As an AI enthusiast and coding aficionado, I recently decided to put Liquid Foundation AI to the test by challenging it to create a Falling Sand game. These games are notoriously tricky for AI to code, making them an excellent benchmark for assessing an AI’s capabilities. Let’s dive into my experience and see how it fared.

The Initial Attempt

I started by giving Liquid Foundation AI a simple prompt to create a Falling Sand game. The response was surprisingly short, and upon running the code, it became clear that we were far from our goal. The output bore no resemblance to a Falling Sand game whatsoever.

Iteration #1: Adding Basic Functionality

Undeterred, I provided more specific instructions, asking for the ability to add sand and create a basic simulation. The AI’s response was longer this time, but the result was still disappointing. The “sand” appeared more like a dispersing cloud, with no semblance of gravity or realistic particle behavior.

Iteration #2: Implementing Gravity

Realizing the lack of gravity was a major issue, I explicitly requested the AI to add a falling effect. Unfortunately, even after this instruction, the simulation still lacked any sense of gravity. The particles continued to behave erratically, far from the realistic sand behavior we were aiming for.

Iteration #3: Expanding Elements and Controls

In a final attempt to salvage the project, I asked the AI to add water, plant, and fire elements, along with the ability to switch between them using number keys. This was admittedly a complex request, but I hoped it might prompt the AI to rethink its approach entirely.

The Verdict

After multiple iterations and attempts to guide the AI, it became clear that Liquid Foundation AI was not up to the task of creating a functional Falling Sand game. While it’s interesting that this isn’t a Transformer-based model, its performance leaves much to be desired.

Key Takeaways:

  1. Liquid Foundation AI struggled with even basic game mechanics like gravity.
  2. The model’s responses, while sometimes lengthy, failed to address the core functionality required.
  3. It’s not open-source, which limits its potential for improvement and customization by the community.

In conclusion, while AI coding assistants have come a long way, Liquid Foundation AI demonstrates that we still have a long road ahead before we can rely on AI for complex coding tasks like game development. Its closed-source nature and limited capabilities make it a less attractive option compared to other AI coding tools available in the market.


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