#software-engineering12 entries

This was a great read. I also believe this to be true and am equally concerned about the consequences.

The reality is thus: the people in charge either have no plan, or see no path forwards other than keeping their heads down.

This is interesting and unsurprising, it seems like it’s just human nature to fall in line, regardless of how absurd the groupthink is. I’ve definitely seen it in every project I’ve been a part of over the last 3 years or so since LLMs became mainstream. Suggesting that we are not, in fact, on the brink of a revolution and should just focus on building something that actually works is heresy.

Frequently the failure is not related to AI itself, but rather that companies are terminally bad at running software projects effectively, and as I have remarked previously, AI projects are subject to all the failure modes of normal projects plus you can get everything right and then still fail because of the method's novelty.

This is what people like me have been saying since the very beginning. The first DORA report on AI adoption came to the same conclusion — AI is just another tool. It will amplify whatever practices you already have in place, good or bad. Most companies have no idea how to ship software and AI doesn’t change that.

They cannot buy sensible software, hire competent talent, communicate honestly with executives about the state of projects, or undertake any sort of sensible initiative.

It’s a shame to hear from someone who has sat at many tables with the big wigs that it has come to this. Again, not really surprising, but it’s unclear what the path forward is.

I really love seeing more and more serious engineering being put into making LLMs actually work in production. They’re too cool a tool not to use or integrate into consumed software, but nobody actually wants more shitty chat bots. Making them work reliably to actually make software better requires structure and effort, and this is one cool way of thinking about it I hadn’t come across before.

Without additional artifacts beyond what human-driven CD requires, agent-generated code accumulates drift and technical debt faster than teams can detect it.

This is undeniably true at this point and it’s cool to see frameworks and systems emerging for addressing it. This is the first time I’d heard of “agentic CD” and it’s super interesting!

I always appreciate these kinds of candid analyses. I’ve become convinced that all the various things people refer to as “AI” are indeed very useful, but they don’t obviate the need for actual engineering. At this point it’s been many years since LLMs became broadly available and the benefits remain largely hypothetical and in the future but the harms are very much real and present today.

This really hit home for me. Lately I've been feeling very overwhelmed and bored at work, feeling like I'm just drowning in AI slop and cannot possibly keep up. AI-generated PRs are flying through our previously robust process, production problems are way up, most of what I read day to day is not AI-generated, and it's frankly exhausting.

It feels like something has shifted just in the last few months. I'm not sure if it actually has, or if my organization has just taken up AI to a new extent recently, but somehow the day to day enjoyment I used to get out of programming has mostly disappeared. I've been reflecting on why that is and what I might need to change going forward, but I'm not sure what that is yet. For now I enjoy lamenting the old way of doing things with other old fogeys around the internet.

I love Randy Au's newsletter in general, but this one makes a particularly useful point: Not thinking about why you're doing something ahead of time is expensive and wasteful.

Randy's discussion of how this issue manifests in software teams in particular is completely on point, in my experience. But it applies to all areas of life. Software engineers get asked all the time to just "log everything", with the assumption being that since we have "big data" tools now, we'll be able to make use of it all later. The reality is that extracting value from heaps of unorganized, unstructured raw data is actually very difficult and potentially expensive, and the time to think about what questions you want to answer is before you start collecting data in the first place. It's also mostly true that once something is being collected or logged, it will go on forever. Another consequence of not thinking through your reporting and monitoring infrastructure ahead of time is that nobody really knows who is depending on what, and so things get left in place "just in case" it breaks something downstream. Of course this is madness, but it's how many organizations operate in reality.

It's true that storage is cheap now, but that doesn't mean accessing said storage is cheap, let alone doing something useful with the mess that's in there. Keeping terabytes of logs in S3 is like having a free warehouse in Labrador for all the tchotchkes you know you don't need but can't bring yourself to throw out.

A lot of this data hoarding stems from organizations wanting to be "data-driven", without ever explaining what that means for their context or why it matters. Collecting data is easy and cheap but does not inherently add value, and the difficulty of extracting value from raw data is a function of its volume (among other things). It's worth spending at least a little bit of time up front thinking about what

This is a really cool tool. I use LLMs extensively in my side projects with great success (my workplace is still investigating whether there are ways we can use them that will align with their privacy needs) and often simulate what this tool is purpose-built to do. I think it's going to save me even more time.

When I first started playing with AI-assistant editors (like Cursor, Windsurf, Co-pilot, etc.) I assumed they would be way better than chatting back-and-forth with an LLM, but it turns out, counterintuitively (to me anyway), that I much prefer chatting over the in-editor suggestions. I find these mostly intrusive and wrong, and they just create more work for me. In a chat interface, though, I can guide the conversation in constructive directions.

It might have something to do with the types of questions I ask them, or maybe just my personality. In any case, I frequently copy-paste dozens of files into a project, describe in great details a project's structure and data model, and then ask questions about it to an LLM. It sounds like this tool can automate and improve that process, which is great news for people like me who frequently engage in this kind of workflow.

I found this article interesting. It's about how AI-generated web UIs might help add more value to the work data scientists do. It was also a little depressing to read as a software engineer, but a good reminder of what our role is morphing into within organizations.

The main takeaway for me was that most code is so shoddy these days that we may as well just get LLMs to spit out UIs that are good enough to serve our immediate needs and then move on. She argues that between the break-neck hype cycle JS frameworks go through, high industry turnover, piles of existing tech debt, and constantly changing requirements, code written by LLMs isn't really that much worse than code written by developers anyway and given the astronomical cost savings, maybe we shouldn't be so skeptical about using generated, disposable UIs.

Of course my first reaction is cringe and outrage, but then I have to immediately admit that she's has a point. I've seen a lot of codebases, and I'm ashamed to say that as far I can tell the average quality of software in the wild is poor to terrible. The sad state of web development these days combined with the increasingly impressive quality of code that LLMs (especially Claude) can write, means I have to agree that it actually makes a lot of sense to use them to write software. They're at least very useful for prototyping and getting started, as she points out.

Ultimately this is just another reminder that software engineering is table stakes now. Anybody doing anything tech-adjacent in many organizations is more or less expected to know how to turn their output into a functioning web app a normal person can see, and despite our best efforts this is still an exceedingly complicated task. Nobody can be an expert in so many things at once, and as long as that remains the industry expectation, I predict that we'll continue to see more and more of the gaps filled by LLMs.