When you’re shaping an org in practice, the work you’re doing usually decomposes to manipulating three levers: structure, incentives, and culture (or, as Andy Grove puts it ‘contractual obligations, economic incentives, and culture’). A good org designer knows that they must pay attention to the interplay between all three elements.

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I bring up the interplay between structure, incentives and culture because I think highlighting this interplay serves one other purpose: it enlarges the discourse around org design. If you focus on org structure alone, every solution to an org design problem is necessarily a restructuring. This is unrealistic. And, from experience, there are whole classes of problems that may be patched by a change to an incentive system or via a tweak to org culture instead.

The philosophy of games, why we play them, and how it relates to modern polarisation and conspiracy theories.

Breaking non-fiction books down into three kinds with advice on how to read them.

A look at how Etsy tailored their search results to individual customers.

Eugene Yan:

Rule #1: Don’t be afraid to launch a product without machine learning.

Machine learning is cool, but it requires data. Theoretically, you can take data from a different problem and then tweak the model for a new product, but this will likely underperform basic heuristics. If you think that machine learning will give you a 100% boost, then a heuristic will get you 50% of the way there.

To recharge themselves, individuals need to recognize the costs of energy-depleting behaviors and then take responsibility for changing them, regardless of the circumstances they’re facing.

The article covers four dimensions of energy: body, emotions, mind, and spirit.

George Fairbanks:

There are two parts to software development: creating a design and expressing it as code. The code is tangible but the design is conceptual. Keeping a project healthy means doing both well. Here’s my concern: whenever you mix the conceptual with the tangible, it’s easier to neglect the conceptual. When you miss a tangible target, it’s obvious, but when you miss a conceptual target, you might not recognize it, or might rationalize that, because it’s impossible to measure, you were really quite close.

Blindly applying a factory process to software development will drive improvements to the tangible part (the code) at the expense of the conceptual part (the design). We see plenty of examples of this today, where teams have great feature velocity at first, are puzzled when velocity slows, and eventually the project is abandoned. As Cunningham warned, if we bolt features onto an existing codebase without consolidating those ideas into the code, the design will suffer, and over time “[e]ntire engineering organizations can be brought to a standstill under the debt load of an unconsolidated implementation.”

Casey Winters:

there’s one big thing that needs to change for wartime CPOs I want to cover today, and that is prioritization and evaluation.

Lara Hogan:

So, when a fraught issue arises, how can we help our organization move forward in a way that actually builds rather than breaks trust? Over time, and after many communication mistakes, I honed a four-part template, covering the following

Ryan Singer:

After I have a lay of the land, the next step is to determine what to work on first, second, third etc. Not every concern in the product provides equal value to the customer. Some parts are core to the problem. Without them the app has no purpose. Others are necessary but have nothing to do with the domain, like user-authentication.

Bungay Stanier explains how advice-giving goes bad; the three personas of your Advice Monster; and the powerful act of staying curious a little longer.

Jonathan Cornelissen:

Relegating all data knowledge to a handful of people within a company is problematic on many levels. Data scientists find it frustrating because it’s hard for them to communicate their findings to colleagues who lack basic data literacy. Business stakeholders are unhappy because data requests take too long to fulfill and often fail to answer the original questions. In some cases, that’s because the questioner failed to explain the question properly to the data scientist.

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A data-literate team makes better requests. Even a basic understanding of tools and resources greatly improves the quality of interaction among colleagues. When the “effort level” — the amount of back-and-forth needed to clarify what is wanted — of each request goes down, speed and quality go up.

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Shared skills improve workplace culture and results in another way, too: They improve mutual understanding. If you know how hard it will be to get a particular data output, you’ll adjust the way you interact with the people in charge of giving you that output. Such adjustments improve the workplace for everyone.

Liz Keogh:

As the Peter Principle suggests, we tend to rise to the level of our incompetence… but that’s not actually such a bad thing, as long as we can learn fast, safely. The best way to do that is to make sure things are safe-to-fail, which usually means putting appropriate feedback loops in place. In a human system, that usually means feedback.

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Sometimes it’s the simplest thing in the world, and we forget to do it. Clarifying why you want something allows people to make autonomous decisions about how best to work towards the outcome you want; or (even more important) give you information about the context you were unaware of that will cause difficulty getting that outcome.

Timothy R. Clark:

Low-velocity decision making. In a nice culture, there’s pressure to go along to get along. A low tolerance for candor makes the necessary discussion and analysis for decision making shallow and slow. You either get an echo chamber in which the homogenization of thought gives you a flawed decision, or you conduct what seem to be endless rounds of discussion in pursuit of consensus. Eventually, this can lead to chronic indecisiveness.

Pete enumerates some patterns of teams and code ownership.