Garrick van Buren

Claude and I talk regularly.

I’ve reached the end of multiple context windows with Claude.

I credit Claude with helping me articulate the long running thread through my career and personal interests (e.g “Find what we forgot”) and clarifying the current positioning of my business (e.g. “When technology breaks your revenue model – call me.”). Once every couple of days I’ll paste an email draft into Claude and say, “I’m having a difficult time saying what I’d like to say succinctly.” and I’ll often take the suggestions.

Additionally, I have a long running chat in Gemini where it scores and ranks companies I offer it against the characteristics of my best clients.

I have the same 4,000 characters of instructions in both Gemini and Claude.

I don’t use ChatGPT anymore.

I would characterize my current usage as:

• Rubber ducking
• Summarizing qualitative patterns
• English-to-English translations
• Ensuring I’m being comprehensive / appreciating conventional wisdom.
• Raising the minimum quality of communication

Overall, I’d characterize this as “Help me minimize common mistakes”. Claude calls it “Sharpening“.

The more I work with Claude, the more it feels like the Mirror of Erised, simply reflecting back whatever you give it in a more narcissistic package.

There’s still nothing new here and I’m still of the position that “AI is amazing about the thing I know nothing about….but it’s absolute garbage at the stuff I’m expert in.”

An Increasingly Worse Response

I don’t pay for Claude, which is to me is a sign it’s not yet become an indispensable co-worker. The reports I’ve read of Claude CoWork and other ‘agentic assistants’ mainly make me wonder why the human operator even bothered with automating the task in the first place.

The greatest value of CoPilot and Claude Code seem to lay in an assumption that all software must start from scratch in Node. As soon as you say, maybe I don’t need to start from scratch, suddenly an entire world of options opens up. Admittedly, they’re not distinct applications that can be discretely monetized. On the flip side, someone else is maintaining the system, has already fixed and squashed thousands bugs you’ve yet to even experience.

But creating software hasn’t been hard for more than 25 years. There have always been piles of frameworks and environments to accelerate the creation of software; Hypercard, VisualBasic, Ruby on Rails, WordPress, etc, etc.

The challenge is in maintaining it, hardening it, justifying why it should continue to exist – that’s the perennial problem.

And that’s the first question I ask, should we create this custom software at all? Or perhaps we frame the problem differently and solve it in a different way altogether; we can minimize our overall maintenance costs while still getting the same output…or completely eliminate problem.

You’re Not Going to Need It

Recently I upgraded all my Google Homes to Gemini…and now the voice is even more tediously verbose. When I set a timer, I want confirmation not a conversation.

I picked up a copy of Bots when it first came out and was enamored by the promise of software applications appearing so human and useful. This promise, like software maintenance itself has held constant over the past 30 years. Perennially unsolved.

On the news of Netflix acquiring Warner Bros, I’m reminded of how good Netflix has been at innovating their business model.

Over the past 27 years, their business model has changed multiple times and each evolution appears to be in direct response to the bottleneck of growth, from maintaining inventory of DVD to acquiring global streaming rights.

Year	Business Model	Bottleneck to Growth
1998	Sell DVDs over the internet	Need to continually replenish DVD inventory,
1999-2006	Rent DVDs over the internet	USPS delivery & return times
2007	Stream movies over the internet	Acquiring US streaming rights to a massive library of movies
2009	Start producing movies (Netflix Originals)	Number of subscribers watching Netflix Originals
2010-2012	Global expansion; Canada, South America, Europe	Maintaining rights globally
2025	Acquire Warner Bros Discovery

Patrick reminded us a decade ago that Everything Happens in Time, said another way: “everything happens at a time – whether deliberate or not.”

It’s been three and a half years since I wrote about my ever evolving calendaring practice. Everything I wrote about in 2022 is still true. The biggest change has been in that time is my heavy use of cal.com as a scheduling service for conversations with clients, prospects, customers of clients, friends, everyone and everything.

Heavily using a scheduling service like Cal.com is great, but I’ve found it requires a couple of non-obvious changes to my calendaring practice.

1. My commitments are now split across 2 calendars; “Committed” and “Tentative”. As I described in 2022, all my hard commitments – my morning run, prep & next steps for client meetings, travel time to & from in-person meetings, fixed personal commitments – whether to myself or others goes, into “Committed”. Everything else goes into “Tentative”. Cal.com integrates with “Committed”. Yes, this means stuff on “Tentative” may get over-ridden – and that’s OK. If there’s nothing on “Committed”, I’m not wondering how to make the best use of the new found time, I’ve got a plan. There’s probably a cleaner way to solve this, but at this point, it’s working fine.
   
2. Some people prefer I declare the date & time. So, if only to keep things moving, I’ve needed to create an Event Type within Cal.com for the times where I’m handle the scheduling. By scheduling it myself – rather than creating a normal calendar event and inviting the person – I still get Cal.com’s auto-population of a video conferencing URL, reminders the day before, and a rescheduling link.
   
   I have 3 standard durations; 30min, 45min, 60min. At a certain level of clarity, every conversation can fit within a 30min or 45min block.

The great thing about Cal.com (and other scheduling tools) is setting a minimum time between bookings, I set it at 15min, and I’ve found it so helpful that I now even space my own commitments by 15min.

A 5 gallon bucket with a spout that’s also a paint tray and a dustpan.

At the risk of evoking all of my past writing on chindogu, this is a nice reminder that there’s always room for improvement.

It’s just a matter of determining that developing improvement is worth your opportunity cost. It might not be. But that doesn’t mean everything’s been invented.

I’m also reminded of the 2010 Design of the Decade winner, Clear RX prescription bottle design;

I recommend AI: How/Why I Use It in its entirety here are just a couple of my favorite passages:

“As any musician knows intimately, the most interesting part of a new musical technology is its glitches: the inventors of the synthesizer hoped to position it as a replacement for strings or horns, but what we loved is the weird blorps; the amplifier was invented just to make a guitar more audible, but we loved distortion; Autotune et al. were invented to correct bad notes, but we loved crazy space-laser voices.”

“Every day the AIs “improve” their ability to make images (actually, I use one of my go-to AIs because it is hilariously bad). I believe that eventually the uncanniness will be refined away, and AIs will evolve from fascinatingly odd to comprehensively mediocre.”

“Expertise will not be sufficient to make a living…Hacks are in trouble. If somebody is making work that is uninspired, and unindividual, then they can indeed be replaced by a machine that just spits up boring chunks of mid-ness.”

Generative AI and LLMs continue to provide the least controversial answer to any question I ask them. For my purposes, this makes them little more than a calculator for words, a generator of historical fiction short stories.

As I mentioned two years ago, this doesn’t make LLMs useless, but it does greatly shrink their usefulness – to those places where you want a general idea of the consensus…whether or not it’s correct, accurate, or legal. Just an average doesn’t necessarily represent any individual datapoint.

For, the more training data the generative AI providers shovel into their models, the greater the drift from credibility toward absurdity the generated consensus.

It’s one thing to train the models on all the scientific research. It’s another to train on all the books ever published (copyright issues aside for the moment). It’s quite another to train it on Reddit and Twitter. It’s yet another thing all together to treat all data equal independent of parody, satire, or propaganda.

18 years ago, I figured out that a 3 in Netflix’s then 5 star rating meant “looks good on paper, but probably not very”. The same seems to be true of the nondeterministic responses from LLMs, an avalanche of Gell-Mann Amnesia Effect or Knoll’s Law of Media Accuracy “AI is amazing about the thing I know nothing about….but it’s absolute garbage at the stuff I’m expert in.”

Again, there are use cases for this (e.g. getting familiar with the basics of a topic in record time), but the moment you expect quality, credibility, or specifics…it collapses like a toy giraffe.

A toy giraffe that, when a person engages with it, can only – collapse.

As a metaphor for new technologies, this toy giraffe’s message is worth considering, “we break when any pressure is applied.”

General purpose LLMs will only get worse the more data they digest. Special purpose LLMs only trained on a specific context, a specific vertical, a rigidly curated & edited set of sources may achieve the level of expert these applications are hyped up to be.

But we may never know they exist because the most valuable use cases – national defense, cybersecurity, fraud detection – will never need (or desire) the visibility the general purpose LLMs require.

When I first got a robot vacuum cleaner,

the first thing I noticed was how much worse its cleaning quality was compared against a person.

The second thing I noticed: it added a job to the household (one that came with zero training):

robot vacuum maintenance

Just as we handwash dishes even though we have an automatic dishwasher, we still vacuum, abeit less frequently, with a non-robotic vacuum.

The introductions of these machines only shifted the labor.

It didn’t eliminate the labor.

Hardware eventually breaks.
Software eventually works.

The work still needs to get done.
That’s why we have people.

Not to mention Jevon’s Paradox or the Bureau of Labor Statistics.

• Technology should require the smallest possible amount of attention
• Technology should inform and create calm
• Technology should make use of the periphery
• Technology should amplify the best of technology and the best of humanity
• Technology can communicate, but doesn’t need to speak
• Technology should work even when it fails
• The right amount of technology is the minimum needed to solve the problem
• Leverage familiar behaviors to introduce new ones

https://www.calmtech.institute/calm-tech-principles

FedEx was started in 1971

The first email was sent in 1971

The Pony Express began in 1860, ceased operations just 2 days after…

…the transcontinental telegraph was completed in 1861