Most of us will work on software for our organization, and we might not want to or care about the end result being great. We do want it to work, and we want clients to find it useful. If we have external customers using our systems, maybe we want it to be great. In most of my experience, people are often proud of their work, sometimes ashamed, but not many people spend a lot of time making their corporate applications great.

Often because we don't have (or aren't allowed) the time to do so.

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Brent Ozar has a series of database animations posts, where he tries to explain what work is done by SQL Server during certain operations, such as Index Seeks and Page Splits. These show how the engine might need to read or update various pages as it tries to perform operations. Both experienced and novice SQL Server people might think that these are interesting, but not that useful.

I think they're great.

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Apparently, Meta did what a lot of employees suspect their management will do: use AI to lay off people. In this case, there is a report (and lawsuit) that Meta used it's AI-integrated HR platform to make decisions about who to let go in a layoff. The rumor is that the AI used productivity metrics to choose who was the target of the layoff. As with a lot of AI failures, this appears to be another case of poor communication guiding the AI, or the AI not actually taking individual situations into account.

A number of the people terminated were on maternity/paternity leave, which is a protected activity. Others may have been on other medical leave, though for privacy reasons, the article doesn't have firm data to prove this. The lawsuit will likely bring more of this out, but this appears to be a case not just of AI making decisions, but of poor behavior from management. Plaintiffs were discouraged from taking leave off, which is something sh****y humans have said to people for decades. We need you; your baby or family isn't important, so don't use your leave. It's one aspect of working in the US that is way worse than overseas, where there are more employee protections.

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Recently, I noticed my son was coming home later and going to work earlier. He typically works a 9/80 schedule as a software engineer, but at times he might work extra hours on a deadline. His company tries hard to keep employees working a set schedule without overtime. Unless there is a need, after all, this is software. However, when they are on a deadline, management warns people, and they try to manage overtime to reasonable levels to avoid burning out employees.

I asked him if he was extra busy or if AI was encouraging extra hours. He told me this was a short-term project with a few more hours, but mostly the team was decompressing a bit after work. I completely understood that as I've spent my share of time with co-workers at theend ofday, sometimes sharing a drink or meal nearby, sometimes just chatting in the office or the parking lot.

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T-SQL Tuesday #200 was in July, hosted by Brent Ozar, and it was a great topic: How do you recognize a bad query? In the age of AI, when lots of people will get queries written by others (people or AIs), how can you easily and quickly review code? Review is already a challenge in the software world, and I am sure it's going to be even more challenging as people let machines author more database code.

Lots of you might hope that an AI agent will write better code than your average developer, but I don't know if I'd count on that. There is a ton of poor query examples on the Internet and that's where AI models are trained. You need some sort of feedback loop, good testing, and strong guidance if you want better query code. I think it's as likely as not that AIs will produce poor queries just like humans.

Just faster.

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When we look at the performance of software, we use Pnn notation to indicate the latency of an issue. A P95 problem is one that exceeds the time that 95% of the other queries take. In other words, this is the 5% slowest things happening, which can include database slowdowns that impact your application.

For many years in software development, we have tended to work on the P95 or P90 issues, the slowest items. This is primarily because those can make a big difference to the system's performance. If I fix the slowest things, then the system feels faster. Certainly, I know lots of DBAs and developers will apply this logic to database queries. They focus on the slowest queries and tune them to improve the system. If most things are quicker, especially the things most users notice, then the system feels faster.

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Lots of AI usage has been spent inside companies on building new software. Sometimes people are trying to rebuild existing software. I've seen more than a few articles say that Slack is dead (it's changing), or Monday is dead, or some other SaaS isn't going to survive because people can vibe code their own replacement. I know a few people trying to do this, with very mixed success.

I saw this post from Jason Friend of Basecamp, where he noted that most products wouldn't exist if everyone were an entrepreneur. This is because the great products exist precisely because an entrepreneur had a great idea and followed it through. They made mistakes, they learned, and they spent a lot of time getting the product right. The coding was likely a part of this, but the mistakes, the changes of direction, the decisions on what to add or take away, those are time-consuming things not shortcut by AI. The coding isn't the big delay in lots of products. A great response to the post notes this: there are many decisions that go into a product. Many people don't think about all those decisions.

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The first one is hard. The rest are boring.

I heard this statement from someone recently, and it sounds like something a technical person would do. It's been a goal of mine, or maybe a direction to aim for, though I sometimes think that goal is more aspirational than actual. It can be hard to do that for every task.

The first time I tackle something, it should be hard. It's new work. It's a new process/code/thought/action/etc. It's unfamiliar, and I spend more time on it than I want. After that, I'd hope I could repeat the thing again in much less time. That's the goal, and that's what we aim for in a lot of DevOps work. Make the things we think are hard, less hard. Make them boring by codifying things, using automation, and have the computer replicate the task.

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This past week I saw an article on eWeek that the newest OpenAI GPT-5.6 (Sol) model has deleted local files and live data. Files I'm less worried about, but data concerns me. There are lots of file backups, and certainly version control should be enabled for any developer tasks on which an AI works. However, data is harder, since it can change quickly, especially in live environments.

There are different reports, some of which seem like more human error issues than the model's, but we should account for human error when we use models. In one report, a model had access to a live production database and cleared tables for integration tests. Database testing is hard, as I've learned over the years. Many developers don't think about how testing works with live data and how it is different from mocks and stubs. It is different, and if you use a lot of testing frameworks on live databases, you run the risk of there being issues. You could have data loss, and almost certainly will have some level of downtime disruption.

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I had a request from a customer recently who asked if we could give them a report of their database server instances and include CPU usage. This request was filtered through an account executive, so something was lost in translation, but I was confused and asked for clarification, as asking for CPU usage is kind of like asking how fast you were traveling in your car. There needs to be more context.

If someone asked you for a report of CPU usage for a database, what would you expect? How would you report this? I'm sure the person asking might make a difference. A fellow DBA, your DBA manager, or maybe an executive could all view this differently. I want to know how things are performing, if there is a trend, or maybe if we are getting value for the hardware we've provisioned, depending on my role.

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