Koodihommia

I've been working on an OTel sink recently. To be more specific, it concerns receiving telemetry data from LLM interactions. Fortunately, there is a spec for all of this called GenAI Semantic Conventions . That is what I based my work on, so I can make assumptions about key attributes to highlight in the sink's UI, which is actually a CLI in my case. I dogfooded my handrolled sink using Pydantic AI (my favorite LLM framework), and all good, it follows the spec well. Key attributes flow in as specified. Pydantic AI also includes some of its own attributes, but that is expected. However, pick something like Claude Code, (via anthropic agent sdk), the reality is a bit different. It does not follow the spec at all. It has its own custom attribute layout . Same direction, different flavor with LangChain. The Python source has zero opentelemetry imports anywhere. None. The ls_* namespace ( ls_provider , ls_model_name , ls_temperature ) is its own thing, with LangSmith as the on...

There is no lack of people complaining about MacOS Tahoe, mostly about rounded corners and inconsistent design decisions. I have not paid that much attentention to that, but there is one mac bug i have paid attention to. It hasn't been a visual or ux but rather few system processes pegging the CPU. trustd , alongside with ecosystemd and ecosystemanalyticsd all reported high CPU usage. I can't exactly recall when this started, it might have predated my Tahoe upgrade but anyway, the trio of processes all had high CPU usage. Sure, it may have been the virtual efficiency cores and whether it affeced battery life or slowed down other processes i don't know to be hon...

When I started at my current job 3ish years ago, we had nothing but a few PowerPoint slides and a Figma prototype. I recall talking with one of the founders, whom I had worked with some 10 years ago, and pointing out that the very next step after PowerPoint and Figma was still the same as it had always been: translating those slides and mockups into CRUDs, forms, and other basic building blocks. Sure, all the new fancy tools and frameworks (we had jQuery then) help, but I still had to tinker with very basic primitives like tables and forms. I could not just say, "here is a table, here is a form." Well, yes, technically I could, but I still needed to write a lot of code to make a working form or table. This was around the time of GPT-3, Copilot beta, and very early Cursor. I did attempt to use them, but they could only perform very basic tasks and were pretty much useless for frontend work. We, being one of the first-wave "ChatGPT wrapper" startups has naturally pus...

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...