hosh

LLMs tends towards closed-worlds and bias towards decidability. Risk assessment is terrible, as are creating opportunities — no animal instincts to draw from.

Despite having this in the training data, I am constantly having to remind my coding agents to reason and design with grammars. On the other hand, once all of that is settled, and the task is no longer open-ended, then they are very good at driving towards completion.

Crafting the automation itself can be fun. That is one of my favorite part of designing systems running on Kubernetes.

I have also come to the conclusion that, right now, AIs lack imagination. So designing human-AI collaboration that lets human imagination in the loop would be interesting.

If we’re going by water, land, etc as an argument against wasteful activities … Coming from the permaculture world, I can tell you just how wasteful the pre-AI world is. The ideas in the mainstream environmental-friendly practices are themselves very wasteful.

I have sometimes had to work in bed while running a low fever.

I do that because I believe in the mission and work is assessed by outcome.

The flip side too — I learned well before COVID that the Kanban principle of “make work visible” applies to WFH

I wonder if the employer is in violation of ADA laws, and whether exemption from monitoring is “reasonable accommodations”.

Not that I think the broader practice for monitoring remote workers is something I think is a good idea.

Code as documentation works better when the code is declarative or a DSL. These capture intent and promises (as in promise theory) better.

When it is not, it has to be reasoned out and does not work well for documentation.

Other things that code and tests alone do not capture well:

- promises (as in Promise Theory) made to other parties. Claude already has PT in its training data.

- Constraints-inducing-properites, as in Roy Fielding / Christopher Alexander. While tests, and property testing can capture properties, there is no formal connection to the constraints that induces fhem. By constraints, I am not talking about business requirements and business value — those are better understood through Promise Theory. I am talking about things like at-least-once delivery or total ordering (from append-only constraint). Claude already has Fielding’s dissertation and Alexander’s works and ideas in its training data.

- grammars, as in pattern panguages (not just patterns) a la Alexander / Fielding are also not captured in code alone. These tell both humans ans AI how to extend a pattern, and how to identify anti-patterns (when they violate a constraint-inducing-property)

- LLMs are trained with many different worldviews and bounded contexts at the same time, and is very capable of translating across it. However, these need to be soelled out, otherwise it would talk in whatever it infers

Specifications written for the exact way components are wited together run into that stale doc problem. Although it takes much more human attention and token burn to describe things in terms of pattern language and promise theory, it becomes easier over time. The actual implementation plan tends to fall out more cleanly when all those other stuff are at least considered. This is where I have been spending most of my time.

If people are happy with Actions API, that sounds great.

I’m not happy with the Actions API. I think Gitlab’s cicd design is much better, and I’m not fighting it all the time when I use it.

Smalltalk also had extensibility for one. But it largely missed the explosion of collaboration when open source code became shareable and people can work on it together.

People also shared Hypercard apps.

I think it is less about client/server and more about how ideas can be shared. Sometimes, data might be shared.

I think as we see more AI participation in code, github stars are going to matter less.

Maybe they got sucked into the cool tech?

I remember my first pass reading of Stephenson’s Snow Crash and Diamond Age, I was bedazzled by some of the tech … it was later readings that the comments like “biomass” (the recent echo in AI “meat proxy”), or how the Primer was ultimately a failure sank in later readings.

Not to mention language communities with constant supply chain attacks because its standard library story is poor, and everyone keeps reinventing new, often half-baked solutions?

Or even that, the very same ecosystem congratulates themselves on the typing system but still relies on linters because the language and runtime themselves allow whole categories of dumb ideas to be written?

This looks a lot less annoying than Typescript, particularly how dynamic() is a lot more useful than any()

I also wonder if this works well with Ruby’s duck-typing and monkeypatching.

In my twenties, I came across an idea from one of Ken Wilbur’s books that helped settle a conundrum for me. What happens when one wants to honor all life? Are they all equal?

He made a distinction between intrinsic value and extrinsic value. Plankton is not as complex of a lifeform as whales, yet whales cannot live without plankton. One has more intrinsic value and the other has more extrinsic value. There is an interrelationship that does not have to flatten value for everything and everyone.

LLMs are trained from the language corpus of our collective consciousness. It reflects our collective, all the wonderful, beautiful, and horrific things we can dream of and put into words.

What people are finding now when building agenic AI orchestrators is that they are reinventing some of the ideas behind BEAM and OTP. The big one being queues (mailboxes), but also fault isolation.

Async runtimes can’t really do preemptive scheduling so those end up using other methods that, while getting low latency, may not improve reliability or reduce variance in latency.

Orchestrating tasks across a number of different services that are not in your control becomes a distributed system where not everything is reliable.

Is it fair to say that Mesos is more top-down while Kubernetes is more bottom-up in scheduling approach?