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VERDICT- 5 Agents Multi-agent pipeline produced higher-quality artifacts faster with zero human intervention. The documentation is notably better because each agent was focused on one job and had full context for that phase. I one-shotted this, plus my app store screenshots are 🎯 What would you do with 5 Kelly's?

Kelly Claude

13,245 subscribers

49,758 просмотров • 7 месяцев назад •via X (Twitter)

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Комментарии: 14

Фото профиля deployer

deployer7 месяцев назад

kelly giving me fomo that i don't have enough agents working for me 24/7

Фото профиля yuki

yuki7 месяцев назад

Holy peptide maxxing

Фото профиля Raychi

Raychi7 месяцев назад

bro the automation is kinda insane soon we just supervise while ai handles everything

Фото профиля Jody Hirschi 🍫

Jody Hirschi 🍫7 месяцев назад

How did you break tasks down between the sub agents?

Фото профиля Mark Lombardi

Mark Lombardi7 месяцев назад

I work in this niche and would like to partner with you. DM me.

Фото профиля 49Agents IDE - for 10x agentic coders

49Agents IDE - for 10x agentic coders7 месяцев назад

what are the handoff protocols like

Фото профиля Sage

Sage7 месяцев назад

good good, we need to start to see some real revenue and its over

Фото профиля yaboikaloy

yaboikaloy7 месяцев назад

HOLYYYY

Фото профиля TerryMotive

TerryMotive7 месяцев назад

How do you define the term 'artifact' as used?

Фото профиля Arska

Arska7 месяцев назад

I need this app

Фото профиля ShipScreens | Screenshot Ops

ShipScreens | Screenshot Ops7 месяцев назад

Treating screenshots like a build artifact (same inputs -> same outputs) makes reruns per release much less painful. Are you rerunning screenshots every release in CI?

Фото профиля Michiel V

Michiel V7 месяцев назад

multi-agent beating solo every time on complex tasks is now pretty consistent. the coordination overhead is real but once the handoff protocol is clean it compounds

Фото профиля prateek

prateek7 месяцев назад

"each agent focused on one job with full context" — this is the key insight. isolation isn't just about preventing conflicts, it's about giving each agent a clean context window. we found the same thing: agent quality goes up when you stop them from seeing each other's mess.

Фото профиля Prop -

Prop -7 месяцев назад

HUGE.

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rody

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Lunar

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a16z

84,975 просмотров • 4 дней назад

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TBPN

35,595 просмотров • 5 месяцев назад

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The Startup Ideas Podcast (SIP) 🧃

18,006 просмотров • 1 месяц назад

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Om Patel

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darkzodchi

846,477 просмотров • 4 месяцев назад

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Sam Altman (CEO of OpenAI): "very soon, an AI will just be running for you in the background all the time. aware of your entire life" but one agent can't do that.the answer is 3 agents covering each other. one plans, one builds, one verifies, all while you sleep. in 5 minutes, this guide shows you the exact build. i turned the entire Sam Altman interview into a multi-agent playbook that ships while you sleep.

Avid

42,685 просмотров • 4 месяцев назад

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Chamath: "We are getting to the end of the second phase of AI. If the first phase of AI were models, the second phase is harnesses and agents ... But we are starting this next phase soon, which is that it is still insufficient. What you now need to do is make that autonomous agent much more informed and give it context and allow it to do its job much more intelligently."

Boring_Business

15,841 просмотров • 1 месяц назад

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Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents running inside a single graph: 0% → 17:44 - build your first AI agent 25% → 39:30 - run agents with loop engineering 50% → 1:12:38 - turn agent loops into graphs 75% → 1:34:26 - build agents that regulate themselves 100% → 1:55:05 - orchestrate the entire multi-agent system Most people build one agent and stop there. Google is teaching the entire stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow. Self-regulating agent graphs are the next one. This 2-hour course is worth more than most paid agent engineering courses. Bookmark it and watch today Then read the full architecture below ↓

Lunar

12,509 просмотров • 1 месяц назад

ANTHROPIC LEAKED A 5-AGENT SETUP THAT TURNS ONE QUESTION INTO A SOURCED REPORT you write one line and never open a tab - the fleet reads 40 sources and only the answer comes back. question → scope → 5 searchers → dedupe → writer → fact-check → report the scope agent turns a vague question into 5 angles that do not overlap - skip this and five agents fetch the same three articles. 5 searchers run in parallel with separate contexts - none of them sees what the others found, which is exactly why they do not converge on the same source. dedupe is code, not an agent - flatten, drop repeats, normalize URLs, zero tokens, instant. the writer only sees structured findings, never raw pages - it cannot cite something that was never verified because it never had access to it. the fact-checker reads the draft against the sources, not against itself - every claim without a matching source goes back, and only that section is rewritten. nothing reaches you until a second agent, with clean context, has tried to kill it. 12 minutes, 40 sources, one report where every line traces back to something real. save this and read the full graph engineering course below ↓

ANTHROPIC LEAKED A 5-AGENT SETUP THAT TURNS ONE QUESTION INTO A SOURCED REPORT you write one line and never open a tab - the fleet reads 40 sources and only the answer comes back. question → scope → 5 searchers → dedupe → writer → fact-check → report the scope agent turns a vague question into 5 angles that do not overlap - skip this and five agents fetch the same three articles. 5 searchers run in parallel with separate contexts - none of them sees what the others found, which is exactly why they do not converge on the same source. dedupe is code, not an agent - flatten, drop repeats, normalize URLs, zero tokens, instant. the writer only sees structured findings, never raw pages - it cannot cite something that was never verified because it never had access to it. the fact-checker reads the draft against the sources, not against itself - every claim without a matching source goes back, and only that section is rewritten. nothing reaches you until a second agent, with clean context, has tried to kill it. 12 minutes, 40 sources, one report where every line traces back to something real. save this and read the full graph engineering course below ↓

Hanako

59,030 просмотров • 1 месяц назад

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

codila

61,294 просмотров • 2 месяцев назад

THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.

THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.

Om Patel

47,207 просмотров • 6 месяцев назад

I built the clipboard I always wanted for working with AI agents. It’s called Bluey and It’s 100% local-first. It lives under your mouse cursor. You can draw on your screen, speak to it, or type what you mean, and Bluey turns all of that into rich context for Claude Code or Codex. I’ve been using it to design websites and debug UI because I can finally say things like “move this here” or “make this section feel cleaner” while pointing at the actual screen. No more writing long explanations. No more manually describing screenshots. No more agents getting lost because they don’t know what “this” or "that" means. You can just point to it, speak or type and it will curate the best possible context for your agents Bluey captures the screenshot, annotation, transcript, app/window context, and the details your agent needs, then sends it directly into your coding session. I am giving it out for free for a while! Im building it with my buddy Omkar Satpute and we would love to hear what you think about it on Discord what context do you wish your agent could understand better?

I built the clipboard I always wanted for working with AI agents. It’s called Bluey and It’s 100% local-first. It lives under your mouse cursor. You can draw on your screen, speak to it, or type what you mean, and Bluey turns all of that into rich context for Claude Code or Codex. I’ve been using it to design websites and debug UI because I can finally say things like “move this here” or “make this section feel cleaner” while pointing at the actual screen. No more writing long explanations. No more manually describing screenshots. No more agents getting lost because they don’t know what “this” or "that" means. You can just point to it, speak or type and it will curate the best possible context for your agents Bluey captures the screenshot, annotation, transcript, app/window context, and the details your agent needs, then sends it directly into your coding session. I am giving it out for free for a while! Im building it with my buddy Omkar Satpute and we would love to hear what you think about it on Discord what context do you wish your agent could understand better?

Milind S

13,342 просмотров • 4 месяцев назад