đź’ˇ System Diagrams are Performance Caches for Cognitive Load (and more)
System Diagrams are Performance Caches for Cognitive Load
I recently mentioned how I like to draw it until it works when I’m ramping up on a new system. Clint Byrum says it so much better in his post System Diagrams are Performance Caches for Cognitive Load. First, this bit resonated with me because it’s exactly the situation I currently find myself in:
Having joined just a few months ago, I was overwhelmed about 5 minutes into the meeting. The individual words and concepts all made sense. JSON parsing slow. Network transit treacherous. Changing things at the source hard. I got all of those components of the discussion, but through the whole thing I was just barely able to follow the overall system conversation and ask very basic questions to understand what was going on. I came away with a bunch of exploratory personal action items, and a very clear hole in my mental model of the system that needs to be filled.
Clint goes on to use a systems analogy for the individual people that make up a team—people and knowledge as components of caching, computation, and storage. This leads to a perfect explanation for why system diagrams are so important:
A single system diagram is where those primed nodes can push the most relevant bits of their information out of their local brain-caches, and into a high-performance distributed cache from which everyone can read. This will preserve precious cognitive load for those critical low-latency tasks. Of course, all of these caches may be stale. The local in-memory ones are particularly hard to test, but at least the system diagram is observable. Everyone can look at it, and if there are nodes with updates, they can update the cache.
So, prime those caches. Draw it until it works!
Google is combining its Android and hardware teams — and it’s all about AI
Maybe it’s my age showing but I’m with Gruber on this one:
I would argue, strenuously, that the phone is the natural AI device. It already has: always-on networking, cameras, a screen, microphones, and speakers. Everyone owns one and almost everyone takes theirs with them almost everywhere they go.
The Language of Business
Bit of a clickbaity title, but there’s some good advice for product managers in this article about making sure the organization understands that product is a profit center, not a cost center. This is the most important point:
Directly tie product to revenue. One way to do this is revenue attribution. In most companies, revenue and revenue growth is tied to marketing or sales. Making the point that product provided the thing to sell and the features that draw in customers is difficult to make. Product, in this regard, looks passive, and marketing or sales are actively doing something. It is easier to attribute recurring revenue to product because it prevents churn and increases upsells and add-on products.
This can be harder to do with some products—like a platform product with lots of internal customers. But the work is important. As Mike Fisher points out in Language of Business:
The lingua franca of business is finance. Each discipline speaks its native language, be that engineering, marketing, product, etc. but when they get together the common language that everyone should understand is finance.
And what that means for PMs:
The core message I want to convey is that understanding the language of finance is not just about adding another skill to your repertoire, although that is worthwhile; it’s about bridging the gap between technical expertise and business acumen. It’s about translating the complex, technical projects we work on into narratives that resonate with stakeholders across the board, narratives that clearly articulate value, risk, and return. This skill set enables technologists, engineers, and product managers to not only defend their projects and ideas but also to align them more closely with the strategic goals of the business.
How to send progress updates
I don’t agree with everything on this list of how to send progress updates, but these two points are especially important and worth remembering:
Acknowledge changes explicitly. If you said
athe last time andbthis time, andbconflicts witha, you need to explain the inconsistency. People perceive acknowledged inconsistencies as cost of doing business, but unacknowledged inconsistencies as broken promises.
I name this section “challenges and requests” in my updates, but the underlying principle is the same:
Add a dedicated section for worries and failures. Be honest, have good plans, and don’t panic. People are drawn to conscientiousness and vulnerability but repelled from haplessness and histrionics.
How cheap, outsourced labour in Africa is shaping AI English
This isn’t entirely surprising but it’s a sad state of affairs, and it’s worth highlighting not just how, but also where LLMs are being trained:
Hundreds of thousands of hours of work goes into providing enough feedback to turn an LLM into a useful chatbot, and that means the large AI companies outsource the work to parts of the global south, where anglophonic knowledge workers are cheap to hire.
I know it’s too dismissive to call chatbots “fancy autocomplete” like many do, but we have to remember that this isn’t magic. The words the bots use come from somewhere. And in the case of “delve”…
I said “delve” was overused by ChatGPT compared to the internet at large. But there’s one part of the internet where “delve” is a much more common word: the African web. In Nigeria, “delve” is much more frequently used in business English than it is in England or the US. So the workers training their systems provided examples of input and output that used the same language, eventually ending up with an AI system that writes slightly like an African.
Thanks for reading Elezea! If you find these resources useful, I’d be grateful if you could share the blog with someone you like.
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PS. You look nice today 👌