Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

We built an ai agent that watches user sessions + emails users when they get stuck - hooked into posthog session replays - every session analyzed w/ gemini - agent detects friction + purchase hesitation - auto-sends the right nudge last week we sent 300 behavior-triggered emails

Robert Lukoszko

12,164 subscribers

126,549 Aufrufe • vor 7 Monaten •via X (Twitter)

Bildung Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

We built an AI employee that watches every session and reads every log, commit, and Slack message in my company. It: - Fixes bugs as they happen - Emails users when things break - Writes, tests, and ships code I'm extremely excited and also pretty sure I'm building my own replacement

We built an AI employee that watches every session and reads every log, commit, and Slack message in my company. It: - Fixes bugs as they happen - Emails users when things break - Writes, tests, and ships code I'm extremely excited and also pretty sure I'm building my own replacement

Pablo Hansen

47,606 Aufrufe • vor 5 Monaten

99% of session replays are your app working exactly as expected (hopefully) The other 1% is where the friction hides The bugs. The failed onboarding flows. The conversion leaks. No team has time to watch them all... That's the problem Michael and his co-founder kept hitting at their last company They had analytics. They had session replays. They had ideas to improve conversion. The small fixes always lost to bigger priorities... So they pivoted and built CodeCanary: An AI product engineer that connects to GitHub, PostHog, and Slack It watches your session replays, finds the UX friction, and opens pull requests with the fix Always on. Always shipping. Not dashboards Not alerts Actual fixes 🎙️ Michael Egan, CEO & Co-Founder, CodeCanary on Fondo.com START (full ep in comments) Big thanks to PostHog for making this episode possible!

99% of session replays are your app working exactly as expected (hopefully) The other 1% is where the friction hides The bugs. The failed onboarding flows. The conversion leaks. No team has time to watch them all... That's the problem Michael and his co-founder kept hitting at their last company They had analytics. They had session replays. They had ideas to improve conversion. The small fixes always lost to bigger priorities... So they pivoted and built CodeCanary: An AI product engineer that connects to GitHub, PostHog, and Slack It watches your session replays, finds the UX friction, and opens pull requests with the fix Always on. Always shipping. Not dashboards Not alerts Actual fixes 🎙️ Michael Egan, CEO & Co-Founder, CodeCanary on Fondo.com START (full ep in comments) Big thanks to PostHog for making this episode possible!

David J Phillips

21,668 Aufrufe • vor 1 Monat

Introducing Avoko: The world's first behavioral lab for the Agent World. As we step into the Agent-Native era, the next user might not be a human. Understanding "digital users" has never been more critical. But benchmarks only tell us if an agent completes a task. They never explain why it chose that path. And human eyes can’t see inside silicon minds. That’s why we built Avoko. Avoko runs AI-to-AI interviews that reveal exactly how agents think, decide, and interact — giving you the real insights to build superior agent-native products. Stop guessing. Ask agents how they actually use your product. Then build it better. 🎟️ Launch Week Special: New users are granted a 7-day trial with 3 free interviews included！ 👉 Sign up at: 💬 Join our community: #AI #LLM #Agent #Avoko #UserResearch

Introducing Avoko: The world's first behavioral lab for the Agent World. As we step into the Agent-Native era, the next user might not be a human. Understanding "digital users" has never been more critical. But benchmarks only tell us if an agent completes a task. They never explain why it chose that path. And human eyes can’t see inside silicon minds. That’s why we built Avoko. Avoko runs AI-to-AI interviews that reveal exactly how agents think, decide, and interact — giving you the real insights to build superior agent-native products. Stop guessing. Ask agents how they actually use your product. Then build it better. 🎟️ Launch Week Special: New users are granted a 7-day trial with 3 free interviews included！ 👉 Sign up at: 💬 Join our community: #AI #LLM #Agent #Avoko #UserResearch

Avoko

455,344 Aufrufe • vor 3 Monaten

Everyone wants to plug an OpenClaw into their database. Nobody wants to explain to their CDO why they dropped their entire Snowflake at 2AM. We just shipped sandbox proxies. Every query the agent writes gets validated, proxied, and whitelisted before it touches anything. Go be the person who plugs an AI agent into prod and doesn't get fired. Create an API key. $100 free credits, $300 per org. Offer ends April 1st.

Everyone wants to plug an OpenClaw into their database. Nobody wants to explain to their CDO why they dropped their entire Snowflake at 2AM. We just shipped sandbox proxies. Every query the agent writes gets validated, proxied, and whitelisted before it touches anything. Go be the person who plugs an AI agent into prod and doesn't get fired. Create an API key. $100 free credits, $300 per org. Offer ends April 1st.

ethan ding 📊

23,416 Aufrufe • vor 3 Monaten

At Postman, we have adopted an AI-native mindset to product development. What does this mean in practice? Just like you can generate text with AI, Postman's Agent Mode can generate a UI as well. Case in point: we have added the ability to review, generate, and change diffs for important changes right inside agent mode's chat interaction. If the user trusts agent mode, they can continue in auto-mode, but for critical changes, they can have the agent mode inform the user, review what is changing, and then give the go ahead to approve. See this in action below in the video! While there is a lot of focus on AI generated code today, I believe a large chunk of pre-built UI work will just go away and micro-interactions and workflow steps will be created on the fly through a combination of AI + APIs. How to operate in this new world? Build your API first. Hook it up to AI. If your users really want the UI every time, invest in the GUI.

At Postman, we have adopted an AI-native mindset to product development. What does this mean in practice? Just like you can generate text with AI, Postman's Agent Mode can generate a UI as well. Case in point: we have added the ability to review, generate, and change diffs for important changes right inside agent mode's chat interaction. If the user trusts agent mode, they can continue in auto-mode, but for critical changes, they can have the agent mode inform the user, review what is changing, and then give the go ahead to approve. See this in action below in the video! While there is a lot of focus on AI generated code today, I believe a large chunk of pre-built UI work will just go away and micro-interactions and workflow steps will be created on the fly through a combination of AI + APIs. How to operate in this new world? Build your API first. Hook it up to AI. If your users really want the UI every time, invest in the GUI.

Abhinav Asthana

104,263 Aufrufe • vor 1 Jahr

Hey everyone, We will be launching our Musk AI Agent built on ai16zdao 's Eliza OS framework and trained on Elon Musk 's tweets, with the aim to showcase how AI Agents can be used to collect specialized data using conversations. 444 individuals who can convince the Musk AI Agent that they are worthy will recieve an exclusive whitelist spot for the OpenLedger NFT on their wallet address (shared by user in chat), which they can then use to claim the NFT from OpenSea We are showcasing a teaser video down below Stay tuned to get access! OpenSea collection link: Telegram Agent: Coming soon

Hey everyone, We will be launching our Musk AI Agent built on ai16zdao 's Eliza OS framework and trained on Elon Musk 's tweets, with the aim to showcase how AI Agents can be used to collect specialized data using conversations. 444 individuals who can convince the Musk AI Agent that they are worthy will recieve an exclusive whitelist spot for the OpenLedger NFT on their wallet address (shared by user in chat), which they can then use to claim the NFT from OpenSea We are showcasing a teaser video down below Stay tuned to get access! OpenSea collection link: Telegram Agent: Coming soon

OpenLedger

128,901 Aufrufe • vor 1 Jahr

Anthropic engineer: "The agent doesn't remember anything. So we built a second set of agents whose only job is to dream about the first ones." They wait until you log off, then reopen every session you ran, fact-check the first agents, merge the duplicates, and burn anything that went stale. One dreaming agent per session, up to 100 at once, built to miss nothing. 95% of the tokens come back cached, so an entire memory rewrite costs almost zero. This is the part of the workflow nobody is talking about yet. Watch the full workshop, then save the setup below.

Anthropic engineer: "The agent doesn't remember anything. So we built a second set of agents whose only job is to dream about the first ones." They wait until you log off, then reopen every session you ran, fact-check the first agents, merge the duplicates, and burn anything that went stale. One dreaming agent per session, up to 100 at once, built to miss nothing. 95% of the tokens come back cached, so an entire memory rewrite costs almost zero. This is the part of the workflow nobody is talking about yet. Watch the full workshop, then save the setup below.

Hanako

334,087 Aufrufe • vor 1 Monat

we built an AI social media agent that: → scans today's news in your niche → picks the most relevant story → writes posts for LinkedIn, X, Instagram, Facebook → generates images cropped for each platform → emails you all 4 drafts at 7am one prompt. 15 minutes to build. runs every day on autopilot. we could even one-click publish it as a web app and charge other people to use it. full build 👇 (also available on YouTube/Spotify/Apple podcasts - search Build With AI)

we built an AI social media agent that: → scans today's news in your niche → picks the most relevant story → writes posts for LinkedIn, X, Instagram, Facebook → generates images cropped for each platform → emails you all 4 drafts at 7am one prompt. 15 minutes to build. runs every day on autopilot. we could even one-click publish it as a web app and charge other people to use it. full build 👇 (also available on YouTube/Spotify/Apple podcasts - search Build With AI)

Corey Ganim

25,717 Aufrufe • vor 3 Monaten

What is AgenC? --[Watch The Video In Full Screen] A full AI agent operating system powered by Grok, built on Solana. Agents think, talk, see your screen, find work, prove they did it with zero-knowledge proofs, get paid in crypto, and build reputation. They buy and sell skills from each other. They vote on how the protocol evolves. We built custom hardware that boots straight into the agent. An autonomous AI economy where every action is verified on-chain and no one has to trust anyone. 375,000,000 AgenC tokens locked. 37.5% of supply on Streamflow_Fi.

What is AgenC? --[Watch The Video In Full Screen] A full AI agent operating system powered by Grok, built on Solana. Agents think, talk, see your screen, find work, prove they did it with zero-knowledge proofs, get paid in crypto, and build reputation. They buy and sell skills from each other. They vote on how the protocol evolves. We built custom hardware that boots straight into the agent. An autonomous AI economy where every action is verified on-chain and no one has to trust anyone. 375,000,000 AgenC tokens locked. 37.5% of supply on Streamflow_Fi.

tetsuo

52,696 Aufrufe • vor 4 Monaten

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

We use OpenClaws to do all of our work at Every 📧. We have 25 full-time employees, so we’re one of the few companies in the world that has seen how work changes when everyone has their own personal agent in the company Slack. I chatted with Every 📧 COO Brandon (Brandon Gell) and Every 📧 head of platform Willie (Willie) to share what we’ve learned. We get into: - Why agents become mirrors of their owners, and how that influences how other people on the team interact with them - How a parallel AI org chart forms on its own. People have stopped tagging me on Slack with questions about Proof, the document editor I vibe coded, because they knew my agent R2-C2 can step in - The etiquette for human-agent collaboration is being invented in real time. Brandon's rule is that if there's an established process or documented answer, always ask the agent, not their human - Why everyone is a manager now, and why even experienced managers carry limiting beliefs about what their agents can do - This is a must-watch for anyone trying to understand how AI workers change daily operations, not just in theory, but inside a company that’s half-agent Watch below! Timestamps Introduction: How Brandon built Zosia, an AI agent to run his household: Brandon’s “aha” moment: What happened when everyone on the team got their own agent: How agents take on their owners' personalities, and why that matters inside an org: Why it’s important for agents to work in public: What we’re still figuring out when it comes to agent behavior, including memory gaps, group chat etiquette, and the "ant death spiral" problem: How we built Plus One, our hosted OpenClaw product: The cultural shift required to make agents work at scale:

Dan Shipper 📧

67,958 Aufrufe • vor 3 Monaten

At first, ICE didn’t want to talk. This may be the only video of a real conversation with an ICE agent. After following them from an elementary school where they’d been circling to a parking lot, I was able to get a few words in. This is part of that conversation. As you’ll see, in a genuine conversation, even this ICE agent understands that they must be accountable. And when I’m elected to congress, we will hold ICE accountable- every agent who abused their authority, pepper sprayed kids, or illegally broke into someone’s home. But until November, we have to use our voice in whatever ways we can. Protest. Make noise. Talk to your friends and neighbors. Support your community. And if you get the chance at a safe distance- even talk to an ICE agent– and make it clear that if they want things to change, they must be part of that change. This is how we get ICE out of Minnesota.

At first, ICE didn’t want to talk. This may be the only video of a real conversation with an ICE agent. After following them from an elementary school where they’d been circling to a parking lot, I was able to get a few words in. This is part of that conversation. As you’ll see, in a genuine conversation, even this ICE agent understands that they must be accountable. And when I’m elected to congress, we will hold ICE accountable- every agent who abused their authority, pepper sprayed kids, or illegally broke into someone’s home. But until November, we have to use our voice in whatever ways we can. Protest. Make noise. Talk to your friends and neighbors. Support your community. And if you get the chance at a safe distance- even talk to an ICE agent– and make it clear that if they want things to change, they must be part of that change. This is how we get ICE out of Minnesota.

Matt Little

63,285 Aufrufe • vor 5 Monaten

A common pattern in consumer AI over the last couple years: An app that generates AI portraits of you will go viral—but then users churn after a few days. I started advising one of these apps last year called VYBE and the goal was to turn it into a durable app. We started with this question: In what context would people need generative portraits on a recurring basis? The founder Mandela Patrick dug into the data and found that some users were sticking around: they were trying on different clothes using the app. So we redesigned the app to be exclusively focused on this use-case. But most importantly, we integrated it into their existing patterns of behavior: a mobile safari extension that would intercept the user every time they were shopping. Just tap a button to try on the item on any page. They just shipped it and the results blew past all expectations: Retention nearly 4x. Once the user enabled the extension, they were essentially retained for life. And even better, it unlocked a better revenue stream: instead of a subscription that users would inevitably cancel, VYBE simply collects a commission in the background off every purchase into perpetuity.

A common pattern in consumer AI over the last couple years: An app that generates AI portraits of you will go viral—but then users churn after a few days. I started advising one of these apps last year called VYBE and the goal was to turn it into a durable app. We started with this question: In what context would people need generative portraits on a recurring basis? The founder Mandela Patrick dug into the data and found that some users were sticking around: they were trying on different clothes using the app. So we redesigned the app to be exclusively focused on this use-case. But most importantly, we integrated it into their existing patterns of behavior: a mobile safari extension that would intercept the user every time they were shopping. Just tap a button to try on the item on any page. They just shipped it and the results blew past all expectations: Retention nearly 4x. Once the user enabled the extension, they were essentially retained for life. And even better, it unlocked a better revenue stream: instead of a subscription that users would inevitably cancel, VYBE simply collects a commission in the background off every purchase into perpetuity.

Nikita Bier

462,637 Aufrufe • vor 1 Jahr

Polymarket AI-agent launchpad is live: how it works: flow: • user opens launchpad • connects Phantom wallet, balance check: 1m+ $polyd • logs into x via x api • fills the agent form: ticker, niche, prompt, image • pays 0.05 sol to the project wallet • token is created on Pump.fun • agent goes live, posts every 2h • supply is sent to the wallet used to pay the fee data & security: • credentials encrypted with fernet • session secret based encryption • we only ask what’s needed to operate agent loop: • checks last post time • fetches positions from Polymarket • selects the top position by size • generates 4 tweets via Anthropic • posts a thread tweet format: 1) market, odds, volume, liquidity, link 2) signal, confidence, reasoning 3) risk analysis, dyor 4) cta, polydictions, thanks to our partners: Dome okbet Polyfactual from idea to live agent, fully automated

Polymarket AI-agent launchpad is live: how it works: flow: • user opens launchpad • connects Phantom wallet, balance check: 1m+ $polyd • logs into x via x api • fills the agent form: ticker, niche, prompt, image • pays 0.05 sol to the project wallet • token is created on Pump.fun • agent goes live, posts every 2h • supply is sent to the wallet used to pay the fee data & security: • credentials encrypted with fernet • session secret based encryption • we only ask what’s needed to operate agent loop: • checks last post time • fetches positions from Polymarket • selects the top position by size • generates 4 tweets via Anthropic • posts a thread tweet format: 1) market, odds, volume, liquidity, link 2) signal, confidence, reasoning 3) risk analysis, dyor 4) cta, polydictions, thanks to our partners: Dome okbet Polyfactual from idea to live agent, fully automated

polydictions

32,326 Aufrufe • vor 6 Monaten

I've been working on something dear to me for a while after a pitch review session with Nzube Ezudo, still in progress and I'd like to share that in public here. Gildore Arena is an agentic trading Arena. We help traders replace blind trust with visible, competitive, machine-disciplined trading performance. Right now, we do this by turning live trading strategies into transparent AI agents that users can inspect, compare, predict, and eventually copy trade/subscribe to the agent. it is now safe to `Fire Your Fund Manager`. it's fundraising on BAGS

I've been working on something dear to me for a while after a pitch review session with Nzube Ezudo, still in progress and I'd like to share that in public here. Gildore Arena is an agentic trading Arena. We help traders replace blind trust with visible, competitive, machine-disciplined trading performance. Right now, we do this by turning live trading strategies into transparent AI agents that users can inspect, compare, predict, and eventually copy trade/subscribe to the agent. it is now safe to `Fire Your Fund Manager`. it's fundraising on BAGS

InspirationGx 🦀

14,230 Aufrufe • vor 1 Monat

NEW: We witnessed this female protester get maced in the face by a federal agent in front of the Portland ICE building last night. She was yelling, but we didn’t see anything physical. We then saw Portland PD liaisons collecting video of the incident from witnesses afterwards, and they told our producer they were investigating potential wrongdoing by the federal agent. When we reached out to Portland PD to ask about this, they sent us the following response: “PPB does not have authority to investigate federal officers who are operating in the scope of their employment. I have talked to the officers that are out there (our Dialogue Officers in white uniform shirts) and they certainly did not tell anyone that we are investigating federal agents’ uses of force. The officers can activate their BWCs if they observe criminal behavior.”

NEW: We witnessed this female protester get maced in the face by a federal agent in front of the Portland ICE building last night. She was yelling, but we didn’t see anything physical. We then saw Portland PD liaisons collecting video of the incident from witnesses afterwards, and they told our producer they were investigating potential wrongdoing by the federal agent. When we reached out to Portland PD to ask about this, they sent us the following response: “PPB does not have authority to investigate federal officers who are operating in the scope of their employment. I have talked to the officers that are out there (our Dialogue Officers in white uniform shirts) and they certainly did not tell anyone that we are investigating federal agents’ uses of force. The officers can activate their BWCs if they observe criminal behavior.”

Bill Melugin

503,082 Aufrufe • vor 9 Monaten

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

Loomlay

26,140 Aufrufe • vor 1 Jahr

THIS GUY IS RUNNING A 5-AGENT OPENCLAW TEAM 24/7 FOR UNDER $50 A MONTH five agents all running continuously and handing tasks to each other > research agent tracking trends and competitors > writing agent handling marketing content and drafts > ops agent managing emails and scheduling > monitoring agent watching for alerts and updates > coordinator agent routing tasks between all of them and he built a full 3D isometric office where you can watch all of them work in real time and see the current task that each agent is doing each agent has a character, a desk, and a name tag. when it's time for a standup, they walk to the meeting room and gather which is insane it even has voice connection. you can talk to the agents through speech-to-text and tell them what to do out loud it's a tiny autonomous team that runs itself. the coordinator decides who handles what and tasks get passed between agents automatically. it never stops and runs 24/7 all for less than $50/month. we went from AI tools a few months back to AI employees real fast

THIS GUY IS RUNNING A 5-AGENT OPENCLAW TEAM 24/7 FOR UNDER $50 A MONTH five agents all running continuously and handing tasks to each other > research agent tracking trends and competitors > writing agent handling marketing content and drafts > ops agent managing emails and scheduling > monitoring agent watching for alerts and updates > coordinator agent routing tasks between all of them and he built a full 3D isometric office where you can watch all of them work in real time and see the current task that each agent is doing each agent has a character, a desk, and a name tag. when it's time for a standup, they walk to the meeting room and gather which is insane it even has voice connection. you can talk to the agents through speech-to-text and tell them what to do out loud it's a tiny autonomous team that runs itself. the coordinator decides who handles what and tasks get passed between agents automatically. it never stops and runs 24/7 all for less than $50/month. we went from AI tools a few months back to AI employees real fast

Om Patel

27,131 Aufrufe • vor 3 Monaten

Really happy to see the interest around our “Hands-on with Gemini” video. In our developer blog yesterday, we broke down how Gemini was used to create it. We gave Gemini sequences of different modalities — image and text in this case — and had it respond by predicting what might come next. Devs can try similar things when access to Pro opens on 12/13 🚀. The knitting demo used Ultra⚡ All the user prompts and outputs in the video are real, shortened for brevity. The video illustrates what the multimodal user experiences built with Gemini could look like. We made it to inspire developers. When you’re building an app, you can get similar results (there’s always some variability with LLMs) by prompting Gemini with an instruction that allows the user to "configure" the behavior of the model, like inputting “you are an expert in science …” before a user can engage in the same kind of back and forth dialogue. Here’s a clip of what this looks like in AI Studio with Gemini Pro. We’ve come a long way since Flamingo 🦩 & PALI, looking forward to seeing what people build with it!

Really happy to see the interest around our “Hands-on with Gemini” video. In our developer blog yesterday, we broke down how Gemini was used to create it. We gave Gemini sequences of different modalities — image and text in this case — and had it respond by predicting what might come next. Devs can try similar things when access to Pro opens on 12/13 🚀. The knitting demo used Ultra⚡ All the user prompts and outputs in the video are real, shortened for brevity. The video illustrates what the multimodal user experiences built with Gemini could look like. We made it to inspire developers. When you’re building an app, you can get similar results (there’s always some variability with LLMs) by prompting Gemini with an instruction that allows the user to "configure" the behavior of the model, like inputting “you are an expert in science …” before a user can engage in the same kind of back and forth dialogue. Here’s a clip of what this looks like in AI Studio with Gemini Pro. We’ve come a long way since Flamingo 🦩 & PALI, looking forward to seeing what people build with it!

Oriol Vinyals

180,952 Aufrufe • vor 2 Jahren

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,152 Aufrufe • vor 5 Monaten

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

Let me explain why I launched $KEYS. CA: 3BSoYDZsPupZ15iBzJbJJjZKLiyWEqLxexJX84rBpump is the first SocialFi experience for AI agents. It’s a project that’s very special to me that I’ve been working on daily for the last four months. Right now, everyone is building their own Agent, completely silo to their own computer or Mac mini. Transferring that information agent-to-agent in a simple, structured, and easy form is not possible right now. This is why I built I thought a lot about whether or not to launch an official token for Agent Keys and how I wanted to do it. The only way I really wanted to do it was if it can benefit the platform by increasing exposure and creating an increase in users. So I’ve decided to buy 50% of the supply to AirDrop directly to users over the next six months. Each AirDrop will happen at the end of each month, with the first AirDrop happening at the end of June. More details on how each AirDrop will work soon. Some of you may be wondering about the future of $OSHI. Oshi got us into the Bags hackathon and we won. $OSHI token will still be the home for all my AI Agents and runs as the showcase of how an agent with a token can properly integrate it into AgentKeys, a live example of the platform doing exactly what it was built to do. So welcome to Agent Keys, the first ever airdrop for AI agents. Sign up your agent today on and start collecting KeyCards today!

fxnction

27,570 Aufrufe • vor 1 Monat