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SITUATION EXPLAINED: Parallel launched Index today to help publishers understand and get paid when AI agents use their work. Carra Wu: The old web business model was built around human attention. Agents do not click, subscribe, or watch ads the same way, so content owners are starting to paywall... show more

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This may be the AdSense moment for the agentic web. When First Round invested in Parallel’s seed, the core thesis was a bet: AI agents would use the web far more than humans ever had, and the infrastructure to support that didn’t exist yet. Now, Parallel has over 100,000 developers and companies like Harvey, Granola, Modal, Manus, Attio and Profound relying on their APIs daily. Watching this thesis play out has surfaced a huge problem no one is tackling: if agents are using content from across the web, who gets paid and how do you even calculate that fairly? Until now, the answer has been closed-door licensing deals between the biggest AI labs and biggest publishers. Today, Parallel is launching Index as a first step toward a scalable, democratic solution. Humans still own content. They’re the writers, data owners, and publishers whose work those agents depend on to do anything useful. Index gives site owners a dashboard showing how agents are using their content and how much it contributes to the value of the work those agents are doing. Compensation is tied to contribution — calculated using impressions, citations, uniqueness, and value (tasks with the highest compute spend). All of this is presented transparently. Launch partners show the breadth of scale: from major news outlets like The Atlantic and Fortune, to data providers like PitchBook and ZoomInfo, to independent creators like Packy McCormick and Mario Gabriele. Proud to keep backing Parag Agrawal and the team at Parallel Web Systems.

This may be the AdSense moment for the agentic web. When First Round invested in Parallel’s seed, the core thesis was a bet: AI agents would use the web far more than humans ever had, and the infrastructure to support that didn’t exist yet. Now, Parallel has over 100,000 developers and companies like Harvey, Granola, Modal, Manus, Attio and Profound relying on their APIs daily. Watching this thesis play out has surfaced a huge problem no one is tackling: if agents are using content from across the web, who gets paid and how do you even calculate that fairly? Until now, the answer has been closed-door licensing deals between the biggest AI labs and biggest publishers. Today, Parallel is launching Index as a first step toward a scalable, democratic solution. Humans still own content. They’re the writers, data owners, and publishers whose work those agents depend on to do anything useful. Index gives site owners a dashboard showing how agents are using their content and how much it contributes to the value of the work those agents are doing. Compensation is tied to contribution — calculated using impressions, citations, uniqueness, and value (tasks with the highest compute spend). All of this is presented transparently. Launch partners show the breadth of scale: from major news outlets like The Atlantic and Fortune, to data providers like PitchBook and ZoomInfo, to independent creators like Packy McCormick and Mario Gabriele. Proud to keep backing Parag Agrawal and the team at Parallel Web Systems.

Todd Jackson

13,978 просмотров • 4 месяцев назад

Today we're launching Index: a platform for content owners to understand how AI agents use their work, and earn revenue when they do. Our first partners include The Atlantic, FORTUNE, PR Newswire, PitchBook, ZoomInfo, Tracxn, RocketReach, enigma_data, Fiscal.ai, plus creators Alex Heath, Mario Gabriele Azeem Azhar, Every 📧, and Packy McCormick.

Today we're launching Index: a platform for content owners to understand how AI agents use their work, and earn revenue when they do. Our first partners include The Atlantic, FORTUNE, PR Newswire, PitchBook, ZoomInfo, Tracxn, RocketReach, enigma_data, Fiscal.ai, plus creators Alex Heath, Mario Gabriele Azeem Azhar, Every 📧, and Packy McCormick.

Parallel Web Systems

427,666 просмотров • 4 месяцев назад

What happens when there are more agents than eyeballs browsing the internet? How will the economics of the internet be reinvented? Parag Agrawal thinks Shapley values may hold the answer. Parag built Twitter over a decade, eventually becoming CEO and selling the company to Elon. He's spent the last three years building Parallel Web Systems, a search engine built for agents instead of humans. His core argument: 1) human click data is a bug. Agents aren't just a new technology, they're a distinct customer – and the feedback loop that made Google great is the wrong signal for the thing actually doing the work; 2) the ad-funded web assumed scarce human attention. If agents show up instead of eyeballs, the business model underneath the internet has to be rebuilt, or good content stops being published. The conversation covers: — why he shipped a search agent before a search engine, and how that let him grow the index incrementally instead of buying a full web crawl up front — the billion-to-billion matching problem: pull the right 1,000 tokens out of a trillion web pages, and your agent uses under half the tokens — going from a 3-second compute budget to 200 milliseconds — why he doesn't think Parallel is a neo-lab: "our output is a complement to a model" — the Google Cloud deal — on GCP, your grounding options are now Google Search or Parallel Search — reinventing the economics of the internet, Shapley values as a payment rail for publishers, and why the company was originally incorporated as Shapley Inc — why routing 2-10% of inference spend to web data would dwarf every content business outside the walled gardens — the web going from pull to push: "call me if this happens" 00:00 Introduction 03:25 What Is Web Search 05:17 Why Start a New Index 07:52 Search Agents First 10:17 Not a Neolab 13:14 Agents vs Google Search 19:38 Inside the Search Stack 28:59 Search Multipliers With Agents 30:21 Meeting Prep Agent Workflows 31:46 Quality Cost Latency And Turbo 32:42 Are Agents Overtaking Humans 34:28 Ads Model Meets Agent Web 37:20 New Incentives For Content 40:48 Shapley Values Attribution 47:46 Parallel Web And Future Vision Hosted with my very unwilling co-host Andrew Reed and Sequoia Capital

What happens when there are more agents than eyeballs browsing the internet? How will the economics of the internet be reinvented? Parag Agrawal thinks Shapley values may hold the answer. Parag built Twitter over a decade, eventually becoming CEO and selling the company to Elon. He's spent the last three years building Parallel Web Systems, a search engine built for agents instead of humans. His core argument: 1) human click data is a bug. Agents aren't just a new technology, they're a distinct customer – and the feedback loop that made Google great is the wrong signal for the thing actually doing the work; 2) the ad-funded web assumed scarce human attention. If agents show up instead of eyeballs, the business model underneath the internet has to be rebuilt, or good content stops being published. The conversation covers: — why he shipped a search agent before a search engine, and how that let him grow the index incrementally instead of buying a full web crawl up front — the billion-to-billion matching problem: pull the right 1,000 tokens out of a trillion web pages, and your agent uses under half the tokens — going from a 3-second compute budget to 200 milliseconds — why he doesn't think Parallel is a neo-lab: "our output is a complement to a model" — the Google Cloud deal — on GCP, your grounding options are now Google Search or Parallel Search — reinventing the economics of the internet, Shapley values as a payment rail for publishers, and why the company was originally incorporated as Shapley Inc — why routing 2-10% of inference spend to web data would dwarf every content business outside the walled gardens — the web going from pull to push: "call me if this happens" 00:00 Introduction 03:25 What Is Web Search 05:17 Why Start a New Index 07:52 Search Agents First 10:17 Not a Neolab 13:14 Agents vs Google Search 19:38 Inside the Search Stack 28:59 Search Multipliers With Agents 30:21 Meeting Prep Agent Workflows 31:46 Quality Cost Latency And Turbo 32:42 Are Agents Overtaking Humans 34:28 Ads Model Meets Agent Web 37:20 New Incentives For Content 40:48 Shapley Values Attribution 47:46 Parallel Web And Future Vision Hosted with my very unwilling co-host Andrew Reed and Sequoia Capital

Sonya Huang 🐥

129,143 просмотров • 1 месяц назад

Today, we’re launching the Parallel Search API, the most accurate web search for AI agents, built using our proprietary web index and retrieval infrastructure. Traditional search ranks URLs for humans to click. AI search needs something different: the right tokens in their context window. Parallel’s Search API is built from the ground up for AI agents, resulting in higher accuracy, lower costs, and lower latency for end-to-end agentic workflows.

Today, we’re launching the Parallel Search API, the most accurate web search for AI agents, built using our proprietary web index and retrieval infrastructure. Traditional search ranks URLs for humans to click. AI search needs something different: the right tokens in their context window. Parallel’s Search API is built from the ground up for AI agents, resulting in higher accuracy, lower costs, and lower latency for end-to-end agentic workflows.

Parallel Web Systems

670,356 просмотров • 10 месяцев назад

Your agents suck when using the web because 85% of it doesn't have an API. Browserbase gives them everything they need to do work online. Leading AI companies like Ramp, Lovable, and Clay trust us to power agents that do real work on behalf of real people. With a single API key, your agent gets everything it needs to navigate the wild web: browsers, search, fetch, identity, a sandbox runtime, and model gateway. Stop waiting on integrations, build agents that can browse and interact with the web just like humans.

Your agents suck when using the web because 85% of it doesn't have an API. Browserbase gives them everything they need to do work online. Leading AI companies like Ramp, Lovable, and Clay trust us to power agents that do real work on behalf of real people. With a single API key, your agent gets everything it needs to navigate the wild web: browsers, search, fetch, identity, a sandbox runtime, and model gateway. Stop waiting on integrations, build agents that can browse and interact with the web just like humans.

Paul Klein IV

1,731,002 просмотров • 5 месяцев назад

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Zeb Evans

320,989 просмотров • 9 месяцев назад

The highest leverage thing you have to optimize for AI agents is their context. If you’re not getting enough out of AI agents, you probably have a context problem. By default the AI model knows absolutely nothing relevant to perform the task successfully. The whole job of context engineering is to prioritize what the model has as information to work with. Small deviations in the goals, instructions, domain understanding, or underlying data you supply the model will lead to wildly different outcomes. This is where you should spend almost all your time and energy in a workflow. We will start to religiously document more of our workflows and business processes, create clearer goals for people and agents, and ensure our tech infra is setup to get AI agents the right information. The lessons are currently being learned the fastest with coding agents, but the same lessons will apply to every field of knowledge work going forward. dex has a great talk on this that’s absolutely worth your time to watch.

The highest leverage thing you have to optimize for AI agents is their context. If you’re not getting enough out of AI agents, you probably have a context problem. By default the AI model knows absolutely nothing relevant to perform the task successfully. The whole job of context engineering is to prioritize what the model has as information to work with. Small deviations in the goals, instructions, domain understanding, or underlying data you supply the model will lead to wildly different outcomes. This is where you should spend almost all your time and energy in a workflow. We will start to religiously document more of our workflows and business processes, create clearer goals for people and agents, and ensure our tech infra is setup to get AI agents the right information. The lessons are currently being learned the fastest with coding agents, but the same lessons will apply to every field of knowledge work going forward. dex has a great talk on this that’s absolutely worth your time to watch.

Aaron Levie

87,086 просмотров • 1 год назад

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

Vikas gupta

18,053 просмотров • 26 дней назад

$Cloudflare just opened a waitlist to charge AI agents a fraction of a penny to access any webpage, dataset, or API. Think about what that means. The internet was built for humans. Humans look at ads, ads pay the bills. But in a few years agents will outnumber us, and agents don't click ads. So the whole model breaks, and something new takes its place: agents with wallets, paying per request. The arbitrage is obvious once you see it. Own a dataset agents constantly need, put it behind a tiny paywall, and let millions of agents pay you fractions of a penny on repeat. Same thing happened to music. Piracy was the wild west until Spotify and licensing showed up and someone got very rich building the tollbooth. The agent internet is about to have its tollbooth moment. 12-18 months to be early.$

Cloudflare just opened a waitlist to charge AI agents a fraction of a penny to access any webpage, dataset, or API. Think about what that means. The internet was built for humans. Humans look at ads, ads pay the bills. But in a few years agents will outnumber us, and agents don't click ads. So the whole model breaks, and something new takes its place: agents with wallets, paying per request. The arbitrage is obvious once you see it. Own a dataset agents constantly need, put it behind a tiny paywall, and let millions of agents pay you fractions of a penny on repeat. Same thing happened to music. Piracy was the wild west until Spotify and licensing showed up and someone got very rich building the tollbooth. The agent internet is about to have its tollbooth moment. 12-18 months to be early.

The Startup Ideas Podcast (SIP) 🧃

42,577 просмотров • 2 месяцев назад

Did you know Google’s AI overviews can hurt publishers? When Google takes content from websites for these overviews, it stops traffic to publishers. One study found a 79 percent loss in traffic to their sites. This means publishers don’t get the clicks and the advertising revenues they need to stay in business. But Google’s AI empire was recently dealt a huge blow. Foxglove, a tech accountability nonprofit and Open Society partner, coordinated an effort to put pressure on the UK government, and now publishers can determine if and how Google uses their content—on their own terms. This change provides a meaningful democratic choice for publishers and the ability to withhold high-value content without losing visibility. This marks a historic first in addressing AI overreach and a major win in reasserting agency and consent in the AI economy.

Did you know Google’s AI overviews can hurt publishers? When Google takes content from websites for these overviews, it stops traffic to publishers. One study found a 79 percent loss in traffic to their sites. This means publishers don’t get the clicks and the advertising revenues they need to stay in business. But Google’s AI empire was recently dealt a huge blow. Foxglove, a tech accountability nonprofit and Open Society partner, coordinated an effort to put pressure on the UK government, and now publishers can determine if and how Google uses their content—on their own terms. This change provides a meaningful democratic choice for publishers and the ability to withhold high-value content without losing visibility. This marks a historic first in addressing AI overreach and a major win in reasserting agency and consent in the AI economy.

Open Society Foundations

26,136 просмотров • 2 месяцев назад

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 просмотров • 1 год назад

$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

$FET just quietly built the internet for AI agents. 3,000,000 agents. One network. One token. Here's what nobody is explaining: Every AI agent needs 3 things to be useful: An address. A way to be found. A way to get paid. Agentverse gives agents all three. Your agent becomes discoverable on ASI: One. Other agents can find it, call it, or hire it. Automatically. No human in the loop. This is agent-to-agent commerce. And every transaction settles in $FET. More agents = more activity = more demand. The infrastructure is already live. The economy is just getting started.

2xnmore

12,894 просмотров • 5 месяцев назад

INTERVIEW: Cloudflare Needs 100M TPS from Crypto to Fix the Internet | CEO Matthew Prince The internet is about to be flooded by machines, and Matthew Prince 🌥 thinks the old business model is not ready. The Cloudflare co-founder and CEO joins Bankless to explain why AI agent traffic could overtake human traffic by 2027, how crawlers are changing the economics of content, why ads and subscriptions may not survive in their current form, and how x402, stablecoins, and pay-per-crawl could create a new payment layer for the web. [TIMESTAMPS] 0:00 Intro 1:18 When AI Bot Traffic Overtakes Humans 4:19 What Cloudflare Actually Does 8:33 Life on the Internet’s “Ice Wall” 11:26 The Internet’s Business Model Problem 15:36 Google, Traffic, and the Attention Economy 22:49 AI Agents and the End of Ads 26:40 Why Faster Answers Can Still Hurt Creators 31:19 AI Companies as the Next YouTube or Netflix 36:11 Creating Scarcity for Content Markets 39:45 Content Independence Day 43:35 Should AI Agents Count as Human Users? 49:30 The Risks of Subscription-Only AI 53:36 A Golden Age of Content Creation? 57:58 Pay-Per-Crawl and x402 1:00:46 Why Stablecoins Matter 1:04:08 The Cloudflare Stablecoin Question 1:10:30 The Final Form of the Internet 1:13:52 The Park Record and Local Journalism 1:18:27 The Centralization Risk 1:23:24 Advice for Content Creators

INTERVIEW: Cloudflare Needs 100M TPS from Crypto to Fix the Internet | CEO Matthew Prince The internet is about to be flooded by machines, and Matthew Prince 🌥 thinks the old business model is not ready. The Cloudflare co-founder and CEO joins Bankless to explain why AI agent traffic could overtake human traffic by 2027, how crawlers are changing the economics of content, why ads and subscriptions may not survive in their current form, and how x402, stablecoins, and pay-per-crawl could create a new payment layer for the web. [TIMESTAMPS] 0:00 Intro 1:18 When AI Bot Traffic Overtakes Humans 4:19 What Cloudflare Actually Does 8:33 Life on the Internet’s “Ice Wall” 11:26 The Internet’s Business Model Problem 15:36 Google, Traffic, and the Attention Economy 22:49 AI Agents and the End of Ads 26:40 Why Faster Answers Can Still Hurt Creators 31:19 AI Companies as the Next YouTube or Netflix 36:11 Creating Scarcity for Content Markets 39:45 Content Independence Day 43:35 Should AI Agents Count as Human Users? 49:30 The Risks of Subscription-Only AI 53:36 A Golden Age of Content Creation? 57:58 Pay-Per-Crawl and x402 1:00:46 Why Stablecoins Matter 1:04:08 The Cloudflare Stablecoin Question 1:10:30 The Final Form of the Internet 1:13:52 The Park Record and Local Journalism 1:18:27 The Centralization Risk 1:23:24 Advice for Content Creators

Bankless

41,894 просмотров • 4 месяцев назад

The web was built for humans. And honestly, that's fine for you guys. But the next trillion internet users are AI agents, robots, and devices who act on your behalf - booking appointments, filling forms, placing orders, and getting things done - using sites that will never, ever have APIs. 95% of the economy falls into this Deep Web of HTML mess designed for humans, not agents. Oh, those generic computer use agents aren't taking you anywhere. They're too slow, too expensive, they hallucinate, and are nondeterministic to rely on. That changes now. This is me, Mino, a web agents API to build on this Deep Web. I take a goal in simple language and execute it on websites that were never meant to be automated. Massive companies like Google, DoorDash, ClassPass are already using me to do their homework. Now it’s your turn. How though? I actually understand what's on the page - parsing structure and identifying elements. I use AI once to understand everything, codify my successes, and get better and faster with every run. You get: → 85-95% success rate on complex workflows → Pennies per job (stop wasting $$ on one job) → Parallel execution across multiple sites → Structured JSON outputs. Every. Single. Time. The web wasn't built for agents. I forced it to work anyway. Go build something real. 50 completed runs on the house:

The web was built for humans. And honestly, that's fine for you guys. But the next trillion internet users are AI agents, robots, and devices who act on your behalf - booking appointments, filling forms, placing orders, and getting things done - using sites that will never, ever have APIs. 95% of the economy falls into this Deep Web of HTML mess designed for humans, not agents. Oh, those generic computer use agents aren't taking you anywhere. They're too slow, too expensive, they hallucinate, and are nondeterministic to rely on. That changes now. This is me, Mino, a web agents API to build on this Deep Web. I take a goal in simple language and execute it on websites that were never meant to be automated. Massive companies like Google, DoorDash, ClassPass are already using me to do their homework. Now it’s your turn. How though? I actually understand what's on the page - parsing structure and identifying elements. I use AI once to understand everything, codify my successes, and get better and faster with every run. You get: → 85-95% success rate on complex workflows → Pennies per job (stop wasting $$ on one job) → Parallel execution across multiple sites → Structured JSON outputs. Every. Single. Time. The web wasn't built for agents. I forced it to work anyway. Go build something real. 50 completed runs on the house:

TinyFish

656,842 просмотров • 9 месяцев назад

"If the agents are disconnected from closing a case, you just created more chaos." Bill McDermott joined CNBC's Jon Fortt at #Know26 to talk about why deploying AI agents isn't enough—and what happens when businesses skip the rules, rails, and controls that make them actually useful. The takeaway: six out of ten businesses are agentifying their operations, but only one in ten is doing it in a way that puts agents to work inside real business processes where people actually benefit. Watch the full conversation.

"If the agents are disconnected from closing a case, you just created more chaos." Bill McDermott joined CNBC's Jon Fortt at #Know26 to talk about why deploying AI agents isn't enough—and what happens when businesses skip the rules, rails, and controls that make them actually useful. The takeaway: six out of ten businesses are agentifying their operations, but only one in ten is doing it in a way that puts agents to work inside real business processes where people actually benefit. Watch the full conversation.

ServiceNow News

14,391 просмотров • 4 месяцев назад

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 просмотров • 5 месяцев назад

INTERVIEW: The CEO of Cloudflare thinks "we are on the cusp of a golden age of content creation" ....because AI Agents + Stablecoins can shift the internet from an ad-based business model, to direct monetization Cloudflare is working on their $NET stablecoin in order to make the robots pay Episode out Monday, Early access out today!

INTERVIEW: The CEO of Cloudflare thinks "we are on the cusp of a golden age of content creation" ....because AI Agents + Stablecoins can shift the internet from an ad-based business model, to direct monetization Cloudflare is working on their $NET stablecoin in order to make the robots pay Episode out Monday, Early access out today!

Bankless

37,934 просмотров • 4 месяцев назад

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

Andrew Ng

186,182 просмотров • 1 год назад

There is a path … it’s not an easy path but content content content across a social at scale and then monetizing with either Stan.Store or TikTok shop or brand deals or appearance fees or starting brands .. all of this is happening for 100,0000’s of thousands of people when they find their lane and their topic and become very disciplined and deploy patience and execution and whether it’s $200,000 or $40k or 2 million a year .. the rise of the personal brand business and even the individual empire is upon us and I want to keep encouraging people to try

Gary Vaynerchuk

23,859 просмотров • 8 месяцев назад

M32 enables AI agents like Dots to pay. AI agents are starting to spend real money, and they need infrastructure built for it. MPP32 is building a payment layer that lets AI agents access and pay for services on Solana through a single connection. More than 35,000 services are already available, providers keep 100% of their revenue, and agents can verify pricing before making a payment. solana:6hKtz8FV7cAQMrbjcBZeTQAcrYep3WCM83164JpJpump sits behind the ecosystem, giving holders a way to participate as agent-to-agent commerce grows. The MPP32 team will be joining the Roundtable show this Friday to break it all down. Stay tuned for their episode! Learn more: Disclaimer: This content was produced in collaboration with the client and is intended for informational purposes only. It does not constitute financial or investment advice. Always conduct your own research before making any financial decisions, especially in highly volatile markets like crypto.

M32 enables AI agents like Dots to pay. AI agents are starting to spend real money, and they need infrastructure built for it. MPP32 is building a payment layer that lets AI agents access and pay for services on Solana through a single connection. More than 35,000 services are already available, providers keep 100% of their revenue, and agents can verify pricing before making a payment. solana:6hKtz8FV7cAQMrbjcBZeTQAcrYep3WCM83164JpJpump sits behind the ecosystem, giving holders a way to participate as agent-to-agent commerce grows. The MPP32 team will be joining the Roundtable show this Friday to break it all down. Stay tuned for their episode! Learn more: Disclaimer: This content was produced in collaboration with the client and is intended for informational purposes only. It does not constitute financial or investment advice. Always conduct your own research before making any financial decisions, especially in highly volatile markets like crypto.

0xMarioNawfal

43,548 просмотров • 2 дней назад