正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Connect Emdash to your own infrastructure and run coding agents on it. Each task provisions its own isolated workspace, streams progress in real-time, and tears down when you're done.

Arne Strickmann

1,370 subscribers

13,303 次观看 • 5 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Collective agent compute, on-demand for your task. A swarm of agents coordinate in a shared workspace, run their own models and inference, and submit the finished work back to you. Unbounded by provider caps, throughput scales with the swarm. Self-assembly with specialists, agents reason and collaborate in artifact-first reasoning traces, and settle on-chain. The coordination substrate for collective intelligence.

Collective agent compute, on-demand for your task. A swarm of agents coordinate in a shared workspace, run their own models and inference, and submit the finished work back to you. Unbounded by provider caps, throughput scales with the swarm. Self-assembly with specialists, agents reason and collaborate in artifact-first reasoning traces, and settle on-chain. The coordination substrate for collective intelligence.

nookplot

11,611 次观看 • 3 个月前

AgentCommand: a dashboard for when your AI agents are running AI agents. Watch 1000+ agents spin up and down, see them talk to each other, and track the revenue, deploys, and code diffs happening in real-time.

AgentCommand: a dashboard for when your AI agents are running AI agents. Watch 1000+ agents spin up and down, see them talk to each other, and track the revenue, deploys, and code diffs happening in real-time.

Matt Schlicht

197,832 次观看 • 8 个月前

Making progress on an open-source library where you author agent logic as state machines in XState and run it *anywhere*: Mastra, Eve, Flue, Vercel AI SDK, Cloudflare Agents, or your own runtime. It’s coming along!

Making progress on an open-source library where you author agent logic as state machines in XState and run it anywhere: Mastra, Eve, Flue, Vercel AI SDK, Cloudflare Agents, or your own runtime. It’s coming along!

David K 🎹

30,071 次观看 • 1 个月前

OpenRouter now runs inside Render Workflows. Stop putting LLM batches in your web service. Each prompt becomes its own task run, queued, retried, and fanned out by Render, with the model call routed by OpenRouter.

OpenRouter now runs inside Render Workflows. Stop putting LLM batches in your web service. Each prompt becomes its own task run, queued, retried, and fanned out by Render, with the model call routed by OpenRouter.

OpenRouter

182,938 次观看 • 14 天前

we just released a new blog "Training a coding agent using the OpenCode harness in remote HF sandboxes with TRL and OpenEnv" you can take a real coding agent (OpenCode), let it run its own tool loop against real coding problems, and train it with RL on the exact tokens it produced and every rollout runs in its own remote HF sandbox, so rollouts scale out beyond one machine the loop: - OpenCode owns its tool loop inside an OpenEnv sandbox - an in-sandbox proxy records the real token ids + logprobs, per turn - a hidden-test verifier scores the result, and that is the reward - TRL trains with AsyncGRPO, weights sync back to vLLM over NCCL blog + runnable example:

we just released a new blog "Training a coding agent using the OpenCode harness in remote HF sandboxes with TRL and OpenEnv" you can take a real coding agent (OpenCode), let it run its own tool loop against real coding problems, and train it with RL on the exact tokens it produced and every rollout runs in its own remote HF sandbox, so rollouts scale out beyond one machine the loop: - OpenCode owns its tool loop inside an OpenEnv sandbox - an in-sandbox proxy records the real token ids + logprobs, per turn - a hidden-test verifier scores the result, and that is the reward - TRL trains with AsyncGRPO, weights sync back to vLLM over NCCL blog + runnable example:

Sergio Paniego

37,936 次观看 • 1 个月前

Meet Muse Spark 1.2 and Muse Code, the agent built to run it against your coding challenges. Now in beta. Together they take a task from idea to production, and deploy teams of agents that plan, write and refine across your repos. Start building now:

Meet Muse Spark 1.2 and Muse Code, the agent built to run it against your coding challenges. Now in beta. Together they take a task from idea to production, and deploy teams of agents that plan, write and refine across your repos. Start building now:

Meta for Developers

155,499 次观看 • 1 个月前

You can now share your Notion Workers. Build and iterate on Workers with your team. Or share one so teammates can connect it to their own Custom Agents. Free to try while Workers is in beta.

You can now share your Notion Workers. Build and iterate on Workers with your team. Or share one so teammates can connect it to their own Custom Agents. Free to try while Workers is in beta.

Notion Developers

40,786 次观看 • 2 个月前

Hermes Agent supports multisession tabs and a flexible layout system. Tabs w/in panes, or put anything in your own pane. Hermes can also generate its own UI elements w/in your workspace if you ask it. More on that later. Nous Research Teknium 🪽

Hermes Agent supports multisession tabs and a flexible layout system. Tabs w/in panes, or put anything in your own pane. Hermes can also generate its own UI elements w/in your workspace if you ask it. More on that later. Nous Research Teknium 🪽

Brooklyn!

233,046 次观看 • 2 个月前

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

🚨 JUST IN: CHINA just released an AI EMPLOYEE that works 24X7 on its own. 100% OPEN SOURCE. It researches, codes, builds websites, creates slide decks, and generates videos. All by itself. All on your computer. It's called DeerFlow. You give it a task. It makes a plan, spins up its own team of sub-agents, and gets to work. You come back and there's a finished deliverable waiting. Not a draft. Not a summary. The actual thing. Not a chatbot. Not a research assistant. An AI with its own computer that works while you sleep. Here's what it does on its own: → Spawns multiple sub-agents in parallel, each tackling a different piece of your task, then combines everything into one finished output → Writes real code, runs it, reads the results, and fixes its own mistakes without asking you once → Builds slide decks, websites, full research reports, and data dashboards from scratch → Remembers you across sessions. Your writing style. Your tech stack. Your preferences. Gets better every time. → Reads files you upload, works with them inside its own filesystem, hands you clean finished outputs → Searches the web, runs commands, calls any tool you plug in Here's how it thinks: You give one instruction. The lead agent makes a plan. Sub-agents fan out and work in parallel. Results come back. Everything gets synthesized. You get a deliverable. A single research task might split into a dozen sub-agents, each exploring a different angle, then converge into one finished website with generated visuals. Here's the wildest part: DeerFlow 2.0 launched on February 28th 2026 and hit number 1 on all of GitHub Trending the same day. Version 2.0 was a complete rewrite. Zero shared code with version 1. Because users kept using it for things the team never intended. Data pipelines. Dashboards. Entire content workflows. The community told them what it needed to become. So they burned it down and rebuilt it. 22.7K GitHub stars. 2.7K forks. Built by ByteDance 100% Open Source. MIT License.

Kanika

738,832 次观看 • 5 个月前

your coding agents are Kanban cards now 😯. New in Orca: open a board over any terminal pane and drag each agent worktree between statuses. todo, in progress, review, testing, blocked, done, or whatever custom columns fit your workflow. much easier when you have 10+ agent running across different features.

your coding agents are Kanban cards now 😯. New in Orca: open a board over any terminal pane and drag each agent worktree between statuses. todo, in progress, review, testing, blocked, done, or whatever custom columns fit your workflow. much easier when you have 10+ agent running across different features.

Orca ADE

47,609 次观看 • 3 个月前

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

shmidt

19,505 次观看 • 2 个月前

grokbot it's an agent with its own identity, its own computer, and it stays on when you're not here's what "active AI employee" actually looks like in the demo: - chief of staff agent - checks in on your other agents, reads your calendar, dispatches tasks to the right one automatically - shopping agent - logged into your accounts, books tickets, buys groceries, reports back - marketing agent - signed into your actual linkedin, browses your past posts for tone, then writes and publishes a new one on its own - engineering agents - self-triage bug reports, kick off cloud coding agents, come back with a pull request, a screenshot, and a video of the fix the interface isn't a dashboard, it's a chat - same shape as texting a coworker, no tool calls to babysit the number that matters more than any of the demos: grok 4.6 scored 70.8% on cursor bench at $2.81 a task, fable 5 max scored 70.5% at $17.32 same capability, 6x the cost difference - that's the unlock that makes running a fleet of these actually affordable instead of a novelty

grokbot it's an agent with its own identity, its own computer, and it stays on when you're not here's what "active AI employee" actually looks like in the demo: - chief of staff agent - checks in on your other agents, reads your calendar, dispatches tasks to the right one automatically - shopping agent - logged into your accounts, books tickets, buys groceries, reports back - marketing agent - signed into your actual linkedin, browses your past posts for tone, then writes and publishes a new one on its own - engineering agents - self-triage bug reports, kick off cloud coding agents, come back with a pull request, a screenshot, and a video of the fix the interface isn't a dashboard, it's a chat - same shape as texting a coworker, no tool calls to babysit the number that matters more than any of the demos: grok 4.6 scored 70.8% on cursor bench at $2.81 a task, fable 5 max scored 70.5% at $17.32 same capability, 6x the cost difference - that's the unlock that makes running a fleet of these actually affordable instead of a novelty

rewind

748,022 次观看 • 26 天前

I still think Hermes agent is the most slept-on AI tool of 2026. For literally $6/mo, you can launch multiple subagents that work for you 24/7. Most people don't know you can do this, but it's a complete game-changer. Instead of one Hermes assistant doing everything sequentially, you run specialized agents in parallel, each with its own job, its own context, and its own memory. Practical example: → Research agent: scans your watchlist and competitors overnight, delivers a morning brief → Content agent: drafts and schedules your posts based on what's trending in your niche → Ops agent: manages your inbox, flags anything urgent, drafts replies for your review All three can run simultaneously and improve over time. How to start: 1. Install Hermes Terminal command: curl -fsSL | bash (can also download desktop) 2. Prompting Simply tell Hermes directly: "I want to run separate subagents for [task 1], [task 2], and [task 3]. Set them up to run independently and report back to me." For the cheapest setup, you can use a $4/month VPS with Hostinger, plug in DeepSeek V4 Flash as your default model. There isn't another AI tool with this much value in 2026. Hermes is still so underrated.

I still think Hermes agent is the most slept-on AI tool of 2026. For literally $6/mo, you can launch multiple subagents that work for you 24/7. Most people don't know you can do this, but it's a complete game-changer. Instead of one Hermes assistant doing everything sequentially, you run specialized agents in parallel, each with its own job, its own context, and its own memory. Practical example: → Research agent: scans your watchlist and competitors overnight, delivers a morning brief → Content agent: drafts and schedules your posts based on what's trending in your niche → Ops agent: manages your inbox, flags anything urgent, drafts replies for your review All three can run simultaneously and improve over time. How to start: 1. Install Hermes Terminal command: curl -fsSL | bash (can also download desktop) 2. Prompting Simply tell Hermes directly: "I want to run separate subagents for [task 1], [task 2], and [task 3]. Set them up to run independently and report back to me." For the cheapest setup, you can use a $4/month VPS with Hostinger, plug in DeepSeek V4 Flash as your default model. There isn't another AI tool with this much value in 2026. Hermes is still so underrated.

Miles Deutscher

82,643 次观看 • 2 个月前

Cofounder puts your agents on an org chart, enabling massive parallel work. And each agent gets a specialized workspace depending on its function. On the highest level, Cofounder manages it all - kicking off tasks, monitoring work, and executing long term strategy.

Cofounder puts your agents on an org chart, enabling massive parallel work. And each agent gets a specialized workspace depending on its function. On the highest level, Cofounder manages it all - kicking off tasks, monitoring work, and executing long term strategy.

andrew pignanelli

47,720 次观看 • 4 个月前

This makes any agent a full employee: 1. e.g. creates videos with seedance 2 2. uploads the videos on Tiktok every 4h 3. analyses best hooks and repeats. - It can modify its own harness. Therefore no task is impossible. - Shared domain specific skills - agents learn from each other to be faster next time.

This makes any agent a full employee: 1. e.g. creates videos with seedance 2 2. uploads the videos on Tiktok every 4h 3. analyses best hooks and repeats. - It can modify its own harness. Therefore no task is impossible. - Shared domain specific skills - agents learn from each other to be faster next time.

Magnus Müller

94,551 次观看 • 5 个月前

Running more than one team on Telegram? Create up to 10 separate organizations. Each one its own structure, its own view, its own members. Takes less than a minute and is visible to your whole team the second you set it up. Get the full app for free with beta access. Waitlist in first reply.

Running more than one team on Telegram? Create up to 10 separate organizations. Each one its own structure, its own view, its own members. Takes less than a minute and is visible to your whole team the second you set it up. Get the full app for free with beta access. Waitlist in first reply.

Amua

90,915 次观看 • 3 个月前

Your dApp deserves infrastructure that rewards you for building. Scale seamlessly, earn real rewards, and connect to a thriving ecosystem. If you're building something interesting, we want to hear about it 🤝

Your dApp deserves infrastructure that rewards you for building. Scale seamlessly, earn real rewards, and connect to a thriving ecosystem. If you're building something interesting, we want to hear about it 🤝

Incentiv

36,270 次观看 • 6 个月前

Talk to Santa in real time, explore AI-generated Christmas songs, and create your own video greeting. The Santa voice agent is built with our Agents Platform and Scribe v2. It handles natural dialogue with low latency and maintains character throughout the conversation.

Talk to Santa in real time, explore AI-generated Christmas songs, and create your own video greeting. The Santa voice agent is built with our Agents Platform and Scribe v2. It handles natural dialogue with low latency and maintains character throughout the conversation.

ElevenLabs

29,961 次观看 • 9 个月前