Privacy Sessions are back this Thursday at 6PM UTC!
@CiaraNightingal and @jp4g_ break down how Aztec, Canton, Starknet, Tempo, and zkSync actually handle selective disclosure.
Every privacy network claims it, but who really holds the keys?
RSVP: https://t.co/zYQh3cNang
For those starting with AI coding, I just shared my CLAUDE.md (also works with Gemini and Codex BTW - see how-to). Since working with this my efficiency went way up. A lot of ideas come from @garrytan & @karpathy, while others are mine from my own experience.
Make sure to personalize it and add/remove what makes sense for you. And please LMK if you have ideas to make it better, always happy to learn more :)
dl it here: https://t.co/9RDZ2y2iMj
As token budgets take on a larger part of operating expenses over time, model routing is the inevitable conclusion. This is also one of the biggest areas of differentiation for the applied AI layer over time.
By understanding the different work patterns in your domain, and having strong evals for that domain, you’ll be able to cost/performance optimize effectively.
We’re still likely at the point where most use-cases will need frontier performance for the foreseeable future; but soon you will be able to peel off individual use-cases and send them to lower cost models once the quality is sufficient for the task.
Enterprises individually trying to figure this out themselves at scale will likely not be possible, so the products that can intelligently route these workflows to the right tier of model will be in a strong position to aggregate more demand.