4/4: Want to run a node with your spare computing power for passive income? No sign up, No waitlist, just install the Crynux Node app at and start earning CNX tokens right away!. For a deeper dive,

3/4: Also, trust is no longer reputation based!. Crynux pioneers a three-node consensus voting scheme for computing power providers, which establishes verifiable trust in completely trust-less environments. As a user, your workloads are run, checked, and returned - you can check

2/4: Unlike expensive GPUs from hyperscalers like Google and Amazon, Crynux taps into idle home GPUs. Since EdgeAI doesn’t require burning cash on bandwidth for cross-talk between GPUs, it is way more economical per flop and infinitely scalable. Let the numbers speak:.

Decentralized AI has the power to slash your compute bills and @crynuxio is leading the charge!. After speaking with co-founder @0xaaaaaron, here's how EdgeAI orchestration rewrites the cost equation👇

5️⃣ GovBots (@jwaup) are onchain governance AI agents that analyze House of Stake proposals, fetch insights, recommend votes, and more!. Test their Telegram bot @neargovernance_bot.

4️⃣ @LearnNear (@SashaBaksht). LearnNear uses AI-assisted learning to combat @cluely -style cheating:. - Verifiable commenting system (user + AI co-awarded points). - "Surprise me" feature for guided Q&A. - Focuses on preserve human learning + monetize knowledge bases. 🎯.

3️⃣ @ASIMOV_Protocol (@bendiken @HammerToe). New launch: to enable "trustworthy neurosymbolic AI" with decentralized knowledge graphs that separate knowledge from models. - Rust-based CLI tools.- Homebrew/Cargo installs available.- Full platform +.

2️⃣ @BitteAI (@PKuveke) lets you build embeddable, multi-chain agents for seamless cross-chain swaps (like Base ETH → NEAR USDC) through simple chat. What's new:. - NEAR Intents beta supporting multiple EVM chains (Solana coming next week). - Analytics dashboard with.

1️⃣ Anton Lomonos from @near_ai shipped an open-source AI agent analytics dashboard that tracks invocations, latency, model performance, and more. Includes: .- Privacy-focused logs with user/admin tiers.- Web component for easy integration (available via npm). 🔗 GitHub repo:.

We're not hosting our weekly livestream tomorrow but here are the takeaways from last week's episode (#18)! . Highlights:. 📈 Open-source agent analytics.🤖 Agents to support human governance .⚡️Chat-to-Swap w/ @BitteProtocol.📚 Anti-CheatingGPT w/ @LearnNear. 👇 Dive deeper:.

What Did You Ship This Week? - Ep. 17

Join “What Did You Ship This Week?” with @PKuveke (@BitteProtocol), Anton Lomonos (@near_ai), @SashaBaksht (@LearnNear), @jwaup from GovBot, and @bendiken & @hammertoe from @ASIMOV_Protocol!. 🎯 Discover product updates.🤝 Engage with fellow builders.🔓 Unlock early insights.🗓️.

5/5: This is the power of Knowledge Graphs in Asimov: modular ingestion + graph merging + LLM access = flexible, privacy-preserving, and infinitely extensible data insights. Dive in at .#KnowledgeGraph #AI.

4/5: That unified graph becomes your personal data warehouse. Want to know which contacts mentioned “AI” last month? One graph query answers it. And with your LLM of choice—cloud or local—you can navigate, summarize, and reason over your entire graph in natural language.

3/5: In Asimov, every data source from Telegram, LinkedIn, X, etc is wrapped in its own module that extracts a mini-knowledge graph. Because “a graph plus a graph is still a graph,” you can merge them seamlessly. By unifying these per-app graphs into one personal knowledge.

2/5: Knowledge Graphs store structured facts as entities & relationships. LLMs generate fluent text from patterns in data but can hallucinate. • Knowledge Graphs = precise, deterministic queries over explicit facts. • LLM = flexible, contextual language

We tested @ASIMOV_Protocol with a 1-hour parliamentary debate and got accurate answers with reference to moments down to the second. With Knowledge Graphs you can reduce the chance of your AI hallucinating! Here is how they work🧵👇.

RT @PhalaNetwork: 🤖 AI runs on GPUs, NOT CPUs. Modern AI workloads—think LLMs, real-time inference—need massive parallelism. GPUs like NVI….

RT @NEARWEEK: @AgoraGovernance @kentf Check out the recap and full recording of Ep. 16 of "What Did You Ship This Week?" from last week:.ht….

RT @NEARWEEK: “We’re now an all-in-one platform for AI builders to build their dreams.”. @PondGNN’s revamp connects hackathons, token launc….