The Project Graveyard: A Practical Guide to Text Classification and Lessons from Abandoned Ideas

The graveyard of abandoned projects is often fertile ground for new ideas and valuable lessons. A recent online conversation highlighted several shelved concepts, revealing common roadblocks like technical hurdles, shifting personal interests, and prohibitive costs. More importantly, it surfaced a highly practical and detailed guide for building a custom text classification and recommendation engine.

A Practical Recipe for a Text Recommender

For anyone looking to filter and recommend content, one developer shared their robust and efficient system built for an RSS reader. This approach avoids the complexities and often unreliable training processes of fine-tuned transformer models, opting for a more straightforward pipeline that 'just works'.

The process is as follows:

Vectorize Content: Raw text from RSS feeds is converted into numerical vectors using SBERT. This captures the semantic meaning or 'gist' of each article.
Cluster for Topics: The resulting vectors are then clustered using scikit-learn's k-means algorithm to group articles into a set number of topics automatically.
Curate and Present: A selection of the highest-scoring articles from each topic, plus a random sample, is presented to the user.
Train on Feedback: The user provides simple 'thumbs up' or 'thumbs down' judgments. These judgments (y) and the article vectors (X) are used to train a Support Vector Machine (SVM) classifier, which then scores all future articles to create personalized recommendations.

This system is praised for its speed and reliability, training over 20 models in just a few minutes to find the best fit. While it may not capture nuances like negation or sentiment, it's highly effective for its intended purpose, where user preferences can be inconsistent.

Ambitious Ideas and Their Roadblocks

Beyond the text classifier, several other intriguing-but-abandoned projects offer insight into common development challenges:

High-Resolution VR Galleries: An idea to create a WebXR-based art gallery for stereo photographs hit a major technical wall: memory constraints on standalone headsets like the Meta Quest 3. A single high-resolution DSLR photo (e.g., 6000x4000 pixels) can consume over 72MB of RAM when unpacked as a texture, quickly overwhelming the device's limited resources. This highlights the critical need for performance tuning and memory management in standalone VR development.
Decentralized Backup System: A concept for a free, peer-to-peer backup service, similar to the old CrashPlan "Home Family" plan, would allow trusted friends to share unused hard drive space. A clever extension involved a virtual file system to avoid duplicating common OS and software files, instead pulling them over the network when needed. The project was ultimately shelved because making it robust would require licensing a file hash database like VirusTotal, turning a free hobby project into a costly commercial venture.
Passion Projects and Shifting Tides: Simpler projects, like a Python chess program built to play against a son or a tool to generate Final Cut Pro XML files, were abandoned for a common reason: the original motivation faded. These serve as a reminder that personal projects are often tied to fleeting interests, and their value lies as much in the learning process as in the final product.