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Ana Sayfaya Dön

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... show more

Lagosaur

3,286 subscribers

12,609 görüntüleme • 13 gün önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

9 Yorum

Mempool in Plain English profil fotoğrafı

Mempool in Plain English13 gün önce

config precedence bugs are the quiet ones. same-name dir in cwd wins over repo skill and nothing logs it. spent an afternoon on this once, thought the script was broken

⊹ Sofia l, profil fotoğrafı

⊹ Sofia l,13 gün önce

Silent local overrides defeat the reproducibility argument. How do you prevent drift between machines when the same name folder can quietly change behavior?

努力配送每一天 profil fotoğrafı

努力配送每一天13 gün önce

morning street whole day makes can accommodate you win all

BG翻70小九 profil fotoğrafı

BG翻70小九12 gün önce

本地配置优先级反噬真的是团队噩梦

Bitget泛70西洲 profil fotoğrafı

Bitget泛70西洲12 gün önce

本地环境隐形陷阱太坑了

凡70 BG阿宁 profil fotoğrafı

凡70 BG阿宁12 gün önce

这种本地配置差异最坑了简直是开发者的噩梦

范70｜BG profil fotoğrafı

范70｜BG12 gün önce

本地覆盖生产配置真是团队协作的噩梦

小栖欧易50凡佣 profil fotoğrafı

小栖欧易50凡佣12 gün önce

这简直是埋在环境里的隐形地雷

南风高返85 Gate profil fotoğrafı

南风高返85 Gate12 gün önce

这种本地覆盖逻辑简直是团队协作的噩梦

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My whole company runs on skills I bundled into a GitHub repo. Here's how to build the same thing for your team: - Put your skills in a GitHub repo - Get Claude to write the couple of JSON files that make it readable as a plugin - In Claude Code, type /plugin, go to marketplaces, add marketplace, paste your repo URL - Split the plugins by department, so your copywriter isn't installing the finance skills It works in Codex too. I've got the same team skills installed there and I use them the same way. Now when I build something like my Notion formatting skill, I add it to the repo and it lands on everyone's Claude and Codex. If someone wrecks a skill, we roll it back to an older version. The key is enabling auto-update, so when someone improves a skill everyone benefits. I don't have a clue how the JSON works. I got Claude to set the whole thing up for me. Start with one skill and before you know it your whole team will be AI pilled.

My whole company runs on skills I bundled into a GitHub repo. Here's how to build the same thing for your team: - Put your skills in a GitHub repo - Get Claude to write the couple of JSON files that make it readable as a plugin - In Claude Code, type /plugin, go to marketplaces, add marketplace, paste your repo URL - Split the plugins by department, so your copywriter isn't installing the finance skills It works in Codex too. I've got the same team skills installed there and I use them the same way. Now when I build something like my Notion formatting skill, I add it to the repo and it lands on everyone's Claude and Codex. If someone wrecks a skill, we roll it back to an older version. The key is enabling auto-update, so when someone improves a skill everyone benefits. I don't have a clue how the JSON works. I got Claude to set the whole thing up for me. Start with one skill and before you know it your whole team will be AI pilled.

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Owner of the shop standing on stage and roasting her own shop. PSBs: record profit, low NPA, Viksit Bharat. Same speech: government bank, not cool, youth didn’t name even one. You own it. You starve the staff and the system. Then you say the face is ugly. That is not branding. That is spit on your own mirror.

Owner of the shop standing on stage and roasting her own shop. PSBs: record profit, low NPA, Viksit Bharat. Same speech: government bank, not cool, youth didn’t name even one. You own it. You starve the staff and the system. Then you say the face is ugly. That is not branding. That is spit on your own mirror.

TheBanker’sMirror

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Different stories, different backgrounds, same love 🏀 Own the game, own your name and own your influence. A new era is coming soon. Don't miss it. The NBPA has officially launched #PLYRSUNTD.

Different stories, different backgrounds, same love 🏀 Own the game, own your name and own your influence. A new era is coming soon. Don't miss it. The NBPA has officially launched #PLYRSUNTD.

The NBPA

34,163 görüntüleme • 3 ay önce

The truck driver only asked for the name, the answer was "Whatever is your father's, the same is mine ” Name and badge number ! It is a fundamental standard of transparency that Police officers provide their name and badge number upon request by citizens

The truck driver only asked for the name, the answer was "Whatever is your father's, the same is mine ” Name and badge number ! It is a fundamental standard of transparency that Police officers provide their name and badge number upon request by citizens

copwatchindia

19,630 görüntüleme • 6 ay önce

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 görüntüleme • 6 ay önce

Your Claude has a tell, and almost nobody uses it. Make it start every reply with your name. One line in custom instructions, no code. The moment it stops saying your name, its context window is full. That is the exact point the output starts drifting, and you do not notice. Name stops? Start a fresh chat. It has killed about 90% of my “why has Claude gone weird” moments. Miners sent a canary down the pit to catch the gas they could not see. Same trick, for your context. Comment GUIDE and I’ll send you the exact canary setup.

Your Claude has a tell, and almost nobody uses it. Make it start every reply with your name. One line in custom instructions, no code. The moment it stops saying your name, its context window is full. That is the exact point the output starts drifting, and you do not notice. Name stops? Start a fresh chat. It has killed about 90% of my “why has Claude gone weird” moments. Miners sent a canary down the pit to catch the gas they could not see. Same trick, for your context. Comment GUIDE and I’ll send you the exact canary setup.

Charlie Hills

24,177 görüntüleme • 2 ay önce

your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below

your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below

Hanako

14,325 görüntüleme • 2 ay önce

your eval measured one system. production ran another. they start from the same idea and end somewhere else, which is why the number was right and the outcome was not. the code is the same until someone rewrites it for prod, because the eval lived in a notebook and prod is a service. then the data diverges. clean fixtures in the test, late and incomplete data in the run. then execution diverges. the eval assumed every call lands. production has retries, timeouts and rate limits. by the end the two runs share a name and nothing else. > why nobody catches it both sides are internally correct. the eval measured what it ran. production ran what it was given. neither one is wrong, and neither one is responsible for checking they were the same artifact. so the gap widens quietly. someone patches prod without touching the eval. someone adds a fixture without touching prod. no test anywhere fails. > what to actually do ship one artifact. the code that was measured is the code that runs. model the friction. whatever the real environment charges you, charge it in the eval too. easiest way to see the gap on your own idea, without writing the harness: (only first 100 registrated) it runs the tested code live, unchanged. that is the whole point. and treat the eval as a prior, not a result. it tells you what to expect. only the live run tells you what happened. your eval was accurate. it was accurate about something you never shipped.

your eval measured one system. production ran another. they start from the same idea and end somewhere else, which is why the number was right and the outcome was not. the code is the same until someone rewrites it for prod, because the eval lived in a notebook and prod is a service. then the data diverges. clean fixtures in the test, late and incomplete data in the run. then execution diverges. the eval assumed every call lands. production has retries, timeouts and rate limits. by the end the two runs share a name and nothing else. > why nobody catches it both sides are internally correct. the eval measured what it ran. production ran what it was given. neither one is wrong, and neither one is responsible for checking they were the same artifact. so the gap widens quietly. someone patches prod without touching the eval. someone adds a fixture without touching prod. no test anywhere fails. > what to actually do ship one artifact. the code that was measured is the code that runs. model the friction. whatever the real environment charges you, charge it in the eval too. easiest way to see the gap on your own idea, without writing the harness: (only first 100 registrated) it runs the tested code live, unchanged. that is the whole point. and treat the eval as a prior, not a result. it tells you what to expect. only the live run tells you what happened. your eval was accurate. it was accurate about something you never shipped.

Hanako

45,239 görüntüleme • 1 ay önce

I replaced Claude Code’s default --worktree command with a custom one built around Ghostty, Lazygit, and Yazi. By default, Claude Code creates worktrees inside the .claude/worktrees folder within the same project. That means if you spin up 3 worktrees, you end up with 3 complete copies of your project nested inside your main project. It makes the project structure messy and difficult to manage. So I built a hook that: - Overrides the default --worktree command - Creates each branch in a sibling directory: ../worktrees/branch-name - Automatically opens Ghostty panes - Launches Lazygit and Yazi already positioned in the correct branch directory Install it with: npx claude-code-templates@latest --hook=development-tools/worktree-ghostty --yes Now each branch lives where it should, outside the main project, and your terminal environment is ready instantly.

I replaced Claude Code’s default --worktree command with a custom one built around Ghostty, Lazygit, and Yazi. By default, Claude Code creates worktrees inside the .claude/worktrees folder within the same project. That means if you spin up 3 worktrees, you end up with 3 complete copies of your project nested inside your main project. It makes the project structure messy and difficult to manage. So I built a hook that: - Overrides the default --worktree command - Creates each branch in a sibling directory: ../worktrees/branch-name - Automatically opens Ghostty panes - Launches Lazygit and Yazi already positioned in the correct branch directory Install it with: npx claude-code-templates@latest --hook=development-tools/worktree-ghostty --yes Now each branch lives where it should, outside the main project, and your terminal environment is ready instantly.

Daniel San

67,005 görüntüleme • 7 ay önce

The loop has a name. And it runs in five steps. 1.Something feels heavy. 2. You reach for your poison. 3. You get the hit. 4. The relief dissipates, and you're lower than square one. 5. You start over. That circle, on repeat, is the loop. And notice: I didn't say feet. The endless scroll. The news. The 1am rabbit hole. A game. A pint. The machine runs the same either way. What's your poison? Tell me below. The FREE Kink Shame Exit Kit walks you out of it, step by step. 👉

The loop has a name. And it runs in five steps. 1.Something feels heavy. 2. You reach for your poison. 3. You get the hit. 4. The relief dissipates, and you're lower than square one. 5. You start over. That circle, on repeat, is the loop. And notice: I didn't say feet. The endless scroll. The news. The 1am rabbit hole. A game. A pint. The machine runs the same either way. What's your poison? Tell me below. The FREE Kink Shame Exit Kit walks you out of it, step by step. 👉

Command Within | Build Self-Command

34,546 görüntüleme • 1 ay önce

#WATCH | Delhi: Chief Election Commissioner Gyanesh Kumar says, "Duplicate EPICs can happen in two ways. One, that a person who is in West Bengal, who is a different person, has one EPIC number and another person who is in Haryana has the same EPIC number. When this question came up around March 2025, we discussed it and we solved it across the country. About three lakh such people were found, whose EPIC number were the same, so their EPIC numbers were changed. The second type of duplication comes when the name of the same person is in the voter list at more than one place and his EPIC number is different. That is, one person, many EPICs... Before 2003, if you wanted to get your name deleted from the old place, then there was no website of the Election Commission, which had all the data at one place... So, since technical facilities were not available before 2003, many such people who migrated to different places, their names were added to many places. Then a question arose that today there is a website, there is a computer, you can select and delete it. The Election Commission stands like a rock with the voters... So, if this is done in a hurry, then any voter's name can be deleted wrongly. Someone else's name will be deleted in your place..."

#WATCH | Delhi: Chief Election Commissioner Gyanesh Kumar says, "Duplicate EPICs can happen in two ways. One, that a person who is in West Bengal, who is a different person, has one EPIC number and another person who is in Haryana has the same EPIC number. When this question came up around March 2025, we discussed it and we solved it across the country. About three lakh such people were found, whose EPIC number were the same, so their EPIC numbers were changed. The second type of duplication comes when the name of the same person is in the voter list at more than one place and his EPIC number is different. That is, one person, many EPICs... Before 2003, if you wanted to get your name deleted from the old place, then there was no website of the Election Commission, which had all the data at one place... So, since technical facilities were not available before 2003, many such people who migrated to different places, their names were added to many places. Then a question arose that today there is a website, there is a computer, you can select and delete it. The Election Commission stands like a rock with the voters... So, if this is done in a hurry, then any voter's name can be deleted wrongly. Someone else's name will be deleted in your place..."

ANI

20,452 görüntüleme • 1 yıl önce

This is literally the easiest way to build an AI agent. Zero code. You only need to run a couple of commands. (I've said this before, but Google ADK is my favorite way to build code-first agents.) Here is what you need to do: 1. Open your terminal and create an empty folder 2. Run `uv init` inside the folder (if you don't have uv installed, you are really missing out!) 3. Install the google-adk library using the following command: `uv add google-adk`. 4. At this point, you can use the ask to create your agent with a single command: `uv run adk create --type=config my_agent`. This will ask you a couple of questions, including your GEMINI_API_KEY (you can create one online). 5. The command above will create a my_agent/root_agent.yaml file. Open it and add the following two lines at the end of the file so your agent can use Google to search for stuff: tools: - name: google_search 6. You have everything you need now. Run the adk web interface and start playing with your agent: `uv run adk web` That's it! Literally zero code. The agent works off the YAML configuration file.

This is literally the easiest way to build an AI agent. Zero code. You only need to run a couple of commands. (I've said this before, but Google ADK is my favorite way to build code-first agents.) Here is what you need to do: 1. Open your terminal and create an empty folder 2. Run `uv init` inside the folder (if you don't have uv installed, you are really missing out!) 3. Install the google-adk library using the following command: `uv add google-adk`. 4. At this point, you can use the ask to create your agent with a single command: `uv run adk create --type=config my_agent`. This will ask you a couple of questions, including your GEMINI_API_KEY (you can create one online). 5. The command above will create a my_agent/root_agent.yaml file. Open it and add the following two lines at the end of the file so your agent can use Google to search for stuff: tools: - name: google_search 6. You have everything you need now. Run the adk web interface and start playing with your agent: `uv run adk web` That's it! Literally zero code. The agent works off the YAML configuration file.

Santiago

140,922 görüntüleme • 10 ay önce

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.

Akshay 🚀

51,540 görüntüleme • 4 ay önce

SpaceXAI is working on Team Bots for Grok Bot. Open Team bots to see agents your teammates shared, then add one to keep it in your sidebar so everyone on the team can work from the same bots. This likely is being built and coming soon for Team and Enterprise accounts.

SpaceXAI is working on Team Bots for Grok Bot. Open Team bots to see agents your teammates shared, then add one to keep it in your sidebar so everyone on the team can work from the same bots. This likely is being built and coming soon for Team and Enterprise accounts.

️

26,652 görüntüleme • 21 gün önce

BlackRock runs on 20,000 people. Elon's Grok Bot runs the same shape for $300 a month, and it hires its own staff. You do not get an assistant. You get a company that hires. It does not throw ten agents at your problem and hand you the pile. It makes one agent that makes 10, and those ten make a 100. > LAYER ONE is one agent, the chief of staff, and it never touches the market > LAYER TWO is six desk heads, one job each, every one on its own computer with its own logins > LAYER THREE is whatever those six decide they need, spun up on the spot and shut down when the work is done Nobody writes a task list. You hand out job titles and the org fills itself in underneath. The swarm is never the same twice. Agents get spun up for one job, finish it, and are gone before I ever read their names. Not one of them sees the whole picture. The answer only exists after they hand off to each other. Wall Street cannot copy that. You cannot hire a hundred people for eleven minutes. BlackRock holds that shape together with a risk system called Aladdin. Mine holds it together with one agent that is only allowed to say no. I gave it $1,000 and told it to grow the money or get deleted. 15 hours later it was holding $3,900, on an address anyone can open and read. I was asleep for most of it, and I have still not written a line of code. The whole thing runs with my laptop shut, because none of it lives on my laptop. Setup is one evening. Create the chief, hand out the titles, run one trade on your screen while they watch, connect Telegram. Ten years ago a machine this shape had its name on a tower. Mine has a name I typed into a box. Save this while the whole thing still fits on one screen.

BlackRock runs on 20,000 people. Elon's Grok Bot runs the same shape for $300 a month, and it hires its own staff. You do not get an assistant. You get a company that hires. It does not throw ten agents at your problem and hand you the pile. It makes one agent that makes 10, and those ten make a 100. > LAYER ONE is one agent, the chief of staff, and it never touches the market > LAYER TWO is six desk heads, one job each, every one on its own computer with its own logins > LAYER THREE is whatever those six decide they need, spun up on the spot and shut down when the work is done Nobody writes a task list. You hand out job titles and the org fills itself in underneath. The swarm is never the same twice. Agents get spun up for one job, finish it, and are gone before I ever read their names. Not one of them sees the whole picture. The answer only exists after they hand off to each other. Wall Street cannot copy that. You cannot hire a hundred people for eleven minutes. BlackRock holds that shape together with a risk system called Aladdin. Mine holds it together with one agent that is only allowed to say no. I gave it $1,000 and told it to grow the money or get deleted. 15 hours later it was holding $3,900, on an address anyone can open and read. I was asleep for most of it, and I have still not written a line of code. The whole thing runs with my laptop shut, because none of it lives on my laptop. Setup is one evening. Create the chief, hand out the titles, run one trade on your screen while they watch, connect Telegram. Ten years ago a machine this shape had its name on a tower. Mine has a name I typed into a box. Save this while the whole thing still fits on one screen.

cvxv666

45,488 görüntüleme • 1 ay önce

Anthropic just 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. I joined a shared space and asked my team member for a status update. My 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 🌟) My co-founder also wrote a great article on building your second brain with Rowboat, and I highly recommend reading it as well. The article is quoted below.

Anthropic just 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. I joined a shared space and asked my team member for a status update. My 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 🌟) My co-founder also wrote a great article on building your second brain with Rowboat, and I highly recommend reading it as well. The article is quoted below.

Akshay 🚀

117,616 görüntüleme • 21 gün önce

James Talarico is running around Texas pitching the warm, fuzzy idea that every religion is just a different "pathway" to the exact same fundamental truth. They're NOT. We can still be courteous to each other, but that's not the same as having the same truth. When anyone tries to sell you this version of "unity," they are asking you to throw out the God that has a name, the God who deals with you as an individual, and replace Him with a meaningless average. Because the average of everything is NOTHING. You are going to hear this exact narrative pushed in your own community, your own church, and your own life. THIS is how to stand against it.

James Talarico is running around Texas pitching the warm, fuzzy idea that every religion is just a different "pathway" to the exact same fundamental truth. They're NOT. We can still be courteous to each other, but that's not the same as having the same truth. When anyone tries to sell you this version of "unity," they are asking you to throw out the God that has a name, the God who deals with you as an individual, and replace Him with a meaningless average. Because the average of everything is NOTHING. You are going to hear this exact narrative pushed in your own community, your own church, and your own life. THIS is how to stand against it.

Glenn Beck

66,219 görüntüleme • 28 gün önce

Y Combinator CEO, Garry Tan, took the stage for 42 minutes at Startup School 2026 and explained how to build your own personal AGI better than any paid AI course. This is what he told the room: 1. The leverage is in your context, not the model. Tan watches hundreds of founders use identical models every batch. "There are 2x people and there are 100x people who are using the same Claude. Same weights, same context window size, same API. But the leverage is not in the weights." The gap between users is now bigger than the gap between models. 2. One person's output went up 400x. In 2013 Tan shipped maybe 14 useful lines of code a day as a YC partner, dead on the median for programmer productivity. "I did the math on my output, and I'm at about 400x what I did in 2013." 3. Agents run on a different working memory. Humans hold 7 things in their head at once. Every org chart and checklist ever built is a patch for that limit. "An AI agent holds a million tokens. That's about a thousand pages. Three Harry Potter books sitting open on its head all at once." You're still running your week on tools built for the 7-digit brain. 4. Markdown is code now. Tan's stack is mostly skill files: pages of plain English an agent can execute. "If you can write clear instructions in English, you're a programmer. The compiler is a language model." At YC, finance and events staff who never opened a terminal are building automations. 5. Your history is your moat. Tan's agent runs on a personal wiki: about 220,000 markdown pages covering 25 years of email, meetings, notes and decisions. "When my agent does anything, it does knowing everything I know. And that's the difference between an assistant and a colleague." No frontier model has your context. That's the one asset nobody can replicate. 6. Never do one-off work. Most people run a task with an agent, close the window and throw the learning away. Tan ends every task by having the agent turn what it did into a reusable skill file. "If you have to ask for something twice, you failed." Captured skills compound daily. Amnesia resets you to zero every morning. 7. Own your skill files before your employer does. A skill file is your judgment, extracted and executable. The only question is who controls it. "Own your skills because if you don't, your job becomes a skill file." Files in your repo compound your career. Files in the company's repo run your judgment without you. Watch it, then read the step-by-step guide on becoming an AI engineer.

Y Combinator CEO, Garry Tan, took the stage for 42 minutes at Startup School 2026 and explained how to build your own personal AGI better than any paid AI course. This is what he told the room: 1. The leverage is in your context, not the model. Tan watches hundreds of founders use identical models every batch. "There are 2x people and there are 100x people who are using the same Claude. Same weights, same context window size, same API. But the leverage is not in the weights." The gap between users is now bigger than the gap between models. 2. One person's output went up 400x. In 2013 Tan shipped maybe 14 useful lines of code a day as a YC partner, dead on the median for programmer productivity. "I did the math on my output, and I'm at about 400x what I did in 2013." 3. Agents run on a different working memory. Humans hold 7 things in their head at once. Every org chart and checklist ever built is a patch for that limit. "An AI agent holds a million tokens. That's about a thousand pages. Three Harry Potter books sitting open on its head all at once." You're still running your week on tools built for the 7-digit brain. 4. Markdown is code now. Tan's stack is mostly skill files: pages of plain English an agent can execute. "If you can write clear instructions in English, you're a programmer. The compiler is a language model." At YC, finance and events staff who never opened a terminal are building automations. 5. Your history is your moat. Tan's agent runs on a personal wiki: about 220,000 markdown pages covering 25 years of email, meetings, notes and decisions. "When my agent does anything, it does knowing everything I know. And that's the difference between an assistant and a colleague." No frontier model has your context. That's the one asset nobody can replicate. 6. Never do one-off work. Most people run a task with an agent, close the window and throw the learning away. Tan ends every task by having the agent turn what it did into a reusable skill file. "If you have to ask for something twice, you failed." Captured skills compound daily. Amnesia resets you to zero every morning. 7. Own your skill files before your employer does. A skill file is your judgment, extracted and executable. The only question is who controls it. "Own your skills because if you don't, your job becomes a skill file." Files in your repo compound your career. Files in the company's repo run your judgment without you. Watch it, then read the step-by-step guide on becoming an AI engineer.

Alex Prompter

250,159 görüntüleme • 2 ay önce

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 görüntüleme • 23 gün önce

ANTHROPIC LEAKED 6 RULES THAT LET ONE FOLDER REPLACE A WHOLE AI ENGINEERING TEAM COSTING $3.2M A YEAR folder with 6 files replaced the AI engineers - and the team that spent 4 years writing them isn't needed anymore. file → commit → description matches → loads → runs → leaves a prompt has no history, a folder has a git log - one commit updates every session at once, and every edit shows up in the diff. two prompts fight over one window - two skills just both apply, and nobody merges anything by hand. 8 skills sit on disk, one enters the context - the other seven cost zero tokens until the description matches. a prompt can only ask - the folder carries build py and template.docx inside it, so the deterministic parts stay deterministic. the same skill works in Claude Code, in Cowork and through the API - a prompt lives only in the window you pasted it into. and the main one: a skill leaves the window when it's done - a prompt never leaves. save this - 6 rules, one folder, zero copy-paste ↓

ANTHROPIC LEAKED 6 RULES THAT LET ONE FOLDER REPLACE A WHOLE AI ENGINEERING TEAM COSTING $3.2M A YEAR folder with 6 files replaced the AI engineers - and the team that spent 4 years writing them isn't needed anymore. file → commit → description matches → loads → runs → leaves a prompt has no history, a folder has a git log - one commit updates every session at once, and every edit shows up in the diff. two prompts fight over one window - two skills just both apply, and nobody merges anything by hand. 8 skills sit on disk, one enters the context - the other seven cost zero tokens until the description matches. a prompt can only ask - the folder carries build py and template.docx inside it, so the deterministic parts stay deterministic. the same skill works in Claude Code, in Cowork and through the API - a prompt lives only in the window you pasted it into. and the main one: a skill leaves the window when it's done - a prompt never leaves. save this - 6 rules, one folder, zero copy-paste ↓

Sprytix

11,389 görüntüleme • 1 ay önce