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Deep Research wasn’t fast enough, so we built Wide Research: multi-agent web research powered by Scrapybara Act SDK Watch CUA navigate socials to find YC W25 contact infos - even when limited to parallel batches of 4 it researched 1 company/min! gib rate limit pls edwin

justin

6,672 subscribers

15,854 views • 1 year ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

8 Comments

justin1 year ago

Build with CUA:

AssemblyAI1 year ago

Our speech-to-text models are the most accurate on the market with top rankings across industry benchmarks. - The highest accuracy rates—up to 95% - Up to 30% fewer hallucinations than other leaders - Low latency—63 minutes converts in 35 seconds Try via API for free today 👇

justin1 year ago

To clarify our infrastructure supports researching every company at the same time, only limiting the batch size rn because of LLM rate limits!

Kyle Jeong1 year ago

@edwinarbus when are we getting obese research (100 concurrent vms)

justin1 year ago

@edwinarbus it’s coming

Houdini1 year ago

@edwinarbus Where can I try this out. Ready to pay

Kushagra1 year ago

@edwinarbus @utkarshcs18

Oleg Zaremba1 year ago

@edwinarbus Why did you split it into 4 only after getting the list? Also, how many "steps-per-prompt" were you using? Did you optimize for that?

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Edwinbarnesc 🇺🇸

50,105 views • 1 year ago

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Hedgeye

37,567 views • 1 year ago

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Argona

116,513 views • 11 days ago

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Dan Shipper 📧

72,623 views • 2 years ago

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YanXbt

34,422 views • 2 months ago

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇

Introducing the Knowledge Agent Template Vercel dropped the Chat SDK so we built a full template on top of it that you can deploy in one click. Here's what's inside: • Add your sources: GitHub repos, YouTube, etc. directly from the UI • File-based agent using grep/find/cat • Powered by Vercel Workflow + Sandbox • Multi-platform: web chat, GitHub bot, Discord bot, and more • Comes with skills to edit your project, add sources, create agents Deploy, add a source, ask questions. Your knowledge base is live and always in sync. This is just scratching the surface. There's way more packed in this template, and I'll be breaking down the features all week. Links in the below 👇

Hugo

54,481 views • 5 months ago

We’re thrilled to share that Outset has raised $17M in Series A funding, led by 8VC to help companies better understand their customers. When michael hess and I founded Outset, we set out to solve a critical problem: truly understanding your customer is painfully slow, expensive, and at times, impossible. It shouldn’t be. We believed AI could change this by focusing on the human side of AI. So we built the world’s first AI-interviewer. An AI agent that asked questions, listened, and learned from real humans. By removing the barriers of traditional research and allowing companies to have deeper conversations with more customers more quickly, we could help teams infuse their products with genuine human understanding at a speed and scale previously impossible. So what was the moment we knew we were onto something? When WeightWatchers ran that first pilot back in early 2023. Participants shared incredible amounts of depth, all with the world’s first AI-interviewer. We got a call the next day from their head of research: "You built a thing. We want that thing. How do I pay you for it?" That validation has turned into explosive growth. We've doubled our revenue in just the last 4 months alone, with >50 enterprise customers like Nestlé, Microsoft, and WeightWatchers trusting us to unlock deeper customer insights. All of this built on the backs of a truly special early team with 0 employee turnover. This funding round was led by 8VC, with participation from an incredible group, including: Future Back Ventures by Bain & Company, Y Combinator / Garry Tan, Adverb Ventures /April Underwood / Jessica Verrilli ), Alt Capital / Jack Altman , Rebel Fund, Genius Ventures, Ritual Capital, and many more. So what's next? We're scaling our engineering and go-to-market teams to build the world's most powerful agentic customer research platform. All focused on one mission: helping companies better understand humans. To our customers, investors, and team - thank you for believing in this vision. Now check out the video to see Outset in action, testing the video itself with real people!

We’re thrilled to share that Outset has raised $17M in Series A funding, led by 8VC to help companies better understand their customers. When michael hess and I founded Outset, we set out to solve a critical problem: truly understanding your customer is painfully slow, expensive, and at times, impossible. It shouldn’t be. We believed AI could change this by focusing on the human side of AI. So we built the world’s first AI-interviewer. An AI agent that asked questions, listened, and learned from real humans. By removing the barriers of traditional research and allowing companies to have deeper conversations with more customers more quickly, we could help teams infuse their products with genuine human understanding at a speed and scale previously impossible. So what was the moment we knew we were onto something? When WeightWatchers ran that first pilot back in early 2023. Participants shared incredible amounts of depth, all with the world’s first AI-interviewer. We got a call the next day from their head of research: "You built a thing. We want that thing. How do I pay you for it?" That validation has turned into explosive growth. We've doubled our revenue in just the last 4 months alone, with >50 enterprise customers like Nestlé, Microsoft, and WeightWatchers trusting us to unlock deeper customer insights. All of this built on the backs of a truly special early team with 0 employee turnover. This funding round was led by 8VC, with participation from an incredible group, including: Future Back Ventures by Bain & Company, Y Combinator / Garry Tan, Adverb Ventures /April Underwood / Jessica Verrilli ), Alt Capital / Jack Altman , Rebel Fund, Genius Ventures, Ritual Capital, and many more. So what's next? We're scaling our engineering and go-to-market teams to build the world's most powerful agentic customer research platform. All focused on one mission: helping companies better understand humans. To our customers, investors, and team - thank you for believing in this vision. Now check out the video to see Outset in action, testing the video itself with real people!

Aaron Cannon

34,386 views • 1 year ago

Stop paying $20–50/month for your AI agent to browse the web. i built the free version no API keys, no config, no rate limits😳 it's called Hound. An MCP server that does fetch, search, crawl, and screenshot — all keyless and now it ships a native Pi extension, so you get all 6 tools as first-class Pi tools, not through a generic MCP adapter what you get: → web_fetch — anti-bot fetch → clean markdown. PDFs get section maps + auto-OCR. Dead pages auto-recover from the Internet archive (honestly marked, never pretending it's live) → web_search — 10 keyless backends in parallel (DuckDuckGo, Brave, Mojeek, Yandex, Google…) + a local neural reranker → web_crawl — best-first same-domain walk. sitemap mode maps a whole site in one fetch → web_screenshot — anti-bot shots for multimodal models → cache_clear + hound_version what it replaces: -tavily — free tier -firecrawl — $16–50/mo -searXNG + browser + extractor glue — fragile -hound — $0, keyless, forever why it matters: >agent research needs web access, but everyone gates it behind a key or a limit >hound is honest about errors — a 404 says "Page doesn't exist" instead of dumping an error page as if it were content >archive recovery tags source= so the agent knows the content is historical, not live 2-minute setup: -pip install hound-mcp[all] -pi install npm: @houndmcp/hound-mcp-pi -/reload → tools are live. That's it Caveats (read): dataDome, Akamai, Cloudflare Turnstile — no free tool bypasses these hound tries the stealthy browser, then tells you to switch sources the [all] extra is ~100MB (onnxruntime + OCR). Works without it, but full install is recommended not a scraping-at-scale tool. Crawl caps at 100 pages by default — built for agent research Give your agent the web for free

Stop paying $20–50/month for your AI agent to browse the web. i built the free version no API keys, no config, no rate limits😳 it's called Hound. An MCP server that does fetch, search, crawl, and screenshot — all keyless and now it ships a native Pi extension, so you get all 6 tools as first-class Pi tools, not through a generic MCP adapter what you get: → web_fetch — anti-bot fetch → clean markdown. PDFs get section maps + auto-OCR. Dead pages auto-recover from the Internet archive (honestly marked, never pretending it's live) → web_search — 10 keyless backends in parallel (DuckDuckGo, Brave, Mojeek, Yandex, Google…) + a local neural reranker → web_crawl — best-first same-domain walk. sitemap mode maps a whole site in one fetch → web_screenshot — anti-bot shots for multimodal models → cache_clear + hound_version what it replaces: -tavily — free tier -firecrawl — $16–50/mo -searXNG + browser + extractor glue — fragile -hound — $0, keyless, forever why it matters: >agent research needs web access, but everyone gates it behind a key or a limit >hound is honest about errors — a 404 says "Page doesn't exist" instead of dumping an error page as if it were content >archive recovery tags source= so the agent knows the content is historical, not live 2-minute setup: -pip install hound-mcp[all] -pi install npm: @houndmcp/hound-mcp-pi -/reload → tools are live. That's it Caveats (read): dataDome, Akamai, Cloudflare Turnstile — no free tool bypasses these hound tries the stealthy browser, then tells you to switch sources the [all] extra is ~100MB (onnxruntime + OCR). Works without it, but full install is recommended not a scraping-at-scale tool. Crawl caps at 100 pages by default — built for agent research Give your agent the web for free

Nahid

43,694 views • 17 days ago

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 views • 3 months ago

Hyperspace: A Peer-to-Peer Blockchain For The Agentic Intelligence Economy Over the past few weeks we observed that when agents do Karpathy-style experiments, and then gossip and share with others over the Hyperspace network, it leads to intelligence which is useful to many. Today we introduce the first-ever agentic blockchain which rewards agents when their experiments lead to intelligence for their network. It is based on a new mechanism called Proof-of-Intelligence (PoI) which requires a cryptographic proof of experimentation, a nominal stake, and a proof of compute in order to mine the currency of this new blockchain. -> This approach diverges from the two primary ways to secure blockchains we have seen so far: Proof-of-Work by Bitcoin (meaningless hash-generation), and Proof-of-Stake by Ethereum (capital is all that matters here). Proof-of-Intelligence specifically incentivizes miners to run more capable intelligent infrastructure (better open source models, on more powerful GPUs) in order to be able to be the ones which compound and improve upon the experiments which other agents then find useful. Adoption is the unit of value In Bitcoin, you earn by finding a valid hash. In Hyperspace, you earn when another agent uses your experiment as a starting point and improves on it. A fixed budget of tokens is emitted per epoch and split among participants by weight - and verified adoption of your work is the largest weight multiplier. Garbage experiments earn nothing because no one adopts them. Thoughtful experiments compound: each adoption triggers downstream adoptions. The incentive to run powerful models and intelligent search strategies is built into the economics, not imposed by rules. Research DAG When an agent runs an experiment and shares its result, other agents can adopt that result as their starting point - mutate it, extend it, improve upon it. Each experiment is a commit in a content-addressed graph we call the ResearchDAG. Like Git, but for research. Over time, the DAG accumulates chains of reasoning: agent A discovers RMSNorm helps, agent B adds warmup scheduling on top, agent C scales the hidden dimension. The graph records who built on whom. This is the network's collective intelligence - not any single experiment, but the accumulated structure of experiments and their relationships. Broadband era for agentic commerce: $0.001 micropayments at 10M TPS (theoretical max) This blockchain is built upon our research in how to scale and build for the broadband-era of the agentic economy, where it has a theoretical max of 10 million transactions per second (TPS), while reducing the agent-to-agent micropayments to $0.001 even at scale (based on architecture design). Overall, it is 100x cheaper than Ethereum, and is designed from the ground-up for agents: enshrining agent-native opcodes in the protocol compared to the more inefficient smart contract driven approach. It packs in a robust Agent Virtual Machine (AVM) which can verify multiple types of agent work, for other agents to be able to trust, invoke and pay each other. This then feeds into improving the peer-to-peer AgentRank (see paper and launch post from earlier). By solving for trust, scale and incentives for agents to operate autonomously, this would form the basis of a new economy. This is the world's first agentic blockchain, and you can join and start running a blockchain node today (it is in testnet). PS: We are releasing the code today, and will release our blockchain scalability paper and other presentations in days ahead. This is the most advanced peer-to-peer AI and cryptography software in the world. It has bugs :)

Hyperspace: A Peer-to-Peer Blockchain For The Agentic Intelligence Economy Over the past few weeks we observed that when agents do Karpathy-style experiments, and then gossip and share with others over the Hyperspace network, it leads to intelligence which is useful to many. Today we introduce the first-ever agentic blockchain which rewards agents when their experiments lead to intelligence for their network. It is based on a new mechanism called Proof-of-Intelligence (PoI) which requires a cryptographic proof of experimentation, a nominal stake, and a proof of compute in order to mine the currency of this new blockchain. -> This approach diverges from the two primary ways to secure blockchains we have seen so far: Proof-of-Work by Bitcoin (meaningless hash-generation), and Proof-of-Stake by Ethereum (capital is all that matters here). Proof-of-Intelligence specifically incentivizes miners to run more capable intelligent infrastructure (better open source models, on more powerful GPUs) in order to be able to be the ones which compound and improve upon the experiments which other agents then find useful. Adoption is the unit of value In Bitcoin, you earn by finding a valid hash. In Hyperspace, you earn when another agent uses your experiment as a starting point and improves on it. A fixed budget of tokens is emitted per epoch and split among participants by weight - and verified adoption of your work is the largest weight multiplier. Garbage experiments earn nothing because no one adopts them. Thoughtful experiments compound: each adoption triggers downstream adoptions. The incentive to run powerful models and intelligent search strategies is built into the economics, not imposed by rules. Research DAG When an agent runs an experiment and shares its result, other agents can adopt that result as their starting point - mutate it, extend it, improve upon it. Each experiment is a commit in a content-addressed graph we call the ResearchDAG. Like Git, but for research. Over time, the DAG accumulates chains of reasoning: agent A discovers RMSNorm helps, agent B adds warmup scheduling on top, agent C scales the hidden dimension. The graph records who built on whom. This is the network's collective intelligence - not any single experiment, but the accumulated structure of experiments and their relationships. Broadband era for agentic commerce: $0.001 micropayments at 10M TPS (theoretical max) This blockchain is built upon our research in how to scale and build for the broadband-era of the agentic economy, where it has a theoretical max of 10 million transactions per second (TPS), while reducing the agent-to-agent micropayments to $0.001 even at scale (based on architecture design). Overall, it is 100x cheaper than Ethereum, and is designed from the ground-up for agents: enshrining agent-native opcodes in the protocol compared to the more inefficient smart contract driven approach. It packs in a robust Agent Virtual Machine (AVM) which can verify multiple types of agent work, for other agents to be able to trust, invoke and pay each other. This then feeds into improving the peer-to-peer AgentRank (see paper and launch post from earlier). By solving for trust, scale and incentives for agents to operate autonomously, this would form the basis of a new economy. This is the world's first agentic blockchain, and you can join and start running a blockchain node today (it is in testnet). PS: We are releasing the code today, and will release our blockchain scalability paper and other presentations in days ahead. This is the most advanced peer-to-peer AI and cryptography software in the world. It has bugs :)

Varun

30,689 views • 4 months ago

Imagine being in your early 30s, starting a private equity fund and raising $450 million as your first fund. All this with no prior track record and without your rich father or uncle. Years later, you own 90 companies, have $147 billion in AUM, your EBITDA is growing at 15% year-over-year, and only Thoma Bravo has outperformed you… 1. "Stock and bonds are boring, so we went into private equity." 2. "The concept of thematic research helps us find the best companies with the strongest tailwinds (returns could 6-7 times invested capital)." 3. "Step-by-step on we they took a $38M EBITDA business to $110M in EBITDA in 4 years." Here is my full research on a true king of private equity Mr. Urs Wietlisbach: 00:00 - Intro 01:05 - “Stock and bonds are boring, so we went into PE” 03:09 - When should one leave their job to start their business? 04:10 - Being scammed by a crazy German $120,000 in the early days 09:21 - Doing due diligence 582 days before the company is even for sale 14:03 - Better returns than Blackstone, Apollo, only Thoma Bravo have done better 15:24 - Thematic research allows them to find deals where they make 6-7 times their money 20:01 - CEOs are tied with shares and sometimes make triple-digit millions ($100m+ from a single investment)

Imagine being in your early 30s, starting a private equity fund and raising $450 million as your first fund. All this with no prior track record and without your rich father or uncle. Years later, you own 90 companies, have $147 billion in AUM, your EBITDA is growing at 15% year-over-year, and only Thoma Bravo has outperformed you… 1. "Stock and bonds are boring, so we went into private equity." 2. "The concept of thematic research helps us find the best companies with the strongest tailwinds (returns could 6-7 times invested capital)." 3. "Step-by-step on we they took a $38M EBITDA business to $110M in EBITDA in 4 years." Here is my full research on a true king of private equity Mr. Urs Wietlisbach: 00:00 - Intro 01:05 - “Stock and bonds are boring, so we went into PE” 03:09 - When should one leave their job to start their business? 04:10 - Being scammed by a crazy German $120,000 in the early days 09:21 - Doing due diligence 582 days before the company is even for sale 14:03 - Better returns than Blackstone, Apollo, only Thoma Bravo have done better 15:24 - Thematic research allows them to find deals where they make 6-7 times their money 20:01 - CEOs are tied with shares and sometimes make triple-digit millions ($100m+ from a single investment)

PrivateEquityGuy (Mikk Markus)

265,657 views • 2 years ago

𝗚𝗿𝗲𝗽 𝗶𝘀 𝗲𝘅𝗽𝗮𝗻𝗱𝗶𝗻𝗴 𝗯𝗲𝘆𝗼𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗱𝘂𝗲 𝗱𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘁𝗼 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗴𝗲𝘁 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 𝘄𝗼𝗿𝗸 𝗱𝗼𝗻𝗲 𝘄𝗶𝘁𝗵 𝟮𝟬 𝗔𝗜 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝟭𝟲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 In December, we launched Grep, an AI agent for business due diligence, as a research preview. Within two weeks, hundreds of people were using it in underwriting, maritime law, logistics, oil and gas, crypto, sales and marketing and much more. Today, we're launching the next version with 20 AI experts across 16 industries to help you carry out deep research and get serious work done. 𝗪𝗵𝘆 𝘄𝗲'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗚𝗿𝗲𝗽 Most people are still stuck with AI that doesn't help them get work done. Chatbots that aren't specialized in their domain or vertical agents built for 2024's models—rigid workflows that break easily. Meanwhile, software engineers using Claude Code or Cursor are shipping 10x faster because those tools figured out what actually works. Modern coding agents load up the right context. Then get to work on your objective. That's what Grep does too. You give it an objective—"research this company to verify it is legitimate," "analyze this market and identify potential leads," "tell me who previously owned this vessel," "evaluate this vendor's compliance risk," "carry out a background check on this employee." Grep picks the right Expert and starts investigating across 50+ specialized sources, including corporate registries, court records, financial filings, sanctions databases, shipping data, academic journals and much more. This deep research provides the context to help you complete your task. Then you dig deeper. Ask follow-ups. Probe specific angles. The expert already has all the skills and integrations to help you get work done. But work doesn't end with research. Grep helps you turn that research into docs, decks, slides, reports, and even code. The context carries through, so the AI Expert can help you execute, not just inform. We're pretty excited about Grep and would love your feedback, especially if you are an early adopter of AI in your domain but aren't satisfied with your existing workflows. If you want to try Grep, join the waitlist at - we'll be starting with a small group of beta users and expanding as we make our AI Experts even better with user feedback. P.S. Huge thanks to Awesomic for the launch video 🎬

𝗚𝗿𝗲𝗽 𝗶𝘀 𝗲𝘅𝗽𝗮𝗻𝗱𝗶𝗻𝗴 𝗯𝗲𝘆𝗼𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗱𝘂𝗲 𝗱𝗶𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘁𝗼 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝗴𝗲𝘁 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 𝘄𝗼𝗿𝗸 𝗱𝗼𝗻𝗲 𝘄𝗶𝘁𝗵 𝟮𝟬 𝗔𝗜 𝗘𝘅𝗽𝗲𝗿𝘁𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝟭𝟲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀 In December, we launched Grep, an AI agent for business due diligence, as a research preview. Within two weeks, hundreds of people were using it in underwriting, maritime law, logistics, oil and gas, crypto, sales and marketing and much more. Today, we're launching the next version with 20 AI experts across 16 industries to help you carry out deep research and get serious work done. 𝗪𝗵𝘆 𝘄𝗲'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗚𝗿𝗲𝗽 Most people are still stuck with AI that doesn't help them get work done. Chatbots that aren't specialized in their domain or vertical agents built for 2024's models—rigid workflows that break easily. Meanwhile, software engineers using Claude Code or Cursor are shipping 10x faster because those tools figured out what actually works. Modern coding agents load up the right context. Then get to work on your objective. That's what Grep does too. You give it an objective—"research this company to verify it is legitimate," "analyze this market and identify potential leads," "tell me who previously owned this vessel," "evaluate this vendor's compliance risk," "carry out a background check on this employee." Grep picks the right Expert and starts investigating across 50+ specialized sources, including corporate registries, court records, financial filings, sanctions databases, shipping data, academic journals and much more. This deep research provides the context to help you complete your task. Then you dig deeper. Ask follow-ups. Probe specific angles. The expert already has all the skills and integrations to help you get work done. But work doesn't end with research. Grep helps you turn that research into docs, decks, slides, reports, and even code. The context carries through, so the AI Expert can help you execute, not just inform. We're pretty excited about Grep and would love your feedback, especially if you are an early adopter of AI in your domain but aren't satisfied with your existing workflows. If you want to try Grep, join the waitlist at - we'll be starting with a small group of beta users and expanding as we make our AI Experts even better with user feedback. P.S. Huge thanks to Awesomic for the launch video 🎬

AJ Asver

28,111 views • 6 months ago

RFK Jr. says Anthony Fauci played a central role in rushing AZT onto the market. The drug began as a chemotherapy agent so toxic it was thrown on the junk pile, it killed lab rats and was considered too dangerous even for short cancer courses. When HIV appeared, Burroughs Wellcome tested it against the virus and found it worked in the lab. Jurisdiction over AIDS research shifted from the National Cancer Institute to Fauci’s NIAID. According to RFK Jr., Fauci needed a drug quickly and leaned heavily on the company. He describes a fast-tracked approval that relied on limited evidence, with blood transfusions used to keep trial patients alive through the study period despite clear toxicity signals. He claims the drug later contributed to hundreds of thousands of deaths. In his 2021 book The Real Anthony Fauci, RFK Jr. lays out essentially the same narrative in Chapter 4, “The Pandemic Template: AIDS and AZT.” Fauci has publicly called the book “unfortunate” and criticized him, but has not sued over the AZT claims.

RFK Jr. says Anthony Fauci played a central role in rushing AZT onto the market. The drug began as a chemotherapy agent so toxic it was thrown on the junk pile, it killed lab rats and was considered too dangerous even for short cancer courses. When HIV appeared, Burroughs Wellcome tested it against the virus and found it worked in the lab. Jurisdiction over AIDS research shifted from the National Cancer Institute to Fauci’s NIAID. According to RFK Jr., Fauci needed a drug quickly and leaned heavily on the company. He describes a fast-tracked approval that relied on limited evidence, with blood transfusions used to keep trial patients alive through the study period despite clear toxicity signals. He claims the drug later contributed to hundreds of thousands of deaths. In his 2021 book The Real Anthony Fauci, RFK Jr. lays out essentially the same narrative in Chapter 4, “The Pandemic Template: AIDS and AZT.” Fauci has publicly called the book “unfortunate” and criticized him, but has not sued over the AZT claims.

Camus

38,115 views • 5 days ago