​​How much do amd64 microarchitecture levels help in Go? is a benchmarking article that shows the compute time improvements you can get if you'd build your apps for modern x64 processors only. You likely use modern processors already and do not plan to run your apps on the decade old hardware.

Still, it's important to remember that while such articles are nice; your real applications probably don't just calculate bit vectors all day. It's much more likely your real performance bottleneck is I/O and not the fact that your apps are built with the support for old hardware. Still, you can get some easy wins here by just adding a compilation flag, if you're using Go.

#performance #go #programming

A Reddit thread with some useful tools for Kubernetes and kubectl plugins0.

Some things there are well-known, but you may find some new interesting things there. I did :)

#kubernetes

A new issue of the CatOps Digest is here!

https://newsletter.catops.dev/p/catops-digest-2026-06-13

#digest #newsletter

​​For today's Donations Monday, I'd like to share with you a fundraiser that our friends at DOU started for the 2nd separate corps of the National Guard of Ukraine «Хартія». The goal of this fundraiser is to buy heavy bomber drones "Vampire" for the Kupiansk direction.

Monobank jar: https://send.monobank.ua/jar/26mrQPQ3PZ

#donations #Ukraine

I will post AI-related articles this week, because why not?

The first one is from Charity Majors called AI demands more engineering discipline. Not less, in which she follows up on her another article.

This one is on technical aspects of moving to the disposable code. It also has a lot of links to other articles, which is also cool.

#ai

​​Harness engineering for coding agent users is a new guest article in Martin Fowler's blog that summarizes approaches to improve AI output and make it more manageable.

If you're actively using AI agents day-to-day, things described in this article won't be news to you, but it helps to structure one's thoughts.

#ai

Continuing with our AI week.

AI in SRE: What's Actually Coming in 2026 is telling a story of AI coming for help with incident response.

The article suggests trying an AI tool for real investigation or data collection for postmortems. To clarify this, in my experience, you don’t need to have a dedicated tool, a general purpose AI agent with some harness (skills and scripts) would do. You should try it! AI does the job of data gathering incredibly well. Yet, the results are indeed not perfect.

Another good point in this article is data quality. AI results are as good as context you provide. I witnessed two prominent failure modes so far:

1. Inference on incomplete data: a person with limited access (typically a developer) asks their agent to investigate an alert. The agent comes to some conclusion. At the same time, a person with elevated access (typically a systems engineer) asks their agent to investigate the same alert and gets a different result, likely because some data is only available via kubectl events, etc. The fix for that is not to allow everyone to do everything, the fix is to revisit your observability pipelines and ensure that you ship all the relevant data, which is easier said than done.
2. Agent that cries "wolves": if you have a pollutant in your logs, or simply an event that happens very often, agents like to correlate it with everything. If your clusters are elastic, an agent could blame node count fluctuations for every error. The problem here is that once node count fluctuation actually causes a problem, you will be the one to ignore this hint from an agent, because it suggests it every single time.

If you are ready to share more AI failure modes specifically related to SRE in Ukrainian, welcome to our chat.

#ai #sre

So, that's for AI in the companies, but what about AI in the wild i.e. in open source?

We have cases like curl, that had to take down their bug bounty program due to the influx of slop bug reports. Yet, the industry adapts.

Here's a study by Redmonk on the stance of various foundations and standalone open source projects on AI, including their major concerns, and openness to AI-generated contributions.

#ai #open_source

We continue supporting DOU with their fundraiser for the 2nd separate corps of the National Guard of Ukraine «Хартія». The goal of this fundraiser is to buy heavy bomber drones "Vampire" for the Kupiansk direction.

Monobank jar: https://send.monobank.ua/jar/26mrQPQ3PZ

#donations #Ukraine

Not all index scans are equal is an article by Datadog, where they describe the idea of targeted DB indices and when to use those.

There is also some praise for their database monitoring tooling, but this is a vendor article after all.

The only thing is that they didn't mention that too many indices also comes with a price: you need to store and update them. So, always evaluate the performance for some period of time after adding indices.

#databases #observability

​​An article about optimizing the symbolicator - the part of the observability stack that translates stack traces of minified code into human-readable ones.

It’s an interesting read about what a design optimization can achieve. In the discussion on Reddit, commentators rightfully pointed out that the drastic difference between this new symbolicator and the baseline is due to the approach that author uses, and that a C/Rust version would still perform batter compared to the example in Go. Yet, this is kinda the point: by designing your application in a clever way, you can achieve better performance with “slower” technologies compared to brute-forcing the solution using “faster” technologies.

#programming

Save a list of Kubernetes defaults in one place, so you don't forget, and don't need to find them every time.

#kubernetes