Chosen links

Links - 22nd May 2022

Magnitudes of exploration

I’ve struggled for some time to articulate the right trade off here, but in a recent conversation with my coworker Qi Jin, he suggested a rule of thumb that resonates deeply.

Standardization is so powerful that we should default to consolidating on few platforms, and invest heavily into their success. However, we should pursue explorations that offers at least one order of magnitude improvement over the existing technology.

This improvement shouldn’t improve on a single dimension with meaningful regressions across other aspects, but instead it should approximately as strong on all dimensions and at least one dimension must show at least one order of magnitude improvement.

A few possible examples, although they’d all require significant evidence to prove the improvement:

  1. moving to a storage engine that is about as fast and as expressive, but is ten times cheaper to operate,

  2. changing from a batch compute model like Hadoop to use a streaming computation model like Flink, enabling the move from a daily cadence of data to a real-time cadence (assuming you can keep the operational complexity and costs relatively constant),

  3. moving from engineers developing on their laptops and spending days debugging issues to working on VMs which they can instantly reprovision from scratch when the encounter an environment problem.

The most valuable — and unsurprisingly the hardest — part is quantifying the improvement, and agreeing about that the baselines haven’t degraded. It’s quite challenging to compare the perfect vision of something non-existent with the reality of something flawed but real, which is why explorations are essential to pull both into reality for a measured evaluation.


There’s this writing style in popular non-fiction that I’ll call the “Malcolm Gladwell method of shoving-a-story-in-your-face”. It substitutes argumentation for storytelling and anecdote, and in so doing sidesteps the difficulty of making a case, since the reader is too distracted by narrative to comprehend the point the author is actually attempting to make.

Whenever this happens, I take care to pay special attention, because often the point is banal, or flawed, or too inconsequential to stand on its own. (I happen to know this because I’ve used this technique a few times on this very blog, and I know from reader feedback how effective it is).

The different kinds of notes

So, when I step back, squint a bit, and stare at my blackboard and my notes, it strikes me that most of the notetaking methods the interviewees described are trying to answer at least one of the three following questions:

  1. How do I manage my creativity?

  2. How do I manage my knowledge?

  3. How do I manage my understanding?

Or, more to the point, the notetaking methods are a way for the writer to continuously ask themselves these questions and adjust their tactics as needed. None of the methods are prescriptive or rigid; they are all constantly being adapted.

The job of notes for creativity is to:

  • Generate ideas in a structured way through research and sketching.

  • Preserve those ideas.

  • Explore the ideas until they have gelled into a cohesive plan or solved a problem.

The job of notes for knowledge is to:

  • Extend your memory to help you keep track of useful information (client data, meeting notes, references).

  • Connect that information to your current tasks or projects so that you can find it when you need it.

The job of notes for understanding is to:

  • Break apart, reframe, and contextualise information and ideas so that they become a part of your own thought process.

  • Turn learning into something you can outline in your own words.

The purpose of the various journalling tactics as well as some of the mapping tactics (like the evidence board) is to get you into the habit of reframing and contextualising your thoughts, your reading, and your ideas. By reframing them in words (or sketches) you integrate them which makes them available to your thinking and decision-making processes. If you don’t integrate what you collect, are just building an ever more intimidating database of opaque words and alien ideas.


Technique Goal: An efficient heuristic for evaluating who to ask feedback from. Also worth using during decision-making: i.e. “believability weighted opinions”. The example here being a) you need to make a decision, b) you approach a variety of people to get advice, c) you incorporate their views and weight their advice according to their believability.

Technique summary: believable people are people who have 1) a record of at least three relevant successes and 2) have great explanations of their approach when probed.

You may evaluate a person’s believability on the subject matter at hand by applying this heuristic. When interacting with them:

  1. If you’re talking to a more believable person, suppress your instinct to debate and instead ask questions to understand their approach. This is far more effective in getting to the truth than wasting time debating.

  2. You’re only allowed to debate someone who has roughly equal believability compared to you.

  3. If you’re dealing with someone with lower believability, spend the minimum amount of time to see if they have objections that you’d not considered before. Otherwise, don’t spend that much time on them.