Plutonic Rainbows

Plutonic Rainbows

Improvements

Today's improvements focused on fixing errors and enhancing the prompt analysis display. I resolved React errors caused by the diff-match-patch library by implementing a local version of the library instead of relying on an external CDN. We then completely redesigned the prompt analysis section to make it more user-friendly, first by simplifying it to a clean, concise summary and then by adding intelligent keyword highlighting that automatically identifies and color-codes important terms like clarity, specificity, and scientific to help users quickly understand what makes their prompts effective. A color-coded legend was added below the summary to explain what each highlight represents. These changes not only fixed the technical issues but also transformed the analysis into a more readable, visually engaging component that gives users immediate insight into their prompt's strengths.

Colder

Big temperature drop today. Feels more like March should be. Decided to watch John Carpenter's The Thing, which I haven't watched for a few years. Also bought some albums from Brian Grainger.

Also just discovered Michael J. Blood - Spaces In Between.

Sounds good, especially if you like the work of Actress, Gescom, Larry Heard or Shinichi Atobe.

Updates

Made several design improvements to the prompt engineering form, enabling easier data capture and allowing rankings in multiple ways. This will enhance training effectiveness. Additionally, I have included a copy icon to conveniently transfer the text to another agent.

I’ve finally decided to use both Claude Code and Cursor for my editing, as each has strengths in different areas. Claude Code operates via the command line, whereas Cursor is a standalone application, making it beneficial to have both available. For many months, I’ve been waiting for OpenAI to provide complete agent integration, but even now, my preferred application, Nova by Panic, lacks support.

True agent integration likely only became fully available with Cursor about a month ago, and Claude Code itself is also very new, having been released on February 24th.

Chinese AI Influence in the West

The rapid advancement of Chinese artificial intelligence has significantly reshaped the global AI landscape, particularly in the Western world. Companies like Baidu, Alibaba, and Tencent have developed sophisticated AI models and applications that have forced Western tech giants to accelerate their own AI development. The emergence of Chinese AI companies offering competitive products at lower costs has created a new dynamic in the market, pushing Western companies to innovate faster and consider more aggressive AI deployment strategies. This influence extends beyond just technology companies, as Chinese AI solutions are increasingly being adopted by Western businesses seeking to maintain competitive advantages in an increasingly automated world.

The impact of Chinese AI development has also led to significant shifts in Western policy and regulatory frameworks. Governments and organizations in the West are now grappling with how to balance the benefits of Chinese AI technology with concerns about data privacy, national security, and technological sovereignty. This has resulted in new regulations and restrictions on Chinese AI investments and partnerships, while also prompting increased investment in domestic AI capabilities. The competitive pressure from Chinese AI development has catalyzed a new era of technological innovation in the West, as companies and governments work to maintain their technological edge while navigating the complex geopolitical landscape of AI development.

Evaluation Updates

I have implemented a fine-tuning system that collects and utilises user feedback to improve prompt refinements. The system stores feedback data (including prompts, ratings, and comments) in a SQLite database, and once I have collected 50 or more high-quality samples (rated 4 or 5 stars), I can initiate a fine-tuning process with OpenAI. This creates a custom model that's specifically tuned to my use cases and feedback.

When users interact with the /refine endpoint, the system now intelligently checks for the availability of a fine-tuned model and uses it if one exists, falling back to the standard GPT-4 model if necessary. I've added new endpoints to manage the fine-tuning process for the processes (/train and /training-status), while maintaining all existing API functionality. However, it's worth noting that the actual fine-tuning occurs on OpenAI's servers and requires OpenAI credits, so the system's effectiveness depends on both the quality of collected feedback and available resources.