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I did not know how much of a data advantage Google has over the other AI companies. Matthew Prince 🌥 says they can access 3X as much data as OpenAI and nearly 5X as much as Bing. This could make it hard for those models—and Grok—to beat Gemini in the long run.

I did not know how much of a data advantage Google has over the other AI companies. Matthew Prince 🌥 says they can access 3X as much data as OpenAI and nearly 5X as much as Bing. This could make it hard for those models—and Grok—to beat Gemini in the long run.

nxthompson

22,536 просмотров • 7 месяцев назад

Beautiful new raster data layers are now available on Google Earth’s intuitive, no-code canvas. 🗺️👉 With the analytical power of Google Earth Engine directly integrated into Google Earth, geospatial analysis is as easy as point and click. Rolling out over the coming days, you will have access to ten new raster layers from Earth Engine's iconic data catalog, including: • Slope • Aspect • Land cover • and more It's never been easier to quickly combine raster data, vector data, imagery, and your own data to save time and make better decisions. Explore these new data layers, now.

Beautiful new raster data layers are now available on Google Earth’s intuitive, no-code canvas. 🗺️👉 With the analytical power of Google Earth Engine directly integrated into Google Earth, geospatial analysis is as easy as point and click. Rolling out over the coming days, you will have access to ten new raster layers from Earth Engine's iconic data catalog, including: • Slope • Aspect • Land cover • and more It's never been easier to quickly combine raster data, vector data, imagery, and your own data to save time and make better decisions. Explore these new data layers, now.

Google Earth

15,693 просмотров • 5 месяцев назад

$PLTR is one of the cleanest ways to play the AI cycle because it benefits from the entire infrastructure buildout without financing the data centers, chips, memory, power or networking underneath it. Palantir is also emerging as one of the biggest sovereign AI winners by building the control layer that determines what AI understands, which proprietary data it can access and what decisions or actions it can influence inside an organization. OpenAI and Anthropic create the intelligence while Palantir connects it to permissions, workflows and real operating systems where measurable economic value is produced.

$PLTR is one of the cleanest ways to play the AI cycle because it benefits from the entire infrastructure buildout without financing the data centers, chips, memory, power or networking underneath it. Palantir is also emerging as one of the biggest sovereign AI winners by building the control layer that determines what AI understands, which proprietary data it can access and what decisions or actions it can influence inside an organization. OpenAI and Anthropic create the intelligence while Palantir connects it to permissions, workflows and real operating systems where measurable economic value is produced.

Shay Boloor

200,100 просмотров • 1 месяц назад

$SNOW is becoming data layer for the new agentic AI economy because agents need secure access to proprietary enterprise data and business context before they can do anything useful. That makes Snowflake even more valuable as models get cheaper and more interchangeable because it can bring the best model to the data while AI products like CoCo and CoWork drive a ton more storage, compute and core platform consumption underneath.

$SNOW is becoming data layer for the new agentic AI economy because agents need secure access to proprietary enterprise data and business context before they can do anything useful. That makes Snowflake even more valuable as models get cheaper and more interchangeable because it can bring the best model to the data while AI products like CoCo and CoWork drive a ton more storage, compute and core platform consumption underneath.

Shay Boloor

62,547 просмотров • 11 дней назад

Larry Ellison basically explained why OpenAI is positioned to win the AI race. Today’s models all trained on the same thing: public data. The internet. Books. Papers. General knowledge. Impressive, but capped. Ellison’s point: peak value only comes when AI is trained on private data. That’s where OpenAI is moving: • partnerships with universities to work with unpublished research • experts teaching models real job tasks, not just theory • deep access to how science, medicine, law, and finance actually work Public data makes AI smart. Private data makes it decisive. The next AI leap won’t come from reading more of the internet. It’ll come from being invited behind the curtain. That’s the real moat.

Larry Ellison basically explained why OpenAI is positioned to win the AI race. Today’s models all trained on the same thing: public data. The internet. Books. Papers. General knowledge. Impressive, but capped. Ellison’s point: peak value only comes when AI is trained on private data. That’s where OpenAI is moving: • partnerships with universities to work with unpublished research • experts teaching models real job tasks, not just theory • deep access to how science, medicine, law, and finance actually work Public data makes AI smart. Private data makes it decisive. The next AI leap won’t come from reading more of the internet. It’ll come from being invited behind the curtain. That’s the real moat.

VraserX e/acc

167,094 просмотров • 8 месяцев назад

GEORGE HOTZ: “YOU CAN BREAK AI DOWN INTO 5 TIERS.” “DATA CENTERS — TIER 1, FABS — TIER 2, NVIDIA/AMD — TIER 3, OPENAI/ANTHROPIC — TIER 4, AND COMPLETELY WORTHLESS THINGS LIKE CURSOR AND WINDSURF, WHICH ARE TIER 5.” “OPENAI AND ANTHROPIC WILL EAT ALL THE VALUE FROM THE CURSORS AND WINDSURFS OF THE WORLD. I ARGUE THAT THE TIER 4S (OPENAI/ANTHROPIC) AREN’T EVEN GOING TO HAVE VALUE.”

GEORGE HOTZ: “YOU CAN BREAK AI DOWN INTO 5 TIERS.” “DATA CENTERS — TIER 1, FABS — TIER 2, NVIDIA/AMD — TIER 3, OPENAI/ANTHROPIC — TIER 4, AND COMPLETELY WORTHLESS THINGS LIKE CURSOR AND WINDSURF, WHICH ARE TIER 5.” “OPENAI AND ANTHROPIC WILL EAT ALL THE VALUE FROM THE CURSORS AND WINDSURFS OF THE WORLD. I ARGUE THAT THE TIER 4S (OPENAI/ANTHROPIC) AREN’T EVEN GOING TO HAVE VALUE.”

Vivek Sen

45,335 просмотров • 3 месяцев назад

man anthropic is absolutely cooking microsoft. claude now works natively in word, powerpoint and excel. thats 450M users but how the fuck did anthropic beat microsoft co-pilot? it gets worse - microsoft blew $13B creating co-pilot and owns 27% of openai yet no sign on chatgpt. - claude has access to SHARED context across excel, word and powerpoint. they know the footprint of microsoft's users - very valuable AI data. - only 15M microsoft users pay for copilot, so its clear they need to go model-agnostic - this is the 3rd major microsoft product to leverage claude (cowork and deep research are the other 2) very impressive execution from anthropic tbh, they are trying to be everywhere and anywhere - its working

man anthropic is absolutely cooking microsoft. claude now works natively in word, powerpoint and excel. thats 450M users but how the fuck did anthropic beat microsoft co-pilot? it gets worse - microsoft blew $13B creating co-pilot and owns 27% of openai yet no sign on chatgpt. - claude has access to SHARED context across excel, word and powerpoint. they know the footprint of microsoft's users - very valuable AI data. - only 15M microsoft users pay for copilot, so its clear they need to go model-agnostic - this is the 3rd major microsoft product to leverage claude (cowork and deep research are the other 2) very impressive execution from anthropic tbh, they are trying to be everywhere and anywhere - its working

Ejaaz

473,868 просмотров • 5 месяцев назад

🚨OPENAI AND ANTHROPIC JUST FOLDED ON THEIR DATA RETENTION POLICY AFTER PALANTIR CEO EXPOSED THEIR REAL BUSINESS MODEL Alex Karp: >"something has gone completely wrong" >they want "access to my data" so they can "build my alpha" >"i'm gonna get no value, and they're gonna get my IP" >"this is effing insane" >"trying to drug addict us to a future they believe they control" >"who owns the data? where is it cached? are the prompts secured?" >"we need to rebuild trust" Yesterday: OpenAI started testing "private safety processing" to avoid retaining customer data Today: Anthropic reversing course, will allow enterprise customers to keep data on THEIR OWN cloud infrastructure instead of Anthropic's Alex Karp was right.

🚨OPENAI AND ANTHROPIC JUST FOLDED ON THEIR DATA RETENTION POLICY AFTER PALANTIR CEO EXPOSED THEIR REAL BUSINESS MODEL Alex Karp: >"something has gone completely wrong" >they want "access to my data" so they can "build my alpha" >"i'm gonna get no value, and they're gonna get my IP" >"this is effing insane" >"trying to drug addict us to a future they believe they control" >"who owns the data? where is it cached? are the prompts secured?" >"we need to rebuild trust" Yesterday: OpenAI started testing "private safety processing" to avoid retaining customer data Today: Anthropic reversing course, will allow enterprise customers to keep data on THEIR OWN cloud infrastructure instead of Anthropic's Alex Karp was right.

NIK

308,222 просмотров • 25 дней назад

Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

Over the last few months, we’ve been thinking about how to learn from “off-domain” data - data from non-robot sources like video or simulation. These data sources are not quite good enough to learn policies (even monolithic VLA models) directly, but they still contain lots of information that can be useful for generalizable robot control. How can we develop robot learning models that are able to make use of this type of data for generalizable control? In new work, that we call HAMSTER, we show that VLMs can be useful for enabling robotic learning from off-domain data, but specifically when used through hierarchical VLA architectures. We show that this class of models can learn generalizable robot policies for the real world from large-scale, off-domain data. A 🧵 (1/10)

Abhishek Gupta

11,994 просмотров • 1 год назад

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Chris Camillo made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Milk Road Stocks

41,351 просмотров • 1 месяц назад

Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Chris Camillo just made the most bullish case on $AMZN. Everyone worried when Amazon raised their capex to $200 billion Camillo believes they might make that $200 billion back on their Anthropic investment alone when Anthropic IPOs. What Amazon has done in the last 18 months is remarkable in scope. They didn't try to win the foundation model race against OpenAI and Google directly. They went a completely different direction, positioning themselves as the infrastructure underneath everything that is AI. Trainium chips, cloud buildout, advertising integration and equity stakes in two of the three most important AI companies on the planet. First Anthropic. Now $50 billion into OpenAI on top of that. And that OpenAI investment isn't just a financial bet. It ties OpenAI into Amazon's cloud ecosystem, probably permanently. Two of the big three AI labs now run on Amazon infrastructure. Google is the only one that doesn't. The market has spent two years debating who builds the best model. Amazon decided to own the plumbing those models run on and bought equity in the winners while they were at it.

Milk Road Macro

55,169 просмотров • 3 месяцев назад

First upgrade of PhoenixONE is here - featuring agent thought log. As the research agent is thinking, get a glimpse into the data, insight extraction, and underlying processes. On an average query, Phoenix Crypto Research & Deep Research performs rapid AI contextual search & analysis on 40-50 data sources. On the user growth side - Vietnam Wave 2 coming right up. Meanwhile, Turkey Wave 1 is about to start! $PHB #PhoenixAI #DeepResearch

First upgrade of PhoenixONE is here - featuring agent thought log. As the research agent is thinking, get a glimpse into the data, insight extraction, and underlying processes. On an average query, Phoenix Crypto Research & Deep Research performs rapid AI contextual search & analysis on 40-50 data sources. On the user growth side - Vietnam Wave 2 coming right up. Meanwhile, Turkey Wave 1 is about to start! $PHB #PhoenixAI #DeepResearch

Phoenix AI Labs

21,901 просмотров • 1 год назад

Elon Musk says Anthropic puts more care into AI safety than OpenAI and explains how rival companies should test one another’s models. “All the AI companies have a test harness, a series of tests that you give to any model to see if it’s going to build bioweapons or nuclear bombs or be deliberately deceptive. Everyone applying everyone else’s test harness is probably the best thing we can do to ensure our safety. We should try to do that as soon as possible.” “It’s the only thing I can think of that we could probably get all parties, including China, to agree to. You basically provide API access in advance of the model release. If they don’t solve what’s problematic, competitors can go public with the fact that they think the model being released is unsafe. The legal liability would be enormous.” “The two leading AI companies are Anthropic and OpenAI. Their models are quite close in capability, so it’s difficult for either one to slow down without handing the lead to the other. On balance, I think Anthropic puts more care into their safety than OpenAI. But even Anthropic acknowledges they are worried about their models. Many people from Anthropic have publicly voiced concern that their models are scaring them, that they’re getting scary smart.”

Elon Musk says Anthropic puts more care into AI safety than OpenAI and explains how rival companies should test one another’s models. “All the AI companies have a test harness, a series of tests that you give to any model to see if it’s going to build bioweapons or nuclear bombs or be deliberately deceptive. Everyone applying everyone else’s test harness is probably the best thing we can do to ensure our safety. We should try to do that as soon as possible.” “It’s the only thing I can think of that we could probably get all parties, including China, to agree to. You basically provide API access in advance of the model release. If they don’t solve what’s problematic, competitors can go public with the fact that they think the model being released is unsafe. The legal liability would be enormous.” “The two leading AI companies are Anthropic and OpenAI. Their models are quite close in capability, so it’s difficult for either one to slow down without handing the lead to the other. On balance, I think Anthropic puts more care into their safety than OpenAI. But even Anthropic acknowledges they are worried about their models. Many people from Anthropic have publicly voiced concern that their models are scaring them, that they’re getting scary smart.”

DogeDesigner

3,728,348 просмотров • 12 часов назад

As part of our walkthrough series, today we are covering labor in the physical AI era—why real robots and real jobs hinge on grounded data, human judgment, and a pipeline from capture to delivery, not simulation alone. If you’re following Harbor or the future of work in AI, this one connects the dots in a few minutes.

As part of our walkthrough series, today we are covering labor in the physical AI era—why real robots and real jobs hinge on grounded data, human judgment, and a pipeline from capture to delivery, not simulation alone. If you’re following Harbor or the future of work in AI, this one connects the dots in a few minutes.

Harbor ML

13,472 просмотров • 4 месяцев назад

Sergey Brin on the genius of Jeff Dean. He credits Jeff’s early obsession with neural networks, back when they were “telling cats from dogs”, as the spark for everything. TPU was Jeff’s idea. He calculated that if users spoke to Google for just three minutes a day, Google would have to double its CPU data centers. Instead of buying more CPUs, Jeff decided to build a new chip for AI. What’s fascinating is that when AI was not as big as today, when it can only “tell cats from dogs,” Larry and Sergey were like, “Cool, let’s make a custom chip for it.” That’s a huge show of confidence in deep tech and top-tier technical talent. It makes Google the only AI company that has the top models, top chips, and top data centers.

Sergey Brin on the genius of Jeff Dean. He credits Jeff’s early obsession with neural networks, back when they were “telling cats from dogs”, as the spark for everything. TPU was Jeff’s idea. He calculated that if users spoke to Google for just three minutes a day, Google would have to double its CPU data centers. Instead of buying more CPUs, Jeff decided to build a new chip for AI. What’s fascinating is that when AI was not as big as today, when it can only “tell cats from dogs,” Larry and Sergey were like, “Cool, let’s make a custom chip for it.” That’s a huge show of confidence in deep tech and top-tier technical talent. It makes Google the only AI company that has the top models, top chips, and top data centers.

Yuchen Jin

526,544 просмотров • 9 месяцев назад

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.

Bob McGrew (Head of Research OpenAI) explains why proprietary data no longer provides companies with a competitive advantage in the AI era. Finance companies once believed their years of accumulated data would give them an edge. They planned to train specialized models on top of GPT or Llama using their exclusive information. The results shocked them. Their industry-specific models performed worse than the next generation of general purpose models. The ability to synthesize new information proved more valuable than memorizing old data. McGrew introduces the concept of "embodied labor" - the human work behind data collection. Companies spent years having employees call customers, analyze case studies, and gather information through manual processes. This accumulated knowledge required massive time and money to build. It represented thousands of hours of human effort that companies thought couldn't be replicated by competitors. But AI changes everything. Instead of years of customer calls, AI can conduct comprehensive surveys instantly. Rather than manual case analysis, AI processes thousands of examples in hours. The core insight is that value wasn't in the data itself but in the labor required to collect it. Since AI makes that labor essentially free, the advantage disappears. Companies can no longer rely on their proprietary data as a protective moat. Any competitor can use AI to replicate years of data gathering almost instantly.

Aish

240,743 просмотров • 1 год назад

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership

What if your personal data became an asset you actually owned? In this May 2026 AMA, Matrix AI Network explores one of the most important questions facing AI, Web3, and the future digital economy: Who owns the data that powers artificial intelligence? As AI models continue to improve, the next major bottleneck is no longer computing power—it is access to high-quality, verified human data. The discussion covers how personal biodata can become a valuable digital asset, how data quality is measured, and why future AI companies may depend directly on users as data providers. Topics include: ✅ Biodata as a new asset class ✅ AI-grade data quality scoring ✅ Wearable devices for raw data ✅ Data ownership and monetization models ✅ Preventing fake data and device spoofing ✅ Real-world applications in healthcare, sports science and mental wellness ✅ Hypnus and the future biodata ecosystem ✅ Project direction The future may not belong to companies that own data. It belongs to the people who create it. Watch now 👇 #AI #Web3 #DataOwnership

Matrix AI Network

61,436 просмотров • 3 месяцев назад

"You can break AI down into 5 tiers." - George Hotz "Data centers - tier 1, fabs - tier 2, Nvidia/AMD - tier 3, OpenAI/Anthropic - tier 4, and completely worthless things like Cursor and Windsurf, which are tier 5." "OpenAI and Anthropic will eat all the value from the Cursors and Windsurfs of the world. I argue that the tier 4s (OpenAI/Anthropic) aren't even going to have value." From his June 2025 appearance, George Hotz 🌑 really raised eyebrows with this clip when it hit the timeline.

"You can break AI down into 5 tiers." - George Hotz "Data centers - tier 1, fabs - tier 2, Nvidia/AMD - tier 3, OpenAI/Anthropic - tier 4, and completely worthless things like Cursor and Windsurf, which are tier 5." "OpenAI and Anthropic will eat all the value from the Cursors and Windsurfs of the world. I argue that the tier 4s (OpenAI/Anthropic) aren't even going to have value." From his June 2025 appearance, George Hotz 🌑 really raised eyebrows with this clip when it hit the timeline.

TBPN

1,052,145 просмотров • 10 месяцев 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 • 10 дней назад