I carefully bottle feed every function by hand, and let it out to pasture at least twice a day.

The same. Think in systems, interfaces, integrations, the problem at hand. Zoom out, leave the grunt work and yak shaving to the machine, and keep your eyes on the price. If you only get satisfaction when you hand set functions in a particular way, then the only thing to say is that time is moving on.

Same here. Loved hacking on things from all angles, still do. The only thing lost to me now is the yak shaving, and good riddance. I want to think in systems, solutions, integration, and first and foremost the higher level problem, not nitty gritty implementation details.

Ever since Fable and Sol level LLMs/agents came online, I have ceased to look at the code. I recently launched a project for a client that integrated many many systems, has considerable traffic with low latency requirements and everything just.. works. I've never had to do this little firefighting when launching a project of that complexity and scale. It's been liberating and I was able to focus mostly on polish and QoL improvements of the stack and systems. What a time to be alive for a technical builder.

Well, this one folds, so you get a much larger screen surface that still fits in your pocket. And $200 won't get you that from any vendor. And while the low end phones have gotten amazingly good, a flag ship will still have a much better camera, better screen, better battery etc. If that's worth the price is a decision everyone has to make for themselves.

Sidenote, I've not had a single one of my smartphones crack a screen since 2008 when I got my first smartphone, take that for what it's worth.

Na ja, "keiner" ist vielleicht etwas kurz gegriffen bei der nach Englisch am häufigst gesprochenen Sprache.

If capability increase continues as it has, then an incident that cannot be resolved by AI will stump humans no matter the practice.

I like the plane example from the article,but I think in reality it will be like code. 1.5 years ago engineers would routinely say that they still write code by hand here or there to keep their skills sharp, and that's just not something you hear much if at all.

If an SRE is faced with a situation an AI can't solve, then said SRE will use the AI systems to triage further, point it to different places and so on.

This works for SREs with pre-AI experience and intuition, possibly less so with new recruits coming in post-AI. I don't know what the solution to this is, maybe practice drills is it, but I have a hunch the entire field will be subsumed, same as many other engineering fields.

There is only so much need for taste and judgement, before even that has been incorporated into the models.

It’s tough. The models can at this point very quickly identify issues in a Kubernetes cluster, for example. This because these systems give you a TON of observability, and it can easily see all the different moving parts.

That doesn’t mean the proposed solution is always right, but it is absolutely landing on the root issue faster than most humans would be able to, even pre-AI. Just because it can remember and run through a bunch of commands more quickly that I can.

There are lots of incidents where the symptom doesn’t always clearly point to the issue, so having something that can fairly exhaustively check a lot of different things very quickly is pretty useful!

But I at least partly agree, I think the more complicated and obtuse a system is, the harder it is for AI to do this. But we’ve invested time in making systems much more straightforward to understand and operate from one place (Kubernetes in general), and AI thrives on that.

And yeah, it means your debugging skills wane a bit because, yeah, if the bot can diagnose the issue in 5 minutes, it’d be irresponsible to not use it.

And I’m not really happy about it, and personally I’ve always been able to figure out a tricky bug given enough time. I don’t want to loose that skill. But everyone is under a lot of time pressure these days.

Like the fact that software "engineering" is mostly nothing like real engineering (and it’s further regressing now due to LLM coding!), the general lack of drilling is again one of the things that make software-related stuff look really naive and amateurish from the perspective of those dealing with the real world. Imagine if the military, police, fire service, and so on did not drill and rehearse incident response?

There are drills, tho. It's just that usually they're only done above a certain level. Small companies, "lean" teams and so on don't have (or didn't have) the capacity to implement all those things. Maybe with the exception of netflix and their chaos thing (bring down systems regularly to make sure the whole still works).

But that's also likely to change with AI assistance. Even an "average" system is better than none. So now teams will have the capacity to bring that in to their systems. Backups / recovery drills that are actually tested (either because they're implementing testing or because the AI screws something up and they need to recover). Either way, it'll be included. Same for security ops. And devops.

I still strongly believe that AI assistance is a catalyst / accelerator, and that the "floor" will rise in most domains. So a small team that only had bandwidth to deal with the happy path previously, will now be able to start incorporating processes and procedures that were historically only done at corporate level. And that's a good thing. Even if it won't look like that in the beginning. But we'll get there, eventually.

Netflix's chaos monkey was this, in a way.

If you are in a situation where you dont know what happened when something goes wrong, the business incentives will not accept “its too complex” as an answer.

Firms aren’t just selling products, they are selling reliability and taking on liability.

Nah, LLM models are already the new compilers. A commodity only engineers know how to use (in the context of software engineering in production environments)

Out of context, but to address "AI will replace engineers".

Recently discussed something about economy/investing with a friend while at work at a slaughterhouse. I really didn't want him to get scammed buying crypto. So, used ChatGPT to find some sources in Somali, a 3 videos with short description why it's worth watching. Intro into investing, intro about cryptocurrencies and about buying them. Had the text shortened down to 3 pretty short paragraphs, not more than twice this post.

He's a smart guy, but only went to primary Qur'an school. Doesn't read or such, mostly consumes internet in form of video/media. He couldn't read those 3 paragraphs, it was too long. Or rather, it wasn't just 3 paragraphs, it was a lot to read.

Maybe we're already dividing into murlocs and the surface dwellers?

I may or may not have told my agent to run the "open" command on a text file.

There are strong indications that brains are prediction engines. Tokens are an implementation detail of LLMs and irrelevant to the discussion.

There are strong indications that brains use predictive encoding and that each individual neuron has an internal model of itself and its environment.

That's nothing like a neuron in a neural network and especially nothing like the current transformer based LLMs that do not use predictive coding at all.

The closest equivalent to the human nervous system is to think of LLMs as a single massive neuron.

So we know that implementation doesn't matter as long as it is somehow making predictions (and predictions also on different things)?

Most living things are prediction engines in a way, why compare to the brain then to start with and not, e.g., bacteria?

Implementation does matter, my point is that different implementations can lead to the same result.

And why compare to the brain? Mostly because of complexity. I can't interface with a bacteria in any meaningful way, but I can interface with an LLM to a significant degree.

"I can't interface with a bacteria in any meaningful way"

But you do. There are more bacterias in and on the body, than body cells. We are bacterias forming lasting bonds and we still interact with the free floating ones in various ways. Mainly in the gut and that has many effects, also on the brain, but also in various other ways we are beginning to understand.

https://en.wikipedia.org/wiki/Human_microbiome

So no idea about a microbiome consciousness - but who am I to know.

But we don't know - just saying it could isn't enough.

Bacteria are quite complex, btw.

This is getting incredibly stupid. The implementation defines the compute budget and the ability to learn continuously and consequently the ability to retain knowledge.

According to you, a model that can simply predict the entire future and then pre-record the answers would be considered intelligence simply because you're obsessed with the hypothetical power of prediction.

The truth is that the intelligence doesn't sit inside the model parameters, the model parameters are just the current state of the intelligence. The training process itself is the intelligence and the model parameters are just an artifact that can be copied around.

Not more than a bunch of neurons have conciousness. But put them together and wire them up just right..

We are seeing the same. We are moving significantly faster and ship more complex stuff, and there are still bugs as they were in the before times, but nothing catastrophic, no gremlins in the machine. Our main worry is about process and communication, not the actual coding really.