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🏆 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 :)
Rid retweeted
🥉 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
Rid retweeted
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
Rid retweeted
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
Rid retweeted
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
claude-eyes is finally a native feature
xitter.cf/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
Introducing Clutter Bot
A robot that understands simple english commands and actually executes them.
Natural language → robot action. Made using @GeminiApp and @Vultr
Built with @danielwu28 for the @lablabai AI Meets Robotics hackathon!
went to sf for Physical AI hack!
we tried to teach a robot to do latte art. a really fun weekend, met awesome people and learned a ton!
thanks @DhruvDiddi @GZinMetaverse @stsqit @astridwilde @DevSodhi and @fdotinc!
can't for the next one 🔥
