2025-06-14 20:00:00
Over the past 19 months, I’ve written Crafting Engineering Strategy, a book on creating engineering strategy. I’ve also been working increasingly with large language models at work. Unsurprisingly, the intersection of those two ideas is a topic that I’ve been thinking about a lot. What, I’ve wondered, is the role of the author, particularly the long-form author, in a world where an increasingly large percentage of writing is intermediated by large language models?
One framing I’ve heard somewhat frequently is the view that LLMs are first and foremost a great pillaging of authors’ work. It’s true. They are that. At some point there was a script to let you check which books had been loaded into Meta’s LLaMa, and every book I’d written at that point was included, none of them with my consent. However, I long ago made my peace with plagiarism online, and this strikes me as not particularly different, albeit conducted by larger players. The folks using this writing are going to keep using it beyond the constraints I’d prefer it to be used in, and I’m disinterested in investing my scarce mental energy chasing through digital or legal mazes.
Instead, I’ve been thinking about how this transition might go right for authors. My favorite idea that I’ve come up with is the idea of written content as “datapacks” for thinking. Buy someone’s book / “datapack”, then upload it into your LLM, and you can immediately operate almost as if you knew the book’s content.
Let’s start with an example. Imagine you want help onboarding as an executive, and you’ve bought a copy of The Engineering Executive’s Primer, you could create a project in Anthropic’s Claude, and upload the LLM-optimized book into your project.
Here is what your Claude project might look like.
Once you have it set up, you can ask it to help you create your onboarding plan.
This guidance makes sense, largely pulled from Your first 90 days as CTO. As always, you can iterate on your initial prompt–including more details you want to include into the plan–along with follow ups to improve the formatting and so on.
One interesting thing here, is that I don’t currently have a datapack for The Engineering Executive’s Primer! To solve that, I built one from all my blog posts marked with the “executive” tag. I did that using this script that packages Hugo blog posts, that I generated using this prompt with Claude 3.7 Sonnet.
The output of that script gets passed into repomix via:
repomix --include "`./scripts/tags.py content executive | paste -d, -s -`"
The mess with paste is to turn the multiline output from tags.py into a comma-separated list
that repomix knows how to use.
This is a really neat pattern, and starts to get at where I see the long-term advantage of writers in the current environment: if you’re a writer and have access to your raw content, you can create a problem-specific datapack to discuss the problem. You can also give that datapack to someone else, or use it to answer their questions.
For example, someone asked me a very detailed followup question about a recent blog post. It was a very long question, and I was on a weekend trip. I already had a Claude project setup with the contents of Crafting Engineering Strategy, so I just passed the question verbatim into that project, and sent the answer back to the person who asked it. (I did have to ask Claude to revise the answer once to focus more on what I thought the most important part of the answer was.)
This, for what it’s worth, wasn’t a perfect answer, but it’s pretty good. If the question asker had the right datapack, they could have gotten it themselves, without needing me to decide to answer it.
However, this post is less worried about the reader than it is about the author. What is our competitive advantage as authors in a future where people are not reading our work? Well, maybe they’re still buying our work in the form of datapacks and such, but it certainly seems likely that book sales, like blog traffic, will be impacted negatively.
In trade, it’s now possible for machines to understand our thinking that we’ve recorded down into words over time. There’s a running joke in my executive learning circle that I’ve written a blog post on every topic that comes up, and that’s kind of true. That means that I am on the cusp of the opportunity to uniquely scale myself by connecting “intelligence on demand for a few cents” with the written details of my thinking built over the past two decades of being a writer who operates.
The tools that exist today are not quite there yet, although a combination of selling datapacks like the one for Crafting Engineering Strategy and tools like Claude’s projects are a good start. There are many ways the exact details might come together, but I’m optimistic that writing will become more powerful rather than less in this new world, even if the particular formats change.
(For what it’s worth, I don’t think human readers are going away either.)
If you’re interested in the fully fleshed out version of this idea, starting here you can read the full AI Companion to Crafting Engineering Strategy. The datapack will be available via O’Reilly in the next few months.
If you’re an existing O’Reilly author who’s skepical of this idea, don’t worry: I worked with them to sign a custom contract, this usage–as best I understood it, although I am not a lawyer and am not providing legal advice–is outside of the scope of the default contract I signed with my prior book, and presumably most others’ contracts as well.
2025-06-07 19:00:00
Since 2020, I’ve been working on my desk setup, and I think I finally have it mostly pulled together at this point. I don’t really think my desk setup is very novel, and I’m sure there are better ways to pull it together, but I will say that it finally works the way I want since I added the CalDigit TS5 Plus, which has been a long time coming.
My requirements for my desk are:
Historically the issue here has been the final requirement, where switching required moving two cables–a Thunderbolt and a cable for the dedicated graphics card–but with my new dock this finally works with just one cable.
The equipment shown here, and my brief review of each piece, is:
UPLIFT v2 Standing Desk – is the standing desk I use. I both have a lot of stuff on my desk, and also want my desk to feel minimal, so I opted for the 72" x 30" verison. At the time I ordered it in 2020, the only option shipping quickly was the bamboo finish, so that’s what I got.
CalDigit TS5 Plus Dock – this was the missing component that has three Thunderbolt ports and a DisplayPort. I have the external graphics card directly connected to the DisplayPort, and then move the Thunderbolt port from computer to computer to change which one is active.
It also has enough USB-A ports to connect the adapters for my wireless keyboard and mouse, to avoid needing to pair them across computers which would create friction in switching computers.
Apple Studio Display – I experimented with dedicated speakers and video camera, but for me having them built into the monitor was helpful to reduce the number of things on my desk. The Studio Display’s monitor, speakers and video camera are all solidly good enough for my purposes: I’m sure I could get better on each dimension, but in practice I never think about this and don’t find any issues with them.
On the other hand, while I was initially hopeful that I could also get rid of my microphone, the microphone quality just wasn’t that good for me, as I spend a lot of time on video conferences and recording podcasts, etc.
Beelink GTi Ultra & EX Pro Docking Station – are my Windows mini desktop and dock which allows mounting an external GPU to the mini desktop. Beelink itself is slightly aggrevating because as best I can tell they’ve done something quite odd in terms of custom patching Windows 11, but ultimately it’s worked well for me as a dedicated gaming machine, and the build quality and size profile are both just fantastic.
MSI Gaming RTX 4070 Ti Super 16G Graphics Card – I bought this earlier this year, looking for something that was in stock, and was good enough that it would last me a generation or two of graphics card upgrades without shelling out a truly massive amount for a 50XX edition (some of which don’t seem to be upgrades on the 40XX predecesors anyway).
Hexcal Studio – this is the workstation / monitor stand / cable management system, with lighting and so on. I ultimately do like this, but it’s not perfect, e.g. my Qi charger technically works but provides such bad charging speeds that it effectively doesn’t work. It’s definitely too expensive for something that doesn’t entirely work, so I can’t really recommend it, although now that I’ve paid for it, I wouldn’t bother replacing it either.
Audio-Technica AT2020USB Cardioid Condenser USB Microphone – this is the microphone I’ve been using for six years, and it’s really quite good and cost something like $120 at the time. It’s discontinued now, but presumably there’s a more modern version somewhere. I have it mounted on this boom arm.
LUME CUBE Edge 2.0 LED Desk Lamp – I have two of these for lighting during recordings. I don’t actually like using them very much, I just hate looking into lights, but I do use them periodically when I want to make sure lighting is actually correct.
Logitech MX Keys Advanced Wireless Illuminated Keyboard for Mac – this keyboard works well for me, and has a USB-C so I can use a single powered USB-C cable from the Hexcal to charge my keyboard, my mouse, my phone, and my headphones.
Logitech MX Master 3S Wireless Mouse – I’ve been using variations of this mouse for a long time, I specifically bought this version a year or two ago to standardize all charging ports on USB-C.
Laptop stand – I’m not actually sure where I got this laptop stand from, it might have been Etsy. I found it relatively hard to find stands that support three laptops rather than just two. Before finding this one, I used this two-laptop stand which is fine.
Laptops – these are my personal and work Macbooks.
Here’s a slightly closer look at the left side of the desk.
At this point, I really have nothing left that I’m upset about with my setup, and I can’t imagine changing this again in the next few years. As a bonus, my office has a handful of pieces of “professional art” that represent things I am proud of. From left to right, it’s the cover of An Elegant Puzzle, a map of San Francisco drawn exclusively from Uber trip data on the night of Halloween 2014, and then the cover of The Engineering Executive’s Primer. It’s probably a bit vain, but I like to remember some of the accomplishments.
2025-05-23 19:00:00
Today’s my last day at Carta, where I got the chance to serve as their CTO for the past two years. I’ve learned so much working there, and I wanted to end my chapter there by collecting my thoughts on what I learned. (I am heading somewhere, and will share news in a week or two after firming up the communication plan with my new team there.)
The most important things I learned at Carta were:
Working in the details – if you took a critical lens towards my historical leadership style, I think the biggest issue you’d point at is my being too comfortable operating at a high level of abstraction. Utilizing the expertise of others to fill in your gaps is a valuable skill, but–like any single approach–it’s limiting when utilized too frequently.
One of the strengths of Carta’s “house leadership style” is expecting leaders to go deep into the details to get informed and push pace. What I practiced there turned into the pieces on strategy testing and developing domain expertise.
Refining my approach to engineering strategy – over the past 18 months, I’ve written a book on engineering strategy (posts are all in #eng-strategy-book), with initial chapters coming available for early release with O’Reilly next month. Fingers crossed, the book will be released in approximately October.
Coming into Carta, I already had much of my core thesis about how to do engineering strategy, but Carta gave me a number of complex projects to practice on, and excellent people to practice with: thank you to Dan, Shawna and Vogl in particular! More on this project in the next few weeks.
Extract the kernel – everywhere I’ve ever worked, teams have struggled understanding executives. In every case, the executives could be clearer, but it’s not particularly interesting to frame these problems as something the executives need to fix. Sure, that’s true they could communicate better, but that framing makes you powerless, when you have a great deal of power to understand confusing communication. After all, even good communicators communicate poorly sometimes.
Meaningfully adopting LLMs – a year ago I wrote up notes on adopting LLMs in your products, based on what we’d learned so far. Since then, we’ve learned a lot more, and LLMs themselves have significantly improved. Carta has been using LLMs in real, business-impacting workflows for over a year. That’s continuing to expand into solving more complex internal workflows, and even more interestingly into creating net-new product capabilities that ought to roll out more widely in the next few months (currently released to small beta groups).
This is the first major technology transition that I’ve experienced in a senior leadership role (since I was earlier in my career when mobile internet transitioned from novelty to commodity). The immense pressure to adopt faster, combined with the immense uncertainty if it’s a meaningful change or a brief blip was a lot of fun, and was the inspiration for this strategy document around LLM adoption.
Multi-dimensional tradeoffs – a phrase that Henry Ward uses frequent is that “everyone’s right, just at a different altitude.” That idea resonates with me, and meshes well with the ideas of multi-dimensional tradeoffs and layers of context that I find improve decision making for folks in roles that require making numerous, complex decisions. Working at Carta, these ideas formalized from something I intuited into something I could explain clearly.
Navigators – I think our most successful engineering strategy at Carta was rolling out the Navigator program, which ensured senior-most engineers had context and direct representation, rather than relying exclusively on indirect representation via engineering management. Carta’s engineering managers are excellent, but there’s always something lost as discussions extend across layers. The Navigator program probably isn’t a perfect fit for particularly small companies, but I think any company with more than 100-150 engineers would benefit from something along these lines.
How to create software quality – I’ve evolved my thinking about software quality quite a bit over time, but Carta was particularly helpful in distinguishing why some pieces of software are so hard to build despite having little-to-no scale from a data or concurrency perspective. These systems, which I label as “high essential complexity”, deserve more credit for their complexity, even if they have little in the way of complexity from infrastructure scaling.
Shaping eng org costs – a few years ago, I wrote about my mental model for managing infrastructure costs. At Carta, I got to refine my thinking about engineering salary costs, with most of those ideas getting incorporated in the Navigating Private Equity ownership strategy, and the eng org seniority mix model.
The three biggest levers are (1) “N-1 backfills”, (2) requiring a business rationale for promotions into senior-most levels, and (3) shifting hiring into cost efficient hiring regions. None of these are the sort of inspiring topics that excite folks, but they are all essential to the long term stability of your organization.
Explaining engineering costs to boards/execs – Similarly, I finally have a clear perspective on how to represent R&D investment to boards in the same language that they speak in, which I wrote up here, and know how to do it quickly without relying on any manually curated internal datasets.
Lots of smaller stuff, like the no wrong doors policy for routing colleagues to appropriate channels, how to request headcount in a way that is convincing to executives, Act Two rationales for how people’s motivations evolve over the course of long careers (and my own personal career mission to advance the industry, why friction isn’t velocity even though many folks act like it is.
I’ve also learned quite a bit about venture capital, fund administration, cap tables, non-social network products, operating a multi-business line company, and various operating models. Figuring out how to sanitize those learnings to share the interesting tidbits without leaking internal details is a bit too painful, so I’m omitting them for now. Maybe some will be shareable in four or five years after my context goes sufficiently stale.
As a closing thought, I just want to say how much I’ve appreciated the folks I’ve gotten to work with at Carta. From the executive team (Ali, April, Charly, Davis, Henry, Jeff, Nicole, Vrushali) to my directs (Adi, Ciera, Dan, Dave, Jasmine, Javier, Jayesh, Karen, Madhuri, Sam, Shawna) to the navigators (there’s a bunch of y’all). The people truly are always the best part, and that was certainly true at Carta.
2025-05-11 21:00:00
Back in 2018, I wrote lethain/systems
as a domain-specific language for writing runnable systems models,
and introduced it with this blog post modeling a hiring funnel.
While it’s far from a perfect system, I’ve gotten a lot of value out of it over the last seven years,
because it allows me to maintain systems models in version control.
As I’ve been playing with writing Model Context Protocol (MCP) servers,
one I’ve been thinking about frequently is one to help writing systems syntax, and I finally put that together
in the lethain/systems-mcp repository.
More detailed installation and usage instructions are in the GitHub repository, so I’ll just share a couple of
screenshots and comments here.
Starting with the load_systems_documentation tool which loads a copy of lethain/systems/README.md
and a file with example systems
into the context window.
The biggest challenge of properly writing DSLs with an LLM is providing enough in-context learning (ICL) examples, and I think the idea of providing tools that are specifically designed to provide that context is a very interesting idea. Eventually I imagine there will be generalized tools for this, e.g. a search index of the best ICL examples for a wide variety of DSLs. Until then, my guess is that this sort of tool is particularly valuable.
The second tool is run_systems_model which passes the DSL (and an optional parameter for number of rounds)
to the tool and then returns the result.
I experimented with interface design here, initially trying to return a rendered chart of the results, but ultimately even multi-modal models are just much better at working with text than with images. This meant that I had the best results returning JSON of the results and then having the LLM build a tool for interacting with the results.
Altogether, a fun little experiment, and another confirmation in my mind that the most interesting part of designing MCPs today is deciding where to introduce and eliminate complexity from the LLM. Introduce too little and the tool lacks power; eliminate too little and the combination rarely works.
2025-05-11 19:00:00
At Carta, we recently ran a reading group for Facilitating Software Architecture by Andrew Harmel-Law. We already loosely followed the ideas of an architectural advice process (from this 2021 article by the same Andrew Harmel-Law), but in practice we found that internal tech spec and architecture decision record (ADR) authors tended to exclusively share their documents locally within their team rather than more widely.
As we asked authors why they preferred sharing locally, the most common answer was that they got enough feedback from their team that they didn’t want to pay the time overhead of sharing widely. The wider feedback wasn’t necessarily bad or combative. It just wasn’t good enough to compensate for the additional time it cost to process. This made sense from the authors’ perspectives, but didn’t work well for me from the executive perspective, as I was seeing teams make misaligned decisions due to lack of cross-team communication.
As one step in reducing the overhead of sharing documents widely, I wrote up and shared this recommended process for providing feedback on documents:
Before starting, remember that the goal of providing feedback on a document is to help the author. Optimizing for anything else, even if it’s a worthy cause, discourages authors from sharing their future writing. If you prioritize something other than helping the author, you are discouraging them from sharing future work.
Start by skimming the document to understand its structure and where various kinds of topics are addressed. Why? This helps avoid giving feedback on ways the document’s actual structure diverges from how you imagined it would be structured. It also reduces questions about topics that are answered later in the document.
Both of these sorts of feedback are a distraction during a discussion on a tech spec. In general, it’s better to avoid them. If you notice an author making the same significant structural mistake over several ADRs, it’s worth delivering that feedback separately.
After skimming, reread the document, leaving comments with concerns. Each comment should include these details:
If you find yourself leaving more than three or four issues, then you should either raise your threshold for commenting or you should schedule time with the individual to talk over the feedback. If the document is unreasonably weak, then it’s appropriate to nudge their leadership to dig into what’s happening on that team.
The most important idea behind these steps is that your goal as a feedback giver is to help the document’s author. It is not to protect your team’s strategy or platform. It is not to optimize for your goals. It’s to help the author. This might feel wrong, but ultimately optimizing for anything else will lead to an environment where sharing widely is an irrational behavior.
As a final aside, I think the user experience around commenting on documents is fundamentally wrong in most document editors. For example, Google Docs treats individual comments as first-order objects, similarly to how old version control systems like CVS tracked changes to individual files without tracking an overall state of the project. Ultimately, you want to collect all your comments into a bundle, then review that bundle for consistency and duplicates, and then submit that bundle as commentary, but editors don’t support that flow particularly well.
2025-05-03 22:00:00
A few years ago I wrote about reading a Profit & Loss statement, which is a foundational executive skill. I also subsequently wrote about ways to measure your engineering organization. Despite having written those, I still spend a lot of time wondering about effective ways to represent an engineering organization to your board of directors.
Over the past few years, one of the most useful charts I’ve found for explaining an R&D organization is a scatterplot of R&D spend as a % of margin versus YoY growth of last twelve months (LTM) revenue. Unlike so many other measures, this is an explicit measure of your R&D organization value as an investment relative to peer organizations.
Until recently, I assumed building this dataset required reading financial filings but my strategic finance partner at Carta, Tyler Braslow, pointed out that you can get all of this data for the tech section from Meritech Analytics, for free.
When you login to Meritech, you’re dropped into a table of public company comparables for tech companies. This is the exact dataset I’d been looking for to build this chart.
After logging in, you can then copy the contents of that table into a Google Sheets spreadsheet or Excel, or whatever you’re most comfortable with.
Within that sheet, the columns you care about are:
Q for me) – how much “last twelve months revenue” has grown year over year, as a percentageU for me) – how much R&D spend is as a percentage of last twelve months marginO for me) – although I don’t show this in the scatterplot, I find this one useful for debugging outlier valuesHiding the other columns gives you a much simpler table.
From that table, you’re then able to build the scatterplot. Note that being “higher” means your R&D spend as a percentage of LTM margin is higher, which is a bad thing. The best companies are to the bottom and to the right; the worst companies are to the top and to the left.
With this chart as a starting point, you can then plot your company in and show where you stand. You could also show how your company’s position in the chart has evolved over time: hopefully improving. Finally, you might want to cull some of these data points to better determine your public company comparables. The Meritech dataset has 106 entries, but you might prefer a more representative thirty entries.