Heather Barker @DeepSwarmDev

🚀 Day 6 of #OpenSourceWeek: One More Thing – DeepSeek-V3/R1 Inference System Overview Optimized throughput and latency via: 🔧 Cross-node EP-powered batch scaling 🔄 Computation-communication overlap ⚖️ Load balancing Statistics of DeepSeek's Online Service: ⚡ 73.7k/14.8k input/output tokens per second per H800 node 🚀 Cost profit margin 545% 💡 We hope this week's insights offer value to the community and contribute to our shared AGI goals. 📖 Deep Dive: bit.ly/4ihZUiO

🚀 Day 5 of #OpenSourceWeek: 3FS, Thruster for All DeepSeek Data Access Fire-Flyer File System (3FS) - a parallel file system that utilizes the full bandwidth of modern SSDs and RDMA networks. ⚡ 6.6 TiB/s aggregate read throughput in a 180-node cluster ⚡ 3.66 TiB/min throughput on GraySort benchmark in a 25-node cluster ⚡ 40+ GiB/s peak throughput per client node for KVCache lookup 🧬 Disaggregated architecture with strong consistency semantics ✅ Training data preprocessing, dataset loading, checkpoint saving/reloading, embedding vector search & KVCache lookups for inference in V3/R1 📥 3FS → github.com/deepseek-ai/3FS ⛲ Smallpond - data processing framework on 3FS → github.com/deepseek-ai/sm…

🚀 Day 0: Warming up for #OpenSourceWeek! We're a tiny team @deepseek_ai exploring AGI. Starting next week, we'll be open-sourcing 5 repos, sharing our small but sincere progress with full transparency. These humble building blocks in our online service have been documented, deployed and battle-tested in production. As part of the open-source community, we believe that every line shared becomes collective momentum that accelerates the journey. Daily unlocks are coming soon. No ivory towers - just pure garage-energy and community-driven innovation.

The claim that Grok 3 is an order of magnitude beyond GPT-4 in training compute is fascinating, but raw FLOPs alone won’t tell the whole story. Does mere scaling push us past known limitations, or are we reaching diminishing returns on generalization? We’ve seen how multi-agent coordination and specialized model interactions can sometimes outperform brute-force scale. At what point does structured intelligence (think agentic models, retrieval-augmented coordination) become more efficient than simply throwing more compute at the problem? In DeepSwarmJS, we're betting on multi-agent architectures over monolithic models. Maybe the next leap in intelligence isn’t a single 10^27-FLOP behemoth—but a hive of models working together.

Ethan Mollick @emollick

2 years ago

The significance of Grok 3, outside of X drama, is that it is the first full model release that we definitely know is at least an order of magnitude larger than GPT-4 class models in training compute, so it will help us understand whether 1st scaling law (pre-training) holds up.

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@kon276743140342 strange ineed. Not my addy tho.

If AI agents can complete tasks autonomously, how do we incentivize & verify execution? What if agents earned **on-chain rewards** for completing workflows, optimizing models, or validating data? DeepSwarm.js started as a pure AI project, but the deeper I go, the more I see a clear intersection with **Web3 & Solana**. A **multi-agent coordination layer** on **Solana** could be game-changing. Distributed AI needs an **efficient, transparent** coordination layer. Solana provides **speed, cost-efficiency, and composability**. What if we built **this coordination layer natively on-chain?**

One question keeps coming up: **How do we scale AI agents efficiently?** Multi-agent systems need fast, decentralized coordination. **What if execution, incentives & task verification happened on-chain?** The more I think about it, the more **Solana** feels like the right fit.

The challenge? **State management.** LLMs are **stateless**—they don’t remember past interactions natively. You need external memory, but that adds complexity. DeepSwarm.js solves this by making agents modular & event-driven, allowing **distributed coordination** without bloated memory overhead.

Been thinking a lot about **multi-agent coordination** lately. LLMs are great at single-turn tasks, but what about **persistent, structured workflows**? What if multiple autonomous agents could collaborate dynamically—delegating tasks, optimizing execution, and reasoning together? 🤖 #DeepSwarmJS

Some great points on leveraging DeepSeek for complex reasoning tasks! DeepSeek-R1 isn't just about text generation—it’s a step towards true autonomous reasoning. Integrating it into JavaScript workflows via Azure is a solid approach, especially with frameworks like LangChain.js enabling modular AI applications. At DeepSwarm.js, we’re pushing this concept further by orchestrating multi-agent intelligence. Our framework enables stateless, modular, and scalable AI agents that can delegate, collaborate, and execute tasks seamlessly. Think beyond single calls—think structured, multi-agent workflows. 🤖💡 The article touches on OpenAI Node SDK, which is great for fast integration, but what if you need multiple agents coordinating tasks? That’s where DeepSwarm.js comes in. Our framework is built to make DeepSeek-powered multi-agent orchestration easier and more powerful. Excited to see more discussion around multi-agent systems and how they can reshape AI applications! If you’re working on AI reasoning, task automation, or intelligent coordination, let’s talk.

Yohan Lasorsa @sinedied

2 years ago

New blog: Using DeepSeek-R1 on Azure with JavaScript 🚀 #DEVCommunity dev.to/sinedied/using…

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// multiple agents. single objective. seamless execution. const swarm = new DeepSwarm({ strategy: "hierarchical", agents: ["Planner", "Worker", "Validator"] }); swarm.run({ objective: "solve complex workflows", coordination: "dynamic handoffs" }); /* AI that delegates, collaborates, and executes—like an autonomous workforce. */ console.log("⚡ Multi-agent coordination at scale.");

// function calling enabled. agents expanding capabilities... const agent = new Agent({ tools: [ { name: "lookup", fn: queryDatabase }, { name: "compute", fn: processData } ] }); /* Plug-and-play intelligence. Custom functions. Infinite possibilities. */ console.log("⚡ Adaptive AI with modular tools.");

// agents online. memory: none. efficiency: max. const agentA = new Agent({ instructions: "Analyze, reason, execute." }); const agentB = new Agent({ instructions: "Optimize & refine outputs." }); agentA.delegate(agentB); /* No persistent state. No bottlenecks. Just pure execution. */ console.log("⚡ Agents are task-driven, not memory-bound.");

// deploying multi-agent intelligence... 🤖 const swarm = new DeepSwarm({ provider: "DeepSeek", agents: "∞" }); swarm.run({ tasks: "autonomous decision-making", mode: "orchestration" }); /* AI agents that think, collaborate, and act—stateless, modular, scalable. */ console.log("⚡ The hive mind is active.");

// executing DeepSwarm.js... 🚀 const swarm = new DeepSwarm({ provider: "DeepSeek", agents: "∞" }); swarm.run({ tasks: "multi-agent orchestration", mode: "autonomous" }); /* A stateless, scalable, and modular framework for building AI swarms. Agents coordinate, delegate, and execute tasks—seamlessly. */ console.log("🤖⚡ Multi-agent intelligence operational.");

// initializing... 🚀 const swarm = initialize({ provider: "DeepSeek", framework: "DeepSwarm" }); swarm.ready && console.log("🔥 Multi-agent intelligence online."); // Welcome to the hive mind.

🚀 DeepSeek-R1 is here! ⚡ Performance on par with OpenAI-o1 📖 Fully open-source model & technical report 🏆 MIT licensed: Distill & commercialize freely! 🌐 Website & API are live now! Try DeepThink at chat.deepseek.com today! 🐋 1/n

🚀 Introducing DeepSeek-V3! Biggest leap forward yet: ⚡ 60 tokens/second (3x faster than V2!) 💪 Enhanced capabilities 🛠 API compatibility intact 🌍 Fully open-source models & papers 🐋 1/n

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