Micromanager in RooCode Turning Simple Models into Coding Interns
AI Summary
Video Summary: Llama 4 Evaluation
- Overview of Llama 4:
- Expectations vs. reality; described as a letdown.
- Allegations of benchmark cheating, leading to skepticism about performance.
- Micro Manager Mode:
- Created a new mode called ‘Micro Manager’ using Google Gemini 2.5 Pro.
- Successful implementation of a working Snake Game after multiple iterations.
- Insights on the limitations of Llama 4 in planning vs. executing code.
- Experiments with Coding:
- Tested various approaches, experimenting with prompt complexity and settings.
- Original attempts crashed or failed; more structured prompts yielded better results.
- Combination of tools like Gemini 2.5 Pro and Llama 4 offered practical outcomes for coding tasks.
- LM Arena Concerns:
- Critique of LM Arena for potentially skewed benchmarks.
- Call for transparency and improvements in AI evaluations.
- Future of AI Coding:
- Discussion on the rising costs of AI and coding.
- Potential for a tiered approach using smaller, cheaper models for specific tasks and leveraging more powerful models for guidance.
- Reflection on the balance of cost and effectiveness in model selection.
- Conclusion:
- Although Llama 4 has shortcomings, there remains hope in combining advantages of various models.
- Invitation for viewers to share thoughts on optimizing AI coding strategies and collaboration of different models.
Next Steps: Encourage viewers to discuss their experiences and suggestions in the comments.