Tag: Dive Deep

Knee of the Curve

Knee of the Curve

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There’s so much ink being spilled about AI that it’s hard to keep track of it all. But I’m doing my best to stay connected to the important stuff, or at least things most relevant to my job.

Here’s a list of articles and essays that I’ve read recently that I found memorable for one reason or another, roughly in descending order of broad relevance:

The first three are especially powerful. If you’re reading this, read them instead.

Coming Up For Air

Coming Up For Air

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Believe it or not, I’m not going to say anything about Claude today.

I wrote a post a couple years ago about statistics I tracked while doing daily crossword puzzles. I took a couple years off, but last year I was back at it, this time using a calendar from the New York Times.

The NYT crosswords are supposed to get harder as the week goes on, with Monday being easiest, and weekend ones being the most difficult. I wanted to prove that out, so I noted my average solve time (capped at 30 minutes) on every puzzle, and then computed an average solve time for each day. The results are below:

Lo and behold, my experience aligns perfectly! I thought that was cool.

Over My Skis

Over My Skis

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A few minutes ago I just published my first Go module. But here’s the thing: I don’t know Go. What madness is this?

Granted, I’ve been by myself most of this weekend, but in that time I’ve published 3 new public projects:

Plus, I have a fourth project in the works that’ll affect this blog materially. And I’ve built a sophisticated “AI Chief of Staff” for my own use (not published yet, but I will eventually in some form), and I’ve made a handful of smaller one-off utilities. And I’ve started spec-ing out a major project. And I’ve matured my local Claude Code configuration and spruced up my dotfiles. And, and, and.

It’s absolutely bonkers the throughput coding agents enable. Knee of the curve indeed.

Let’s Reason Together

Let’s Reason Together

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For the past few weeks I’ve made considerable gains in learning AI-related tools. Not through some formal training process, but by just doing it. I guess it’s good to heed my own advice?

Professional learning needn’t be solely focused on seemingly professional stuff, either. Part of what helped free the mental logjam of diving deep was allowing myself to use AI for fun stuff, such as creative writing. Going through that process has revealed both the power and limitations of LLMs; experiences I’ll be able to carry forward into professional use cases.

One pretty clear lesson is that, past a certain size, projects need to have some degree of structure, lest context get lost in a sea of tokens. Another lesson is that motivation for learning often comes when working on a project together with others.

To support the above, as an aid for creative writers using AI, I created this story framework repository. It contains all the scaffolding required to keep track of large creative writing projects, along with instructions to a number of AI tools on how to use it. And since it’s based on git and plain text files with markdown, it naturally supports group collaboration through branching, pull requests, and commit history.

Want to try your hand at AI-assisted storytelling? Give it a try!

Patience Isn’t A Virtue

Patience Isn’t A Virtue

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I’ve had space on the brain recently, having in the last month visited both the Jet Propulsion Laboratory and the Johnson Space Center. There’s a paradox of sorts when thinking about interstellar travel called the wait calculation (or, in simpler form, the wait/walk dilemma). The gist is that we should not launch a slow spacecraft now because one sent later with a faster propulsion system would simply overtake it. Repeat that argument ad infinitum, and you’ll never launch, hence the paradox.

These days I’m feeling caught in a similar sort of trap when it comes to learning about AI, with announcements almost daily (just this week, GPT-5 launched, right on the heels of the release of a bunch of open weight models that can run all sorts of places, including Amazon Bedrock). Just when I think I’ve identified the technology I want to really embrace, new ones arrive that create new capabilities, deprecate old ones, and demand rethinking workflows. It’s disorienting.

You know what isn’t disorienting? Photos of cool command centers. Here’s three of them from my recent travels:

The room where it happened
The room where it’s been happening a while
The room where it’s still happening

I’m a sucker for a good command center, that’s for sure. But enough distractions; I know I just need to dig into deeper AI learning. That’s the trick: just start.

Random Content

Random Content

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Last week I stumbled onto this presentation I made for a job interview I did nearly 11 years ago. Python generators are pretty cool! I also dig the vaguely LCARS styling, which was a built-in theme of Google Sheets.

Thought it’d be fun to share here. Enjoy!

(Oh, and in posting this I also learned how to ensure a consistent aspect ratio in CSS. Cool!)

TL;DR

TL;DR

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I’ve discovered a couple highly practical uses for GenAI this week relative to performance reviews.

In one case, I collected various stakeholder feedback for an individual on my team (using these questions from the other day) and used AI to extract common themes (both strengths and growth areas) that I could share without revealing who wrote what (which I’d promised in the hopes of getting more honest responses). It was also useful for me as a manager to see what emerged so we could review together.

In another, I took all the self-evaluations across a team and asked AI to identify and then summarize specific portions that would be relevant to pass along to the broader leadership group (e.g. feedback they had about the organization, common challenges that might be indicators of systemic problems). It’s a lot to ask of an executive to read these docs in their entirety, but there’s valuable insights to be gleaned. Building this summary was the best of both worlds.

Something my years at Amazon taught me is the usefulness of discussing performance in light of shared values. Our evaluation forms this year broke down questions along those lines (at my suggestion), but I’m now seeing it may be a bit too structured and artificially constraining. So next year I might see if we can keep the reflection questions a bit more open-ended, and then use AI tooling to align people’s responses to our specific guiding principles. Will that be effective? Not sure! But worth a try.