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Codex Dynamic workflows are great. Try it out. 1. Go to GitHub link below 2. Copy install instruction 3. Type /dynamic to invoke the skill Recreates the same orchestration logic as Claude Dynamic Workflows. > Generates orchestration script. > Spins up a swarm of subagents. > Enters /goal mode... show more

Dan McAteer

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131,919 次观看 • 1 个月前 •via X (Twitter)

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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.

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.

Ado

152,944 次观看 • 1 个月前

You can now orchestrate Fable 5, Sol, and any model inside Codex with one plugin. It's called Codex-Orchestration. Assign Fable 5 as the advisor, Sol as the executor, or any model to any role. Then define the order they work in. Codex handles the routing. I ran Fable 5 High as planner with GPT-5.6 Sol Extra High as executor on a set of issues Opus and GPT-5.5 always struggled with. Done in 30 minutes. 40% fewer limit hits. 2x faster implementation. Install it by pasting this into Codex: "Install Codex Orchestration: codex plugin marketplace add Cjbuilds/Codex-Orchestration codex plugin add codex-orchestration@codex-orchestration Verify the installation, then tell me to start a new task." Then assign your models: @ codex-orchestration advisor: Claude Fable 5 High, Executor: GPT-5.6 Sol High Open source. Tweak the routing however you want.

You can now orchestrate Fable 5, Sol, and any model inside Codex with one plugin. It's called Codex-Orchestration. Assign Fable 5 as the advisor, Sol as the executor, or any model to any role. Then define the order they work in. Codex handles the routing. I ran Fable 5 High as planner with GPT-5.6 Sol Extra High as executor on a set of issues Opus and GPT-5.5 always struggled with. Done in 30 minutes. 40% fewer limit hits. 2x faster implementation. Install it by pasting this into Codex: "Install Codex Orchestration: codex plugin marketplace add Cjbuilds/Codex-Orchestration codex plugin add codex-orchestration@codex-orchestration Verify the installation, then tell me to start a new task." Then assign your models: @ codex-orchestration advisor: Claude Fable 5 High, Executor: GPT-5.6 Sol High Open source. Tweak the routing however you want.

Alvaro Cintas

89,595 次观看 • 9 天前

Sonnet 5 is here. It's worse than Opus 4.8 on nearly every benchmark... Does that mean it's useless? Absolutely not. Use it with Claude Code Dynamic Workflows! 1. /model set to Sonnet 5 2. /effort set to Ultracode 3. Any complex task will kick off a dynamic workflow This will only become more powerful when Fable is back. You'll use Fable 5 as the superintelligent advisor and Sonnet 5 as the fast and efficient implementer.

Sonnet 5 is here. It's worse than Opus 4.8 on nearly every benchmark... Does that mean it's useless? Absolutely not. Use it with Claude Code Dynamic Workflows! 1. /model set to Sonnet 5 2. /effort set to Ultracode 3. Any complex task will kick off a dynamic workflow This will only become more powerful when Fable is back. You'll use Fable 5 as the superintelligent advisor and Sonnet 5 as the fast and efficient implementer.

Dan McAteer

660,774 次观看 • 25 天前

Updated the subagents system in my custom Pi extension to include: - Pi subagents - Codex subagents - Claude Code subagents The Pi thread can call and spin up any of them, and instead of just using the "codex exec" or whatever, it'll do a tool call that wraps it up in a first class way. Have a decent UI for them already added in as well. Pi being the main thread then having Codex/Claude Code as tools when needed feels so good. A flow I'm already using a ton is: "Implement feature ___, have a cc fable subagent research and plan out the api design + feature, then have a codex subagent implement it, then have a cc fable subagent do a review/fixes focused on simplicity and correctness" I also have a dynamic workflows extension setup and working, but it's not nearly as polished yet. Probably gonna make the subagent system more robust, then built it around that so u could have a workflow that flows between codex/cc/pi...

Updated the subagents system in my custom Pi extension to include: - Pi subagents - Codex subagents - Claude Code subagents The Pi thread can call and spin up any of them, and instead of just using the "codex exec" or whatever, it'll do a tool call that wraps it up in a first class way. Have a decent UI for them already added in as well. Pi being the main thread then having Codex/Claude Code as tools when needed feels so good. A flow I'm already using a ton is: "Implement feature ___, have a cc fable subagent research and plan out the api design + feature, then have a codex subagent implement it, then have a cc fable subagent do a review/fixes focused on simplicity and correctness" I also have a dynamic workflows extension setup and working, but it's not nearly as polished yet. Probably gonna make the subagent system more robust, then built it around that so u could have a workflow that flows between codex/cc/pi...

Ben Davis

24,140 次观看 • 11 天前

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.

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.

eric zakariasson

57,554 次观看 • 11 个月前

How to actually get the most out of Codex instead of bookmarking another great guide you’ll never open again: We turned this article into an interactive workbench. Before prompting Codex, open the workbench, choose your task type, and check whether you’re using the right Codex capabilities. Built by Codex inside Open Design. If you want the workbench, reply “Open Design” below and I’ll send it to you.

How to actually get the most out of Codex instead of bookmarking another great guide you’ll never open again: We turned this article into an interactive workbench. Before prompting Codex, open the workbench, choose your task type, and check whether you’re using the right Codex capabilities. Built by Codex inside Open Design. If you want the workbench, reply “Open Design” below and I’ll send it to you.

Open Design

67,044 次观看 • 2 个月前

This is the one GitHub repo that everyone needs to save. The CEO of Obsidian literally open-sourced his entire Claude Skills vault. It's his personal Obsidian vault template, and it's built to 10x your AI productivity. To get started: 1. Download the vault in the GitHub link below 2. Unzip the .zip file (Claude can help here) 3. Open Obsidian & create a new vault pointing to that folder →

This is the one GitHub repo that everyone needs to save. The CEO of Obsidian literally open-sourced his entire Claude Skills vault. It's his personal Obsidian vault template, and it's built to 10x your AI productivity. To get started: 1. Download the vault in the GitHub link below 2. Unzip the .zip file (Claude can help here) 3. Open Obsidian & create a new vault pointing to that folder →

Miles Deutscher

126,853 次观看 • 6 天前

I built a plugin to generate 3D assets directly from Codex, using the latest update that introduces image generation. It generates an image, calls the Tripo3D API, then starts an MCP server and a local viewer directly inside Codex. It uses the paid Tripo3D API (12 free generations, 600 credits included). Everything is open source on my GitHub. Link below. Feel free to contribute. Room for improvement: skeleton/animation support and multi-provider 3D support.

I built a plugin to generate 3D assets directly from Codex, using the latest update that introduces image generation. It generates an image, calls the Tripo3D API, then starts an MCP server and a local viewer directly inside Codex. It uses the paid Tripo3D API (12 free generations, 600 credits included). Everything is open source on my GitHub. Link below. Feel free to contribute. Room for improvement: skeleton/animation support and multi-provider 3D support.

Defend Intelligence (Anis Ayari)

11,983 次观看 • 3 个月前

How To Buy $BUFFY (Simplified) 1. Install Tonkeeper wallet 2. Load it up with Ton ($TON) 3. Copy and paste the link below into your browser and connect 4. Enter the amount of $TON you would swap for $BUFFY

How To Buy $BUFFY (Simplified) 1. Install Tonkeeper wallet 2. Load it up with Ton ($TON) 3. Copy and paste the link below into your browser and connect 4. Enter the amount of $TON you would swap for $BUFFY

$BUFFY | Telegram Founder’s dog

45,798 次观看 • 2 年前

Claude Code Scheduled Tasks is now available... here's a solid idea to connect it with Telegram Save this so you don't forget to set it up! First, ask Claude to add a simple Telegram messaging module to your repo. You can use the Telegram Bot Builder Skill from Link: Install command: npx claude-code-templates@latest --skill enterprise-communication/telegram-bot-builder Once the module is in your project, grab your bot credentials from BotFather and add the bot ID to your .env file That's it! ✅ Now every Scheduled Task you create should end with an instruction for Claude to send the task result to Telegram using that module. Claude will handle the delivery automatically on every task it runs

Claude Code Scheduled Tasks is now available... here's a solid idea to connect it with Telegram Save this so you don't forget to set it up! First, ask Claude to add a simple Telegram messaging module to your repo. You can use the Telegram Bot Builder Skill from Link: Install command: npx claude-code-templates@latest --skill enterprise-communication/telegram-bot-builder Once the module is in your project, grab your bot credentials from BotFather and add the bot ID to your .env file That's it! ✅ Now every Scheduled Task you create should end with an instruction for Claude to send the task result to Telegram using that module. Claude will handle the delivery automatically on every task it runs

Daniel San

91,123 次观看 • 4 个月前

Opal, our no-code visual builder for AI workflows, just got a major upgrade. 🧠💎 We’ve added a new agent step that analyzes your goal, determines the best approach, and automatically calls the right tools — such as Veo for video or web search for research — to complete the task. We’re also adding new tools to make the agent even more capable: 💾 Memory – Remember info, like a user’s name or your style preferences across sessions. 🚀 Dynamic Routing – Let the agent choose the next best step using the “@ Go to” tool. 💬 Interactive Chat – Initiate user interactions to gather missing information or present options before moving on. Try it now →

Opal, our no-code visual builder for AI workflows, just got a major upgrade. 🧠💎 We’ve added a new agent step that analyzes your goal, determines the best approach, and automatically calls the right tools — such as Veo for video or web search for research — to complete the task. We’re also adding new tools to make the agent even more capable: 💾 Memory – Remember info, like a user’s name or your style preferences across sessions. 🚀 Dynamic Routing – Let the agent choose the next best step using the “@ Go to” tool. 💬 Interactive Chat – Initiate user interactions to gather missing information or present options before moving on. Try it now →

Google Labs

1,007,514 次观看 • 5 个月前

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.

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.

elvis

30,588 次观看 • 2 天前

We've launched a Collector Crypt skill for our agents Use AI to analyze over 79,000 listings, monitor collections, mass bid, and execute workflows using natural language This is an independent tool built for the Collector Crypt community, try it out in the link below 👇

We've launched a Collector Crypt skill for our agents Use AI to analyze over 79,000 listings, monitor collections, mass bid, and execute workflows using natural language This is an independent tool built for the Collector Crypt community, try it out in the link below 👇

Starchild

15,552 次观看 • 11 天前

Having 2 players stood over the ball at Wide free kicks are great for creating opportunities through dynamic advantage As we see Hellas Verona use the third man to disrupt the Roma defensive set up The covering defender expects an overlap but is drawn into a 3v2 situation

Having 2 players stood over the ball at Wide free kicks are great for creating opportunities through dynamic advantage As we see Hellas Verona use the third man to disrupt the Roma defensive set up The covering defender expects an overlap but is drawn into a 3v2 situation

HybridMarking

18,663 次观看 • 2 个月前

I built a custom TradingView indicator with Claude Code & Fable 5. It's called the Storm Gauge and is built off a real quant trading strategy. I open-sourced the full code on GitHub. Free to install, free to fork, yours to improve. Here's how to install a quant indicator on your TradingView chart: What it actually is The Storm Gauge is a live implementation of the GARCH model, a Nobel Prize-winning volatility framework that real quant desks run daily. It forecasts how "violent" tomorrow's market could be by combining three inputs: an asset's baseline volatility, yesterday's shock, and where volatility was already sitting before that shock happened. It doesn't predict market direction. Instead, it measures risk, in real time, on your actual chart. How to install it Method 1. Plugin command Open the GitHub repo: Find the installation section, copy the command, and paste it into Claude Code. It runs the plugin install automatically. Method 2. Manual config Open garchmethod.md in the repo, copy the entire file, and paste it into Claude Code. It fetches the skill files directly and verifies the strategy for you. (you only need one method; I'm just showing both) Getting it onto your TradingView chart Inside the repo, there's a Pine Script folder. Open it, copy the entire file. Go into TradingView's Pine Editor, paste it in, hit Enter, and refresh. That's it. The Storm Gauge now runs live on your chart as a real number. Once it's installed, just talk to it: → "What's the volatility forecast on Bitcoin?" → "Explain what the current volatility forecast means on $BTC and how it should impact my position sizing" → "Help me size my S&P500 position according to current market volatility" Does it actually work? I backtested the same EMA cross strategy two ways across 15 years of BTC data. Same entries, same exits. → Fixed position sizing: $17,957 final equity → Storm Gauge (GARCH) sizing: $21,205 final equity Fewer drawdowns, less risk, better result. Full breakdown of the entire build process in my recent article - pinned on my profile.

Miles Deutscher

52,931 次观看 • 3 天前

Anthropic dropped 33 pages for Claude trading bots Last night I decided to try writing one and it worked out for me In 10 hours this script made me $561 The bot has a win rate of about 71% Wallet: Copytrade: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions, scripts, and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three-layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed, reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated, the skill executes a predefined multi-step process These workflows can include data analysis, document generation, automation scripts, or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions, Claude performs tasks using consistent methodology rather than ad-hoc prompting Testing and iteration Skills are continuously refined through triggering tests, functional validation, and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time, the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity, improves output consistency, and scales productivity across thousands of tasks

Anthropic dropped 33 pages for Claude trading bots Last night I decided to try writing one and it worked out for me In 10 hours this script made me $561 The bot has a win rate of about 71% Wallet: Copytrade: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions, scripts, and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three-layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed, reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated, the skill executes a predefined multi-step process These workflows can include data analysis, document generation, automation scripts, or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions, Claude performs tasks using consistent methodology rather than ad-hoc prompting Testing and iteration Skills are continuously refined through triggering tests, functional validation, and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time, the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity, improves output consistency, and scales productivity across thousands of tasks

winkle.

334,570 次观看 • 4 个月前

Claude Code Desktop now opens a new window for each session This makes it much easier to visualize multiple Claude Code agents running in parallel My current stack depends on the task: - Ghostty: when starting a project. Bash commands, git, env variables, provider connections. All manual through the terminal with a Claude panel running alongside. - Claude Code Desktop: once everything is configured. GitHub connected, CLAUDE.md, Skills, subagents and Hooks ready. Claude Code runs on its own, no more terminal setup, just panels running and outputs to review. - VSCode: when I need to review code by hand. I use it less and less, but there are moments where I have to confirm Claude got it right. I usually open the Claude extension inside VSCode, but it lacks most of the CLI features so it's limited Solid update. Worth trying once your workflows are already set up 👇

Claude Code Desktop now opens a new window for each session This makes it much easier to visualize multiple Claude Code agents running in parallel My current stack depends on the task: - Ghostty: when starting a project. Bash commands, git, env variables, provider connections. All manual through the terminal with a Claude panel running alongside. - Claude Code Desktop: once everything is configured. GitHub connected, CLAUDE.md, Skills, subagents and Hooks ready. Claude Code runs on its own, no more terminal setup, just panels running and outputs to review. - VSCode: when I need to review code by hand. I use it less and less, but there are moments where I have to confirm Claude got it right. I usually open the Claude extension inside VSCode, but it lacks most of the CLI features so it's limited Solid update. Worth trying once your workflows are already set up 👇

Daniel San

38,726 次观看 • 2 个月前

SOMEONE TURNED THE IPHONE DYNAMIC ISLAND INTO A WORKING POLAROID CAMERA the whole thing lives in your dynamic island. > drag the island down to open the camera > tap to snap, then it morphs and ejects the photo out of the slot > the pic slowly develops like a real polaroid, and you shake your phone to reveal it faster > every sound and interaction is mapped to haptics, so you feel each step > fully native swift, and he spent a month keeping the whole app under 5mb its called pico cam and its pure skeuomorphism done right absolutely insane what you can do with codex now

SOMEONE TURNED THE IPHONE DYNAMIC ISLAND INTO A WORKING POLAROID CAMERA the whole thing lives in your dynamic island. > drag the island down to open the camera > tap to snap, then it morphs and ejects the photo out of the slot > the pic slowly develops like a real polaroid, and you shake your phone to reveal it faster > every sound and interaction is mapped to haptics, so you feel each step > fully native swift, and he spent a month keeping the whole app under 5mb its called pico cam and its pure skeuomorphism done right absolutely insane what you can do with codex now

Om Patel

394,592 次观看 • 1 个月前

With the release of Fable 5, I thought it would be fun to build and open-source a new MacOS screensaver using a metal shader, that way could also try building your own! Totally free, link to the repo below

With the release of Fable 5, I thought it would be fun to build and open-source a new MacOS screensaver using a metal shader, that way could also try building your own! Totally free, link to the repo below

Daniel Destefanis

54,180 次观看 • 20 天前

I’ve built a background daemon that connects to Linear for one of my enterprise clients (multi-billion company). It runs autonomous AI workflows: – Creates PRDs from tickets – Waits for human review – Implements, verifies, and opens PRs automatically Built with LangGraph for orchestration, Codex + Claude for deep code understanding, direct LLM's API calls (via OpenRouter) for more deterministic tasks. It’s pretty cool watching the whole flow execute live in the console ⚡️

I’ve built a background daemon that connects to Linear for one of my enterprise clients (multi-billion company). It runs autonomous AI workflows: – Creates PRDs from tickets – Waits for human review – Implements, verifies, and opens PRs automatically Built with LangGraph for orchestration, Codex + Claude for deep code understanding, direct LLM's API calls (via OpenRouter) for more deterministic tasks. It’s pretty cool watching the whole flow execute live in the console ⚡️

Krzysztof Zabłocki

17,019 次观看 • 9 个月前