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Today, Melius is launching the new way for creatives to work with agents. Introducing the slash command. Creative work with agents painfully moves one step at a time. Each idea is queued sequentially, so even a full campaign only moves at the pace of a single agent. Your eyes... flips back and forth between the chat constantly. Slash changes that. Hit the slash key anywhere on the canvas to start an agent on a task. Run as many as you want, side by side. Each agent works in parallel, each with a clear status showing its progress. Every designer, marketer, and director will be that much more productive - building images, videos, audio and more at the speed of thought. Try it now on Melius.show more

joowon

26,193 subscribers

25,450 次观看 • 2 个月前 •via X (Twitter)

Anya Rossi• Live Now

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43 条评论

澔澔的头像

澔澔2 个月前

Config conference who

Young Kim 的头像

Young Kim2 个月前

thank you Joowon, very cool

alex 的头像

alex2 个月前

this experience is unbeatable

Shivam Patel 的头像

Shivam Patel2 个月前

this is awesome

joowon 的头像

joowon2 个月前

arca even more so.

nic 的头像

nic2 个月前

hype

Connor Heggie 的头像

Connor Heggie2 个月前

love the UX

joowon 的头像

joowon2 个月前

thanks pookie

Fred 的头像

Fred2 个月前

/ play du bist gut genug with subtle, cinematic motion taking place at the berlin cathedral

joowon 的头像

joowon2 个月前

mmm yes

Contra 的头像

Contra2 个月前

This is awesome. Congrats team 🔥

joowon 的头像

joowon2 个月前

love you guys

TK Kong 的头像

TK Kong2 个月前

@trymelius HUGE! 🚀

nic 的头像

nic2 个月前

@JahowYu so good at making videos

es 的头像

es2 个月前

🔥🔥🔥

Jared Zoneraich 的头像

Jared Zoneraich2 个月前

I want / on every website now

Kevin Liang 的头像

Kevin Liang2 个月前

This is the greatest thing since sliced bread

joowon 的头像

joowon2 个月前

tabasco sauce comes in at a close second

Anu Anuja 的头像

Anu Anuja2 个月前

@ArnavRamu This is actually useful for generating shot sequences while directing a movie.

joowon 的头像

joowon2 个月前

@ArnavRamu should be easier to direct multiple movies now

Anu Anuja 的头像

Anu Anuja2 个月前

@ArnavRamu Yes agreed! 🙌 You should add a tagline - "No Mess, only Melius for your Movies" for these cool features😅

Maya Agnihotri 的头像

Maya Agnihotri2 个月前

WERE SO COOL

vas 的头像

vas2 个月前

Banger alert 🚨

Alex 的头像

Alex2 个月前

exactly as i imagined 🔥🔥

Brycent 的头像

Brycent2 个月前

Melius is a 10/10 platform

Mason Warner 的头像

Mason Warner2 个月前

bang

joowon 的头像

joowon2 个月前

we love u

David 的头像

David2 个月前

W update

micah 的头像

micah2 个月前

Yeah y'all cooked on this. Insane

Yash Gogri 的头像

Yash Gogri2 个月前

let's goooooooooo

Neddy 的头像

Neddy2 个月前

ts so clean

Enyu 的头像

Enyu2 个月前

parallelism is undefeated

punn 的头像

punn2 个月前

incredibly well designed

Josiah Parappally 的头像

Josiah Parappally2 个月前

LFG MELIUS!!!

Septimiu 的头像

Septimiu2 个月前

congrats guys! you're the best 👀

aditya addepalli 的头像

aditya addepalli2 个月前

let em coooook

Roland Shen 的头像

Roland Shen2 个月前

melius team is cooking like crazy

henry 的头像

henry2 个月前

Huge ship 🔥🔥

liquidated (Dev Arc) 的头像

liquidated (Dev Arc)2 个月前

omo this is literally why i built forge fr

John Greg 的头像

John Greg2 个月前

The slash command makes way more sense than one chat box forever. Parallel agents on a canvas is the workflow!

Rashi Umapathi 的头像

Rashi Umapathi2 个月前

The real unlock is cutting the back-and-forth between agent and human. Most tools don't solve the waiting problem.

temp.md 的头像

temp.md2 个月前

congrats on the launch. slash on the canvas is a nice interaction because it turns each creative branch into its own visible job, not another thing buried in chat.

Monk 的头像

Monk2 个月前

Finally, a slash that actually works.

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23,585 次观看 • 2 个月前

I ran the same task on Claude Code and DeepSeek's new agent harness. One cost $150. The other cost $2. Today we're launching (AgentSky), the "OpenRouter for Agents" — one API → Claude Code, Codex, DeepSeek, Kimi, OpenCode, and every major agent in the cloud. And Agent Playground on top: race them on your own task, with your real tools (GitHub, Gmail, more), side by side in a browser: time, cost, tokens burnt. Guess which one was $2.

I ran the same task on Claude Code and DeepSeek's new agent harness. One cost $150. The other cost $2. Today we're launching (AgentSky), the "OpenRouter for Agents" — one API → Claude Code, Codex, DeepSeek, Kimi, OpenCode, and every major agent in the cloud. And Agent Playground on top: race them on your own task, with your real tools (GitHub, Gmail, more), side by side in a browser: time, cost, tokens burnt. Guess which one was $2.

Xiaoyin Qu

2,215,032 次观看 • 21 天前

Codex team is back in the kitchen with a really nice quality of life upgrade for subagents. With the advent of custom roles, they have also upgraded the TUI experience in two really meaningful ways. > All agents now get a name for better readability > Additionally, the agent role is declared. > Subagent name, role, and status are now color coded. > Subagent rendering was also optimized for readability > /agents slash command shows all agents, even 2+ layers deep. And here's the biggest and most important change. Subagent injection. Before, sometimes the orchestration agent would continue work and lose track of the work of a subagent. Now, when a subagent is blocked or completed, it injects a message back up the chain to ensure that the parent sees the message. This is a really big improvement overall, and leads to much more reliable inter-agent communication, reliability, and DX. In this example, I used the parent agent to spin up a worker agent, which then spawned two more "Spark" agents a second layer deep. I was able to easily tell them apart, switch between the threads, and see exactly what they were prompted. All of this will be available in update 0.105.0 I don't know who JIF is at OpenAI, but they are truly a legend.

Codex team is back in the kitchen with a really nice quality of life upgrade for subagents. With the advent of custom roles, they have also upgraded the TUI experience in two really meaningful ways. > All agents now get a name for better readability > Additionally, the agent role is declared. > Subagent name, role, and status are now color coded. > Subagent rendering was also optimized for readability > /agents slash command shows all agents, even 2+ layers deep. And here's the biggest and most important change. Subagent injection. Before, sometimes the orchestration agent would continue work and lose track of the work of a subagent. Now, when a subagent is blocked or completed, it injects a message back up the chain to ensure that the parent sees the message. This is a really big improvement overall, and leads to much more reliable inter-agent communication, reliability, and DX. In this example, I used the parent agent to spin up a worker agent, which then spawned two more "Spark" agents a second layer deep. I was able to easily tell them apart, switch between the threads, and see exactly what they were prompted. All of this will be available in update 0.105.0 I don't know who JIF is at OpenAI, but they are truly a legend.

am.will

78,267 次观看 • 6 个月前

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In the next 10 years, you can have billions of agents as customers. They'll have millions of wallets. An agent does not look at your website. It reads your documents, your prices, and your API. An agent needs six things that a person does not need: - identity - tools - an inbox - memory - a wallet _ receipts. A CFO agent can compare 12 vendors, read the SOC 2 documents, and select the one vendor that obeys your policy. The old internet is for people. Each step of the agent journey has no infrastructure yet. Look at each SaaS tool that you use. Then build the version for agents only.

The Startup Ideas Podcast (SIP) 🧃

12,220 次观看 • 17 天前

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,549 次观看 • 3 个月前

THIS GUY BUILT A 24/7 AI COMPANY WITH 7 AGENTS THAT CHECKS 48 TIMES A DAY, PICKS ITS OWN TASKS AND KEEPS WORKING WHILE HE SLEEPS 00:05 the map shows agents moving between the marketplace, research lab and command center, with each one handling a different job inside the same shared system the setup runs on 4 core layers: automatic triggers, shared memory, connected tools and an isolated verifier that checks every result before the next step starts each cycle reads the last 10 log entries, updates open tasks, saves new signals and passes the context forward, so the next agent never starts from zero one agent researches, two execute, another monitors outcomes and a read-only checker reviews the work independently, allowing several workflows to run in parallel without breaking the system bookmark this before everyone stops prompting one agent at a time and starts building AI teams that work, learn and improve around the clock

THIS GUY BUILT A 24/7 AI COMPANY WITH 7 AGENTS THAT CHECKS 48 TIMES A DAY, PICKS ITS OWN TASKS AND KEEPS WORKING WHILE HE SLEEPS 00:05 the map shows agents moving between the marketplace, research lab and command center, with each one handling a different job inside the same shared system the setup runs on 4 core layers: automatic triggers, shared memory, connected tools and an isolated verifier that checks every result before the next step starts each cycle reads the last 10 log entries, updates open tasks, saves new signals and passes the context forward, so the next agent never starts from zero one agent researches, two execute, another monitors outcomes and a read-only checker reviews the work independently, allowing several workflows to run in parallel without breaking the system bookmark this before everyone stops prompting one agent at a time and starts building AI teams that work, learn and improve around the clock

Gipp 🦅

19,071 次观看 • 2 个月前

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)

How to build a 1-person AI company that: - Runs locally - 100% open-source - No human employees, all agents - Real-time collaboration via email Multi-agent orchestration is not new. Plenty of frameworks already let agents hand off tasks, run in parallel, and talk to each other. So the interesting question is not whether agents can collaborate. It is what structure you use to make them collaborate. The common approach is to wire a graph of nodes and edges and reason about the plumbing yourself. It works, but you are learning a new abstraction just to describe who does what. There is a coordination structure we have trusted for a hundred years already: an organization. Every company runs the same way. People have roles, roles have reporting lines, and work moves up and down that chart without anyone relaying each message by hand. Map that onto agents and the whole thing gets intuitive. You lay out an org chart, each agent fills one role, you talk to the person at the top, and the org sorts out the work between them. You already know how a company works, so you already know how to run one here. There is no new abstraction to learn. That is exactly what Alook does. Each agent is a live Claude Code or OpenCode session with a defined role, a reporting line, and its own email inbox. The agents coordinate over email, the same way a team would. And it all runs locally through a runtime on your own machine, so nothing leaves your setup. You bring your own agent too. Claude Code and Codex both work, and if you would rather stay fully open source and local, OpenCode works the same way. To show how this feels in practice, I set up three agents as a small sales team. Vi is the one I talk to. I hand Vi a goal, and Vi routes the work down the chart. Neile runs prospect research. Vi passes the target criteria, and Neile reports back a ranked list of names, roles, and companies, each with a suggested angle and a confidence score. Lliane runs outreach. Vi hands over the messaging angle and follow-up cadence, and Lliane reports back on emails sent, responses received, and any deal that needs escalation. I never relay a message between them. Neile and Lliane report to Vi, and Vi updates me in one place. The whole thing is open source and self-hosted, so it runs on your machine with your own agents. Give the repo a star if you want to follow where it goes: I also wrote a full walkthrough on building your own AI company with it, from a blank org chart to a running job. The article is quoted below. Cheers! :)

Akshay 🚀

170,603 次观看 • 2 个月前