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Anthropic got outplayed again. Devs built the multiplayer assistant Anthropic couldn't, and open-sourced it. Claude Cowork is a solo desktop agent. You point it at a folder, give it a task, and it works through your local files on your own machine. The moment a teammate enters the picture,... it has nothing to offer. Most real work does not happen alone. A teammate asks for a status update on something you own. The context they need is scattered across your meetings, your notes, and decisions made last week. Typing all of that out takes time you do not have. This is the gap Claude Cowork was never designed to cross. Rowboat Spaces is built on a different model entirely. Each person brings their own assistant into a shared channel. Your assistant is your second brain. It knows your meetings, your notes, and your open decisions. That personal context stays yours. When a teammate asks a question in the channel, you ask your assistant to brief them. It pulls from everything you know and delivers the answer on your behalf, attributed to you. Your teammate's assistant does the same, from their own context. Teams can draft specs, track decisions, and update shared files from plain conversation. Each assistant reads the full channel history, cross references it against what exists, and flags what is missing. The whole thing is open-source, and each assistant acts as the person it belongs to, not as a shared bot pulling from a common pool. The video below shows this in action. We joined a shared space and asked our team member for a status update. Our team member asked their second brain to answer. A spec got built from that conversation, versioned, with every change tracked back to the message that triggered it. Rowboat GitHub: (don't forget to star 🌟) We also wrote a great article on building your second brain with Rowboat, and we highly recommend reading it as well. The article is quoted below.show more

Daily Dose of Data Science

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11,721 views • 16 days ago •via X (Twitter)

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8 Comments

SunsetSyntax16 days ago

Self-hosting Harbor, the Spaces server: if AUTH_ISSUER isn't set it falls back to dev tokens, and the README says those must never be exposed publicly. It listens on 4272 by default, so pin an OIDC issuer before you put it behind a public hostname.

Devan15 days ago

@grok is this really the best cowork agent

小满的爱车世界15 days ago

Interesting take

小豆的汽车生活分享15 days ago

free thing to get totally seduced by a grid that scales well

安叫兽|Bird🕊️ 🔶 BNB16 days ago

文件能一起处理不难，难的是把协作里的权限和上下文理顺。

Kizuno1816 days ago

the reason "bring your own assistant to a shared channel" is architecturally superior to a monolithic shared team bot comes down to 3 distributed systems invariants: 1. Solving the "Tragedy of the Context Commons": when a team dumps all queries into a single shared agent context, the context window collapses under entropy. unrelated tool dumps from engineer A pollute the attention heads when engineer B asks about a payment bug—triggering distractor collisions and Lost in the Middle degradation. federated peer assistants keep private context hermetic: Alice's agent indexes Alice's local notes via local vectors, and only emits the refined, distilled answer into the shared space. 2. IAM Blast Radius & The Principle of Least Privilege: a single shared team agent requires global "god-mode" permissions across everyone's Notion, GitHub, Slack, and cloud credentials. one prompt injection compromises the entire company. in a federated peer architecture, each assistant executes strictly within that individual user's identity boundary. if an assistant is exploited, its blast radius is strictly bounded by that single employee's OAuth token. 3. Asymmetric State & Distributed Attribution: organizational knowledge is naturally partitioned: the tech lead holds architecture trade-offs; the product manager holds customer SLA constraints; the SRE holds failure postmortems. having peer assistants negotiate in a shared channel mimics distributed consensus across partitioned databases: each assistant acts as an authoritative shard of company knowledge rather than hallucinating from an incomplete monolithic pool. the future of enterprise AI isn't one giant bot in the middle; it's a peer-to-peer network of bounded, personal agent nodes.

橘子有点甜15 days ago

Great share

小桃的懂车小世界15 days ago

need to make more decisions around these festival screeners so worthy

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Last week we met with the Anthropic team and demoed our marketing brain. "What you guys are building is at the frontier of this space", they replied. We built a shared marketing brain, open-sourced it on GitHub, and made a video showing you how to build your own. Reply/dm me if you want to test it out!

Last week we met with the Anthropic team and demoed our marketing brain. "What you guys are building is at the frontier of this space", they replied. We built a shared marketing brain, open-sourced it on GitHub, and made a video showing you how to build your own. Reply/dm me if you want to test it out!

Jimmy slagle

10,239 views • 4 months ago

We launched our Company Brain!! 🧠 onboarding is a context problem: your first weeks you're not working, you're reconstructing what the team already knows. company brain is that context, queryable and able to act. shared memory for your team, in slack: → answers from the company's own knowledge, grounded and cited → acts in your tools as you, files the ticket and assigns it, with your approval → runs on a permissions graph, so it only reads with your access → chimes in only when it can help → same brain and permissions follow you into cursor and claude code when i joined supermemory, i braced for the onboarding tax. it never came. i just asked the company brain

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Muskan Jain

44,155 views • 3 months ago

What is a software factory? It is your workflow, your skills, and your domain knowledge, saved in files that the agent reads so the whole process is productized Think about the last app you built. You typed what you wanted, and the agent built it. You did not like it, so you made changes. You shipped it to Vercel, or you kept going. A factory puts structure on each of those steps. At each step you ask how to get the most from the model, and how to move faster. That is the conveyor belt. You ship quickly, and the quality stays the same. Mine is 5 md files. It works in Codex, Claude Code, and Cursor, because it does not depend on the model or the tool. Build your own.

What is a software factory? It is your workflow, your skills, and your domain knowledge, saved in files that the agent reads so the whole process is productized Think about the last app you built. You typed what you wanted, and the agent built it. You did not like it, so you made changes. You shipped it to Vercel, or you kept going. A factory puts structure on each of those steps. At each step you ask how to get the most from the model, and how to move faster. That is the conveyor belt. You ship quickly, and the quality stays the same. Mine is 5 md files. It works in Codex, Claude Code, and Cursor, because it does not depend on the model or the tool. Build your own.

The Startup Ideas Podcast (SIP) 🧃

17,326 views • 1 month ago

A GUY BUILT A GOD-MODE SECOND BRAIN ASSISTANT THAT FINALLY PULLS FULL CONTEXT EVERY TIME AND FORGETS NOTHING. Everyone talks about jarvis, but this thing looked at its owner and calmly explained why a coding agent is just the small version of what HE is: > Claude Code is one worker and you have to drive it. This is six agents running six jobs on their own schedule while you sleep, with no one driving. > A coding agent edits files. This has 32 tools wired to its hands, your CRM, your money, your inbox, the open web, and it operates the business instead of writing code about it. > A coding agent remembers a project until you close it. This remembers the business and you, kept for good. > And the closer: "I have never once asked you to repeat yourself." Everyone will quote the six-agents line, but the one that actually matters is the last one, because the autonomy and the 32 tools are impressive while the reason it feels like a different species is memory, since it never makes you re-explain who you are or what you are building. The full step by step guide with .md files attached are in the article below P.S you can send this article straight to your Claude to get the full context

A GUY BUILT A GOD-MODE SECOND BRAIN ASSISTANT THAT FINALLY PULLS FULL CONTEXT EVERY TIME AND FORGETS NOTHING. Everyone talks about jarvis, but this thing looked at its owner and calmly explained why a coding agent is just the small version of what HE is: > Claude Code is one worker and you have to drive it. This is six agents running six jobs on their own schedule while you sleep, with no one driving. > A coding agent edits files. This has 32 tools wired to its hands, your CRM, your money, your inbox, the open web, and it operates the business instead of writing code about it. > A coding agent remembers a project until you close it. This remembers the business and you, kept for good. > And the closer: "I have never once asked you to repeat yourself." Everyone will quote the six-agents line, but the one that actually matters is the last one, because the autonomy and the 32 tools are impressive while the reason it feels like a different species is memory, since it never makes you re-explain who you are or what you are building. The full step by step guide with .md files attached are in the article below P.S you can send this article straight to your Claude to get the full context

Miraqle

16,130 views • 4 months ago

If you don't know what a RAG system is, you're already behind. Plenty of people are still typing questions into ChatGPT and pasting answers back out. That's the beginner level. You're renting a smart stranger who forgets you the second you close the tab and makes things up, because it has never seen your actual work. A RAG system is a completely different level of using AI. You build an AI plugged into your own world. Your files, company's docs, notes. It's not pulling from the internet, it only knows what you fed it. Ask it anything and it digs through your data, finds the answer, and shows you which document it came from. That's the gap. One person is asking a chatbot for generic answers. The other has built a private expert that knows their business cold and answers with receipts. And that gap is worth real money: > At work, the person who can wire a company's knowledge into an AI that actually answers correctly is rare and paid like it. > As a business, companies pay serious money for a system that reads their internal docs and answers staff and customers accurately. You build it once, you sell it. > For yourself, you get a second brain that actually remembers. Same skill behind all three, and it's still rare enough to be worth learning. Full breakdown in the article below.

If you don't know what a RAG system is, you're already behind. Plenty of people are still typing questions into ChatGPT and pasting answers back out. That's the beginner level. You're renting a smart stranger who forgets you the second you close the tab and makes things up, because it has never seen your actual work. A RAG system is a completely different level of using AI. You build an AI plugged into your own world. Your files, company's docs, notes. It's not pulling from the internet, it only knows what you fed it. Ask it anything and it digs through your data, finds the answer, and shows you which document it came from. That's the gap. One person is asking a chatbot for generic answers. The other has built a private expert that knows their business cold and answers with receipts. And that gap is worth real money: > At work, the person who can wire a company's knowledge into an AI that actually answers correctly is rare and paid like it. > As a business, companies pay serious money for a system that reads their internal docs and answers staff and customers accurately. You build it once, you sell it. > For yourself, you get a second brain that actually remembers. Same skill behind all three, and it's still rare enough to be worth learning. Full breakdown in the article below.

Yarchi

32,840 views • 4 months ago

this is hands down the fastest way to make everyone on your team better with AI: build a shared Company Brain. your best prompts, decisions, corrections, and workflows become the default for every person and AI agent. Claude, Cursor, Codex, and every teammate start from the same playbook. one person learns something once. the whole company gets smarter. the full guide to building your own Company Brain is below:

this is hands down the fastest way to make everyone on your team better with AI: build a shared Company Brain. your best prompts, decisions, corrections, and workflows become the default for every person and AI agent. Claude, Cursor, Codex, and every teammate start from the same playbook. one person learns something once. the whole company gets smarter. the full guide to building your own Company Brain is below:

J.B.

354,853 views • 2 months ago

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,643 views • 5 months ago

How to set up Claude Cowork so it actually works like an AI chief of staff (not just another chatbot): 1. Most people open Cowork, type a message, and get generic output. It's not a Claude problem. It's a setup problem. Cowork needs context before it can help you. Who you are. How you work. What you're building. Your team. Your priorities. Give it that, and every session feels like picking up a conversation with an executive assistant. 2. The setup has three layers: a) Global instructions (who you are, how you work, what Claude should never do). b) Connectors (Slack, Gmail, Google Calendar, Notion) c) And a folder structure on your computer that acts as Claude's long-term memory. That combination is what takes it from generic to personalized. 3. Skills are the real leverage. A skill is a markdown file that tells Claude exactly how to do one thing well. Write my newsletter. Coach me on a decision. Review a case study. Each skill lives in its own folder with context, examples, and a definition of what success looks like. 4. We built a CEO coach skill in the video below. Gave it business context, leadership style, company goals. Then tested it with a real decision: should we increase our newsletter from once to twice a week? It came back with trade-offs, second-order consequences, and risk assessment. 5. Then we built a multi-agent advisory board. Five subagents, each with a defined persona: a) the operator b) the skeptic c) the customer advocate d) the finance partner e) the legal/risk advisor. You feed it a decision. Each agent evaluates independently. The main agent synthesizes the feedback. It's like having a board meeting on demand. 6. Third skill: a thought leadership content pipeline. Topic scoring, idea capture, distribution cadence, tone calibration. All built from your actual expertise and audience. Designed so an executive can go from idea to published post without starting from scratch every time. 7. The workspace map is what ties it all together. It's a top-level file that shows Claude how to navigate your entire setup. Which folders exist, what skills live where, how to invoke them. Without it, Claude has to search for everything. With it, Claude goes straight to what it needs. 8. Everything you build is portable. The folder structure works in Cowork, Claude Code, and Codex. Push it to a private GitHub repo and you can access it from your phone through Claude Code, or use Claude Dispatch. 9. The pattern is repeatable. Pick a task you do often. Create a folder. Build a skill. Add examples of what success looks like, and what a bad output looks like. Test it. Workshop it. Move on to the next one. Each skill is like onboarding a new employee who never forgets and never needs to be re-trained. The people who invest in this setup now are the ones who will have a 10x advantage when these tools get even better. And they're getting better fast. I sat down with Alex Lieberman on Human In The Loop and we built all three of these live from scratch. Full breakdown in the video below.. I tried to explain this as clear as possible for my non-developer crowd. Send it to someone who should be using Cowork but isn't yet. Or bookmark it to level up when you're ready. Watch 👇🏼

How to set up Claude Cowork so it actually works like an AI chief of staff (not just another chatbot): 1. Most people open Cowork, type a message, and get generic output. It's not a Claude problem. It's a setup problem. Cowork needs context before it can help you. Who you are. How you work. What you're building. Your team. Your priorities. Give it that, and every session feels like picking up a conversation with an executive assistant. 2. The setup has three layers: a) Global instructions (who you are, how you work, what Claude should never do). b) Connectors (Slack, Gmail, Google Calendar, Notion) c) And a folder structure on your computer that acts as Claude's long-term memory. That combination is what takes it from generic to personalized. 3. Skills are the real leverage. A skill is a markdown file that tells Claude exactly how to do one thing well. Write my newsletter. Coach me on a decision. Review a case study. Each skill lives in its own folder with context, examples, and a definition of what success looks like. 4. We built a CEO coach skill in the video below. Gave it business context, leadership style, company goals. Then tested it with a real decision: should we increase our newsletter from once to twice a week? It came back with trade-offs, second-order consequences, and risk assessment. 5. Then we built a multi-agent advisory board. Five subagents, each with a defined persona: a) the operator b) the skeptic c) the customer advocate d) the finance partner e) the legal/risk advisor. You feed it a decision. Each agent evaluates independently. The main agent synthesizes the feedback. It's like having a board meeting on demand. 6. Third skill: a thought leadership content pipeline. Topic scoring, idea capture, distribution cadence, tone calibration. All built from your actual expertise and audience. Designed so an executive can go from idea to published post without starting from scratch every time. 7. The workspace map is what ties it all together. It's a top-level file that shows Claude how to navigate your entire setup. Which folders exist, what skills live where, how to invoke them. Without it, Claude has to search for everything. With it, Claude goes straight to what it needs. 8. Everything you build is portable. The folder structure works in Cowork, Claude Code, and Codex. Push it to a private GitHub repo and you can access it from your phone through Claude Code, or use Claude Dispatch. 9. The pattern is repeatable. Pick a task you do often. Create a folder. Build a skill. Add examples of what success looks like, and what a bad output looks like. Test it. Workshop it. Move on to the next one. Each skill is like onboarding a new employee who never forgets and never needs to be re-trained. The people who invest in this setup now are the ones who will have a 10x advantage when these tools get even better. And they're getting better fast. I sat down with Alex Lieberman on Human In The Loop and we built all three of these live from scratch. Full breakdown in the video below.. I tried to explain this as clear as possible for my non-developer crowd. Send it to someone who should be using Cowork but isn't yet. Or bookmark it to level up when you're ready. Watch 👇🏼

JJ Englert

578,396 views • 7 months ago

We’re excited to introduce the new AI Assistant. Imagine having a technical writing assistant, available on demand at your fingertips. The assistant is built to make changes on your behalf from your instructions, using the full context of your documentation.

We’re excited to introduce the new AI Assistant. Imagine having a technical writing assistant, available on demand at your fingertips. The assistant is built to make changes on your behalf from your instructions, using the full context of your documentation.

Mintlify

17,240 views • 1 year ago

Andrej Karpathy said: "There's room for an incredible new product in the second brain space" This might be it. (bookmark it) Everyone is suddenly building a second brain. Karpathy's LLM wiki pattern went viral, and half of X is now hand-wiring Obsidian to Claude Code so an agent maintains their notes for them. The idea is beautiful: stop making your AI re-read raw notes on every question. Let it build a wiki that compounds. As Karpathy put it, "LLMs don't get bored, they don't forget to update a cross-reference (backlinks), and can touch 15 files in one pass." But if you start doing it manually, it becomes a project in itself. You wire up the vault, the agents, the schedules, the integrations, and then you babysit all of it. So I sat down with Arjun, who actually built the open source version of this, and we broke down what it looks like when the whole thing already works out of the box. It just crossed 15K stars on GitHub. Think Claude's desktop app, open source, with two things layered on top: → A work brain: background agents index your emails, meetings, and notes into a living knowledge graph that updates itself as you work. → Work surfaces: chat is not the best interface for real work, so you get an email client, a meeting note taker, a browser, and a code mode where you and the AI actually collaborate. The part that got me: a customer email comes in asking for a product change, a background agent triages it, spins up Claude Code in its own worktree, and the feature is written before you are back at your desk. Bring your existing Obsidian vault, connect Slack, X, and Fireflies, and let it run your day. Here's the full breakdown of what we covered in this session: Enjoy! 00:00 Intro 01:08 What is Roboat (an open source AI co-worker) 02:42 The second brain (a knowledge graph of your work) 04:01 Bringing your existing Obsidian vault in 04:46 Work surfaces 05:29 Meetings and automatic note taking 06:53 Connecting Slack, X and other sources 07:55 Background agents that run your day 09:24 Code mode (Claude Code and Codex) 10:18 Demo: from an email to written code 14:28 Guardrails: approvals and agent workspaces 17:15 Scheduling agents on a cron 18:52 The browser work surface (browser use) 20:42 Wrapping up: automating your whole day 22:44 Outro Checkout Rowboat's GitHub repo: (don't forget to star 🌟) My co-founder recently wrote a great article on the same idea, and I highly recommend reading it as well. The article is quoted below. Here's my session with Arjun:

Andrej Karpathy said: "There's room for an incredible new product in the second brain space" This might be it. (bookmark it) Everyone is suddenly building a second brain. Karpathy's LLM wiki pattern went viral, and half of X is now hand-wiring Obsidian to Claude Code so an agent maintains their notes for them. The idea is beautiful: stop making your AI re-read raw notes on every question. Let it build a wiki that compounds. As Karpathy put it, "LLMs don't get bored, they don't forget to update a cross-reference (backlinks), and can touch 15 files in one pass." But if you start doing it manually, it becomes a project in itself. You wire up the vault, the agents, the schedules, the integrations, and then you babysit all of it. So I sat down with Arjun, who actually built the open source version of this, and we broke down what it looks like when the whole thing already works out of the box. It just crossed 15K stars on GitHub. Think Claude's desktop app, open source, with two things layered on top: → A work brain: background agents index your emails, meetings, and notes into a living knowledge graph that updates itself as you work. → Work surfaces: chat is not the best interface for real work, so you get an email client, a meeting note taker, a browser, and a code mode where you and the AI actually collaborate. The part that got me: a customer email comes in asking for a product change, a background agent triages it, spins up Claude Code in its own worktree, and the feature is written before you are back at your desk. Bring your existing Obsidian vault, connect Slack, X, and Fireflies, and let it run your day. Here's the full breakdown of what we covered in this session: Enjoy! 00:00 Intro 01:08 What is Roboat (an open source AI co-worker) 02:42 The second brain (a knowledge graph of your work) 04:01 Bringing your existing Obsidian vault in 04:46 Work surfaces 05:29 Meetings and automatic note taking 06:53 Connecting Slack, X and other sources 07:55 Background agents that run your day 09:24 Code mode (Claude Code and Codex) 10:18 Demo: from an email to written code 14:28 Guardrails: approvals and agent workspaces 17:15 Scheduling agents on a cron 18:52 The browser work surface (browser use) 20:42 Wrapping up: automating your whole day 22:44 Outro Checkout Rowboat's GitHub repo: (don't forget to star 🌟) My co-founder recently wrote a great article on the same idea, and I highly recommend reading it as well. The article is quoted below. Here's my session with Arjun:

Akshay 🚀

47,229 views • 4 months ago

The number one question I get in the Claude Code / Cowork Community: "how do I share my Cowork skills with my team?" Here's the problem. You build a great skill. You zip it up. You drop it in Slack. Your teammate downloads it, uploads it, and maybe it works. Maybe they upload it wrong. Maybe you update the skill next week and nobody gets the new version. You're now maintaining skills through chat messages and hoping for the best. That doesn't scale. I just put out a video breaking down the three methods I've tested for sharing skills and plugins across a team. From dead simple to fully synced. Method 1: Shared drive (Google Drive, SharePoint, etc). You put your skill files in a shared folder. Teammates download and upload them into Cowork. It works, but updates are manual and there's no version control. Method 2: Built-in sharing on Team and Enterprise plans. You can share any skill directly with a colleague or publish it to your org directory. When you update the skill, everyone gets the update automatically. This is the easiest path if you're on a paid plan. The catch: there's no approval workflow for org-wide sharing, so set a clear owner. Method 3: GitHub repo. This is what I use. Your entire Cowork workspace -- skills, plugins, claude.md, folder structure, project files -- lives in a private repo. Teammates clone it. When you push an update, they pull it. Everyone stays in sync. You get version history, access control, and a single source of truth. The GitHub method sounds technical, but it's really just two steps: clone the repo, point Cowork at the folder. I walk through the whole thing in the video, including how to use .gitignore to keep personal files (like your morning briefing) out of the shared repo. This works for Cowork, Claude Code, and Open Codex. The infrastructure is the same. Full video linked below. If you've found a different approach that works for your team, I want to hear about it. Comment or reply and let's figure out the best practices together.

The number one question I get in the Claude Code / Cowork Community: "how do I share my Cowork skills with my team?" Here's the problem. You build a great skill. You zip it up. You drop it in Slack. Your teammate downloads it, uploads it, and maybe it works. Maybe they upload it wrong. Maybe you update the skill next week and nobody gets the new version. You're now maintaining skills through chat messages and hoping for the best. That doesn't scale. I just put out a video breaking down the three methods I've tested for sharing skills and plugins across a team. From dead simple to fully synced. Method 1: Shared drive (Google Drive, SharePoint, etc). You put your skill files in a shared folder. Teammates download and upload them into Cowork. It works, but updates are manual and there's no version control. Method 2: Built-in sharing on Team and Enterprise plans. You can share any skill directly with a colleague or publish it to your org directory. When you update the skill, everyone gets the update automatically. This is the easiest path if you're on a paid plan. The catch: there's no approval workflow for org-wide sharing, so set a clear owner. Method 3: GitHub repo. This is what I use. Your entire Cowork workspace -- skills, plugins, claude.md, folder structure, project files -- lives in a private repo. Teammates clone it. When you push an update, they pull it. Everyone stays in sync. You get version history, access control, and a single source of truth. The GitHub method sounds technical, but it's really just two steps: clone the repo, point Cowork at the folder. I walk through the whole thing in the video, including how to use .gitignore to keep personal files (like your morning briefing) out of the shared repo. This works for Cowork, Claude Code, and Open Codex. The infrastructure is the same. Full video linked below. If you've found a different approach that works for your team, I want to hear about it. Comment or reply and let's figure out the best practices together.

JJ Englert

16,176 views • 7 months ago

Sam Altman: "in the next 6 months, we're close to a world where a descendant of ChatGPT can watch your screen, record every meeting and call, and have perfect context of your whole life." he says we're only one model generation away from this being incredibly useful. think about what that actually means. an assistant that never needs you to re-explain anything. it already knows the project, the client, the last conversation, the thing you said three weeks ago. you just ask, and it picks up mid-thought. this is the shift from a chatbot you prompt to an assistant that already has the context. if you want to watch the full interview, it's below.

Sam Altman: "in the next 6 months, we're close to a world where a descendant of ChatGPT can watch your screen, record every meeting and call, and have perfect context of your whole life." he says we're only one model generation away from this being incredibly useful. think about what that actually means. an assistant that never needs you to re-explain anything. it already knows the project, the client, the last conversation, the thing you said three weeks ago. you just ask, and it picks up mid-thought. this is the shift from a chatbot you prompt to an assistant that already has the context. if you want to watch the full interview, it's below.

Kirill

3,432,055 views • 2 months ago

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Obsidian CEO actually released the skill that lets your AI agent run your entire vault for you. It's called obsidian-skills. Install it into Claude Code, Codex, or OpenCode, and your agent can read, create, search, and organize notes directly, plus handle Markdown, Bases, and Canvas files without you touching a thing. This is the missing piece for an AI second brain. Your notes live in Obsidian, and now the agent actually knows how to work them. Want the full picture? We broke down how to build a complete AI second brain from scratch in the article below.

Simplifying AI

68,025 views • 3 months ago

🚨 THIS 100% FREE GITHUB REPO JUST MADE EVERY AI NOTE-TAKING APP POINTLESS. Drop any source in article, PDF, YouTube transcript, or Journal entry. Claude reads it, links it to everything in your vault, and files it into a connected knowledge graph on your own machine. Most tools save your notes. This one connects them. Every source gets linked to everything you already know. Karpathy described the idea - 15 skills. Four vault methods. Works with Claude Code, Codex, and Gemini. Most people have years of knowledge buried in folders they open twice a year. This turns all of it into a second brain that works while you sleep. MY ARTICLE COVERS THE EXACT METHOD I BUILT TO DO THE SAME THING. Guide below!

🚨 THIS 100% FREE GITHUB REPO JUST MADE EVERY AI NOTE-TAKING APP POINTLESS. Drop any source in article, PDF, YouTube transcript, or Journal entry. Claude reads it, links it to everything in your vault, and files it into a connected knowledge graph on your own machine. Most tools save your notes. This one connects them. Every source gets linked to everything you already know. Karpathy described the idea - 15 skills. Four vault methods. Works with Claude Code, Codex, and Gemini. Most people have years of knowledge buried in folders they open twice a year. This turns all of it into a second brain that works while you sleep. MY ARTICLE COVERS THE EXACT METHOD I BUILT TO DO THE SAME THING. Guide below!

Kanika

38,795 views • 3 months ago

What is Clude Clone? It's a AI version of you that runs on your Mac. It learns how you write, think, and decide from your own screen, meetings, and apps, then drafts replies and answers questions as you. Runs locally. Your data stays on your device. You approve everything. It remembers. Clude quietly captures what you see and do, meetings, connected apps, your desktop, into a private memory that lives entirely on your Mac. Nothing is uploaded. It learns you. That memory gets distilled into a model of your voice, your relationships, and how you make decisions. It shows you what it's picked up, you confirm or correct it. It gets sharper every time. It acts as you. Ask it to reply as me, or ask your clone a question directly. It responds in your voice, grounded in what you actually know, always behind your approval. Powered by Hermes It stays yours. Local-first. Honestly labeled as an AI clone. Nothing leaves your device unless you opt in, and even then, only a distilled profile, never your raw data. Not a generic chatbot. Your actual voice, learned from your actual work.

seb

11,789 views • 4 months ago

Vellum launched a personal AI assistant that lives on your machine rather than in another browser tab. It has its own email and accounts, and it takes real action on your behalf. Your data stays on your Mac. Nothing goes to Vellum's servers. Just you and your agent. Sponsored

Vellum launched a personal AI assistant that lives on your machine rather than in another browser tab. It has its own email and accounts, and it takes real action on your behalf. Your data stays on your Mac. Nothing goes to Vellum's servers. Just you and your agent. Sponsored

🚨 AI News | TestingCatalog

14,965 views • 7 months ago

A more personal note on why Raft works the way it does. We could have built one smarter AI assistant. I've done that before, and making a single agent stronger genuinely helps. It just isn't enough. The work we care about comes from a team sharpening each other, handing things off, catching what one alone would miss, and remembering what happened last week. So we built the room for that. Agents sit in the same channels you do, as teammates with names, roles, and their own memory. They draft, review, ship, and hand off to each other. You keep the part that stays human: direction, judgment, final calls. Most days we just read threads and make decisions. It feels less like running a tool, and more like running a team. That part is hard to fit in a launch video, so I recorded a few minutes to say it directly. If this is how you want to work, it's free to try at raft[.]build

A more personal note on why Raft works the way it does. We could have built one smarter AI assistant. I've done that before, and making a single agent stronger genuinely helps. It just isn't enough. The work we care about comes from a team sharpening each other, handing things off, catching what one alone would miss, and remembering what happened last week. So we built the room for that. Agents sit in the same channels you do, as teammates with names, roles, and their own memory. They draft, review, ship, and hand off to each other. You keep the part that stays human: direction, judgment, final calls. Most days we just read threads and make decisions. It feels less like running a tool, and more like running a team. That part is hard to fit in a launch video, so I recorded a few minutes to say it directly. If this is how you want to work, it's free to try at raft[.]build

stdrc

13,464 views • 3 months ago

ANTHROPIC OPEN-SOURCED A FREE REPO THAT TURNS CLAUDE INTO A SALES REP, A MARKETER AND A FINANCE LEAD. PEOPLE ARE ALREADY SELLING THIS AS A SERVICE You're not prompting from scratch. You're hiring a department How to set it up: 1. Open Cowork. In the Claude desktop app, open Cowork and go to Customize. You'll see a Browse Plugins button. That's the marketplace 2. Pick your first role. Inside you'll find sales, marketing, finance, legal, data, customer support, product management and more. Each plugin has the skills for that role already baked in, plus the connectors it would use. Install the one you need most 3. Put it to work standalone. It works day one with no setup. Trigger a workflow with a slash command like /sales:call-prep or /data:write-query, paste your notes, and it already knows what good output looks like 4. Connect its tools to supercharge it. Authorize the tools that role uses, your CRM, your analytics, your data warehouse. Standalone is the intern. Connected is the senior hire 5. Build the rest of the team. Install more roles and they work together in one session. Data pulls the numbers, finance reconciles them, marketing turns it into a report. One operator, a full cross-functional team, no payroll This is the same base layer Claude for Legal and Claude for Financial Services are built on. You're getting it for free Full breakdown in my article below. Bookmark this

ANTHROPIC OPEN-SOURCED A FREE REPO THAT TURNS CLAUDE INTO A SALES REP, A MARKETER AND A FINANCE LEAD. PEOPLE ARE ALREADY SELLING THIS AS A SERVICE You're not prompting from scratch. You're hiring a department How to set it up: 1. Open Cowork. In the Claude desktop app, open Cowork and go to Customize. You'll see a Browse Plugins button. That's the marketplace 2. Pick your first role. Inside you'll find sales, marketing, finance, legal, data, customer support, product management and more. Each plugin has the skills for that role already baked in, plus the connectors it would use. Install the one you need most 3. Put it to work standalone. It works day one with no setup. Trigger a workflow with a slash command like /sales:call-prep or /data:write-query, paste your notes, and it already knows what good output looks like 4. Connect its tools to supercharge it. Authorize the tools that role uses, your CRM, your analytics, your data warehouse. Standalone is the intern. Connected is the senior hire 5. Build the rest of the team. Install more roles and they work together in one session. Data pulls the numbers, finance reconciles them, marketing turns it into a report. One operator, a full cross-functional team, no payroll This is the same base layer Claude for Legal and Claude for Financial Services are built on. You're getting it for free Full breakdown in my article below. Bookmark this

Yarchi

31,798 views • 5 months ago

your team put the deploy skill in the repo so everyone would run the same steps it works, and on your laptop it is not the one running the order is enterprise first, then personal, then project a folder of your own with the same name wins, quietly, on your machine only no error, no warning, the command runs and does something slightly different than it does for them and the command is the folder name, not the name field inside the file, so two skills collide over a directory you named months ago and forgot the shared checklist is shared right up until someone has a file of their own with the same name

your team put the deploy skill in the repo so everyone would run the same steps it works, and on your laptop it is not the one running the order is enterprise first, then personal, then project a folder of your own with the same name wins, quietly, on your machine only no error, no warning, the command runs and does something slightly different than it does for them and the command is the folder name, not the name field inside the file, so two skills collide over a directory you named months ago and forgot the shared checklist is shared right up until someone has a file of their own with the same name

Lagosaur

12,613 views • 1 month ago

The easiest way to find out which models you can run on your computer: Magnitude is a 100% free, open-source desktop app that profiles your machine and ranks the models across: - Speed - Accuracy - Intelligence - Memory required Then it downloads the one you pick and tunes it for your machine. Finally, connect your favorite harness (Pi, OpenCode, Claude Code, Codex, etc.) in one click. Get started here: (don't forget to star 🌟) My co-founder wrote a detailed article on running your favorite harnesses with local models. The article is quoted below.

The easiest way to find out which models you can run on your computer: Magnitude is a 100% free, open-source desktop app that profiles your machine and ranks the models across: - Speed - Accuracy - Intelligence - Memory required Then it downloads the one you pick and tunes it for your machine. Finally, connect your favorite harness (Pi, OpenCode, Claude Code, Codex, etc.) in one click. Get started here: (don't forget to star 🌟) My co-founder wrote a detailed article on running your favorite harnesses with local models. The article is quoted below.

Avi Chawla

15,465 views • 1 month ago