"You are a friendly breadwinner"

A recent blog post by Pete Koomen about how we still lack truly "AI-native" software got me thinking about the kinds of applications I’d like to see. As the blog post says, AI should handle the boring stuff and leave the interesting parts for me.

I listed down a few tasks I've dealt with recently and wrote some system prompts for potential agentic AIs:

Check that the GDPR subprocessor list is up to date. Also, ensure we have a signed data processing agreement in place with the necessary vendors. Write a summary of what you did and highlight any oddities or potentially outdated vendors.
Review our product’s public-facing API. Ensure the domain objects are named consistently. Here's a link to our documentation describing the domain.
Conduct a SOC 2 audit of our system and write a report with your findings. Send the report to Slack. Once you get approval, start implementing the necessary changes. These could include HR-related updates, changes to cloud infrastructure, or revisions to documentation and process descriptions.
Facilitate retrospectives for the team. Note down all actionable items and ensure the team changes its behavior. If agreed-upon actions aren’t being followed, raise the issue in Slack. You need to nag a bit while still being understanding.
Monitor our competitors and their products. Write a daily report of your findings and suggest action points and feature focus areas for our weekly meetings.
Reach out to startups that could be potential customers. Before contacting them, create a tailored solution that addresses likely challenges using our <secret feature>, and include it in a personalized message.

Personally, I’d love to have this one too. Not sure if my boss would, though. 😉

Create a steady stream of monthly income for me. You can use my existing company. My bank account number is XYZ, and here are the company details. I've created your accounts on...

Comments

I'm not a passionate developer

A family friend of mine is an airlane pilot. A dream job for most, right? As a child, I certainly thought so. Now that I can have grown-up talks with him, I have discovered a more accurate description of his profession. He says that the truth about the job is that it is boring. To me, that is not that surprising. Airplanes are cool and all, but when you are in the middle of the Atlantic sitting next to the colleague you have been talking to past five years, how stimulating can that be? When he says the job is boring, it is not a bad kind of boring. It is a very specific boring. The "boring" you would want as a passenger. Uneventful. Yet, he loves his job. According to him, an experienced pilot is most pleased when each and every tiny thing in the flight plan - goes according to plan. Passengers in the cabin of an expert pilot sit in the comfort of not even noticing who is flying. As someone employed in a field where being boring is not exactly in high demand, this sounds pro...

PydanticAI + evals + LiteLLM pipeline

I gave a tech talk at a Python meetup titled "Overengineering an LLM pipeline". It's based on my experiences of building production-grade stuff with LLMs I'm not sure how overengineered it actually turned out. Experimental would be a better term as it is using PydanticAI graphs library, which is in its very early stages as of writing this, although arguably already better than some of the pipeline libraries. Anyway, here is a link to it. It is a CLI poker app where you play one hand against an LLM. The LLM (theoretically) gets better with a self-correcting mechanism based on the evaluation score from another LLM. It uses the annotated past games as an additional context to potentially improve its decision-making. https://github.com/juho-y/archipylago-poker

Careful with externalTrafficPolicy

On a project I am working on is hosted in an EKS cluster with the NGINX ingress controller (the one maintained by Kubernetes). It is deployed using it's official official Helm chart, which I realized, after a lengthy debugging session, was a mistake. The initial setup I aimed to improve had several flaws. Firstly, we were using the AWS Classic Load Balancer in front of the nginx ingress in the cluster, which has been deprecated for some time (years?). Continuing to use it makes little sense to us. The second issue was that we were only running one(!) nginx pod, which is quite sketchy since the exposed web services had essentially no high availability. I switched to the Network Load Balancer (NLB), which was straightforward - I just needed to change the ingress-nginx service annotation to specify the load balancer type as NLB: service.beta.kubernetes.io/aws-load-balancer-type: nlb However, increasing the replica count turned out to be tricky. When I bumped it up to two, I began to ...

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