Pipeline so far has gone like this:

* Use the search engine's API to query a bunch of depravity

* Use qwen3.5 to label the search results and generate training data

* Try to use fasttext to create a fast model

* Get good results in theory but awful results in practice because it picks up weird features

* Yolo implement a small neural net using hand selected input features instead

* Train using fasttext training data

* Do a pretty good job

* for (;;) Apply the model to real a world link database and relabel positive findings with qwen to provide more training data

Currently this is where I'm at

  Accuracy:   90.90%
  True  Positive: 1021
  False Positive: 154
  True  Negative: 2816
  False Negative: 230
  Precision:  0.8689
  Recall:     0.8161
  F1:         0.8417

AIOs are a black hole - we dont know when they appear and whats in it. so i creates a tool thats starts with GSC data and enriches it via AIO data

works good and the major finding by now

the best AIOs you can get are ..... none.

doesn't matter if you are in it or not - as soon as they show up the CTR to tour web-property goes down massively ~60% to 70%

the CTR on the AIOs are ~0%

    const app = new App("com.apple.finder")

    const window = app.$({role: "window"})
    const someButton = window.$(/* another query */)

    someButton.press()

Targeted towards music producers and AI (there's one collection of snippets that starts an MCP server and exposes some basic functionality) in the beginning.

I started developing a city builder called Metropolis 1998 [1], but wanted to take the genre in new directions, building on top of what modern games have to offer:

- Watch what's happening inside buildings and design your own (optional)

- Change demand to a per-business level

- Bring the pixel art 3D render aesthetic back from the dead (e.g RollerCoaster Tycoon) [2]

I just updated my Steam page with some recent snapshots from my game. Im really happy with how the game is turning out!

[1] https://store.steampowered.com/app/2287430/Metropolis_1998/

[2] The art in my game is hand drawn though

We’ve continued to get some paid customers and have exited beta last week, given everyone seemed to be quite satisfied and there hadn't been requests for changes, only some specific search providers.

Because of bots there isn’t a free trial easily available, but if you’re a human and you’d like to try it for a couple of days for free, reach out with your account number and we’ll set that up!

Thanks.

P.S.: Because people have asked before, our tech stack is intentionally very "boring" (as in, it generates and serves the HTML + bits of JS to enhance settings and such — search can be done without JS), using Deno in the backend (for easier TypeScript), PostgreSQL for the DB, and Docker for easier deploying.

[1] https://uruky.com

Making improvements on this tetris meets block puzzle game

https://github.com/keithn/yt

Also working on a language for embedded bare-metal devices with built-in cooperative multitasking.

A lot of embedded projects introduce an RTOS and then end up inheriting the complexity that comes with it. The idea here is to keep the mental model simple: every `[]` block runs independently and automatically yields after each logical line of code.

There is also an event/messaging system:

- Blocks can be triggered by events: `[>event params ...]`

- Blocks can wait for events internally

- Events can also be injected from interrupts

This makes it easy to model embedded systems as independent state machines while still monitoring device state.

Right now it’s mostly an interpreter written in Rust, but it can also emit C code. I’m still experimenting with syntax.

Example:

    module WaterTank {
      type Direction = UP|DOWN
      let direction = UP
      let current = 0

      [>open_valve direction |> direction]
      [>update level |> current]

      [
        for 0..30 |> iteration {
          when direction {
            UP   -> !update level=current + 1 |> min(100)
            DOWN -> !update level=current - 1 |> max(0)
          } ~
          %'{iteration} {current}'
        }
      ]

      [>update level |> when {
          0..10  -> %'shallow'
          11..15 -> %'good'
          16..   -> %'too much!' then !open_valve direction=DOWN
        }
      ]
    }

I have been working on it as side project for over two years and now, with funding from the EU for the next 2.5 years, I hope I can make of it a real product for everyone to use that can compete with the likes of Excel and Googl;e Sheets.

I can oly say, I am overly, off the Moon excited

[1]: https://www.ironcalc.com

https://IsThisAI.lol

The content is hand picked from tiktok, Instagram, Facebook, Reddit and other AI generating platforms.

Honestly I don't know where I'm going with this, but I felt the urge to create it, so here it is.

I learned how to optimize serving assets on CloudFlare.

Feedback welcome.