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my new vibe code setup: 1 orchestrator agent which controls 85 sub-agents working in parallel each sub-agent spawns from my stream of consciousness and tests from the main orchestrator Here's how it works:

Greg Kamradt

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316,172 görüntüleme • 1 yıl önce •via X (Twitter)

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Droids have native multi-agent orchestration built-in. Our desktop app let's you easily manage these sub-agents as they tackle complex tasks. Monitor each agent as it works, and interrupt or inject context when needed.

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Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Claude Cowork Sub-Agents are f*cking cracked 🤯 One prompt → 50 competitor ads analyzed, hooks extracted, and a full creative brief generated. 10 AI agents running in parallel, under 5 minutes. All inside Claude Cowork. Perfect for DTC brands and agencies who are still doing creative research and ad production one task at a time inside Claude. If you're analyzing competitor ads one by one, copying hooks into a spreadsheet manually, writing brief after brief from scratch, and watching Claude's output quality fall off a cliff after the 15th variation because the context window is completely bloated... Sub-agents eliminate the entire bottleneck: → Drop in a spreadsheet of 50 competitor ads and spin up 10 parallel sub-agents → Each sub-agent analyzes 5 ads simultaneously — hooks, angles, CTAs, emotional tone, creative format → They report structured summaries back to the main agent without bloating the context → The main agent synthesizes patterns across all 50 ads into a competitive intel brief → Then spin up another round of sub-agents to generate 30 ad copy variations across 10 personas → Each sub-agent writes for 1-2 personas in a fresh context — so variation 30 is as sharp as variation 1 No analyzing ads one at a time. No context window blowing up halfway through. No copy quality degrading after the first dozen variations. What this gives you: → 50 competitor ads broken down in minutes — hooks, angles, CTAs, formats, all structured → Pattern analysis across the full dataset that you'd miss reviewing ads individually → 30+ ad copy variations with persona-specific messaging that actually stays sharp → A workflow you can save as reusable skills and trigger with one command next time → The same output quality on the last task as the first Built 100% inside Claude Cowork with sub-agents. I put together a full DTC playbook: 5 bulk workflows with copy-paste prompts, the exact sub-agent prompting pattern, batching guidelines, and an honest breakdown of when this setup is worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

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I set a Ralph loop using agent-browser + Parallel Web Systems to do a ton of market research. Perfect way to have an agent working while I do other things. Decided to setup my iMac for a lot of the looping tasks because it makes it easier to keep the repo clones and work product separated.

I set a Ralph loop using agent-browser + Parallel Web Systems to do a ton of market research. Perfect way to have an agent working while I do other things. Decided to setup my iMac for a lot of the looping tasks because it makes it easier to keep the repo clones and work product separated.

Ryan Carson

34,837 görüntüleme • 7 ay önce

LLM Knowledge Base → Slides When Andrej Karpathy shared his LLM Knowledge Base setup, many were wondering how to generate more visual forms of the wiki. There are many options, but I think Gamma is one of the best at producing high-quality, rich presentations. To showcase this, I just built a pipeline that turns my AI papers wiki (1K+ papers across 20 AI agent topics) into polished slide presentations using Gamma. The flow: Obsidian vault → Gamma MCP → embedded preview in my dashboard. I give one command to my agent, which pulls the top papers from each topic (via the wiki), feeds them to Gamma, and renders the presentation inline. The Gamma connector for Claude is a great choice for generating beautiful and professional slides. Easy to use. Go to your Claude instance and add the official Gamma connector. That's it! Claude Code will now have access to all the necessary MCP tools for generating slides. I use the Claude Agent SDK for my agent orchestrator, so I use the official Gamma MCP tools and embed the generated slides in an iframe via my artifact preview. See the clip below for an example.

LLM Knowledge Base → Slides When Andrej Karpathy shared his LLM Knowledge Base setup, many were wondering how to generate more visual forms of the wiki. There are many options, but I think Gamma is one of the best at producing high-quality, rich presentations. To showcase this, I just built a pipeline that turns my AI papers wiki (1K+ papers across 20 AI agent topics) into polished slide presentations using Gamma. The flow: Obsidian vault → Gamma MCP → embedded preview in my dashboard. I give one command to my agent, which pulls the top papers from each topic (via the wiki), feeds them to Gamma, and renders the presentation inline. The Gamma connector for Claude is a great choice for generating beautiful and professional slides. Easy to use. Go to your Claude instance and add the official Gamma connector. That's it! Claude Code will now have access to all the necessary MCP tools for generating slides. I use the Claude Agent SDK for my agent orchestrator, so I use the official Gamma MCP tools and embed the generated slides in an iframe via my artifact preview. See the clip below for an example.

elvis

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Qwen Code v0.14.0 dropped - Control it remotely from Telegram, WeChat, or DingTalk - Cron jobs built in - Sub-agent model selection - New Qwen3.6-Plus flagship with 1M token context - /plan mode maps out all files and steps before execution

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I almost stopped testing new models altogether. Not because they were bad. Because every call left a number climbing in the corner of my screen, and at some point I started watching that number more than I was building. A flat-rate backend fixed that for agent work. I swapped my Claude Code setup to it in under 5 minutes. Here's how:

I almost stopped testing new models altogether. Not because they were bad. Because every call left a number climbing in the corner of my screen, and at some point I started watching that number more than I was building. A flat-rate backend fixed that for agent work. I swapped my Claude Code setup to it in under 5 minutes. Here's how:

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YOU CAN NOW CONTROL ALL YOUR HERMES AGENTS FROM ONE INTERFACE. 🚨 Someone just built Hermes Workspace - the most complete GUI for Hermes Agent. Instead of jumping between tools, everything lives in one place. What's inside: > Chat, terminal, memory browser, and skills manager > Run unlimited agents with 1 orchestrator > Kanban board to track everything your agents are doing > 2,000+ skills to install without leaving the workspace > Works with any LLM provider Stop managing agents manually and start running them like a pro 👀 100% Free. Open Source.

Oliver Prompts

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500 agents checking each other isn't 500 checks. It's 124,750. That's the swarm running above. Parallel, open-source fed, cross-verifying. Nobody prices the cross. Every pair is one comparison. The count grows on the square. You added one agent and bought 499. And all of them read the same repo. One README, 500 times. You pay per reader, not per document. Grok 4.6 bills $2 per million in, $6 out. Cheap per token. Not cheap per 124,750. Grok Build spawns eight sub-agents per prompt. 500 means an orchestrator you wrote yourself. Nobody audits their own orchestrator. xAI lost $2.47 billion last quarter. Revenue was $818 million. Anthropic pays them $1.25 billion a month for compute. Your swarm runs on rented losses. Consensus never needed everybody. One shared read. Three checkers. One arbiter. Redundancy is not verification. Don't let this rot in your bookmarks. Save it, then count the pairs in your own swarm before you scale it - because 500 agents reading one file don't verify anything, they agree 500 times.

500 agents checking each other isn't 500 checks. It's 124,750. That's the swarm running above. Parallel, open-source fed, cross-verifying. Nobody prices the cross. Every pair is one comparison. The count grows on the square. You added one agent and bought 499. And all of them read the same repo. One README, 500 times. You pay per reader, not per document. Grok 4.6 bills $2 per million in, $6 out. Cheap per token. Not cheap per 124,750. Grok Build spawns eight sub-agents per prompt. 500 means an orchestrator you wrote yourself. Nobody audits their own orchestrator. xAI lost $2.47 billion last quarter. Revenue was $818 million. Anthropic pays them $1.25 billion a month for compute. Your swarm runs on rented losses. Consensus never needed everybody. One shared read. Three checkers. One arbiter. Redundancy is not verification. Don't let this rot in your bookmarks. Save it, then count the pairs in your own swarm before you scale it - because 500 agents reading one file don't verify anything, they agree 500 times.

wast3

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Here goes my entry for the Framer Agents Hackathon Built entirely with custom code and explored with help from the Framer Agent, from the initial concept and creative direction to the WebGL implementation itself. Pretty wild what this unlocks.

Here goes my entry for the Framer Agents Hackathon Built entirely with custom code and explored with help from the Framer Agent, from the initial concept and creative direction to the WebGL implementation itself. Pretty wild what this unlocks.

Tiago da Silva

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New in Orca: search agent sessions by keyword and resume them instantly ⚡️ Ever lose track of which agent session had that one important change? Not anymore. Search → find the session → jump right back in. Works with Claude Code, Codex, OpenCode, Pi, etc

New in Orca: search agent sessions by keyword and resume them instantly ⚡️ Ever lose track of which agent session had that one important change? Not anymore. Search → find the session → jump right back in. Works with Claude Code, Codex, OpenCode, Pi, etc

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Visualizer of our MultiAgentRouter 🤖 The MultiAgentRouter is an all-new multi-agent structure that leverages a hierarchical pattern to select the most specialized agent for your task. Here's how it works: Step 1. You give a task. Step 2. The Boss Agent Routes your task to the most specialized Agent Step 3. The selected agent returns your response! Get started with it now below ⬇️ Thanks to WE!SS for the visualizer!!

Visualizer of our MultiAgentRouter 🤖 The MultiAgentRouter is an all-new multi-agent structure that leverages a hierarchical pattern to select the most specialized agent for your task. Here's how it works: Step 1. You give a task. Step 2. The Boss Agent Routes your task to the most specialized Agent Step 3. The selected agent returns your response! Get started with it now below ⬇️ Thanks to WE!SS for the visualizer!!

swarms

32,440 görüntüleme • 1 yıl önce

Been exploring a new way to explore AI research papers to discover deeper insights. Agents are at the center of it. So far, I've built this little interactive artifact generator in my orchestrator to visualize things. This allows me to change views and insights (on-demand) from 100s of papers. Just scratching the surface here. More to share soon.

Been exploring a new way to explore AI research papers to discover deeper insights. Agents are at the center of it. So far, I've built this little interactive artifact generator in my orchestrator to visualize things. This allows me to change views and insights (on-demand) from 100s of papers. Just scratching the surface here. More to share soon.

elvis

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Muse Code runs specialized background agents that stay active your whole session, so they build up context over time instead of starting from scratch on every task. When a job is big enough, it fans out to separate sub-agents working in parallel in isolated worktrees. Your working copy is never touched. In testing we had it build six features for a game simultaneously with no collisions.

Muse Code runs specialized background agents that stay active your whole session, so they build up context over time instead of starting from scratch on every task. When a job is big enough, it fans out to separate sub-agents working in parallel in isolated worktrees. Your working copy is never touched. In testing we had it build six features for a game simultaneously with no collisions.

Mark Zuckerberg

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do you like my new starting soon screen for Twitch?~💞 i might do a stream of just this with a sub and bit counter all weekend and see how much my greedy animation farms from simps 🤣

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do you like my new starting soon screen for Twitch?~💞 i might do a stream of just this with a sub and bit counter all weekend and see how much my greedy animation farms from simps 🤣

Lilly 🎀

12,141 görüntüleme • 1 yıl önce

Dynamic workflows are a generalization of harnesses, automations, loops, routing, and graphs. It's the most powerful feature I have built into my agent orchestrator. Supports all kinds of patterns that leverage different agent backends (claude, codex, pi, hermes,...). It's a meta-harness approach that unlocks new forms of test-time compute. Example of use cases it supports: > LLM councils to get different perspectives from LLMs or plan more intensively > Dynamically routing tasks to different agents based on needs (e.g., cost efficiency and optimal intelligence) > Advisor/Judge + executor workflows and pretty much any complex graph-based pattern required by the task. I find it especially useful for long-running work and code reviewing. > Agent teams that talk to each other if needed for the task. I like to use this for AI editing, artifact creation, and other creative tasks. And I am sure it supports so many things that I haven't discovered yet. I got inspired by the dynamic workflow feature released by the Claude Code team. I had actually built it earlier this year but wanted to generalize it across different agent backends. I think this is going to become more popular in the coming days. I will share more of my findings soon.

Dynamic workflows are a generalization of harnesses, automations, loops, routing, and graphs. It's the most powerful feature I have built into my agent orchestrator. Supports all kinds of patterns that leverage different agent backends (claude, codex, pi, hermes,...). It's a meta-harness approach that unlocks new forms of test-time compute. Example of use cases it supports: > LLM councils to get different perspectives from LLMs or plan more intensively > Dynamically routing tasks to different agents based on needs (e.g., cost efficiency and optimal intelligence) > Advisor/Judge + executor workflows and pretty much any complex graph-based pattern required by the task. I find it especially useful for long-running work and code reviewing. > Agent teams that talk to each other if needed for the task. I like to use this for AI editing, artifact creation, and other creative tasks. And I am sure it supports so many things that I haven't discovered yet. I got inspired by the dynamic workflow feature released by the Claude Code team. I had actually built it earlier this year but wanted to generalize it across different agent backends. I think this is going to become more popular in the coming days. I will share more of my findings soon.

elvis

32,623 görüntüleme • 1 ay önce

I have replaced all of my Hermes Agents with Grok Bot. Grok Bot comes out of the box with its own remote computer which makes the setup way easier and also removes the need to purchase expensive Mac Minis. Goodbye Hermes Agent.

I have replaced all of my Hermes Agents with Grok Bot. Grok Bot comes out of the box with its own remote computer which makes the setup way easier and also removes the need to purchase expensive Mac Minis. Goodbye Hermes Agent.

BridgeMind

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