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.
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.
Estimates matter because most people and businesses are date-driven.
Estimation is difficult but developing it as a skill is helpful for delivering software.
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.
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.
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.
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.
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.
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.
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.
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.
BFFs are not about the shape of your endpoints, but about giving your client applications autonomy.
Rough consensus relies on the distinction between two types of objections:
“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.”
Fundamental flaws: “I believe Solution A is unacceptable because XYZ.”
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.
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”.
A leader doesn’t shape people – a leader shapes an environment.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
First and foremost, autonomous teams need to live with the consequences of their decisions.
A tired subject but a solid analogy.
“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.
The man can tell a story.
I’ve been playing with Sketch.systems a bit already. This post looking into adding verification on top of it.
Dark is a holistic programming language, structured editor, and infrastructure, for building backend web services. It’s aimed at frontend, backend, and mobile engineers.
Soup to nuts.
Australia is a place with more land than people, more geography than architecture. But it is not and never has been empty. Few landscapes have been so deeply known.
A post that builds up from simple princples. It looks into React, its programming model, its goals, and the trade offs it takes in solving its design challenges.
Startup strategy is like Kung Fu. There are many styles that work. But in a bar fight, you’re going to get punched in the face regardless.
I can only teach you my style. Others can only teach you theirs.
Lots to chew on.
the research is clear: Telling people what we think of their performance doesn’t help them thrive and excel, and telling people how we think they should improve actually hinders learning.
The only realm in which humans are an unimpeachable source of truth is that of their own feelings and experiences.
Speaking of lifting others up, your core group of friends can make or break your life. And your participation can make or break theirs as well.
Instead of working with a thing you love, think about how to work in a way you love.
This is totally my bag.
Still trying to learn how to think better.
Cindy Sridharan quoting Joe Armstrong:
We should identify the error kernel. The error kernel of a system is that part which must be correct. That’s what the error kernel is. All the other code can be incorrect, it doesn’t matter. The error kernel is the part of the system that must be correct. If it’s incorrect, then all bets are off. The error kernel must be correct.
John D. Cook:
The rule of three gives a quick and dirty way to estimate these kinds of probabilities. It says that if you’ve tested N cases and haven’t found what you’re looking for, a reasonable estimate is that the probability is less than 3/N. So in our proofreading example, if you haven’t found any typos in 20 pages, you could estimate that the probability of a page having a typo is less than 15%.
Hashing syntax trees and storing them directly in a database is very interesting. I’ve long wondered what will come after the grab bag of text files approach we’ve been using to date.
Embrace the power of compounding.
Now you can learn them too.
I ❤️ every time Sam goes on The Watch.
Alex Blumberg interviews Ira Glass.
Come for the Feynman anecdote, stay for the exploration of technology and culture.
A look behind the curtain with Nick Cave.
An improvisational approach.
Ryan Singer on the Product Love podcast talking about strategy, design, and outcomes.
A nice summary of the various parts in a typical web application.
Having all the answers is not the goal. Motivating the team to find the answers is the goal.
To evaluate the strength of a manager, look at the strength of their team.
The first time any of the above happens on your watch, it’s always new and hard, no matter how many books you’ve read on the topic. But the fifth or tenth time or 20th time it happens, you’re no longer freaked out. You realize that you’ll be fine.
Lots more interesting points in this and part 2.
A look at conflict in online communities and a suggestion on how to handle it.
So what are the management skills that are needed to achieve [Engineering productivity]? At the first level of management, they look like:
- Breaking down the scope of projects to help your team ship frequently. An eye for the MVP, for sequencing work and for predicting likely risks and bottlenecks for project completion are the skills here. This is why I think project management is such an important part of engineering leadership development, and why I hate to hand it off to professional project managers for work that doesn’t cross teams or organizations.
In product development, the closer to the customer a piece of software is, the more often it changes.
In short: stdout is for output, stderr is for messaging.
Some good tips sprinkled throughout.
Nice coverage of different approaches for testing in Production.
Nephew James Stein of Los Angeles claimed his uncle was an A&R consultant for Bad Boy records and ran a chain of legal recreational marijuana dispensaries in Colorado called Casablunta.
Now, let’s try a much better, O(1) control plane. DUMB AS ROCKS. Imagine if instead that the customer API pretty much edits a document directly, like a file on S3 or whatever. And imagine the servers just pull that file every few seconds, WHETHER IT CHANGED OR NOT.
This system is O(1). The whole config can change, or none of it, and the servers don’t even care. They are dumb. They just carry on. This is MUCH MUCH more robust. It never lags, it self-heals very quickly, and it’s always ready for a set of events like everyone changing everything at once. The SYSTEM DOES NOT CARE.
This contains many things I don’t understand.
Applying evolutionary theory to the origins and functions of religion.
Learning something new all the time, and staying healthy. Getting paid. Interacting with smart people. Having the chance to pass something along to others.
If you think about it this way, you might recognize that in many software teams, our limitation is not how much we can do, but how much we can know. To change a sufficiently complex system, we need more knowledge than one or two people can hold. Otherwise we are very slow, or we mess it up and the unintended effects of our change create a ton more work.
An explicit approach to delegation.
So the next time you find yourself tempted to volunteer to take over responsibility for something from someone who reports to you, pause. Instead of taking it over, ask them what they think the next steps should be. Give your feedback, and let them bring the follow-ups to you in the next appropriate touch base. You owe it to them to stop taking over their work in the name of helpfulness.
There are three types of product features, a seasoned head of product told me recently. MMRs, neutralizers, and differentiators. MMRs are minimum market requirements; basic features that every customer expects and demands. Neutralizers mitigate competitive threat. Differentiators are your startup’s competitive advantage.
As the product team talks to customers, they are likely to hear feedback encouraging more investment in MMRs and neutralizers. “Your product is missing this feature. We need this capability that exists in another piece of software in yours.”
customers rarely push vendors to further their differentiation. By definition, the unique selling proposition doesn’t exist elsewhere in the market. So advances or improvements to the differentiator may not be obvious to customers.
A simple application to get your macOS menu bar under control.
it’s important to build up a solid instinct for separating the technologies and products worth spending time on from the ones that will fade into obscurity after their 15 minutes of fame is over
I especially like the idea of recording yourself working to help guide improvement.
A look at the costs of implementing single page applications.
Looks like a useful visualisation for projects.
Some good tips and resources for remote workers and companies.
The Tesla’s big battery in South Australia has already taken a 55 per cent share in the state’s frequency and ancillary services market, and lowered prices in that market by 90 per cent, new data has shown.
And the 100MW battery has achieved over 55 per cent of the FCAS revenues in South Australia. So it’s 2 per cent of the capacity in South Australia achieving 55 per cent of the revenues in South Australia.
Various estimates have put the cost savings to consumers from the FCAS market alone at around $35 million, just in the first four months of its operation.
That’s a pretty good bang for the buck for the estimated $50 million investment by the South Australia government. South Australia is the only state that has experienced a decline in FCAS prices over the past few months.
This comes up every couple of years and each time I read it I laugh.
Things unfold in unexpected ways.
A pretty good list.
There’s hope yet that I can draw me some comics one day.
Covers the challenges of employing people around the world.