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<li><a href="/">Home</a></li>
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<li><a href="https://roundabout-host.com/roundabout">Roundabout-host</a></li>
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<h1>Projects</h1>
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<article class="content-area">
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<h2><a href="/projects/ampoule.html" class="article-title">Ampoule</a></h2>
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<p><p>Ampoule is a lightweight, simple yet flexible, static site generator written in Python.
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It uses Jinja2 for templating. This site was generated using Ampoule.
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</p></p>
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</article>
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<article class="content-area">
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<h2><a href="/projects/echo.html" class="article-title">Echo</a></h2>
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<p><p>Echo is a very basic status tracker for web endpoints. I don't recommend you use it. It was
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made for a programming competition (hackathon) organised by the Automation and Computing faculty
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of the Politehnica University of Timișoara, in 3 days, alone. I decided to publish it because
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it might be useful for someone and doesn't hurt me to have it online, and the contest encourages
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publishing the works.
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</p></p>
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</article>
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<article class="content-area">
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<h2><a href="/projects/gigadata.html" class="article-title">Gigadata</a></h2>
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<p><p>Gigadata is an image dataset collection and annotation platform. It allows anyone
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to easily contribute to the dataset by uploading images and annotating objects,
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and to use the dataset for training machine learning models.
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</p></p>
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</article>
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<article class="content-area">
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<h2><a href="/projects/roundabout.html" class="article-title">The roundabout</a></h2>
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<p><p>The roundabout is a <strong class="emphasis-2">git repository hosting</strong> server software. It is designed to be a complete
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alternative to GitHub, GitLab, BitBucket, Gogs/Gitea/Forgejo, and other similar services. It
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is still in development and not yet ready for production use. As of version 0.3.0 development
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stage, it supports:
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</p></p>
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</article>
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<article class="content-area">
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<h2><a href="/projects/waste.html" class="article-title">Waste detection</a></h2>
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<p><p>An YOLO model specifically trained to detect waste in images. We collected a custom dataset of waste,
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classified and annotated it, and trained the model using YOLOv8 on Keras. The model is able to detect
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the position and class of waste in images. It can separate the following classes of waste:
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</p></p>
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</article>
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<article class="content-area">
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<h2><a href="/projects/wordle.html" class="article-title">Teletype Wordle</a></h2>
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<p><p>Just like the original Wordle, you have to guess a 5-letter word in 6 tries and it tells you
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how many letters are correct and in the right position, how many are correct but in the wrong
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position, and how many are not in the word at all. The only difference is that this one is
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played in the terminal. It's a great SSH toy for when you're bored.
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</p></p>
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