Hence I built Yamlet: A CLI tool with accompanying Claude Code skillset, that merges both deterministic checks with human like interface to help you define requirements - including acceptance criteria, which are then promptly used to generate adequate Gherking .feature test files. The details are stored in a minimized fashion as .yamlet.yaml files, which allow quick and easy review - without the usual multi page slop specs.

- And that's it. Everything else, depends entirely on the system you're working on. I stopped here because for me, this can not only be used in a system of pure agentic coding, but also with a normal dev team.

Comments URL: https://news.ycombinator.com/item?id=49123254

Points: 1

# Comments: 0

I stumbled across this UX mock about living dashboards, and thought that it was a neat idea - why not have interactions on a dashboard automatically align it to the users needs? If a user focuses more on one KPI, why not make the first interaction with it on the page more descriptive?

After some short talks with the original designer, I decided to write a library for it. Should work for whatever FE framework you favour, or none. Also 0 dependencies by itself, in addition to outputting a simple JSON you can store as you want.

Just putting this out there, for people that might want something like this :)

Comments URL: https://news.ycombinator.com/item?id=47753560

Points: 2

# Comments: 0

It's really simple: docker compose, micro services connected to some very basic data pipelines using rabbitMQ, and some local NLP. The NLP portion is a bit slow as it's pure CPU and doesn't leverage GPU/NPE if you have them.

The tool offers you to define your own "labels" and "relations", which are then picked up by the local NLP models to assign it to news articles. Standard, there are only a few included.

But the goal is to have your own personal news aggregator, and adding/changing news sources (if they have a rss feed) should be pretty easy through the docker compose file.

Comments URL: https://news.ycombinator.com/item?id=47528914

Points: 6

# Comments: 0

I've built a simple Java library that tracks exceptions for you and deals with posting them to $PROJECT_PLANNING_TOOL. This way, you will automatically get tickets for exceptions that are thrown on your system and potentially never discovered, because nobody is monitoring the logs 24/7. It always felt weird to me that you pay for $PROJECT_PLANNING_TOOL that contains your tasks, but then pay again for $BUGS_TRACKING_TOOL for yet another dashboard (and library to collect the information).

I did build this using AI (CC to be specific), to some larger parts to get more experience with using these tools. I try to keep the code in a state where I have no problem with `git blame` showing my name under it.

At the same time it's something I've tried building 8 years ago; but sadly work has gotten to a point where I never had the motivation in me to spend my free time on this. But somehow, I've gained it back - using CC has allowed me to focus on the fun parts of building things like these!

Let me know your feedback :) As it stands now, I'm trying to get some testing feedback on it (and it might be that some parts don't work practically!), but if you have an opinion on this I will gladly listen to it

Comments URL: https://news.ycombinator.com/item?id=47440846

Points: 1

# Comments: 0

I have always hated how we deal with feature flags; often times as a dev I implement it, but then business decides how the flags are used, which comes back to us having to set them. I want to make this something that can be used by both easily, and priced fairly - looking at how much others charge for a feature flag SaaS is insane.

Currently I'm in heavy testing by myself, as I am quite worried about issues (that's just my personality), but yeah. It started as a normal side project for me to train a bit, then I added a bit of agentic coding to it to also learn it, and now it's here.

This is quite an interesting topic, which would be fascinating to deeper understand, because from a superficial look it does appear that introducing directed hate makes masses much more susceptible to being controlled. Also helps my theory, that whilst the left did like Elon for quite some time, that doesn't mind they blindly trusted him and pointed out his issues when they came up, which pushed him to a more... comforting? right wing embracement.