Koodihommia

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...

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 ...

  On a project I've been working on, I've been preparing for SOC 2 Type II certification. My responsibilities have mostly been on the engineering/IT side, ensuring that our SaaS is deployed and developed according to SOC 2 processes. This isn't something a developer would willingly or enthusiastically take on, right? I can’t believe I’m saying this out loud, but actually ... it hasn’t been that bad. The biggest reason for this has definitely been Vanta . Vanta has distilled the rather hard-to-decipher process descriptions into actionable items. As far as I know, SOC 2 isn’t a one-size-fits-all (SaaS provider) certification; it differs according to the stack - which makes sense. Plugging in all our services, from cloud providers to issue trackers, spits out a tailored task list. The task list can even include literal Terraform code examples, which you can copy-paste with minor changes. You could kind of get a similar list from AWS Audit Manager or AWS Security ...

On a project I'm working on we are using PgCat as the PostgreSQL frontend. We chose it mainly based on gut feeling as pgbouncer seems a bit dated, although it would have arguably been the safe choice. I was looking into the connection times using our tracing tool (Sentry) and noticed that establishing connections takes about 50ms.  That is a bit slow, right? It was easy enough to confirm that it is indeed very slow. Establishing a direct connection to the mostly idle Postgres is in the sub-5ms range. I quickly found a  ticket  about connection slowness,  hinting that the problem could be related to TCP_NODELAY. Essentially, it disables Nagle's algorithm , which batches small packets together. I guess that establishing connections from the client to PgCat is such a light process that the extra buffering is actively harmful. And sure enough, after upgrading PgCat, we see sub 5ms connection times.  So why use PgCat at all? For us, it is for scaling purposes but not...

I bought or technically leased a Canyon Precede:ON 7 (2022) electric bike last fall. This post is about my experiences with it after riding for about 2000 km this winter. The season was a bit colder than usual, and we had more snow than in years, so I properly put the bike through its paces. I've been cycling for almost 20 years. I've never owned a car nor used public transport regularly. I pedal all distances below 30km in all seasons. Besides commuting, I've mountain biked and raced BMX, and I still actively ride my road bike during the spring and summer months. I've owned a handful of bikes and kept them until their frames failed. Buying new bikes or gear has not been a major part of my hobby, and frankly, I'm quite sceptical about the benefits of updating bikes or gear frequently. I've never owned an E-bike before, but I've rented one a couple of times. The bike arrived in a hilariously large box. I suppose there's no need to worry about damage durin...