I have a few core observations about work that influence how I approach AI:
1. There is one-time work (development, personal assistant) where the input and output are net new. There is also back office work (billing, FP&A, questions about data) that is quite repetitive--working within existing datasets.
2. Back office work exists within SaaS products all of which are including AI within their platforms. Those SaaS products are often integrated with each other.
3. Business logic needs to be transferred between systems in order for each SaaS AI to be accurate. (Asking a Salesforce AI and a Xero AI "What is our revenue?" may get you different answers if either system has custom logic that defines any input to that question differently.)
Based on this, I believe that the optimal path forward for back office work is:
1. Centralize agents (business logic) across an organization
2. Decouple agents from orchestration
Agents that exist at the end of an API call--which can be from a SaaS, Airflow, vibe-coded app, or model harness--are a better answer than platform specific AI. They provide a centralized repository for business logic while reducing vendor reliance (and lock in). Additionally, you get better observability and auditabilty.
Sleeper Service is the result of this thinking--an enterprise AI framework.
I see that as well. Sometimes I can see that they are prioritizing speed to print for breaking news (publishing to the web happens continuously vs the cadence of a daily newspaper) but often it is just being sloppy.
When most people are consuming _their_ news (as opposed to _the_ news) in the form of a thumbnail in an algorithmic social media feed, those copy errors matter less.
Linking to work, where ownership and attribution is clear and the owner has the ability to commercialise is a very different thing to “laundering” content through the model, quoting the midjourney developers here
> "We just need to launder it through a fine-tuned codex." [0]
Under “conduct requirements” imposed by the CMA in June, UK websites are able to activate an opt-out to stop Google from scraping their content to power search features such as AI overviews - very similar conceptually to the news law passed in Australia.
Every once in a while I would put Flight Simulator 2020 on my desktop and work on my laptop. I found that watching the digital world pass by while working put me in a slightly different headspace. Not sure it increased productivity but working while sitting in an actual plane does provide me a bit of focus.
Train ride videos on YouTube scratch some of the same itch.
The Weber kettle grill is one of my favorite examples of quality in another way—function. It is built well but it also is such a wonderfully simple design that provides quite a bit of flexibility.
I have the Smokey Mountain which is amazing at holding heat but a total PIA to clean. Have one of their gas grills as well on a built in line, and it is fairly high quality also.
A feature I would like on LinkedIn would be a 100% verified human content flag for users. LinkedIn can then do the scans and flip that to false for any users it catches posting AI assisted content. Let me filter that out of the feed.
If we don’t do something to maintain some standard of discourse, we lose intellectually and as a piece of our humanity.
LinkedIn is the only social media I use and it is on thin ice.
When I moved from LA to St Louis many years ago, there wasn’t an IKEA yet. I was truly confused about where one was supposed to go to buy lamps. Any furniture stores that had them were selling ones that gave off strong grandma vibes.
Very much agree on Fable. Over the past month or so it has shown to be the only Anthropic model that can understand a largish dbt model codebase. Opus 5 gets almost everything wrong. (High reasoning on both)
More importantly other way cheaper models can do what Opus 5 does. So you can pay for Claude to use Fable 5 exclusively for harder stuff and planning, then get the same value you'd otherwise get from switching back to Opus by using other cheap LLMs for day-to-day coding tasks.
The irony is, as I read the OP I thought it was very much in the style of McSweeneys and then there is this.
It is a pretty obvious humor device so I assume the ideas are independent. And the Socratic dialog framing adds another layer of humor since this is closer to a juxtaposition, lampooning both Swift's vapidity and Plato's writing style.
1. There is one-time work (development, personal assistant) where the input and output are net new. There is also back office work (billing, FP&A, questions about data) that is quite repetitive--working within existing datasets.
2. Back office work exists within SaaS products all of which are including AI within their platforms. Those SaaS products are often integrated with each other.
3. Business logic needs to be transferred between systems in order for each SaaS AI to be accurate. (Asking a Salesforce AI and a Xero AI "What is our revenue?" may get you different answers if either system has custom logic that defines any input to that question differently.)
Based on this, I believe that the optimal path forward for back office work is:
1. Centralize agents (business logic) across an organization
2. Decouple agents from orchestration
Agents that exist at the end of an API call--which can be from a SaaS, Airflow, vibe-coded app, or model harness--are a better answer than platform specific AI. They provide a centralized repository for business logic while reducing vendor reliance (and lock in). Additionally, you get better observability and auditabilty.
Sleeper Service is the result of this thinking--an enterprise AI framework.
Larger write up of my thought process behind Sleeper Service here: https://zero2data.substack.com/p/agents-as-a-service
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