2025년 8월 24일의 Show HN

This runs YOLOv8 + bytetrack with Tinygrad detections (depending on user config) are saved and can be sent to the companion iOS app along with a notification, all video processing is done locally, all footage is encrypted before leaving your computer, and the sending notifications + videos part is optional. This uses tinygrad, so it runs well on my apple silicon macs and should be able to run on a lot of hardware (or will be able to when I remove other deps).

A few weeks ago a friend sent me grug-brain XSLT (1) which inspired me to redo my personal blog in XSLT.

Rather than just build my own blog on it, I wrote it up for others to use and I've published it on GitHub https://github.com/vgr-land/vgr-xslt-blog-framework (2)

Since others have XSLT on the mind, now seems just as good of a time as any to share it with the world. Evidlo@ did a fine job explaining the "how" xslt works (3)

The short version on how to publish using this framework is:

1. Create a new post in HTML wrapped in the XML headers and footers the framework expects.

2. Tag the post so that its unique and the framework can find it on build

3. Add the post to the posts.xml file

And that's it. No build system to update menus, no RSS file to update (posts.xml is the rss file). As a reusable framework, there are likely bugs lurking in CSS, but otherwise I'm finding it perfectly usable for my needs.

Finally, it'd be a shame if XSLT is removed from the HTML spec (4), I've found it quite eloquent in its simplicity.

(1) https://news.ycombinator.com/item?id=44393817

(2) https://github.com/vgr-land/vgr-xslt-blog-framework

(3) https://news.ycombinator.com/item?id=44988271

(4) https://news.ycombinator.com/item?id=44952185

(Aside - First time caller long time listener to hn, thanks!)

I've frequently found myself using [nvitop](https://github.com/XuehaiPan/nvitop) to diagnose GPU/CPU contention issues.

The two best things about it are:

- It's easy to install if I can access pip in the container

- It makes a compelling screenshot (which helps me communicate with coworkers.)

With those two lessons in mind: Here is [sping](https://dseltzer.gitlab.io/sping/docs/)

Purpose: Help observe and diagnose latency issues at layer 4+ (TCP/HTTP/HTTPS)

Two good things about it:

- It's easy to install if you have pip. (Available at [service-ping-sping](https://pypi.org/project/service-ping-sping/) on PyPi)

- It makes a compelling screenshot.

Not sure if this is the kind of thing that anyone else would be interested in. But I've enjoyed making it and intend to keep using it.

(Repost, this got shadowbanned, then unbanned, sorry there's two posts now)

This runs YOLOv8 + bytetrack with Tinygrad detections (depending on user config) are saved and can be sent to the companion iOS app along with a notification, all video processing is done locally, all footage is encrypted before leaving your computer, and the sending notifications + videos part is optional.

This uses tinygrad, so it runs well on my apple silicon macs and should be able to run on a lot of hardware (or will be able to when I remove other deps).

Hi everyone!

I built this tool because I couldn't find a simpler solution to my problem (publish a markdown file and make it available online so other people can see). I hope you'll find it useful too!

Currently, what it does is only publish a markdown file, but I'm thinking of adding features like styling, custom URL, password-protected link, etc.

But I don't know whether people would find it useful.

Let me know what you think!

Hi HN

I’ve been working with GraphQL for a while and always felt the tooling around load testing was lacking. Most tools either don’t support GraphQL natively, or they require heavy setup/config.

So I built *LoadGQL* — a single-binary CLI (written in Go) that lets you quickly stress-test a GraphQL endpoint.

*What it does today (v1.0.0):* - Run queries against any GraphQL endpoint (no schema parsing required) - Reports median & p95 latency, throughput (RPS), and error rate - Supports concurrency, duration, and custom headers - Minimal and terminal-first by design

*Roadmap:* p50/p99 latency, output formats (JSON/CSV), multiple query files.

Landing page: [https://apps.devanswers.org](https://apps.devanswers.org)

I’d love feedback from the HN community: - What metrics matter most to you for GraphQL performance? - Any sharp edges you’d expect in a GraphQL load tester?

Thanks for checking it out!

Most developers talk about unifying data. But in reality, data lives everywhere — in lakes, warehouses, databases, streaming systems, AI/ML pipelines. Trying to centralize or replace them is costly, slow, and often fails.

So instead of asking “How do we unify the data?” we asked: “Can we unify the metadata?”

That’s the idea behind Apache Gravitino — an open-source “catalog of catalogs” that sits above your existing systems and provides:

Unified metadata governance without replacing your stack

Federated access to diverse systems (SQL, NoSQL, lakehouses, ML/AI)

A lightweight, extensible platform you can contribute to and extend

Website: Datastrato

Code: Apache Gravitino on GitHub

We’d love feedback from HN: Does focusing on metadata instead of data solve a pain you’ve seen? What gaps do you think still exist in the “data & AI catalog” space?

Comparison of the best i18n solutions for Next.js

A Flux Kontext + Mistral experiment. Upload an image, and let the AIs do the rest of the work.

Hi, I’ve developed an open-source app for practicing basic skills in digital signal processing and computer graphics using OpenGL. It’s written mainly in C++ for data processing and visualization, with Python used for data input and configuration. This makes it easier to run experiments or adjust settings without recompiling the code, lowering the entry barrier for users unfamiliar with C++.

By default, the app captures audio from a microphone in real-time and displays its spectrum on the screen. It’s highly customizable — you can change the number of bars, colors, and the overall color theme. The app runs on both Raspberry Pi and standard Ubuntu desktops.

In my Raspberry Pi setup, I use a HiFiBerry DAC+ DSP to analyze music in real-time. The signal comes via optical input (TOSLINK) from a CD player, but you can also connect a microphone for live audio visualization.

I’ve written instructions and a tutorial to help you get started — feel free to check it out and give it a try!

Demo video (Ubuntu): https://www.youtube.com/watch?v=Sjx05eXpgq4

Demo video (raspberry pi with hifiberry dac+dsp): https://www.youtube.com/watch?v=QA2DYmdZ_Gw

Simplified spec: https://sylwekkominek.github.io/SpectrumAnalyzer/

Hope someone finds it useful or fun to play with!

Mix is an open-source, local agent for multimodal claude code. Claude code users will feel at home.

- Uses ffmpeg and local apps like blender instead of clunky cloud based editors - All project data is stored plain text and native media files - absolutely no lock-in. - The backend is an HTTP server, meaning that the frontend is just one of possible clients. - - Our SDK with stdio interface (similar to claude code SDK) is launching soon.

I recently wrote a short story called “Strapped,” for my short story blog The Put Down. It is about the overwhelming feeling of mental collapse, anxiety, and loneliness and being haunted by trauma. It is emotional intense and written with dark lyrical prose.

Would love to hear your thoughts, and honest feedback or first impressions.

Link: https://theputdown.com/

Hey all, I just released v2.0.0 of FilterQL, a query language and TypeScript library. This version adds support for Operations, which allow you to transform the data after filtering.

If you think this would be useful in a project you're working on, give it a try and let me know what you think!

Spotify has 7 features for each of their music tracks (acousticness, danceability, energy, instrumentalness, liveness, speechiness, valence) which describe the perceptual/emotional content of the song. I wanted to tag my own offline music library with these features so that I could sort my songs into playlists for different occasions (working out, driving, etc.), but unfortunately Spotify doesn't share how they calculate these features. So, I trained my own lightweight neural network to predict these features!

Hi HN,

I've been working on a small tool to make link building less painful. It's a Chrome extension called BacklinkHelper.

It does a few things:

1.Detects backlink submission forms automatically;

2.Autofills site information with one click;

3.Tracks submitted backlinks and checks if they're still live;

4.Website management and backlink management in one place;

5.Lets you manage sites by groups and export anchor text suggestions;

The idea came from my own frustration: submitting backlinks felt repetitive and error-prone, and I wanted something that saves time while keeping things organized.

I know link building isn't everyone's favorite part of SEO, but I’m curious what you think — especially if you’ve tried automating parts of the process before.

Would love feedback on:

1.What features would make this more useful for you?

2.Any pain points in backlink building that I might be missing?

3.Concerns about automation or how you’d want control over it

If you'd like to try it out:

Website: https://backlinkhelper.com

Chrome Web Store: https://chromewebstore.google.com/detail/backlinkhelper-%E2%...

Thanks!