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Helmor has been out as an open-source coding agent orchestrator for less than a week, and we’re already close to 1,000 GitHub stars!!! As a little gift, we shipped a new feature you’re going to love. 👇 Stop copy-pasting GitHub links, Linear tickets, Slack threads, and random notes into...

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Caspian 東澔

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10,805 görüntüleme • 2 ay önce •via X (Twitter)

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Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---
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Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

Shushant Lakhyani

20,423 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.
0:15

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.

Google AI

22,487 görüntüleme • 7 ay önce

SOMEONE BUILT AN OPEN-SOURCE JARVIS WITH 9 AGENTS AND 5 MEMORY BACKENDS AND YOUR DATA NEVER LEAVES YOUR DEVICE Every time you message ChatGPT or Claude your data hits a server you don't control, gets processed by infrastructure you're paying for and comes back with zero guarantee of what happened in between. OpenJarvis runs the entire stack locally - 9 agent types, 5 memory backends, a learning loop that gets smarter every day and a morning digest that connects to Google Drive and surfaces what matters before you open a single app. Most AI tools are exactly as dumb on day 100 as they were on day 1 because they forget everything when the window closes - this one indexes your documents once and automatically injects relevant context into every prompt forever. Custom agent setup for a client is $500-2,000 one time and AI infrastructure retainer is $300-800 a month - and your cost is one afternoon and an open source repo. The repo is free. The advantage it creates is not.
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SOMEONE BUILT AN OPEN-SOURCE JARVIS WITH 9 AGENTS AND 5 MEMORY BACKENDS AND YOUR DATA NEVER LEAVES YOUR DEVICE Every time you message ChatGPT or Claude your data hits a server you don't control, gets processed by infrastructure you're paying for and comes back with zero guarantee of what happened in between. OpenJarvis runs the entire stack locally - 9 agent types, 5 memory backends, a learning loop that gets smarter every day and a morning digest that connects to Google Drive and surfaces what matters before you open a single app. Most AI tools are exactly as dumb on day 100 as they were on day 1 because they forget everything when the window closes - this one indexes your documents once and automatically injects relevant context into every prompt forever. Custom agent setup for a client is $500-2,000 one time and AI infrastructure retainer is $300-800 a month - and your cost is one afternoon and an open source repo. The repo is free. The advantage it creates is not.

Cortex

11,374 görüntüleme • 1 ay önce

OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.
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OpenClaw, but built for normal people. Sim is an open-source platform that lets you build AI agent workflows on a drag-and-drop canvas. Connect them to channels like Telegram and WhatsApp and deploy without writing a single line of code. They also have a built-in Copilot that generates entire workflows from plain English, which you can then tweak and customize in the UI. Key features: - Free and open-source (Apache 2.0) - Vector store integration for RAG-grounded agents - Self-host with one command (`npx simstudio`) - Run fully local with Ollama, no API keys needed - Supports vLLM for production-grade self-hosted inference The thing I really like about Sim is the level of control you get. You can add conditional branching, parallel execution, human-in-the-loop approval gates, and even nest workflows inside other workflows. Everything is visible on the canvas, so you know exactly what your agent is doing at every step. And you can build a workflow in Sim, deploy it as an MCP server, and plug it into any agent, including OpenClaw. I've shared the link to Sim's GitHub repo in the next tweet.

Akshay 🚀

52,426 görüntüleme • 4 ay önce

THIS GUY TURNS NOTES, DOCUMENTS, AND IDEAS INTO AN AI SECOND BRAIN the system stores all the context of your work and helps claude not forget anything how to build one yourself: install obsidian connect claude code keep your projects, notes, and sessions in one vault link similar ideas together use the graph to discover new connections as a result, claude gets memory of your projects and understands the context without you constantly having to explain everything the more you work, the smarter the system gets don’t collect notes build connections between them instead
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THIS GUY TURNS NOTES, DOCUMENTS, AND IDEAS INTO AN AI SECOND BRAIN the system stores all the context of your work and helps claude not forget anything how to build one yourself: install obsidian connect claude code keep your projects, notes, and sessions in one vault link similar ideas together use the graph to discover new connections as a result, claude gets memory of your projects and understands the context without you constantly having to explain everything the more you work, the smarter the system gets don’t collect notes build connections between them instead

Marvin

24,954 görüntüleme • 1 ay önce

SOMEONE TURNED THEIR TEAM'S TASK TRACKER INTO A 3D ISLAND instead of a boring list of tasks, your teams work is a little island that grows as you get stuff done > you assign tasks right in slack, just type who its for, the points, and the due date > finish a task and you get to place a building on the island > get your work rejected and the building collapses into rubble > the rubble stays there forever, so everyone can see it > each new sprint starts a fresh island so over time the island fills up with buildings for all the work your team actually finished, and the rubble is a reminder of what got rejected. its open source, so any team can set it up. way more fun than staring at a to do list all day
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SOMEONE TURNED THEIR TEAM'S TASK TRACKER INTO A 3D ISLAND instead of a boring list of tasks, your teams work is a little island that grows as you get stuff done > you assign tasks right in slack, just type who its for, the points, and the due date > finish a task and you get to place a building on the island > get your work rejected and the building collapses into rubble > the rubble stays there forever, so everyone can see it > each new sprint starts a fresh island so over time the island fills up with buildings for all the work your team actually finished, and the rubble is a reminder of what got rejected. its open source, so any team can set it up. way more fun than staring at a to do list all day

Om Patel

12,496 görüntüleme • 3 gün önce

warp code feels like a combination of a cli agent and cursor-style ux design it's a cli that looks like an ide because it gives you: - editor code view - project explorer - one-click to view command output - switch between agent/cli - context/credit spend tracking - task lists - shared context with warp drive there is a learning curve because it's a different workflow, but the agent was top of terminal bench until recently and i can see why would love to see them add: - subagents - an agent sdk - sidebar fonts increasing with cmd +/- not being paid to post this, btw (feel like i have to add that these days 😉) i have been using warp for a long while as a terminal and sometimes agent on the $15/mo plan
0:18

warp code feels like a combination of a cli agent and cursor-style ux design it's a cli that looks like an ide because it gives you: - editor code view - project explorer - one-click to view command output - switch between agent/cli - context/credit spend tracking - task lists - shared context with warp drive there is a learning curve because it's a different workflow, but the agent was top of terminal bench until recently and i can see why would love to see them add: - subagents - an agent sdk - sidebar fonts increasing with cmd +/- not being paid to post this, btw (feel like i have to add that these days 😉) i have been using warp for a long while as a terminal and sometimes agent on the $15/mo plan

Ian Nuttall

32,665 görüntüleme • 9 ay önce

Claude Mobile starts the idea... Ghostty finishes it I basically use Claude Mobile as a notepad now. Whenever something comes to mind, I open the app, pick a repo, and ask Claude to start exploring. Then I just let it run while I go on with my day. Later I jump into Claude Desktop and I’m right back in the same session, with context, structure, and a clearer shape of the idea. I tweak a few things to set up the coding phase. Finally, one click to Ghostty (video), open a couple of worktrees, and start working on the PR. IMPORTANT: when you move to Ghostty, it must be the exact same repo you started on mobile, otherwise the --teleport command will fail
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Claude Mobile starts the idea... Ghostty finishes it I basically use Claude Mobile as a notepad now. Whenever something comes to mind, I open the app, pick a repo, and ask Claude to start exploring. Then I just let it run while I go on with my day. Later I jump into Claude Desktop and I’m right back in the same session, with context, structure, and a clearer shape of the idea. I tweak a few things to set up the coding phase. Finally, one click to Ghostty (video), open a couple of worktrees, and start working on the PR. IMPORTANT: when you move to Ghostty, it must be the exact same repo you started on mobile, otherwise the --teleport command will fail

Daniel San

67,891 görüntüleme • 5 ay önce

Today we’re introducing Copilot Mode in Edge, our first step in reinventing the browser for the AI age. My favorite feature is multi-tab RAG. You can use Copilot to analyze your open tabs, like I do here with papers our team has published in nature journals over the last year. And there is a lot more to come, including built-in actions so you can delegate tasks as you browse.
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Today we’re introducing Copilot Mode in Edge, our first step in reinventing the browser for the AI age. My favorite feature is multi-tab RAG. You can use Copilot to analyze your open tabs, like I do here with papers our team has published in nature journals over the last year. And there is a lot more to come, including built-in actions so you can delegate tasks as you browse.

Satya Nadella

704,679 görüntüleme • 11 ay önce

It has been an extraordinary run. As I leave the State House for the last time as Governor, I am proud to say we are leaving our children a state that is better than we found it. A New Jersey that is stronger, fairer, and more responsible. To each and every New Jerseyan: It has been the highest honor of my life to serve as your Governor. Thank you for joining me on this journey. Never forget: We’re from Jersey, baby!!
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It has been an extraordinary run. As I leave the State House for the last time as Governor, I am proud to say we are leaving our children a state that is better than we found it. A New Jersey that is stronger, fairer, and more responsible. To each and every New Jerseyan: It has been the highest honor of my life to serve as your Governor. Thank you for joining me on this journey. Never forget: We’re from Jersey, baby!!

Governor Phil Murphy

185,506 görüntüleme • 5 ay önce

We built an interactive 3D module to teach kids about what temperature does to water. - You adjust the temperature in real time - You see the impact on the state of water and what's changing at a molecular level - You can unlock two secret states in there if you pass a quick quizz This is the first of a giant series. We're turning the open source Marble App curriculum into a full bank of these interactive lessons. One for each topic kids learn in primary school. If you are a parent or primary school teacher, let us know in comments which module you want to see next. Link to play with the water lab below 👇
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We built an interactive 3D module to teach kids about what temperature does to water. - You adjust the temperature in real time - You see the impact on the state of water and what's changing at a molecular level - You can unlock two secret states in there if you pass a quick quizz This is the first of a giant series. We're turning the open source Marble App curriculum into a full bank of these interactive lessons. One for each topic kids learn in primary school. If you are a parent or primary school teacher, let us know in comments which module you want to see next. Link to play with the water lab below 👇

Lionel Mora

115,852 görüntüleme • 6 gün önce

The entire timeline is filled with talks on sentient and all, but I love being as informative and precise as possible on pressing issues. Let’s quickly talk about @SentientAGI’s Recursive Open Meta Agent (ROMA); ROMA is an open-source meta-agent framework used to build high performance multi-agent systems. ROMA serves as the conductor in a mass choir, or a captain of a ship . The captain gives commands for the other subordinates to follow to ensure efficiency on all sides. In this like manner, it provides a hierarchical tress system where the parent agents break down complex tasks to create simpler subtasks that are then passed on to children nodes. A family tree has the parents above, likewise the same tree analogy works here, but that’s not all that makes it stand out The results and solutions gotten by these child nodes are then aggregated together and there’s an up flow of results sent back up to the parent nodes. And at the center of it all is ROMA engineering and making sure all is running smoothly without break or fail. Are you really bullish on Sentient and the future of AGIs?
0:12

The entire timeline is filled with talks on sentient and all, but I love being as informative and precise as possible on pressing issues. Let’s quickly talk about @SentientAGI’s Recursive Open Meta Agent (ROMA); ROMA is an open-source meta-agent framework used to build high performance multi-agent systems. ROMA serves as the conductor in a mass choir, or a captain of a ship . The captain gives commands for the other subordinates to follow to ensure efficiency on all sides. In this like manner, it provides a hierarchical tress system where the parent agents break down complex tasks to create simpler subtasks that are then passed on to children nodes. A family tree has the parents above, likewise the same tree analogy works here, but that’s not all that makes it stand out The results and solutions gotten by these child nodes are then aggregated together and there’s an up flow of results sent back up to the parent nodes. And at the center of it all is ROMA engineering and making sure all is running smoothly without break or fail. Are you really bullish on Sentient and the future of AGIs?

OHJAY ⭕️ || 🇬🇧

23,521 görüntüleme • 9 ay önce

Memories are the previous learnings and context. Developers build up state and context as they do work, which is what makes a more tenured developer more effective than a brand new one with a similar skillset. It would be both inefficient and painful to relearn information from scratch around code structure, architecture, etc each time you needed to do a new task. Memories solve for this. As you do work with Cascade, it can automatically choose to “remember” pieces of information that it learns as Memories, and for any later work, it can choose to pull from this memory bank instead of trying to relearn that information from scratch. You also can manually prompt Cascade to remember parts of conversations as Memories and can manually go in and edit Memories post-fact. Here’s a developer asking Cascade to save some knowledge as a Memory:
0:14

Memories are the previous learnings and context. Developers build up state and context as they do work, which is what makes a more tenured developer more effective than a brand new one with a similar skillset. It would be both inefficient and painful to relearn information from scratch around code structure, architecture, etc each time you needed to do a new task. Memories solve for this. As you do work with Cascade, it can automatically choose to “remember” pieces of information that it learns as Memories, and for any later work, it can choose to pull from this memory bank instead of trying to relearn that information from scratch. You also can manually prompt Cascade to remember parts of conversations as Memories and can manually go in and edit Memories post-fact. Here’s a developer asking Cascade to save some knowledge as a Memory:

Windsurf

12,356 görüntüleme • 1 yıl önce

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.
0:15

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Akshay 🚀

254,599 görüntüleme • 16 gün önce

🇺🇸 TESLA’S NEW APP UPDATE LETS YOUR PHONE POINT TO YOUR CAR IN REAL TIME Tesla just rolled out a new app update (v4.51.5), and honestly… this is the kind of feature people assumed should exist years ago. Now, when you open the app, you can rotate your phone and see exactly where your car is - live - with an arrow that shifts as you move. It also shows the precise distance to your car in feet, so no more wandering through parking garages like you’re in a low-budget spy movie. It’s basically Find My iPhone, but for your Model Y, and way more satisfying to use. If Tesla keeps adding features like this, the app is going to end up being more fun than the car itself. Source: Sawyer Merritt
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🇺🇸 TESLA’S NEW APP UPDATE LETS YOUR PHONE POINT TO YOUR CAR IN REAL TIME Tesla just rolled out a new app update (v4.51.5), and honestly… this is the kind of feature people assumed should exist years ago. Now, when you open the app, you can rotate your phone and see exactly where your car is - live - with an arrow that shifts as you move. It also shows the precise distance to your car in feet, so no more wandering through parking garages like you’re in a low-budget spy movie. It’s basically Find My iPhone, but for your Model Y, and way more satisfying to use. If Tesla keeps adding features like this, the app is going to end up being more fun than the car itself. Source: Sawyer Merritt

Mario Nawfal

66,725 görüntüleme • 7 ay önce

last night I built a calorie tracking app, fully automated whilst I was asleep... all of these commits you can see here were on autopilot. I first spent an hour creating a large spec list/plan, split into phases and individual tasks. Kept asking Opus 4.5 to see if there's anything it's unsure of. nailed down the architecture (Bun, Nativewind, MySQL, EffectTS, TanStack Query/Mutate, Barcode Scanner API, Nutrition API details), and the schema. then I built my own version of the Ralph Wiggum bot, which is basically a .md file with a list of commands the AI can use to track task status, latest context, and a loop to keep resetting the context window on task completion. when I woke up in the morning the app was fully working and all tasks were complete. there's also 479 tests across 17 files. I don't know how to feel. This is insane. It works.
0:18

last night I built a calorie tracking app, fully automated whilst I was asleep... all of these commits you can see here were on autopilot. I first spent an hour creating a large spec list/plan, split into phases and individual tasks. Kept asking Opus 4.5 to see if there's anything it's unsure of. nailed down the architecture (Bun, Nativewind, MySQL, EffectTS, TanStack Query/Mutate, Barcode Scanner API, Nutrition API details), and the schema. then I built my own version of the Ralph Wiggum bot, which is basically a .md file with a list of commands the AI can use to track task status, latest context, and a loop to keep resetting the context window on task completion. when I woke up in the morning the app was fully working and all tasks were complete. there's also 479 tests across 17 files. I don't know how to feel. This is insane. It works.

Richie

85,555 görüntüleme • 6 ay önce

Why the internet needs Grokipedia? ➤ Wikipedia isn’t the neutral source of truth it once was. It’s been taken over by far-left activists and often used as a propaganda tool, not an unbiased encyclopedia. ➤ A lot of AIs today get their info from the internet, but the web is full of unfair views, wrong facts, and tons of junk. ➤ Grokipedia will be an open-source encyclopedia focused solely on TRUTH. ➤ Grokipedia won’t be controlled by any activist or political bias. ➤ It will be open-source and free for all to use, a truly public resource without limits. ➤ xAI’s ultimate mission is to understand the universe, and that requires honest, unfiltered information. Grokipedia is a crucial step toward that goal, because you can’t build a truthful AI on biased information. ➤ The vision is for Grokipedia to become the global standard for knowledge. One day, every person and every AI system could rely on it as a trusted source of truth. ➤ Seeking truth is the greatest mission of all and that’s what Grokipedia stands for. Grokipedia is coming.
0:15

Why the internet needs Grokipedia? ➤ Wikipedia isn’t the neutral source of truth it once was. It’s been taken over by far-left activists and often used as a propaganda tool, not an unbiased encyclopedia. ➤ A lot of AIs today get their info from the internet, but the web is full of unfair views, wrong facts, and tons of junk. ➤ Grokipedia will be an open-source encyclopedia focused solely on TRUTH. ➤ Grokipedia won’t be controlled by any activist or political bias. ➤ It will be open-source and free for all to use, a truly public resource without limits. ➤ xAI’s ultimate mission is to understand the universe, and that requires honest, unfiltered information. Grokipedia is a crucial step toward that goal, because you can’t build a truthful AI on biased information. ➤ The vision is for Grokipedia to become the global standard for knowledge. One day, every person and every AI system could rely on it as a trusted source of truth. ➤ Seeking truth is the greatest mission of all and that’s what Grokipedia stands for. Grokipedia is coming.

DogeDesigner

69,804 görüntüleme • 8 ay önce

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:
0:16

HTML Artifacts are a big part of how I work with agents now. Artifacts can be more than just static files. When combined with agents, they can take action or help you take action. This unlocks all kinds of interesting ways to work with agents. This is clearly the future. Check out this writing and scheduler artifact I built in a few minutes. It uses a bit of HTML and JS. All the data is in markdown (Obsidian vaults), so the agent can access and modify it at any time. No DB needed. No sophisticated functionalities. The agent decides all that for me based on the skills, context, and memory it has access to. The best part about this simple stack is that all the important information stays with me. This has allowed me to build a recursive self-improving system and automations that can better tap into coding agents like Codex or Claude Code. I could have paid or built an entire app for scheduling posts, and there are so many of them out there. But I don't need to. I've realized a simple artifact does the job. And the simplicity of it is actually an advantage. Very little maintenance for very high returns on personalization, time, and efficiency. The other benefit of this is that I can add features as I please. That level of personalization feels magical, and we should all be pursuing more of it. All of this just keeps compounding. Of course, this example is just about writing. But I have similar artifacts for research, design, experimentation, evaluation, and so much more. And no, I didn't actually publish the post example I shared in the clip. It was just for demonstration purposes. I actually spend more time than this when writing together with agents. Lastly, having built my own agent orchestrator tool has made me realize that simplifying the tool stack is a superpower. If you are curious about how all this works, I will do a live session next week:

elvis

18,374 görüntüleme • 2 ay önce

Every serious Claude Code user is using this repo. if you're not, you're leaving 90% of Claude Code's power on the table. It's called claude-code-best-practice - 84 sourced tips, implementation examples for every major feature, workflow comparisons across 8 major repos, and the actual tips from Boris Cherny (creator of Claude Code) compiled in one place. Here's what's actually in it: → 84 tips organized by category -- prompting, planning, CLAUDE.md, agents, commands, skills, hooks, workflows, debugging, utilities, daily habits → best practice + implemented examples for every core concept: subagents, commands, skills, hooks, MCP servers, plugins, settings, memory, checkpointing, CLI flags → workflow comparison table -- Superpowers, BMAD-METHOD, Get Shit Done, OpenSpec, gstack, HumanLayer -- what makes each unique, how many agents/commands/skills each has → orchestration workflow -- Command → Agent → Skill pattern with a live demo → Boris Cherny tips compiled across 3 tweet threads (13 + 10 + 12 tips) and 5 podcast/video appearances → "billion dollar questions" section -- open questions about CLAUDE.md, agents vs commands vs skills, specs -- that nobody has definitively answered yet here's a few of the tips that actually change how you use it: → use subagents with "say use subagents" to throw more compute at a problem -- offload tasks to keep your main context clean → spin up a second Claude to review your plan as a staff engineer before executing → CLAUDE.md should target under 200 lines -- wrap domain-specific rules in ` ` tags so Claude doesn't ignore them as files grow → compress KV context at max 50%, not at the end -- avoid the "agent dumb zone" by doing manual /compact proactively → after a mediocre fix: "knowing everything you know now, scrap this and implement the elegant solution" was #1 trending on GitHub in March 2026. 19.7K GitHub stars. 1.7K forks. MIT license. 100% open source. (link in the comments)
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Every serious Claude Code user is using this repo. if you're not, you're leaving 90% of Claude Code's power on the table. It's called claude-code-best-practice - 84 sourced tips, implementation examples for every major feature, workflow comparisons across 8 major repos, and the actual tips from Boris Cherny (creator of Claude Code) compiled in one place. Here's what's actually in it: → 84 tips organized by category -- prompting, planning, CLAUDE.md, agents, commands, skills, hooks, workflows, debugging, utilities, daily habits → best practice + implemented examples for every core concept: subagents, commands, skills, hooks, MCP servers, plugins, settings, memory, checkpointing, CLI flags → workflow comparison table -- Superpowers, BMAD-METHOD, Get Shit Done, OpenSpec, gstack, HumanLayer -- what makes each unique, how many agents/commands/skills each has → orchestration workflow -- Command → Agent → Skill pattern with a live demo → Boris Cherny tips compiled across 3 tweet threads (13 + 10 + 12 tips) and 5 podcast/video appearances → "billion dollar questions" section -- open questions about CLAUDE.md, agents vs commands vs skills, specs -- that nobody has definitively answered yet here's a few of the tips that actually change how you use it: → use subagents with "say use subagents" to throw more compute at a problem -- offload tasks to keep your main context clean → spin up a second Claude to review your plan as a staff engineer before executing → CLAUDE.md should target under 200 lines -- wrap domain-specific rules in ` ` tags so Claude doesn't ignore them as files grow → compress KV context at max 50%, not at the end -- avoid the "agent dumb zone" by doing manual /compact proactively → after a mediocre fix: "knowing everything you know now, scrap this and implement the elegant solution" was #1 trending on GitHub in March 2026. 19.7K GitHub stars. 1.7K forks. MIT license. 100% open source. (link in the comments)

Sukh Sroay

113,759 görüntüleme • 3 ay önce

ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.
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ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

Rohan Paul

50,097 görüntüleme • 4 ay önce