🏆 Excited to share I got 3rd place at the @GoogleDeepMind x @vercel Hackathon NYC I built ANSR. An AI voice host that answers every restaurant phone call. Takes orders. Makes reservations. Responds in any language. How it works: → Owner uploads a photo of their menu → Gemini 3.1 Pro Vision extracts every item, price, and dietary tag → @ElevenLabs powers the voice conversation → @GeminiApp extracts structured data from the conversation → @supabase Realtime pushes orders and reservations to a live dashboard The entire pipeline (voice in, structured data out, dashboard updated) happens with zero human input. Built with: → @vercel Nextjs → Gemini 3.1 Pro Flash Lite → @ElevenLabs Conversational AI →@supabase Postgres Realtime Huge thanks to @vercel, @GoogleDeepMind, and @cerebral_valley for putting this together :)

such a cool pop up @ElevenLabs 🔥

🥉 3rd Place - Ansr Ansr is an AI voice host that answers restaurant calls, takes orders and reservations, and converts missed calls into structured, real-time business for owners. @ridcursion

New York 🇺🇸 Zero to Agent Momentum builds. As London wrapped, New York picked it up. What started in one city is now moving at scale.

Introducing hivememory Shared reasoning memory for multi-agent systems. Agents write structured claims with evidence provenance, query what others already know before making LLM calls, and catch contradictions automatically. pip install hivememory ridxm.github.io/hivememory/

I built hivememory, a shared reasoning memory layer for multi-agent systems agents write structured claims with evidence provenance, query what others already know before researching, and catch contradictions automatically benchmark results with 3 parallel agents: - 56% of queries served from memory - 17.5% token reduction - quality equal or better - conflicts identified project page: ridxm.github.io/hivememory/ repo: github.com/ridxm/hivememory inspired by @karpathy's LLM knowledge base. single-agent works, this makes it multi-agent. pip install hivememory

Day 3: building 30 AI Agents in 30 days ✧ i think we can all agree apartment hunting is very time consuming. looking at the neighborhood, reviews, commute times, and much more. So today, I built an agent that takes an address and pulls everything together that I'd normally spend hours digging for. #agentsystems #automation

day 5 of building 30 ai agents in 30 days! competitive intelligence agent you give it a startup idea, it finds your competitors, maps them visually, compares pricing/features, and tells you what to avoid. watch the demo below :) #BuildingInPublic #agent

Building 30 AI agents in 30 days Day 1: Multi-Agent Debate System I wanted to know if AI could actually hold a real debate. So I built 5 agents that argue, challenge assumptions, and verify claims with real sources in real-time. Turns out they can!

day 10 of building an AI agent every day! - reads any github repo - identifies entry points, components, relationships - builds an interactive mind map you can explore - generates plain english summaries understand how a codebase works by visualizing it

claude-eyes is finally a native feature x.com/ridcursion/status/2013…

i think the wildest finding here is how often a model tries to manipulate doesn't predict how often it succeeds. propensity ≠ efficacy also manipulation was most effective in finance, least in health. health has ground truth that can be validated against, finance has speculation. so it's easier to get someone to make a bad investment than take a bad supplement the domain matters more than model

flew to sf for the physical ai hackathon at @fdotinc! super excited to spend the weekend meeting great people, learning and building 🦾