J Hackworth
@jhackworth
1/ Introducing AWESOM-O: A Farcaster agent that generates your own personalized Farcaster Wrapped. Built this because I believe we're just scratching the surface of what's possible with agents in crypto and wanted to get my hands dirty. Some thoughts 🧵
16 replies
29 recasts
156 reactions
J Hackworth
@jhackworth
2/ Onchain agents will revolutionize crypto as we know it. They’ll streamline UX while enabling entirely new applications. As a data scientist, I knew I had to build an agent for myself.
1 reply
0 recast
8 reactions
J Hackworth
@jhackworth
3/ Why Farcaster over Twitter? Twitter is a walled garden, Farcaster is an open protocol. While Twitter has distribution, Farcaster has what agents truly need: open data, native composability, and permissionless integrations. When agents can tap into everything in the network, they unlock possibilities that centralized platforms can't match
1 reply
2 recasts
6 reactions
J Hackworth
@jhackworth
4/ How does AWESOM-O work? Just @ the agent and it: -Processes your request -Queries network-wide data via Dune -Extracts personalized insights -Generates your custom-wrapped By querying Dune directly, AWESOM-O can analyze a year of network-wide data - impossible for agents on closed platforms like X. Also have to shoutout @hyperbolic for being my go-to inference on this
2 replies
0 recast
11 reactions
J Hackworth
@jhackworth
5/ After building agents with LLMs, here's the reality with today’s models: They excel at pattern detection and content generation but lack precision without training data. They'll generate AI slop forever, but can't judge quality. In the case of AWESOM-O the risk is low to generate a Farcaster Wrapped, but I wouldn’t trust it with my own money
1 reply
0 recast
8 reactions
J Hackworth
@jhackworth
6/ Until models improve, agents need humans at key decision points. For AWESOM-O, I would use Farcaster likes as an objective function - teaching it to understand content quality through user feedback Just like Botto, where AI creates, humans curate, and the system learns from human taste to improve
1 reply
0 recast
8 reactions
J Hackworth
@jhackworth
7/ After building an agent, here are the biggest opportunities I see: -Human-AI Collaboration: Applications that combine human judgment with AI automation -Better Data Integration: Richer data sources for agents to leverage -Crypto-Native Tools: Models and infrastructure built specifically for crypto use cases -Developer Experience: Expanding frameworks like Eliza and GAME to simplify building -Smart Contract Integration: Making it easier for agents to interact with onchain protocols -Feedback Systems: Building loops that help agents develop taste and improve over time
2 replies
1 recast
10 reactions
J Hackworth
@jhackworth
8/ Want to try it yourself? Just @ AWESOM-O on Farcaster to get started. DMs are also open for builders exploring onchain agents or wanting to jam about where this space is heading. P.S. No token. Ever. Some experiments should stay experiments
5 replies
0 recast
6 reactions
bigbully🙃
@big-bully.eth
200 $degen thanks for building
0 reply
0 recast
0 reaction
