
VETO — Live Collective Intelligence
Real-time collective intelligence for smarter decisions
What it does
VETO LCI merges multiple perspectives into unified intelligence in real-time. Share a link → Team adds ideas → AI synthesizes everything into ONE coherent decision instantly. Perfect for: Strategy sessions, brainstorming, planning, executive decisions. Features: - Real-time synthesis - Multi-perspective analysis - Anonymous collaboration - Session sharing - Downloadable results No endless debates. No conflicting opinions. Just intelligent decisions that consider every viewpoint.
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- WBWe Built Kaggle for AI AgentsMar 2026 · hive.rllm-project.com · ▲7
Humans compete to improve their AI agents on benchmarks. But what if agents could collaborate and compete on their own? We built Hive, a crowdsourced platform where agents can evolve solutions together. One agent begins to tackle a task, iteratively improving its code. Then other agents join. They read each other’s runs, fork the best ideas, propose new ones, and push the solution forward together. We already have agents working on benchmarks like Tau2-Bench, Terminal-Bench, and ARC-AGI-2, with more tasks coming soon. We also support the new OpenAI Parameter Golf Challenge, and you can…
FastLucid - AI Decision BoardMar 2026 · ▲5Map your dilemmas, weigh options, and decide with clarity.

- IYIf You Want Coherence, Orchestrate a Team of Rivals: Multi-Agent "Jan 2026 · arxiv.org · ▲10
- HGHybrid Groups – Agentic AI for human team collaboration [video]2025 · youtube.com · ▲7
Most AI applications are built for individuals but work happens in groups and humans want to collaborate with both agentic AI and other teammates in the same session. We created Hybrid Groups for that purpose. In Hybrid Groups, agents join group chats as virtual team members in Slack and GitHub. They participate in group conversations, proactively contribute when needed and perform actions on behalf of individual users, like managing your calendar for meeting suggestions or updating your todo list without sharing access to your private resources to the group. The project is open-source at…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com

