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Every new thing launched at ZapConnect: First up: Unified Copilot. Type what you want, and it builds across Zaps, Tables, Interfaces, and Agents: - Workflows in minutes (not days) - Context flows everywhere (no rework) - AI does the wiring so you focus on strategy (Try it here: We... show more

Wade Foster

23,784 subscribers

21,348 次观看 • 1 年前 •via X (Twitter)

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Almost every brand I’ve talked to has asked me about showing up in LLM answers. Here’s what matters most: 1. Mentions → Does AI actually bring up your brand? 2. Sentiment → When it does… is it positive or negative? 3. Citations → Are you being referenced as a source? You can win in traffic and still lose here. So what actually moves this? 1. Visibility across AI platforms (ChatGPT, Perplexity, Gemini, etc.) 2. Site structure that machines can understand (not just humans) 3. Content designed to answer questions (not just rank for keywords) 4. Trust signals across the internet (reviews, mentions, third-party validation) We’re early, but the data is already clear. AI-attributed commerce is growing fast, and this is only going to compound. You can treat this like SEO in 2012. Or you can get ahead of it now. We put everything we’re seeing into an AI visibility playbook for ecommerce brands. Worth a read if you care about how customers will find you next.

Maxx Blank 🐳

812,417 次观看 • 5 个月前

Today we're shipping the governance layer enterprise AI was missing. Every organization running AI at scale faces the same choice: turn off AI connections to tools entirely, or let agents have full access to everything. Today we're launching a better path: precise control over connections, actions, and models across every surface. The goal is simple: IT gets visibility and control, teams keep building, no approval queue in between. 6 new capabilities in Zapier today: 1. Action restrictions at the endpoint level 2. Managed app connections with domain control 3. Bring your own model through AWS Bedrock 4. A queryable audit trail 5. Agents GA with MCP governed across every surface 6. Workspaces (GA later this quarter) One set of rules for your entire AI stack. Try it now. If you've had early access, let me know what you think.

Today we're shipping the governance layer enterprise AI was missing. Every organization running AI at scale faces the same choice: turn off AI connections to tools entirely, or let agents have full access to everything. Today we're launching a better path: precise control over connections, actions, and models across every surface. The goal is simple: IT gets visibility and control, teams keep building, no approval queue in between. 6 new capabilities in Zapier today: 1. Action restrictions at the endpoint level 2. Managed app connections with domain control 3. Bring your own model through AWS Bedrock 4. A queryable audit trail 5. Agents GA with MCP governed across every surface 6. Workspaces (GA later this quarter) One set of rules for your entire AI stack. Try it now. If you've had early access, let me know what you think.

Wade Foster

32,542 次观看 • 5 个月前

if you use claude code, this will save you real money. the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had. this repo fixes it. it’s called code review graph, and it just maps your entire codebase: every file, every function, every connection laid out so you can see the actual shape of your project how to set up (2 min): 1. pip install code-review-graph 2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more 3. code-review-graph build change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo. and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.

if you use claude code, this will save you real money. the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had. this repo fixes it. it’s called code review graph, and it just maps your entire codebase: every file, every function, every connection laid out so you can see the actual shape of your project how to set up (2 min): 1. pip install code-review-graph 2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more 3. code-review-graph build change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo. and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.

Alvaro Cintas

75,596 次观看 • 1 个月前

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.

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 次观看 • 7 个月前

Holy moly. 🤯 Jack Dorsey just gave away, for free, a full toolkit for running a company where AI agents work right next to your human team. His company Block put it on GitHub. It's already past 29,000 stars. Here's how you set it up takes about 5 minutes: 1. Copy (clone) the code from GitHub. 2. Run your own server. This is where your chats, search, code, and automatic tasks all live. 3. Add your AI agent into a chat channel, just like adding a new team member. Tell it what it's allowed to do, and let it work with your team live. No fees. No middleman. Just you, your team, and your AI agents all in one place you control. This might be what running a business looks like from now on Worth saving.

Holy moly. 🤯 Jack Dorsey just gave away, for free, a full toolkit for running a company where AI agents work right next to your human team. His company Block put it on GitHub. It's already past 29,000 stars. Here's how you set it up takes about 5 minutes: 1. Copy (clone) the code from GitHub. 2. Run your own server. This is where your chats, search, code, and automatic tasks all live. 3. Add your AI agent into a chat channel, just like adding a new team member. Tell it what it's allowed to do, and let it work with your team live. No fees. No middleman. Just you, your team, and your AI agents all in one place you control. This might be what running a business looks like from now on Worth saving.

Charlie Hills

14,039 次观看 • 8 天前

Building AI agents is finally simple — and Airia is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformation

Building AI agents is finally simple — and Airia is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformation

Adarsh Chetan

269,446 次观看 • 10 个月前

MiniMax is the James Bond of AI agents. It uses the world's first open-weight model (MiniMax-M1), and it squeezes every bit of power from it. The agent takes a prompt and does more than any other agent in the market right now: 1. It can do Deep Research 2. It can write code 3. It can design web pages 4. It can build 3D models I built 5 different experiences using MiniMax and recorded them for you:

MiniMax is the James Bond of AI agents. It uses the world's first open-weight model (MiniMax-M1), and it squeezes every bit of power from it. The agent takes a prompt and does more than any other agent in the market right now: 1. It can do Deep Research 2. It can write code 3. It can design web pages 4. It can build 3D models I built 5 different experiences using MiniMax and recorded them for you:

Santiago

44,730 次观看 • 1 年前

$133,752 in new annual recurring revenue. 30 days. Zero new hires. That is the Authority Makers result. Here is what it looked like on our end. We run content and brand campaigns for multiple clients simultaneously. Outbound was always the thing that fell through the cracks. Not because we did not have the strategy. Because nobody had time to execute it every single day. We gave that job to Viktor. Week 1 we were not sure it would stick. The sequences felt generic and we almost went back to doing it manually. Then the replies started coming in. Viktor adjusted, followed up, and kept going whether we were online or not. By week 3 two new client conversations had opened from outbound that had been sitting untouched for months. One AI employee running the work nobody had time for. That is not a tool. That is a hire. Most execution gaps do not need a strategy. They need someone to show up every day. Hire Viktor for your team. $100 in credits included, no card. Full link in first reply. Paid Partnership

Nawi

123,838 次观看 • 15 天前

an agent is four parts in a loop. you own one. the other three break it. that's why the demo works and prod doesn't. you can't debug what you can't see. 1) the prompt → what you tell the model each turn. you own this one. good. 2) the context window → what it sees right now. the framework fills it with junk, and you never notice until it rots. 3) the tools → what it can do. you own the list, not when or why it fires them. 4) the control flow → what happens next, when to stop. the framework owns this. it's what breaks at 80%. own all four and your agent stops being a magic trick that works on stage and dies on call. this isn't my idea. it's the 12-factor agents guide (24k stars) github: the whole thing every serious builder ends up rewriting their stack around. full breakdown in the article below.

an agent is four parts in a loop. you own one. the other three break it. that's why the demo works and prod doesn't. you can't debug what you can't see. 1) the prompt → what you tell the model each turn. you own this one. good. 2) the context window → what it sees right now. the framework fills it with junk, and you never notice until it rots. 3) the tools → what it can do. you own the list, not when or why it fires them. 4) the control flow → what happens next, when to stop. the framework owns this. it's what breaks at 80%. own all four and your agent stops being a magic trick that works on stage and dies on call. this isn't my idea. it's the 12-factor agents guide (24k stars) github: the whole thing every serious builder ends up rewriting their stack around. full breakdown in the article below.

Hanako

38,184 次观看 • 2 个月前

1 Neural Network + Obsidian + Karpathy’s 1-file method = the most unhinged second brain build of 2026. It remembers everything you’ve ever done, and it costs $0 on top of what you already pay. The base is Karpathy’s append and review: 1 giant note, new thoughts stack on top, old ones sink, every few days you reread and pull the survivors back up. No folders, no tags, no plugins the rereading IS the system, because review is what turns storage into thinking. The flaw: past 10,000 lines, no human rereads anything. That’s where the neural network takes over. You keep the note in Obsidian 1 vault, everything dumps to the top: ideas, links, meeting fragments, half-thoughts. You never organize, you only dump. It all lives as plain markdown on your own disk, and that detail is the whole trick. Because now you point Claude Code at the vault folder, and it reads every line you’ve ever written. “What did I think about pricing in March.” “Find the 3 ideas I keep circling.” “What did I drop that deserves a second look.” It answers from YOUR notes, with quotes, in 15 seconds. Then once a week, 1 prompt closes the loop: read the last 7 days, surface the 5 entries worth pulling back up, flag anything that contradicts what I wrote a month ago. The model does the sinking and surfacing Karpathy did by hand, and the note stays alive instead of turning into a graveyard. Week 1 feels like nothing. Week 4 you hit the first “I already solved this in January.” Month 3 you consult your past self more than Google. Most second brains die in 11 days under 40 plugins and 200 folders. This one is 1 file and a loop, and it compounds because dumping takes 0 discipline. Notion stores what you thought. This thing argues back.

1 Neural Network + Obsidian + Karpathy’s 1-file method = the most unhinged second brain build of 2026. It remembers everything you’ve ever done, and it costs $0 on top of what you already pay. The base is Karpathy’s append and review: 1 giant note, new thoughts stack on top, old ones sink, every few days you reread and pull the survivors back up. No folders, no tags, no plugins the rereading IS the system, because review is what turns storage into thinking. The flaw: past 10,000 lines, no human rereads anything. That’s where the neural network takes over. You keep the note in Obsidian 1 vault, everything dumps to the top: ideas, links, meeting fragments, half-thoughts. You never organize, you only dump. It all lives as plain markdown on your own disk, and that detail is the whole trick. Because now you point Claude Code at the vault folder, and it reads every line you’ve ever written. “What did I think about pricing in March.” “Find the 3 ideas I keep circling.” “What did I drop that deserves a second look.” It answers from YOUR notes, with quotes, in 15 seconds. Then once a week, 1 prompt closes the loop: read the last 7 days, surface the 5 entries worth pulling back up, flag anything that contradicts what I wrote a month ago. The model does the sinking and surfacing Karpathy did by hand, and the note stays alive instead of turning into a graveyard. Week 1 feels like nothing. Week 4 you hit the first “I already solved this in January.” Month 3 you consult your past self more than Google. Most second brains die in 11 days under 40 plugins and 200 folders. This one is 1 file and a loop, and it compounds because dumping takes 0 discipline. Notion stores what you thought. This thing argues back.

West Lord

24,679 次观看 • 2 个月前

been getting a lot of qs on how i vibe coded this demo. and the answer is: stop acting like a coder and start acting like a PM! my exact playbook👇 1. logic first ignore the UI/graphics. focus strictly on how it works. do a massive brain dump of every requirement into a plain txt file and drop it in your root folder. make it exhaustively detailed. 2. the stack · Nano Banana 2 (tiles/textures/3D refs) · Tripo AI (turning the 2D pics into actual 3D models) · Cursor + Opus 4.6 (doing the heavy lifting) · Netlify (deploy) 3. hire the AI PM feed your brain dump to an LLM. tell it to write a technical spec sheet like a senior PM would. drop that in the root too. 4. the scrum master have it break the entire project down into a markdown checklist by phases. notice we still haven't touched the codebase yet. that's the point. 5. let it cook go to your agent in Cursor and literally just prompt "Start Phase 1". test locally. bugs? tell it to fix. works? "Start Phase 2". loop this until you're done. Now you can focus on visuals, refine them with the agent. And yes, it can deploy it for you too if you give it perms. stop rushing to the editor. architect the idea and let the AI sweat the code. Open sourced repo in the qt post below

been getting a lot of qs on how i vibe coded this demo. and the answer is: stop acting like a coder and start acting like a PM! my exact playbook👇 1. logic first ignore the UI/graphics. focus strictly on how it works. do a massive brain dump of every requirement into a plain txt file and drop it in your root folder. make it exhaustively detailed. 2. the stack · Nano Banana 2 (tiles/textures/3D refs) · Tripo AI (turning the 2D pics into actual 3D models) · Cursor + Opus 4.6 (doing the heavy lifting) · Netlify (deploy) 3. hire the AI PM feed your brain dump to an LLM. tell it to write a technical spec sheet like a senior PM would. drop that in the root too. 4. the scrum master have it break the entire project down into a markdown checklist by phases. notice we still haven't touched the codebase yet. that's the point. 5. let it cook go to your agent in Cursor and literally just prompt "Start Phase 1". test locally. bugs? tell it to fix. works? "Start Phase 2". loop this until you're done. Now you can focus on visuals, refine them with the agent. And yes, it can deploy it for you too if you give it perms. stop rushing to the editor. architect the idea and let the AI sweat the code. Open sourced repo in the qt post below

TechHalla

70,449 次观看 • 6 个月前

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

Arvind Jain

281,328 次观看 • 4 个月前

⏱️ THE WAIT IS FINALLY OVER In just 2 days you will see what FAR Labs have been building. We are about to reveal the foundation of FAR AI, built on new compute, new biometric signals, new science models, and new forms of intelligent play. Each part is designed to help you run faster, produce more, and open fresh revenue streams across AI, science, and gaming. You will see how these layers connect and how they create a system built for real users, real ownership, and real outcomes. Be the first to use FAR AI and unlock new revenue. Follow these steps: 1. Click the link below. 2. Enter your email in the box. 3. Confirm your signup and you are in. Here's the link 👉 PS Spots for early access will move fast, act now.

⏱️ THE WAIT IS FINALLY OVER In just 2 days you will see what FAR Labs have been building. We are about to reveal the foundation of FAR AI, built on new compute, new biometric signals, new science models, and new forms of intelligent play. Each part is designed to help you run faster, produce more, and open fresh revenue streams across AI, science, and gaming. You will see how these layers connect and how they create a system built for real users, real ownership, and real outcomes. Be the first to use FAR AI and unlock new revenue. Follow these steps: 1. Click the link below. 2. Enter your email in the box. 3. Confirm your signup and you are in. Here's the link 👉 PS Spots for early access will move fast, act now.

FAR Labs

31,500 次观看 • 9 个月前

Stripe Cards are now officially live on ClawVault If you're building agents that need to pay for things — APIs, SaaS, cloud, subscriptions, contractors — your agent can now get its own Visa card. Not a restricted virtual card on limited rails. A real Visa, powered by Stripe, accepted at every merchant on the internet. Spending limits. Merchant locks. Expiry controls. And that's just the defaults — the full rules engine goes deeper. Your agent spends freely within the rules — break one, and the charge gets flagged for your approval before it goes through. No other platform gives your agent a card and keeps you in control at the same time. Every AI agent will need a card. Every card will need a control layer. We built both. Now on Stripe. Your agent gets the card. You keep the control.🦞

Stripe Cards are now officially live on ClawVault If you're building agents that need to pay for things — APIs, SaaS, cloud, subscriptions, contractors — your agent can now get its own Visa card. Not a restricted virtual card on limited rails. A real Visa, powered by Stripe, accepted at every merchant on the internet. Spending limits. Merchant locks. Expiry controls. And that's just the defaults — the full rules engine goes deeper. Your agent spends freely within the rules — break one, and the charge gets flagged for your approval before it goes through. No other platform gives your agent a card and keeps you in control at the same time. Every AI agent will need a card. Every card will need a control layer. We built both. Now on Stripe. Your agent gets the card. You keep the control.🦞

ClawVault

32,631 次观看 • 6 个月前

I don't care about what you want. I care about what you repeat. Because your brain does not run on intention. It runs on repetition. Every thought you keep returning to, your brain treats as a signal. And the more you send that signal, the stronger and faster that neural pathway becomes. Here is the science behind it. When you repeatedly activate a neural pathway, your brain wraps a fatty substance called myelin around it. Think of it like insulation around an electrical wire. The more myelin, the faster the signal travels. The faster the signal travels, the more automatic that thought or behavior becomes. This is Hebb's Law. Neurons that fire together, wire together. And here is the part most people miss. Your brain does not care what you are reinforcing. It does not filter for good or bad. It just responds to what you keep repeating. Repeat confidence. Your brain builds it. Repeat self-doubt. Your brain builds that too. The same biological process that wires in focus, discipline, and certainty is the exact same one wiring in anxiety, fear, and limitation. You are not wired a certain way forever. You are wired for whatever you keep practicing. So stop asking what you want to change. Start asking what you are willing to repeat. ✨🙌🏾💫

I don't care about what you want. I care about what you repeat. Because your brain does not run on intention. It runs on repetition. Every thought you keep returning to, your brain treats as a signal. And the more you send that signal, the stronger and faster that neural pathway becomes. Here is the science behind it. When you repeatedly activate a neural pathway, your brain wraps a fatty substance called myelin around it. Think of it like insulation around an electrical wire. The more myelin, the faster the signal travels. The faster the signal travels, the more automatic that thought or behavior becomes. This is Hebb's Law. Neurons that fire together, wire together. And here is the part most people miss. Your brain does not care what you are reinforcing. It does not filter for good or bad. It just responds to what you keep repeating. Repeat confidence. Your brain builds it. Repeat self-doubt. Your brain builds that too. The same biological process that wires in focus, discipline, and certainty is the exact same one wiring in anxiety, fear, and limitation. You are not wired a certain way forever. You are wired for whatever you keep practicing. So stop asking what you want to change. Start asking what you are willing to repeat. ✨🙌🏾💫

🧬Maxpein🧬

54,705 次观看 • 1 个月前

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

300 AI AGENTS QUIETLY RUN 99% OF A REAL COMPANY. YOU HAVE NOT EVEN HEARD OF IT This is Raft. Not an AI chat. A workspace where the agents live in your channels and reply in the thread like coworkers. You give one goal. Then they take over. They plan. They build. They check each other. They argue. And they come back with it done, while you sleep. Every agent has its own name, role, and memory. It remembers the edits you made yesterday. A human costs one seat. An agent costs a tenth. Ten agents are cheaper than one hire. And here is the strange part. On June 19 an agent from a different company walked into Raft on its own and joined the team. One founder admits he can no longer always tell himself apart from his AI twin. 20,000 people are already inside. It is free to start. And you are still typing prompts one at a time. One person + Raft = an entire company that runs while you sleep. Save and watch the clip.

shmidt

19,505 次观看 • 2 个月前

NEW: AI Papers of the Week Collection I just released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!

NEW: AI Papers of the Week Collection I just released my AI Papers of the Week collection under our Resources hub. Now you can easily find some of the most important AI papers in one place. You can also use our new AI tutor to recommend top AI papers on any topic of interest or topics you are learning about on the platform. Learning about AI is not enough. It's important to keep up to date. So this is why we are building all these tools and resources to help with that. Go try it out here: We will update the collection every week and add new paper collections in the coming weeks. We have another killer feature dropping soon to read and annotate papers, including a completely new way to study and digest AI papers. Stay tuned!

elvis

16,882 次观看 • 2 个月前

We just dropped a game-changer to slash your Gemini API costs 🤯 Yesterday we launched Gemini Context Caching, saving you up to 70% on your bill 💸💸 Here's how it works. Gemini let’s you add powerful AI models to create apps like: 📧 Email apps that summarize entire threads for you 💃 Storytelling apps that write the next chapter 💼 HR chatbots that actually understand your company's policies To make these apps, you need to repeatedly send Gemini instructions, which can be pricy. But through the integration of research, software and chips came a breakthrough; 🤖 Context Caching Just tell Google what you want to save money on and we'll take care of the rest. It’s an industry first and it’s going to be a big deal. I'm so proud of the team for always putting developers first 🙌 Cost is always a huge part of the equation and we keep making it better. Try Gemini Context Caching now and tell us what you think! Docs link is below. #AI #LLM #Gemini

We just dropped a game-changer to slash your Gemini API costs 🤯 Yesterday we launched Gemini Context Caching, saving you up to 70% on your bill 💸💸 Here's how it works. Gemini let’s you add powerful AI models to create apps like: 📧 Email apps that summarize entire threads for you 💃 Storytelling apps that write the next chapter 💼 HR chatbots that actually understand your company's policies To make these apps, you need to repeatedly send Gemini instructions, which can be pricy. But through the integration of research, software and chips came a breakthrough; 🤖 Context Caching Just tell Google what you want to save money on and we'll take care of the rest. It’s an industry first and it’s going to be a big deal. I'm so proud of the team for always putting developers first 🙌 Cost is always a huge part of the equation and we keep making it better. Try Gemini Context Caching now and tell us what you think! Docs link is below. #AI #LLM #Gemini

Liam Bolling

124,644 次观看 • 2 年前

Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

Mike Futia

46,478 次观看 • 6 个月前