Probably raised $9 million to build an AI system that catches hallucinations and factual errors before they reach users. Their data-science tool wraps LLM outputs in a deterministic validator “mech suit,” letting it run smaller models locally while ensuring each answer matches the source data.

• Probably raised $9M from a16z to build a validator system that blocks LLM outputs unless they match source data exactly, aiming for 99.99% accuracy.
• This validation approach lets them use models "four classes weaker" than frontier LLMs, cheap enough to run on a desktop instead of a GPU farm.
• Elias argues big AI labs won't build this themselves because their revenue model benefits from users paying per interaction, including ones spent correcting errors.

The article discusses the shifting landscape for data scientists and machine learning engineers in the age of large language models (LLMs). It emphasizes the importance of data science fundamentals in evaluating AI systems, addressing common pitfalls in metrics, experimental design, and data quality. The author argues that the core work of data scientists remains vital, even as their roles evolve.

• Off-the-shelf eval framework metrics often mislead teams; digging into your own data to find relevant metrics is what data scientists actually do.
• Using LLMs as judges without validating them against human labels is a common, risky shortcut.
• Synthetic test data that isn't grounded in real production logs leads to flawed experimental design and misleading results.
• Outsourcing labeling away from domain experts degrades data quality and undermines the whole evaluation process.

Livedocs is a collaborative platform that merges the functionality of notebooks with app-building simplicity, ideal for various data tasks such as exploration, analysis, and visualization. It supports powerful AI tools, enabling users to perform advanced analytics, create interactive dashboards, and share insights effortlessly.

• Livedocs combines notebook-style flexibility with app-building simplicity for data exploration, analysis, and visualization
• Built-in AI tools enable advanced analytics and let users build interactive dashboards without heavy manual coding
• The platform emphasizes collaboration, making it easy to share insights and outputs with others