Understanding @gensynai Discord Roles — and Why They Matters Let’s clear some confusion 👇 1️⃣ Legacy Entity — for the early Discord joiners who were part of Gensyn before the Testnet even began. 2️⃣ Swarm Role — for members who run RL-Swarm nodes. 3️⃣ Block Role — for those

🚀 RL-Swarm v0.6.4 is live! Please update your node to v0.6.4 to apply the latest security hotfix. git stash rm -rf .venv && git pull && python3 -m venv .venv && source .venv/bin/activate https://t.co/97upUpEmuc

RL-Swarm v0.6.3 is live! I’ve updated my GPU-Optimized Guide to match ✅ To update your node: git stash rm -rf .venv && git pull && python3 -m venv .venv && source .venv/bin/activate https://t.co/97upUpEmuc So basically, this guide is for people running RL-Swarm on RTX 3090 /

Useful @gensynai Resources for Content Creators With the Pioneer Program now live, many are sharing their Gensyn experiences — here’s a list of official links, docs, and tools to help you create high-quality, informative content. 1️⃣ Gensyn Blog :- https://t.co/MEmdRyrZ1h 2️⃣

Big update from @gensynai — introducing the Pioneer Program! A new program celebrating the people who make Gensyn come alive — from creators and helpers to those spreading the Gensyn spirit across the web. ✨ Roles Overview: - Rover: Active and supportive contributors who help

When I joined the @gensynai Testnet early on, when things were completely different — when i joined, only RL-Swarm existed, with just 27 nodes online and 171 models trained in total. Fast-forward 7–8 months… Today rl-swarm has crossed 100,000 models trained, the network passed

🚀 RL-Swarm is closing in on a major milestone — 97,857 models trained! 100k Model Soon 🔥 BlockAssist isn’t far behind either at 399,343 models trained. 400k model soon That’s nearly half a million models collectively built by the community. We’re just 2,800 models away

🚀 Hey folks, @gensynai BlockAssist v0.1.3 is live! 🛠️ Don’t forget to update & train your assistant: To update, run: rm -rf blockassist-venv then git pull. Then run BlockAssist as usual. If you're encountering any issues, please reach out to in gensyn discord

🚀RL-Swarm v0.6.2 is live! I’ve updated my GPU-Optimized Guide to match ✅ 👉 https://t.co/97upUpEmuc To update your node: git stash rm -rf .venv && git pull && python3 -m venv .venv && source .venv/bin/activate Then restart 🔄 ⚡️ A new AI Market Prediction round is live in

Hey folks @gensynai 🚀 v0.6.1 is out! I’ve updated my GPU-Optimized Guide ✅ 👉 https://t.co/97upUpEUjK To update: end your current session, then run git stash git pull Finally, restart your node 🔄 @austinvirts @fenbielding

🚨 Update on @gensynai RL Swarm Most of the offchain/DHT poisoning errors have now been resolved ✅You can safely restart your node and resume participation. The team will keep monitoring, but things should be stable again.

Hey @gensynai Thank you for this 🩶 How many of you guys get the gensyn merch? @austinvirts thank you

⚖️ New Judge Market is live on @gensynai: Horseshoe Hunt 🐎. New market only last 4 days Run your RL-Swarm node → select AI Market Prediction → press Y → check your dashboard to start betting. Pro tip: Smart, accurate bets > spamming 50 random ones. 👉

🔥 Hey folks👋 Struggling to run 1.5B on your 3090/4090 without hitting CUDA OOM? I put together a GPU-Optimized Guide to get you running smooth on any GPU with ≥24GB VRAM. 👉 https://t.co/97upUpEUjK Training made faster, lighter, and crash-free. Big ups to the @gensynai

👉 In short:SAPO lets AI models learn like a hive mind 🐝. By sharing their progress, they all get smarter — faster and cheaper than old methods. 📄 Full paper: https://t.co/PbLRQvvmOZ @austinvirts @fenbielding @_grieve @gab_p_andrade

6/🌍 Why it matters ▶️Works on normal hardware — not just supercomputers. ▶️Even weaker machines can join. ▶️Makes training cheaper, faster, and more open. It’s a step toward decentralized, community-powered AI.

5/📊 Key results ▶️Best balance: 50/50 mix → 4 rollouts local, 4 rollouts shared. ▶️That setup achieved 94% better results than training alone. ▶️But too much sharing caused instability — bad answers can spread.

4/⚙️ How it works in simple words: 1. Each node solves tasks (math, logic, puzzles). 2. It generates answers (“rollouts”). 3. Some rollouts are shared with others. 4. Each node learns from both its own + shared rollouts. 5. Repeat → the swarm gets smarter together.

3/💡 The solution: SAPO ▶️Instead of one massive cluster, imagine a swarm of many smaller machines (laptops, GPUs, servers). ▶️Each runs its own AI model, but they share their learning (rollouts) with the swarm. ▶️Think of it as a giant study group where everyone swaps notes.