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
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.
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.
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 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.”
The intention of this graph is to help those studying for the BJCP Written Exam in prioritizing which styles to focus on for the compare/contrast portion.
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.
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.
There was a time when the internet was mostly silly technology experiments. Where that it worked at all was the win. All the projects were primarily for delight of the creator and delight for others. There was no advertising. No polish. Simply a neopunk aesthetic of “let’s just do it, it’ll be fun.” As the internet has matured and my own children have occasionally referred to me as ‘Mr Business Business’. I’ve longed for a more unstable time, a more optimistic time, a more experimental time.
I thank the creators of the projects below for showing me the we can have internet silliness in 2024.
Heard of ShotSpotter? Microphones are installed across cities in the United States by police to detect gunshots, purported to not be very accurate. This is that, but for music. This is culture surveillance. No one notices, no one consents. But it’s not about catching criminals. It’s about catching vibes.
