I see, so about 900 symbols per 30 minutes. IEX puts out one file per trading day with every trade on IEX, so you get the last trade for about 8,5k US symbols in one download the next day ~12GB compressed though. Has Yahoo ever throttled you at that rate?
It means the exposure of being in HN front page usually causes a sudden spike of visits, and the increased traffic was too much for the server so the service is down for new visitors.
> it takes months, years even, to notice the effects of bad architecture or of unmaintainable code.
I think this is a bad take... if you are going that long without noticing, you are (hopefully) delivering end user value all that time. That is the main driver of code. You can normally dig/hack/rearchitect your way out of an ugly code situation. If you've been building value for the last year based on the hacky code, that's a win.
I have leaned heavily into it… and became good at “agentic thinking”, but now I’m going to reverse. I have become dumber, sloppier, lazier, more distracted. I’m less happy than before. It’s hard to escape the productivity addiction and expectations though.
I've seen this happen to multiple co-workers, in the span of months. Two were laid off so I don't know how it's going with them, and another has backed off but not fully abandoned AI code. I'm resisting it entirely because of these problems.
I’m very interested in building tools for coding agents (where I spend a lot of my time).
I’ve been building https://smalldocs.org, it’s a simply styled, but functionally deep, artifact layer for agents. It’s open source and free to use.
Because it can incorporate charts, diagrams, spreadsheets, etc. in one artifact, it’s good for creating (and optionally sharing) agent created analysis.
It has a local library for all your Markdown files, and a cloud library (my one paid feature) too. Both support tagging and rich search/filtering. I find it turns SmallDoc artifacts into little checkpoints in work I can easily circle back to.
I think this is a very interesting article because it raises an idea I had not considered: these companies found PMF and huge growth through satisfy the demands of coders, it is interesting if they are in a bind where improving the model in one direction worsens it in others
You’re correct–you actually can’t improve the model in one area without changing the characteristics in every other area. It’s almost like the whole thing is just a lot of linear regression…
For a personal project a while ago I peeked at Gemini's reasoning tokens in their coding CLI. I was pretty shocked. It was a bit like discovering Marvin (from Hitchhiker's Guide) was hiding in there all along. There was a lot of concern about my needs, "The user want's us to respond in a simple way...", "The user wants a clean front end...". Under the hood the poor model seemed very anxious to please with a hint of depression. It was a bit sad to see!
For Qwen3.8, my first impression was weirdly Reni from TwoKinds: Immensely powerful but also apparently a ball of insecurities in the thinking traces.
(which makes sense, as I think one motivation for reasoning traces is to explore different options and approaches. So it makes sense that there is a lot "but wait, let me reconsider" in them)
What I found surprising is how strongly "logical contradictions" seem to influence the thinking trace. E.g. I had a situation where I accidentally copied a python file into a repo, but forgot to add a package that the file was depending on. Then I (somewhat carelessly) commited the file without ever testing it and gave the agent a task to work on the file.
If I had run it in Python, I'd have gotten an "cannot resolve import" error and that would have been the end of it.
Instead, the model went absolutely haywire.
The thinking traces were full of utter confusion how the file could possibly resolve its dependencies - but at the same time, entertaining the possibility that a committed file may have an error was apparently Verboten. Hence, the model wrote up ever more outlandish theories how the file could resolve that package and in the end started to make tool calls outside the repo to explore the entire file system before I stopped it.
Moral of the story: Underspecified requests are fine, but beware of anything self contradictory, it can easily send the Qwen model spiraling.
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