into a shape that others will understand. And by then, you’ve explored the contours of the problem space enough that the end result of your noodling doesn’t seem interesting anymore: it seems obvious.

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But as you get into more senior-type engineering roles, your most valuable contributions start to take the form not of concrete labor, but of conceptual labor. You’re able to draw on a rich mental library of abstractions, synthesizing and analyzing concepts in a way that only someone with your experience can do.

Dan Slimmon:

When building a dashboard, start with a set of questions you want to answer about a system’s behavior, and then choose where and how to add instrumentation; not the other way around.

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The incident response team’s common ground is their theory of the system’s behavior – in order to make troubleshooting observations meaningful, that theory needs to be kept up to date with the data.

A look at the Svelte reactive Javascript framework. Compilers strike again.

Peter Coy:

The purpose of quadratic voting is to determine “whether the intense preferences of the minority outweigh the weak preferences of the majority,”

This is something I’d like to try in planning meetings.

We’ve repurposed the idea of a technology tree, popular in many strategy video games, and used that as a vehicle to communicate the Up product roadmap.

It’s effective.

Using Javascript Proxies to provide immutable data with a native feel.

Sarah Leo:

At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers accurately and in a way that best supports the story. But sometimes we get it wrong. We can do better in future if we learn from our mistakes — and other people may be able to learn from them, too.

Herb Caudill:

Once you’ve learned enough that there’s a certain distance between the current version of your product and the best version of that product you can imagine, then the right approach is not to replace your software with a new version, but to build something new next to it — without throwing away what you have.

Gregor Hohpe:

First and foremost, autonomous teams need to live with the consequences of their decisions.

Yep.

A tired subject but a solid analogy.

David Gasca:

“Silent Meetings” are meetings where most of the time is spent working and not talking. When done correctly most of the meeting is spent silently working together.

A look at radical transparency at the hedge fund Bridgewater.