New LLM Agents, OpenRank Integration and Closed Beta Access

This week we have been busy preparing for our closed beta launch. More on that later.

Courtesy of the OpenRank and Neynar collaboration, we have tapped into OpenRank's rich Farcaster social graph rankings, in order to further develop our user context model. As we will discuss later in this update, this has been instrumental in enabling our new Farcaster Relationships agent and we can't wait for you to try it.

Powered by DL News, we have launched a new agent for our in-feed assistant which fetches the latest crypto news via our News Frame. The agent can also tailor its response to show you only a specific subcategory of news.

Example prompt:

@askgina.eth Show me crypto political news.

Powered by a blend of our proprietary model and OpenRank, we have launched a new agent for our in-feed assistant, which can analyse social relationships on Farcaster. We are launching this agent with a narrow scope, focused on just making recommendations for who you should follow on Farcaster, but we have many plans to expand its capabilities.

Example prompt:

@askgina.eth Who should I follow on Farcaster?

Last week we saw a suite of Ethereum spot ETF products launch in US traditional markets. Within 24 hours of launch, one of our team members deployed an LLM-enabled tracker, which publishes a daily update of the capital flows into and out of the various Ethereum spot ETFs.

Follow the /etf channel to get daily updates on both Bitcoin and Ethereum spot ETF flows, as soon as they're published by the issuers.

Our in-feed assistant has seen numerous improvements over the past two weeks, most notably a significant upgrade in the underlying model. Furthermore, through an improved system prompt and additional post-processing, we've been able to make the assistant speak in a clearer, more succinct tone. We will continue to tweak the assistant's tone of voice, in order to find a tone that sounds "Farcaster native" and adapts its level of humour to the task being asked of it.

We will launch a closed beta for our assistant, open to the entire Farcaster community, in the coming weeks. Spots on this whitelist will be driven algorithmically, with those users who show the most support for Gina through likes, shares, comments, feedback, etc. being given a higher whitelist score and thus a higher position on the whitelist. If you'd like access to the assistant from day one, we'd recommend that you get started on supporting Gina sooner rather than later, as interactions that pre-date the whitelist will be considered in your whitelist score.

Last week we published our first paragraph post, which explains part of the underlying model we use to give our in-feed assistant additional context about the person using it. You can read that post here and we plan to produce further data-driven deep dives into Farcaster on a monthly basis. Reach out to us if you have any ideas for these and be sure to subscribe to this paragraph to receive notifications for future Farcaster data-driven deep dives and developer updates.