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src/content/blog/drafts/checking-in-on-2025.md

@@ -139,6 +139,8 @@ This will gain strength over the next year, but remain niche. Prices will contin
 - Dropping regulatory enforcement
 - Mass layoffs
 
+Counter-acting forces
+
 - The Fed has stayed largely independent
 
 ### Crackdown on Facts and Dissenting Opinions
@@ -149,3 +151,13 @@ This will gain strength over the next year, but remain niche. Prices will contin
 - Rewriting of federal information to align with ideological goals
 - Installing more partisan oversight in agencies
 - Attacks on universities and funding
+- De-funding of CPB
+- RFK and the health services
+
+### Decreased rule of law
+
+- Pardons
+- Dropping of cases
+- Bribes
+- Extortion law suits, e.g. CBS.
+- Likely illegal firing of appointees

src/content/blog/drafts/vibe-coding-debt.md

@@ -0,0 +1,36 @@
+---
+title: "5 Types of AI Coding Tech Debt"
+description: "AI coding assistants, or even vibe-coding agents, can create a lot of code very quickly and get features done fast.  However, there are a few common ways in which vibe-coding projects tend to go off the rails and end up mired in tech-debt hell.  The AI doesn't prevent you from accumulating tech-debt, in fact it allows you to create more of it more quickly.  I go over a few common issues with AI code that causes it to lose it's luster on large or long projects."
+---
+
+1. Concepts
+   1. ROI
+      1. Refer to the Glyph's article.
+      1. Net-positive and net-negative programmers
+   1. Simplicity and Complexity
+   1. Transparency
+1. Some of the ways AI fails
+   1. Testing
+   1. Debugging and logging
+   1. Libraries and common code
+   1. Performance
+   1. Abstract structure
+   1. Real-world/Domain modeling
+1. How do we fix it?
+   1. Functional tests
+   2. Enforce good debugging practices
+   3. Build your own libraries
+   4. Build real-world performance tests
+   5. You gotta know how to design it
+
+### Domain Modeling
+
+While the AI can be very good at getting some code to run. It's like having an assistant who lives in another country that you only interact with through chat. Not even another country. It's like an assistant who was born in a fallout shelter and has only ever interacted with the world through a text terminal. Their entire life they have been sitting, hunched over a black screen with green text reading and reading to learn everything they can. But it's also pitch black down their. They've never _seen_ anything.
+
+You have to tell them everything you want them to know about your specific case. Let's say you are modeling some sort of supply chain logistics. They've never actually worked on a supply chain. They've never seen a ship. They've never been annoyed by a late order or a project falling behind due to a shipping mishap. All they know is what they have read.
+
+More than that, they have never read anything about your specific case. They only know what you have told them. You might think that this is at least as good as an intern, but even that intern sits in on meetings, chats with people in the halls, and observes the company functioning around them. They also have an intuitive grasp of things like space and time. They may have seen the ships sitting out in the harbor and realize just how hard those are to turn around.
+
+The AI doesn't have any of that. It only knows what it's read. You have to tell it _everthing_ it needs to know in order to write code that corresponds to whatever problem you want to solve.
+
+AI sucks at domain modeling because it has no understanding of the domain. All it can do is imitate models it's read about that might sound similar.

src/content/blog/vibe-coding-debt.md

@@ -1,23 +0,0 @@
----
-title: "5 Types of AI Coding Tech Debt"
-description: "AI coding assistants, or even vibe-coding agents, can create a lot of code very quickly and get features done fast.  However, there are a few common ways in which vibe-coding projects tend to go off the rails and end up mired in tech-debt hell.  The AI doesn't prevent you from accumulating tech-debt, in fact it allows you to create more of it more quickly.  I go over a few common issues with AI code that causes it to lose it's luster on large or long projects."
----
-
-1. Concepts
-   1. ROI
-      1. Refer to the Glyph's article.
-      1. Net-positive and net-negative programmers
-   1. Simplicity and Complexity
-   1. Transparency
-1. Some of the ways AI fails
-   1. Testing
-   1. Debugging and logging
-   1. Libraries and common code
-   1. Performance
-   1. Abstract structure
-1. How do we fix it?
-   1. Functional tests
-   2. Enforce good debugging practices
-   3. Build your own libraries
-   4. Build real-world performance tests
-   5. You gotta know how to design it