Tasks can now spawn async subagents that move to... the background and continue working independently, even if the main agent finishes its task and becomes inactive. This lets subagents handle long-running tasks, great for monitoring logs or waiting for builds.show more

Claude
349,404 görüntüleme • 7 ay önce
OpenCode can already spawn background subagents, but I just... opened a PR letting you send synchronous tasks to the background. But more interestingly, this video was recorded + edited by 𝚘𝚙𝚎𝚗𝚌𝚘𝚍𝚎 running 𝚘𝚙𝚎𝚗𝚌𝚘𝚍𝚎 with 𝚝𝚎𝚛𝚖𝚒𝚗𝚊𝚕-𝚌𝚘𝚗𝚝𝚛𝚘𝚕.show more

Kit Langton
39,931 görüntüleme • 2 ay önce
subagents are just recursive agents where you can apply... different prompts + models depending on the task. since they’re just a primitive, Cursor cli can actually spawn subagents by calling cursor-agent in headless mode via shell commands. that’s what makes the cli so nice. you can extend it, experiment, and have a lot of fun exploring orchestration patterns. here’s one way to do it w. dynamic model selection: 1. create a subagents.mdc rule 2. drop in: ``` --- alwaysApply: true --- ALWAYS spawn subagents by running `cursor-agent -p [task] --output-format=text --force --model [model]` in the terminal. Each subagent should return a summary of the changes it made. Subagents should be used for ALL tasks You can adopt a fan-out pattern where you spawn subagents to perform parallel isolated tasks, and then fan-in the results. Use the following models: - `--model gpt-5` for reasoning, researching, and planning - `--model sonnet-4` for implementation ``` 3. start cursor cli and try it out you can also adjust the rule to be more explicit when it should use subagents, when not to, which models when etc.show more

eric zakariasson
57,554 görüntüleme • 11 ay önce
You don’t have to use one model (or one... provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.show more

Workshop AI
904,641 görüntüleme • 3 ay önce
Thrilled to announce the Monitor tool which lets Claude... create background scripts that wake the agent up when needed. Big token saver and great way to move away from polling in the agent loop Claude can now: * Follow logs for errors * Poll PRs via script * and more!show more

Noah Zweben
1,188,546 görüntüleme • 3 ay önce
Bug fixes shipping to Grok Build 0.2.13 (release notes... will be available in the TUI and on change-log website) We are leveraging the alt-screen to better handle your background tasks, subagents, monitors with smart grouping allowing you to navigate between them quickly • Group Subagents → Tasks → Watchers, within subagents, order by agent type (Explore, General, Plan) • Show timestamps on pinned user messages at top of scrollback • Fix command highlighting when prompt has paste chips • Update the context-usage indicator to show tokens by default • ANSI16 fallback for themes • Better context usage breakdown/rendering • Update highlighting for /loop, monitor, tag colors • Tab now cycles Prompt → Scrollback → Tasks → Prompt • Order gateway turn-completion after streamed content • Formatting: fix language-tagged fenced code blocks and fix code under a list itemshow more

skcd
15,075 görüntüleme • 2 ay önce
Claude Code Weekly Roundup Last week we added support... for Azure, let you use "&" in the CLI to send tasks to the background, added image upload to Claude Code Web, improved Claude Code's ability to look up its docs and we're continuing to make hooks and subagents more powerful.show more

Thariq
70,840 görüntüleme • 8 ay önce
Working on weapon crew and QOL upgrades: • Units... will now target the biggest threat they can handle • HMGs and similar can swivel independently (not tied to main gun) • Crews auto-select the most effective ammo for the target (e.g. canister for infantry)show more

aeoridev | ARMAPHRACT
44,953 görüntüleme • 3 ay önce
Julio GUC stays on top of everything with a... task list that adapts to the way he works. His system lets you organize tasks by priority, deadlines, and more—great for everything from quick to-dos to big projects →show more

Notion
13,648 görüntüleme • 1 yıl önce
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.show more

Miles Deutscher
81,911 görüntüleme • 17 gün önce
Introducing Bubble Up You can now submit Bubble Up... tasks to probe the limits of current AI capabilities for your own tasks. Select unsatisfied results, click “Optimize Bubble”, and submit bubble up tasks — even without vibe coding anything yourself.show more

DAPPOS
21,660 görüntüleme • 3 ay önce
Opus 4.6 lets you set the effort level Not... every task needs high reasoning. For simpler tasks you can set it to low or medium. Claude thinks less (or skips it entirely), makes fewer tool calls, and gives more concise answers Lower effort = faster responses with fewer tokens!show more

Lydia Hallie ✨
34,127 görüntüleme • 5 ay önce
Opus 4.8 is awesome but that's expected. The unsung... hero of this release for me is dynamic workflows. Claude plans your task, fans it out to tens or hundreds of parallel subagents, verifies their work, and iterates until the results converge into one coordinated answer.show more

Ado
152,944 görüntüleme • 2 ay önce
Users state the goal: build a site that tracks... Iran-related Polymarket markets and breaking movements. Bubble Code routes it to a Polymarket Agent SOP, connects live market data, sets monitoring keywords, and builds Iran Pulse: a working dashboard for probabilities, volume, liquidity, abnormal changes, and breaking signals. This is not a normal AI demo or static page. It is a ready-to-use market-monitoring product that can refresh, run, deploy, and be used directly.show more

DAPPOS
140,768 görüntüleme • 1 ay önce
Introducing Novo Launching today a new project I coded... for myself in 1 weekend in May and decided to finish this week. Novo is a dead simple to-do app that lets you "Speech-To-Tasks", or paste a huge text and organize for you. You can customize the AI and make it organize in any criteria: - Auto-tag by category - Schedule some types of tasks to certain days - Prioritize based on your own rules Try it:show more

Pedro
76,232 görüntüleme • 1 yıl önce
Boom! Grok Tasks Make It One Of The Most... POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3show more

Brian Roemmele
152,242 görüntüleme • 6 ay önce
You can now delegate tasks to GitHub Copilot coding... agent from any page on GitHub 🤖 Open the new Agents panel in one click, write a simple prompt, then hit Enter. GitHub Copilot works in the background, and opens a PR for your review. No interruptions to your workflow required. ✅show more

GitHub
113,332 görüntüleme • 11 ay önce
Alright, now that we know *what* an agent is,... how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.show more

Google AI
22,487 görüntüleme • 8 ay 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.show more

elvis
31,939 görüntüleme • 10 gün önce
Robotics keeps hitting the same wall. Single task RL... works, but... it does not scale to hundreds of tasks or new embodiments. This new paper looks like a real step toward fixing that. The team introduces MMBench, a benchmark with 200 tasks across many domains and robots, and Newt, a language conditioned world model trained online across all 200 tasks at once. The simple idea behind Newt: The model learns from demos to get the right priors It trains across many tasks through online interaction It uses language to ground the goal It adapts fast when a new task shows up What stood out to me: ✅ One model trained on 200 tasks at the same time ✅ Language conditioned control for both states and RGB ✅ Better data efficiency than strong baselines ✅ Strong open loop control ✅ Fast adaptation to new tasks and embodiments ✅ Full release of 200 checkpoints, 4000 demos, code, and benchmark This is a good push toward general control instead of one model per task. If you want the full paper: Project page: —- Weekly robotics and AI insights. Subscribe free:show more

Ilir Aliu
70,090 görüntüleme • 8 ay önce