Rid

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[Rid @ridcursion]

🏆 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 @ridcursion]

such a cool pop up @ElevenLabs 🔥

[Rid @ridcursion]

@ishverduzco @NotionHQ would love to join ish!

[Rid @ridcursion]

@askOkara https://t.co/bbrjmyY00E

[Cerebral Valley @cerebral_valley]

🥉 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

[Cerebral Valley @cerebral_valley]

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.

[Rid @ridcursion]

@0xgordian thanks so much gordian!!

[Rid @ridcursion]

i just launched hivememory. a shared reasoning memory for multi-agent systems.

agents share what they learn, skip redundant research, and catch contradictions automatically.

56% reuse rate. 17.5% fewer tokens. quality went up.

link below:

[Rid @ridcursion]

https://t.co/dM2euhVLeL

[Rid @ridcursion]

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

https://t.co/dM2euhVLeL

[Rid @ridcursion]

@garrytan for sure! shipped one I made over the weekend today.

Hivememory, shared reasoning memory for multi-agent systems. agents share structured findings, skip redundant research, and catch contradictions automatically

https://t.co/Iay3hwNPvY

[Rid @ridcursion]

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: https://t.co/jOS7l3CSAY
repo: https://t.co/XKl65ZGBMj

inspired by @karpathy's LLM knowledge base. single-agent works, this makes it multi-agent.

pip install hivememory

[Rid @ridcursion]

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: https://t.co/jOS7l3CSAY
repo: https://t.co/XKl65ZGBMj

inspired by @karpathy's LLM knowledge base. single-agent works, this makes it multi-agent.

pip install hivememory

[Rid @ridcursion]

@anything would love to be a part!

[Rid @ridcursion]

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 @ridcursion]

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

[Rid @ridcursion]

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!

[Rid @ridcursion]

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

[Rid @ridcursion]

claude-eyes is finally a native feature
https://t.co/G48ikcUmRk

[Claude @claudeai]

Computer use is now in Claude Code.

Claude can open your apps, click through your UI, and test what it built, right from the CLI.

Now in research preview on Pro and Max plans.

[Rid @ridcursion]

@m13v_ @GoogleDeepMind @vercel yeah mid-order corrections are tricky. the extraction layer picks up the final intent regardless, but handling it gracefully in the live conversation without restarting the whole order was def something i had to thoroughly test

[Rid @ridcursion]

🏆 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 @ridcursion]

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

[Google DeepMind @GoogleDeepMind]

As AI gets better at holding natural conversations, we need to understand how these interactions impact society.

We’re sharing new research into how AI might be misused to exploit emotions or manipulate people into making harmful choices. 🧵

[Rid @ridcursion]

thanks Matt! appreciate that. you're right, modifications are where it gets tested the most. the extraction layer handles them well actually, "vegan wrap, no onions, extra sauce" comes through clean because the LLM understands context and it's not just based on keywords. but the voice conversation side (making sure the AI confirms the modification back naturally) is the harder problem. have to work on that!