What I thought was a cardinal sin of empirical problems in ML has been a blessing in disguise.
Been iterating on a model without quantitative metrics, simply looking at model output on a diverse yet manageable (volume wise) eval set.
Oh boy, what intuition you build this way!
you know what
all of these "which is better" polls are silly
use codex or claude code, whatever works best for you
i am grateful we live in a time with such amazing tools, and grateful there is a choice
Just wrapped our quarterly earnings call.
We are focused on delivering AI infrastructure and solutions that empower every business to eval-max their outcomes in this agentic computing era.
Our AI business surpassed a $37 billion annual revenue run rate, up 123%.
We are at the beginning of one of the most consequential platform shifts that will change the entire tech stack as we move from end-user driven workloads to workloads driven by end-users and agents.
This will drive TAM expansion and change the value creation equation across the entire economy.
To capture this opportunity, we are executing against two major priorities:
We're excited to partner with Google to offer Grounding With Exa inside of Gemini models!
Using Exa's agent-first search, Gemini models can now access billions of websites, technical docs, papers, people, companies, and more.
10^18🤝10^100