Plutonic Rainbows

I was having trouble with the suggestions list persisting on my screen even after I selected a suggested prompt, and my cursor kept losing focus on the text area. To solve this, I switched from using onClick to onMouseDown with e.preventDefault(), which prevents the text area from losing focus when I interact with the suggestions. Then, by using a small setTimeout to refocus on the text area, I ensured that the suggestions list disappears as soon as I choose an option, and my cursor remains in the right place to continue typing.

I’ve now built a solid framework for reinforcement learning from human feedback.

• Feedback Collection: I set up a FastAPI backend with endpoints for submitting feedback, refining prompts, and generating insights. This lets users provide valuable feedback that’s stored in a SQLite database.

• Data Management: I integrated SQLAlchemy to handle my SQLite database. The system automatically creates a new feedback.db if one doesn’t exist, giving me a clean slate when needed.

• Training Simulation: I created a script (rlhf_training.py) that retrieves the feedback data, processes it in a dummy training loop, and saves a model checkpoint. This simulates how I could fine-tune my model using the collected human feedback.

• Model Setup: I ensured my model is loaded with the correct number of labels (to match my feedback ratings) and can seamlessly integrate with both the feedback collection and training processes.

This framework sets the stage for continuous improvement. Now, as I gather more feedback, I can use this data to progressively refine and retrain my model.

Some other things happening today:

• A sample of Rosendo Mateu No 5 Elixir arrived. It's very unique.

• Made some small adjustments to Flux.1 [Dev] templates.

• Began reading The King In Yellow by Robert W. Chambers.

• Listened to the new MPU101 album.

I updated all my templates that support image generation to include a high-definition option, while retaining the legacy option because it is likely more cost-effective. The new high-resolution setting now outputs images at 1088×1920 pixels, regardless of whether the orientation is portrait or landscape.

For my Prompt Refiner application, I also added an SQL database to log user feedback ratings on prompts. My plan is to eventually incorporate Reinforcement Learning from Human Feedback (RLHF).

I’ve significantly refined my application’s interface and user experience today by introducing Montserrat as the main font, aligning the two columns so both the refined prompt and AI insights start at the same height, enlarging the text areas for more comfortable typing, and adding a loading spinner that appears whenever a request is processing. I also added a subtle highlight animation for updated content, giving the entire workflow a smoother, more polished feel.