Koodihommia

The most valuable comments I find in any given codebase look like this: Hack! This thing is weird because of this and that reason. I tried to implement a more elegant solution, but due to X and Y constraints, I failed. Hack! This is weird because there is a bug in library X that we depend on. See https://github.com/library/issues/420 Note! I tried options A, B, and C and decided to do this weird thing because, while it looks wrong, it turned out to be the best solution at the time of writing. These comments do not explain what the code does. They explain why the code looks the way it does. They bring into light historical context, failed attempts, and external constraints that are otherwise invisible. We all occasionally fail to communicate our intent to the next developer. That is normal and unavoidable. What matters is leaving a clear mark when something non-obvious or hacky is done on purpose. Increasingly, the “next developer” is a metal-headed clanker: an LLM. ...

There has been a lot of talk about SaaS platforms whose front page essentially just has a textarea for collecting the user's intent, and what comes after that is a canvas for the outcome. Call these agentic or AI native startups, but I think there might be something in this pattern. Most famous examples include Lovable and ChatGPT, but obviously, these will spread to other domains like legal, and I happen to know that some medical startup is also working on this. So what's the big deal here? Firstly, we can eliminate the endless form filling and table layouts. Not that there is that much bad in those - we are all used to them, and they play a part in making websites familiar and easy to use. It’s more than the UIs become more personalized in the sense that you don’t need to squeeze all users through a funnel with forms that have tons of fields and tables or visualizations with dozens of variables. The textarea approach flips this. Instead of asking use...

I’ve been looking into agentic workflows to act as an operations assistant for the SaaS I'm working on. A big part of that work is getting the assistant to make sense of all the alert and monitoring data that pours in every day. Passing a bunch of raw time-series data to an LLM generally doesn’t work that well. You need to tell the LLM to aggregate the data and give it the means to do so. Using aggregates will often lead to better insights from the LLM. This is a well-known fact to anyone who has tinkered with this (at least at the time of writing this). Humans, of course, like to build visualizations and dashboards to solve this issue (yes, yes, dashboards are often useless, but complaining about that is another blog post). LLMs can analyze them as well and in fact are pretty good at that, so the aggregate can be something both humans and LLMs can digest. I’ve been tinkering with the idea of appending some LLM-only content to a dashboard—for example, additional context, specific d...

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 infras...

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

Working with Django in ASGI mode can benefit applications with long requests where the synchronous mode may be a bit wasteful, as you would likely need a ton of workers—especially if those workers are processes (e.g., Gunicorn). Your app may also run some fancy new LLM framework which is often async only. That said, async mode isn't a silver bullet for scaling. In Django 5.1, you still need threads to offload blocking database operations, which remain synchronous by design. Django provides async query wrappers like  aget , acount , and aall , but these are just syntactic sugar for offloading the operation to a worker thread via asgiref.sync_to_async. You can use the asgiref package's  sync_to_async utility to move blocking executions to the worker. By default, that is a ThreadPoolExecutor with one worker. This thread is shared by all sync_to_async calls during the request context, effectively meaning that each request spawns one worker thread for all sync work. So, to summar...