u/beatwad

Here's my idea:
- preprocess the raw text to remove the most obvious signs of AI-generated content: em dashes, zero-width characters, exotic spaces, etc.

The next three steps tackle with core concept of watermarking: specific token-selection distribution. Here how that works:
- send the text to a translator (e.g. Google Translate) and translate it into an intermediate language
- translate text back from the intermediate to the original language
- optionally, rewrite the text using an LLM that is guaranteed not to watermark its output (e.g. an open-weighted model like DeepSeek-v4) to make the resulting text more readable (in case it was degraded during double translation) and to remove any remaining watermarks traces if they were somehow preserved.

I developed a simple application which automates that process. In the application you can select which steps to use and also customize each step (e.g. choosing a specific translator or rephrasing LLM).

Here`s the test results (used Google SynthID watermarking)

z-score:

watermarked text: 11.43

eng-germ-eng transtaled text: 5.68

eng-germ-eng translated + paraphrased text: 1.02

eng-cn-eng transtaled text: 4.10

eng-cn-eng translated + paraphrased text: 2.86

eng-ru-eng transtaled text: 4.19

eng-germ-eng translated + paraphrased text: 0.51

paraphrased only text: -1.18

And here's the link:

https://github.com/beatwad/AI-watermark-remover

Notebook with results can be found inside watermark_detector folder.

At first I just wanted to make AI bot that searches for the interesting vacancies on LinkedIn, but over time it grew into something more.

Here's the link:

https://github.com/beatwad/LinkedIn-AI-Job-Applier-Ultimate

And here’s its main features:

- It can apply on both LinkedIn and Indeed sites.

- It can apply to any job, not just "Easy Apply." Thanks to the browser-use library, the bot can navigate and fill out applications on third-party sites. Of course success rate of applications to non Easy Apply vacancies is not so high comparing to Easy Apply, but if you have bunch of tokens and don't know what to do with them - you can try.

- Web browser GUI with live telemetry, full run history, start/stop/pause controls and total amount of money you spent on access to LLMs.

- Automated LinkedIn networking. I needed to grow my LinkedIn network, so I created a separate script that does this for me. This script looks for Open Networkers (LIONs) — people who accept connection requests from anyone, and sends them a connection request.

- LinkedIn Message manager. Use that tool to handle your message inbox if you have plenty of unread messages and want to do something with this. It uses LLM to decide what to do with each message: archive, spam, skip, or draft a reply based on user’s preferences. I also added additional configuration settings for this script for cases when you want to connect with people of specific professions (e.g. Java developers only).

- Protection of personal data. The bot anonymizes your personal data before sending it to the LLM, so your personal data stays safe.

- AI-powered resume tailoring. Bot analyzes the job description and tailors your resume for each specific vacancy, adapting your skills, experience, and projects, etc. Multiple resume styles a supported: FAANGPath, Modern Blue, Modern Grey, Clean Blue, and others. But if you want, you can use your own ready-made resume for all vacancies.

- Protection from bot-detection systems. Indeed has Cloudflare protection and this makes hard to apply on this site with vanilla Selenium or Playwright frameworks. So I used Patchright — a patched Chromium build that hides automation detection signals.

- If you don't trust application process to LLM, you can just use it in collection mode, in which bot doesn't apply but only gathers information for interesting jobs and saves them to the file. Then you can explore this file and apply these jobs manually.

- Advanced question answering. It uses LLM to automatically parse your resume and intelligently answer all types of application questions and even adds previously asked questions to context if necessary.

- Sending errors and reports via Telegram. You get detailed reports and error notifications sent directly to your Telegram chat.

- The bot can also analyze job descriptions and compile statistics on the most in-demand skills.

- And yes, it has a headless mode, so you can keep working on your computer without a browser window popping up.

- You can pause and resume the bot at any time with a simple Ctrl+X command or Pause button in GUI.

- It also tries to fix apply form errors: if an Easy Apply form has issues, the bot will try to fix them on its own.

And many additional improvements and features that you can read about in README file.

It supports multiple LLM providers like OpenRouter, Gemini, OpenAI, Claude, and local models with Ollama. Plus, it's packed with features to make it more robust and easier to use, like Pydantic configuration and LLM output parsing, secret management, and automated scheduling.

Here's the video that demonstrates how it works.

I'm ready to answer any questions you have in the comments.

If find a bug or want to add a new feature please open an issue or dm me.