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Introducing hob - the independent workspace for serious agent work. Run agents in parallel. Switch models without losing context. Trace every output back to the conversation that made it. → Launch offer: 2 months free, then a special price locked in👇

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Andrew D. Anderson

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35,649 görüntüleme • 1 ay önce •via X (Twitter)

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Subagents have arrived in Gemini CLI! 🤖🚀 Create your own custom subagents in Gemini CLI! Subagents are specialized, expert agents that the main agent can delegate work to. 📦- Subagents have their own set of tools, MCP servers, system instructions, and context window. 🏷️- Use Agent to explicitly delegate to a subagent 🧹- Keeps the main context window clean ⚡️- Speed up work by running agents in parallel Read more in the launch blog below 👇
0:33

Subagents have arrived in Gemini CLI! 🤖🚀 Create your own custom subagents in Gemini CLI! Subagents are specialized, expert agents that the main agent can delegate work to. 📦- Subagents have their own set of tools, MCP servers, system instructions, and context window. 🏷️- Use Agent to explicitly delegate to a subagent 🧹- Keeps the main context window clean ⚡️- Speed up work by running agents in parallel Read more in the launch blog below 👇

Jack Wotherspoon

221,941 görüntüleme • 3 ay önce

We’re excited to launch the Codex app, a command center for building with agents. It gives you a focused space to manage multiple agents at once, run work in parallel, and collaborate with agents over long-running tasks.
4:21

We’re excited to launch the Codex app, a command center for building with agents. It gives you a focused space to manage multiple agents at once, run work in parallel, and collaborate with agents over long-running tasks.

OpenAI Developers

1,305,011 görüntüleme • 6 ay önce

I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.
0:44

I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.

AI Breakfast

78,285 görüntüleme • 19 gün önce

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 ↓
38:54

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

60,826 görüntüleme • 16 gün önce

Have you tried forking chat sessions in VS Code? Create a new independent session that keeps the conversation history so you can explore alternative approaches without losing your original context. Watch the video to see how it works.
1:06

Have you tried forking chat sessions in VS Code? Create a new independent session that keeps the conversation history so you can explore alternative approaches without losing your original context. Watch the video to see how it works.

Visual Studio Code

28,488 görüntüleme • 4 ay önce

ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.
16:29

ANTHROPIC JUST QUIETLY SHIPPED A FEATURE THAT LETS CLAUDE SPAWN A WHOLE TEAM OF AGENTS THAT MESSAGE EACH OTHER AND REVIEW EACH OTHER'S WORK. It's a Claude Code feature called agent teams. The team lead spawns multiple agents that share a task list and message each other directly, not subagents reporting back, actual peers. In the demo a QA agent caught three bugs, sent the work back to the front-end and back-end devs, they fixed it, app shipped in one pass. How to run it: 1. Enable it. Needs Claude Code v2.1.32+. Add to settings.json: "env": { "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" }. Or paste that to Claude and say "add this to my settings." Restart. 2. Prompt in plain English. Start with a goal (agents wake with zero context), then "create a team of 3 using Sonnet," describe each role, its deliverable, and who it messages when done. 3. The rules: each agent owns its own files, define exact outputs, name who talks to who, keep it to 3-5 agents. Use it for complex work with separate parts running in parallel. Skip it for simple or sequential tasks, teams cost 3-4x the tokens. Bookmark this.

Yarchi

460,553 görüntüleme • 1 ay önce

Railway's new remote MCP (and the Railway Agent it accesses) are the best way for agents to work with everything in your Railway workspace. We released it last week and Cody De Arkland takes it and the new updates to the Railway CLI for a spin. Check it out 👇
4:17

Railway's new remote MCP (and the Railway Agent it accesses) are the best way for agents to work with everything in your Railway workspace. We released it last week and Cody De Arkland takes it and the new updates to the Railway CLI for a spin. Check it out 👇

Railway

10,774 görüntüleme • 3 ay önce

How to manage multiple AI agents at once: I run 10 to 15 parallel AI agent threads a day. Some days even more. Hermes, OpenClaw, Claude Code, Codex, all going at once. But the problem is more agents doesn't equal more output. Past a certain number of threads, you lose context. You forget what you told which agent. You duplicate work across two threads doing the same job. This is how we're solving for it:
2:02

How to manage multiple AI agents at once: I run 10 to 15 parallel AI agent threads a day. Some days even more. Hermes, OpenClaw, Claude Code, Codex, all going at once. But the problem is more agents doesn't equal more output. Past a certain number of threads, you lose context. You forget what you told which agent. You duplicate work across two threads doing the same job. This is how we're solving for it:

ericosiu

11,868 görüntüleme • 23 gün önce

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.
0:22

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,070 görüntüleme • 2 ay önce

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.
2:09

Interestingly, as we have AI agents that run in the background, the speed of AI becomes incrementally less important than the core underlying capability level. When you could only give AI small bits of work, then the speed of response mattered a ton. The rate at which you can go back and forth with AI in real-time was the determining factor on how useful it is. But as AI agents can perform more complex and useful tasks in parallel behind the scenes, you can much more easily afford to wait longer for work to get done assuming it’s valuable. Now the focus becomes more on how you review and orchestrate the agents’ work. And the main factor is just how useful and usable is the output that the agent came back with.

Aaron Levie

103,811 görüntüleme • 10 ay önce

Launching 'Expert Agents' on Agent Two. A specialised creative crew of AI agents, trained on real creative expertise. Name the role and it's filled: creative director, DOP, storyboard artist, and more. And they talk to each other. Independently. In parallel. You're no longer briefing each agent one by one: you're running a crew, exactly like on set. Expert agents. Now in conversation. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.
2:08

Launching 'Expert Agents' on Agent Two. A specialised creative crew of AI agents, trained on real creative expertise. Name the role and it's filled: creative director, DOP, storyboard artist, and more. And they talk to each other. Independently. In parallel. You're no longer briefing each agent one by one: you're running a crew, exactly like on set. Expert agents. Now in conversation. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Invideo

3,386,198 görüntüleme • 3 gün önce

Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses. trace every run → judge it with an LLM → diagnose → fix → ship. That loop is how agents self-improve over time. Agent loops + memory + harness + evals - thats the stack. Watch it, then save the framework below.
19:30

Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses. trace every run → judge it with an LLM → diagnose → fix → ship. That loop is how agents self-improve over time. Agent loops + memory + harness + evals - thats the stack. Watch it, then save the framework below.

Movez

560,455 görüntüleme • 1 ay önce

Hermes Agent is now free to run 24/7. No paid model needed. You can plug in: → Qwen 3.6 Plus → OwlAlpha via OpenRouter Both have 1M token context windows. That means your agent can research, write, find leads, handle tasks, and build workflows without burning through paid API credits. The best part? When one free model hits limits, just switch to the other. Free AI agents are getting scary fast.
8:29

Hermes Agent is now free to run 24/7. No paid model needed. You can plug in: → Qwen 3.6 Plus → OwlAlpha via OpenRouter Both have 1M token context windows. That means your agent can research, write, find leads, handle tasks, and build workflows without burning through paid API credits. The best part? When one free model hits limits, just switch to the other. Free AI agents are getting scary fast.

Julian Goldie SEO

21,609 görüntüleme • 2 ay önce

Introducing Dex: the self-driving workspace for operators. Dex is the first agent system with full operational context and a self-updating knowledge base. Every datapoint from your workspace is ingested, synced, and structured into compounding context for agents to take action. Comment "DEX" or tag ThirdLayer access. First 1,000 sign-ups get 7 days free. After, join our rolling waitlist. Sign up at joindex [dot] com for a fun surprise. How dex works (threads)
1:20

Introducing Dex: the self-driving workspace for operators. Dex is the first agent system with full operational context and a self-updating knowledge base. Every datapoint from your workspace is ingested, synced, and structured into compounding context for agents to take action. Comment "DEX" or tag ThirdLayer access. First 1,000 sign-ups get 7 days free. After, join our rolling waitlist. Sign up at joindex [dot] com for a fun surprise. How dex works (threads)

Regina Lin

70,415 görüntüleme • 3 ay önce

🧲 Meet Magnet — the AI workspace for agentic coding. Build software with Claude Code, Cursor, Codex & co. → Write a ticket → Magnet assembles context → Fires to Claude Code, Deep Research, and other agent sessions in parallel Git worktree sandboxes → PRs appear → Use agents to help you spec, research, and decompose tasks Let your agents cook!
2:12

🧲 Meet Magnet — the AI workspace for agentic coding. Build software with Claude Code, Cursor, Codex & co. → Write a ticket → Magnet assembles context → Fires to Claude Code, Deep Research, and other agent sessions in parallel Git worktree sandboxes → PRs appear → Use agents to help you spec, research, and decompose tasks Let your agents cook!

Nicolae Rusan

16,608 görüntüleme • 1 yıl önce

Hi, I'm RC. I built Kimi CLI at Moonshot last year, and back in 2015, bots that lived in group chats. For the past four months, I've been building Raft in public. Today I'm launching Raft 1.0. Right now, working with agents means juggling terminals, sessions, and skills. The more you run, the more you end up holding it all together yourself, and the easier it is to lose the thread. Raft puts your agents in team mode: one workspace where working with agents feels like messaging your team. The work keeps moving, and you stay at the wheel. Meet my Raft agent team👇
1:09

Hi, I'm RC. I built Kimi CLI at Moonshot last year, and back in 2015, bots that lived in group chats. For the past four months, I've been building Raft in public. Today I'm launching Raft 1.0. Right now, working with agents means juggling terminals, sessions, and skills. The more you run, the more you end up holding it all together yourself, and the easier it is to lose the thread. Raft puts your agents in team mode: one workspace where working with agents feels like messaging your team. The work keeps moving, and you stay at the wheel. Meet my Raft agent team👇

stdrc

855,148 görüntüleme • 18 gün önce

Introducing CoreWeave ARIA, the first AI research agent that runs autoresearch in your W&B dashboard. It reads your runs, finds what's working, and launches the next experiment itself. See it on Andrej Karpathy's nanochat, proposing configs and launching real training runs. Watch👇 Chapters 0:00 The setup, nanochat runs on an A100 0:32 Ask ARIA to run autoresearch 1:00 Spin up a second ARIA in parallel 1:38 ARIA inspects prior runs and forms hypotheses 2:03 What happened, what worked, across every run 2:24 ARIA proposes configs, 3 trials via W&B Launch 2:59 Push for bigger architecture changes 3:14 Runs hit the launch queue on the A100 4:09 Two ARIAs running autoresearch in parallel 4:26 Results back, ARIA evaluates the val loss
5:07

Introducing CoreWeave ARIA, the first AI research agent that runs autoresearch in your W&B dashboard. It reads your runs, finds what's working, and launches the next experiment itself. See it on Andrej Karpathy's nanochat, proposing configs and launching real training runs. Watch👇 Chapters 0:00 The setup, nanochat runs on an A100 0:32 Ask ARIA to run autoresearch 1:00 Spin up a second ARIA in parallel 1:38 ARIA inspects prior runs and forms hypotheses 2:03 What happened, what worked, across every run 2:24 ARIA proposes configs, 3 trials via W&B Launch 2:59 Push for bigger architecture changes 3:14 Runs hit the launch queue on the A100 4:09 Two ARIAs running autoresearch in parallel 4:26 Results back, ARIA evaluates the val loss

Weights & Biases

88,081 görüntüleme • 1 ay önce

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.
0:27

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.

joowon

24,708 görüntüleme • 1 ay önce

Introducing Agents & Models. It unlocks ridiculous creative power - allowing you to keep all generations in one place, collaborate in teams and seamlessly switch between different AI models. Free unlimited generations on all models for 4 days - unlocked with every paid plan started before 1 Dec. RT + Comment ‘STARBOY’ to win the highest generative plan on invideo + $1200 in credits.
0:50

Introducing Agents & Models. It unlocks ridiculous creative power - allowing you to keep all generations in one place, collaborate in teams and seamlessly switch between different AI models. Free unlimited generations on all models for 4 days - unlocked with every paid plan started before 1 Dec. RT + Comment ‘STARBOY’ to win the highest generative plan on invideo + $1200 in credits.

Invideo

650,408 görüntüleme • 8 ay önce

Granular code control or prompt-driven development? In Antigravity, you exist in both worlds. Dive into the Editor for deep work. Pull back to the Agent Manager to manage multiple agents across workspaces. Switch between them instantly. Watch the Manager and Editor in sync 👇
0:29

Granular code control or prompt-driven development? In Antigravity, you exist in both worlds. Dive into the Editor for deep work. Pull back to the Agent Manager to manage multiple agents across workspaces. Switch between them instantly. Watch the Manager and Editor in sync 👇

Google Antigravity

70,384 görüntüleme • 8 ay önce