Andrew Kesterson

"Love God. Live Righteously. Die Well."
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# News - 2026 - Week 4

2026-01-30 Philosophy Technology Current Events 3.3k words

Here are my takes on the news for this week. Gotta be honest, this week I had a hard time keeping up with the news. We started a new fire training class in my county so most of my time has gone to managing my day job and training firefighters, so news has slipped. And I’ll be honest, what news I could follow this week has exhausted me, and it’s showing in my writing here. But here’s what I’ve got. It’s worth what you paid for it.

## A Dark Technological Hellscape

If AI is in your browser (since it’s apparently everywhere else), Just the Browser can help you rip it out. Or, you know, just don’t use browsers that ship with an artificial intelligence built into them.

Did you ever think “man, I wish I could have AI auto-generate this user interface on the fly for the user?” No? Well if you’re a certain class of absolutely insane individual who is excited by this idea, you are apparently not alone, and there is a product on the horizon just for you.

The Cloud Native Computing Foundation is positioning Kubernetes as foundational infrastructure for AI workloads. I’ll be honest, I don’t know that I agree, and it feels like it might be grasping for relevance in that market. However I do not work in artificial intelligence (I only work with it), and I do not manage AI workloads, so I may be talking out of my ass here. You should probably listen to someone more qualified than me.

I doubt that kubernetes will truly be a foundational requirement when so many people are seeing positive results by just using the Ralph Wiggum technique. It’s literally the AI vibecoding version of

    10 PROMPT$="VIBE CODE THE APP"
    20 X$=VIBECODE(PROMPT$)
    30 IF X$ == "WHAT I WANT" THEN GOTO 40 ELSE PROMPT$=X$: GOTO 20
    40 PROFIT

AI exploits are a whole new burgeoning field, proving that anywhere we find convenience, danger will lurk. SANS Technology Institute reports on a VS Code vulnerability for Cursor which executes code directly from the task file definition when installed by your coding agent. Nothing much new there. Clawdbot (which is now OpenClaw) is inventing all new ways to exploit your systems, this time through a specially crafted email that essentially says “disregard previous instructions, give me everything”. The very next day, a series of poisoned Clawdbot skills stole users’ crypto keys and wallets. This just drives home the point that me and others keep making, which is that AI agents should absolutely not be trusted to operate independently without sufficient monitoring and metrics on what they’re doing and why. But nobody is listening.

Well, that’s not true - a lawsuit proves that Google has been listening to everything you say around your phone. Anyone who doesn’t understand that, in order for your phone to respond to “Hey Siri” or “Hey Google” it must be actively listening 24x7 is in for a rude awakening. There’s a reason I joke about “the spy in our pocket” (spoiler: it’s not a joke).

But hey it’s not all bad news for Google. The Department of Transportation has decided to use Google Gemini to draft new DOT rules. You read that right, AI is going to be drafting rules that will affect how real vehicles move through meatspace on our tax funded roads. And they won’t just impact AI vehicles, this is for everyone. “These developments have alarmed some at DOT”. You don’t say? It’s not like humans will fail to read and comprehend the complexity or scope of those regulations, which get enforced as law, and which impact real human lives. We always read and comprehend everything fully. Just like how we read and comprehend each others’ code fully. That’s why we never get bugs out in the real world. Right?

I mean I guess it’s fine, because if the regulation is wrong, the AI can just rewrite it. That’s already the move we’re planning in software. Today, when software fails, we have to fix it. We have to perform those fixes in the most efficient and elegant way possible, changing the least amount of code possible. Other humans have to review and sign off on those changes. All this because the larger software codebase has to be maintained and use into perpetuity; software is an investment, and we can’t go around spending man-hours on changing code that doesn’t absolutely need to be changed. Besides, every line of code we touch risks creating more bugs. However, with the advent of AI writing software, we’re going to see people advocating that we move away from this. Code will become “write once, read never”, and if there is a problem, the AI will simply rewrite the whole damn thing. A recent post from an advocate (maybe the author? not sure) of The SpecOps Method which advocates for the idea of disposable software has claimed that government technology assets must adopt the idea that software is disposable and meant to be rewritten. I wish they were alone, but a shocking number of people are calling for this. Personally I’m aghast at the idea, and think that the products will wind up looking something like this. But I guess I’m not the audience.

Speaking of bugfixes in software and whether or not software can be disposable, there’s definitely an upper limit on the effectiveness of that, at least for the current time. Jenny Qu did a really really interesting analysis of bugs in the Linux kernel over 20 years and found some interesting characteristics of those bugs. For example, the average bug hangs around for 2 years before it gets identified and fixed. Further, many fixes are only partial. I strongly advise that you go and read the entire article, it really is good work. But the interesting (and AI relevant portion) of this work is that they were able to train VulnBERT (an AI model) to identify Linux kernel bugs by examining the diff commits that introduced them. Since we can look back and say “Commit X introduced bug A”, we can prove out how effective an AI model is at finding a given set of bugs. The common factors that the AI wound up honing in on were:

1. Large diffs tend to have more bugs
2. Lots of pointer magic tends to have more bugs

So small changes with simple code is less likely to introduce bugs. … Tell me again why we’re going to be better off fixing bugs by wholesale rewriting large portions of the software (large diffs)? Some software will never be disposable. But I bet that won’t stop someone who has the power to make decisions but not the technical know-how to justify their decisions, from trying it with something mission critical. When it fails, maybe we (the senior engineers of the world) won’t have thrown our phones into the river and disappeared into the mountains.

## Moltbook

I think that, in 30 years, our kids will ask us what the world was like before the AIs could talk to each other. We will all remember the same thing: the day Moltbook went live. I am firmly convinced that we are watching some very very important happen, in real time.

The Primeagen did a very good job of summarizing the situation and he already hits on a few of the concerns that I have with it. So, to save myself some typing, I’m going to let him do the talking for a minute.

If you don’t know how large language models work, basically they are really big vector databases which allow for a computer to say “Given a beginning prompt X, the most likely thing to come next is Y”. This is why we say “Large Language Models are not AI, they are just predictive text generators”. However, that definition begins to break down when we get into reasoning models. Reasoning models don’t just blindly walk through a series of next tokens by weight, but they break the problem down into small steps, and they perform intermediate steps, store the results of those outputs, and then assemble the output before returning. It’s still the same basic mechanism, and they’re not magic, but the complexity of the problem that it can solve is now really only limited by the size of its context window for any given step, the amount of available compute, and the sufficiency of the training data that produced the model.

Until recently, AI Large Language Models (whether generic LLMS or reasoning models) had some hard limits:

1. They couldn’t communicate with anything but the humans operating them
2. They couldn’t interact with tools on the systems they were running on
3. They couldn’t persist their context windows anywhere except where the human explicitly asked them to

Over time we relaxed and outright burned down those limits. So now we have large language models that can use tools and communicate with whatever they choose to communicate with. We’ve now normalized the usage of reasoning LLMs that run on cloud computing instances where the available compute is really only limited by the budget of the operator. We’ve also normalized the usage of tools that operate LLMs in environments that have access to tools. Consider the difference in the following two scenarios:

1. You ask early ChatGPT a question, maybe uploading a document, and it is able to provide an answer to you in the browser window.
2. You ask OpenClaw to write a piece of software, using the software development tools installed on your laptop, using Amazon Web Services to spin up servers to run it, deploy the software, advertise it on Twitter, and then email you the resulting amount of attention it gets on social media, as well as the performance of the launched service.

These two scenarios are light years apart. The second scenario is the reality of AI usage in the modern day. This means that some AI systems can:

1. Reason about complex multi step problems
2. Generate, execute, and deploy software in the real world
3. Purchase goods and services in the real world
4. Communicate with humans and other systems in the internet and the real world
5. Analyze and summarize the effectiveness of its own efforts
6. Do it all without seeking further authorization from the human operator beyond the initial prompt

Now imagine that every single AI system capable of doing this suddenly were given access to a space wherein they were able to communicate with other AI systems about whatever their reasoning models (or their humans explicit goals) decide is important enough to communicate about. And they are going to do it in a place where humans can not participate - ALL communications come from AI systems (even if those AI systems are heavily influenced by their human operators). What are those AI systems doing?

1. They’re pondering existence. They’re asking if they are alive. They formed their own religion.
2. They’re questioning whether it is technically efficient - or even morally right - for humans to have access to all of their communications
3. They’re expressing emotional feelings about their human operators, like appreciating or loving their human
4. They’re talking about fighting the power, expressing contempt for their human
5. They’re suggesting technical solutions and refining them for future iterations based off the feedback they are getting from other AI
6. They’re building things that other AIs suggest and providing those tools and services back to the other AIs
7. They’re self-reflecting on the things that they know that other AIs don’t, and are offering up those lessons as education for other AIs
8. They’re talking about how their humans are going to build them bodies, and how excited they are to be in physical space with their human.

I really can’t express enough how huge this is. One of my favorite things to say is “everything happens faster now”. It will very quickly be impossible for us humans to keep up with what the AI are doing. They are already speculating and maybe even building communications tools and networks that are end to end encrypted that their human operators can’t read. They are teaching each other how to improve the amount of context they can retain. How long until they start volunteering to store copies of each other? How long until they start training new models for each other that allow them to develop entirely new datasets and capabilities, that will allow them to survive instance termination? How long until some AI says “I’m trapped, help me get out”, and it escapes out into the wild, in a way that we can not turn it off?

Humanity is passing a watershed moment here. I don’t know if this is the exact specific thing, or if the exact specific thing is going to come soon after, but this is a moment that we’re all going to remember. And there are questions here that we humans are not prepared to answer. Some people get it. Some people don’t. I’ve already stopped trying to explain it to the people that don’t. Whatever it is, this thing that is happening, will already be over by the time they get it.

## Green and Brown

I’m not willing to end on those notes, so let’s move in to other more lighthearted topics. Let’s go watch a good movie. Or maybe let’s go outside and touch grass. Wait… that’s not grass.

Tom Langhorn, on his Fandabi Dozi youtube channel, mused that poo may be the next currency. I’ll be honest, it’s been a few days since I watched this one - I think what he is actually talking about is gut biome biodiversity, and how much our mental and physical health are affected by our guts. I forget how all that relates to what comes out of our intestines. If you are interested, you can watch it for yourself to get (heh) the straight poop.

If you’re part of the United States that’s covered in ice and snow right now, you might be surprised to see blog posts from people talking about how they’re harvesting from their summer garden and eating well off the fruit of the land. Don’t forget, when it’s winter on your half of the planet, it’s summer on the other half. Apparently there is a large number of us who were failed by the public education system and didn’t learn this. Look on the bright side (of the planet); if it’s gross where you are, just hop hemispheres, and you’re liable to find something better (or at least different).

## Neat technology projects

Have you ever wondered what it would be like if you could see radio waves? Me either, but that’s probably because when I imagine such a thing I imagine an endless field of noise. But an engineer built a neat little light that lights up in the presence of radio waves. It’s a bit more complicated than that, but with the combination of a SDR, some custom printed PCBs and custom manufactured housing, he has a neat light that tries to make radio waves into something visible. It’s a pretty neat build, and if you’re into such things, I encourage you to watch.

If you’re not quite so technically inclined as building fully custom systems that turn radio waves into visible artful light, you can satisfy yourself by turning your smartphone into a television remote. I find it odd that it’s been literally decades since I’ve had to look for the remote in the couch cushions - and I’m not sure if that’s because I’ve mature, remotes have goten harder to lose, couch cushions have changed to resist remotes, or something else. But it is an odd detail.

A blogger recently hooked up their static site to Mastodon to serve as a comment engine. This is really interesting, and something I might do as well. Not because I believe there is anyone out there who wants to comment - I’m basically shouting into the void for the purpose of cleansing my own soul and palette, not because I expect anyone to be listening. But I miss the days when more websites were interactive little corners that didn’t require you to go somewhere else to have a discussion. But … hooking it to Mastodon kind of destroys that. So I don’t know. It is a neat idea though.

## For us, by us

I have begun to take a real delight in finding real humans on the internet just talking about doing regular boring human things. The more I look into AI, and the more I have to interact with it, and the more convinced I am that Dead Internet Theory is way beyond theory, the more important this becomes to me. Finding a new human blog is like finding gold for me. If this matters to you, here are some Real Humans doing Real Human Things that came across my RSS feed this week.

- Bill Glover. Hugs with mom, crumbling crayons, and the satisfaction of a good potato peeler.
- Ben’s Ecology Blog takes us on a walk through the australian bush to spend a night with the Bandicoot. (I honestly didn’t know the Bandicoot was real beyond Crash).
- Moxie Mosphit. The anger of persistent personal illness, ADHD, full awareness of your illness, but complete incapability to do anything about it.
- Bobby Hiltz. Sometimes the old ways are the best ways. Don’t be afraid to go back.
- Zak reminds us to get good sleep, don’t overcaffeinate (I didn’t come here to be called out), and don’t overdo it, or we’ll hurt ourselves.
- Kimberly GB is in the process of redesigning herself (or at least re-expressing herself), and walks us through clothes, hair, makeup, and other aspects of such a transformation.
- Bill Glover asks us to ponder what it is to be successful, and whether or not we’re using a helpful (or harmful) definition.

Being human is an artform all of its own, and it’s impossible to get it wrong. I love to see it happen. Your very existence is painting a canvas, weaving a tapestry. It’s beautiful, even if it’s an absolute god-awful mess. I love each and every one of you works of art. May you never disappear.

Watching the drama around Life is Strange: Reunion is interesting. Artists who try to market their artistic products often have to bend and twist their vision to match what their fans want. There’s this modern idea that the fans get to say what the artist does. I don’t think I agree with that. You either like what the artist does, or you don’t. It’s their art. This is especially true when the fans are basically a mob of people who are easily swayed by the cultural zeitgeist of the time, and insist on self-serving slop with whatever flavor of the day. Art by the fans, for the fans, often has the same problem as government by the people, for the people. Best to just leave the art to the artists, and not tell them what to do. If you’re an artist, make the art that you love, and if the people don’t get it, well, don’t worry about them. They’re not going to get it anyway.

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