We reduced the false positive rate from over 40% to 0.38% in our demo system, which is a significant improvement.

We are no longer in a compute-constrained environment for our best models, marking a significant shift in our approach to model training and development.

Elon Musk mentioned that we're seeing a 100x increase in computational and algorithmic efficiencies per year, which aligns with the progress we've made in large language models over the past two years.

We quickly improved our training infrastructure and trained our own models, which allowed us to pull ahead in terms of quality and capabilities compared to GitHub Copilot.

We've learned a lot through the process of training this model, discovering what scales well and what doesn't.

Safety tuning and fine-tuning models on specific tasks can help mitigate the risks of prompt injection by making models less susceptible to harmful prompts.

The two main improvements we made were honing in on the constitution idea and solidifying the jailbreak styles that we trained on, which helped reduce false positives.

We have implemented security measures to protect our AI models, but if a state actor is determined enough, they could still succeed in stealing our model weights.

The challenge of reducing 'slop' in model outputs involves teaching models to produce more elegant and effective solutions, especially in coding.