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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 Aufrufe • vor 1 Jahr •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

8 Kommentare

Profilbild von justin

justinvor 1 Jahr

Build with CUA:

Profilbild von AssemblyAI

AssemblyAIvor 1 Jahr

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 👇

Profilbild von justin

justinvor 1 Jahr

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

Profilbild von Kyle Jeong

Kyle Jeongvor 1 Jahr

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

Profilbild von justin

justinvor 1 Jahr

@edwinarbus it’s coming

Profilbild von Houdini

Houdinivor 1 Jahr

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

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Kushagravor 1 Jahr

@edwinarbus @utkarshcs18

Profilbild von Oleg Zaremba

Oleg Zarembavor 1 Jahr

@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 Aufrufe • vor 1 Jahr

Hedgeye Launches New Mobile App, Giving Investors Instant Access to Market Research 🚨 FOR IMMEDIATE RELEASE Stamford, CT – [March 4, 2025] – Hedgeye Risk Management is excited to announce the launch of the Hedgeye App, a powerful new tool that puts Hedgeye’s market-leading investment research directly in the hands of subscribers. Available now for free download on both the Apple App Store and Google Play, the app delivers a seamless, on-the-go investing experience. With a clean, intuitive interface, the Hedgeye App makes it easier than ever to find the content you care about from Hedgeye’s vast daily research production. The app will help investors to stay ahead of important market trends, manage risk, and make smarter, data-driven decisions—all from their mobile devices. Hedgeye’s "Go Anywhere" full-cycle investing strategy and approach is now truly everywhere—giving users the ability to: ✅Receive real-time notifications on the latest actionable research and investment ideas ✅Watch or listen to The Macro Show, The Call, and other HedgeyeTV programs anytime, anywhere ✅Bookmark and save favorite research reports for easy reference ✅Access complimentary content like "Real Conversations" and additional research insights to explore Hedgeye’s research before subscribing While subscribers unlock full access to Hedgeye’s deep research and market insights, non-subscribers can still benefit from the app by accessing select free content, including market commentary, interviews with top investors, and a preview of Hedgeye’s research. It’s a valuable way to experience Hedgeye’s trusted investing process before committing to a full membership. "Investors need reliable, real-time insights to navigate today’s fast-moving markets," explains Keith McCullough, Founder and CEO of Hedgeye. "The Hedgeye App gives our subscribers instant access to my team’s research and risk management tools they need to protect and grow their wealth—anytime, anywhere." Download the Hedgeye App today on the App Store or Google Play, and take Hedgeye’s market signals, macro insights, ETF and stock ideas, and portfolio coaching wherever you go. Apple ➡️ Google ➡️ ABOUT HEDGEYE Founded in 2008, Hedgeye Risk Management is an independent investment research firm trusted by institutional and retail investors worldwide. With a team of over 40 seasoned analysts covering more than 1,000 stocks and ETFs across 20+ sectors, Hedgeye delivers in-depth, data-driven insights to help investors navigate and profit in any market condition. The firm is renowned for its risk management approach, combining macroeconomic research with bottom-up stock analysis to provide clear, actionable investing signals.

Hedgeye Launches New Mobile App, Giving Investors Instant Access to Market Research 🚨 FOR IMMEDIATE RELEASE Stamford, CT – [March 4, 2025] – Hedgeye Risk Management is excited to announce the launch of the Hedgeye App, a powerful new tool that puts Hedgeye’s market-leading investment research directly in the hands of subscribers. Available now for free download on both the Apple App Store and Google Play, the app delivers a seamless, on-the-go investing experience. With a clean, intuitive interface, the Hedgeye App makes it easier than ever to find the content you care about from Hedgeye’s vast daily research production. The app will help investors to stay ahead of important market trends, manage risk, and make smarter, data-driven decisions—all from their mobile devices. Hedgeye’s "Go Anywhere" full-cycle investing strategy and approach is now truly everywhere—giving users the ability to: ✅Receive real-time notifications on the latest actionable research and investment ideas ✅Watch or listen to The Macro Show, The Call, and other HedgeyeTV programs anytime, anywhere ✅Bookmark and save favorite research reports for easy reference ✅Access complimentary content like "Real Conversations" and additional research insights to explore Hedgeye’s research before subscribing While subscribers unlock full access to Hedgeye’s deep research and market insights, non-subscribers can still benefit from the app by accessing select free content, including market commentary, interviews with top investors, and a preview of Hedgeye’s research. It’s a valuable way to experience Hedgeye’s trusted investing process before committing to a full membership. "Investors need reliable, real-time insights to navigate today’s fast-moving markets," explains Keith McCullough, Founder and CEO of Hedgeye. "The Hedgeye App gives our subscribers instant access to my team’s research and risk management tools they need to protect and grow their wealth—anytime, anywhere." Download the Hedgeye App today on the App Store or Google Play, and take Hedgeye’s market signals, macro insights, ETF and stock ideas, and portfolio coaching wherever you go. Apple ➡️ Google ➡️ ABOUT HEDGEYE Founded in 2008, Hedgeye Risk Management is an independent investment research firm trusted by institutional and retail investors worldwide. With a team of over 40 seasoned analysts covering more than 1,000 stocks and ETFs across 20+ sectors, Hedgeye delivers in-depth, data-driven insights to help investors navigate and profit in any market condition. The firm is renowned for its risk management approach, combining macroeconomic research with bottom-up stock analysis to provide clear, actionable investing signals.

Hedgeye

37,559 Aufrufe • vor 1 Jahr

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!

Anthropic's in trouble, again. The entire Claude experience is now available at 1/6th the price. Kimi now does everything Claude does, powered by K2.6, a 1-trillion-parameter MoE model that activates only 32B parameters per token. It covers all three features Claude has (Chat, Code, and Cowork): 1) Kimi Chat runs in four modes - Instant for fast responses - Thinking for deep reasoning - Agent for multi-step execution - and Agent Swarm for parallel workloads. There's a 262K context window across all of them. 2) Kimi Code is the open-source CLI coding agent with K2.6 as the default backend. K2.6 ranked #1 on OpenRouter's programming leaderboard by weekly usage. 3) Kimi Agent is the Cowork equivalent. It generates: - full websites with database and auth - presentation decks (editable PPTX output) - spreadsheets with formulas and charts - word docs and structured research reports. On top of this, Kimi K2.6 is also trained to decompose tasks into up to 300 parallel sub-agents. This helps it retain coherence even across 4,000+ tool calls in a single run, with sessions sustaining up to 13 hours. On SWE-Bench Pro: - Kimi K2.6 → 58.6 - GPT-5.4 xhigh → 57.7 - Gemini 3.1 Pro → 54.2 - Claude Opus 4.6 → 53.4 Kimi K2.6 model is open weights and self-hostable on 4x H100s in INT4. Find the link to the HuggingFace model page in the replies!

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109,238 Aufrufe • vor 2 Monaten

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Anne-Laure Le Cunff (Anne-Laure Le Cunff) might be the most productive person I’ve ever met. She’s simultaneously running a successful business, writing a book being published by a major publisher, and getting a PhD in neuroscience. She says it’s only possible because of ChatGPT. It saves her time on the administrative tasks required to run her business. It helps her create better outlines so that she can write pieces more efficiently. It even breaks down complex research papers so that she can incorporate them into her PhD work. We spent an hour and a half exploring in-detail every part of her ChatGPT workflow—and we even used it live to help her fine-tune her meditation practice. We get into: - How ChatGPT saves her time running her business - Tips to break down research papers into digestible insights - How she leverages ChatGPT to revamp her YouTube thumbnails - Using ChatGPT to write original articles - Doing deep online research using ChatGPT - How to use ChatGPT to generate advice tailored for your needs - How to surface useful insights from your journal using ChatGPT This is a must-watch for curious, creative people who want to get more done. Watch below! ---- Timestamps: Introduction 01:10 How to use ChatGPT to save time running a business 02:11 Tips to breakdown research papers with ChatGPT 05:17 How to use ChatGPT to generate explanations tailored for you 09:38 Leveraging ChatGPT to find hidden gems on the internet (like recipes for obscure cheese) 19:51 How to create awesome YouTube thumbnails with ChatGPT 33:47 Incorporating ChatGPT into your writing process 51:13 Rapid fire questions from X 56:52 Surfacing useful insights from Anne-Laure’s meditation journal 1:13:01 The case for journaling in the age of AI 1:29:04

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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 Aufrufe • vor 4 Monaten

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

34,342 Aufrufe • vor 1 Jahr

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

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

Om Patel

27,131 Aufrufe • vor 3 Monaten

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

29,171 Aufrufe • vor 3 Monaten

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)

265,595 Aufrufe • vor 2 Jahren

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

28,077 Aufrufe • vor 5 Monaten

A clip from Li Lu's greatest-ever public speech. The Columbia Lecture for Bruce Greenwald's 2006 Columbia Class. Here are 9 Learnings from Li Lu's Investment Strategy: 1. Why Value Investing Works The market isn’t built for value investors. It is built in a way that increases the urge to speculate. That’s why businesses are so often misprized in the short term. Value investors can benefit from this circumstance. 2. Understand Who You Are You’ll be more interested in some industries/topics than in others. And in investing, you can choose in what industries you’ll look for opportunities. Investors should use this advantage and be sure about their circle of competence. 3. Be a Journalist Being an investor is a lot like being a research journalist. You have to dig into the company on a level that journalists do when they research their stories. You also need to clearly articulate your thesis and research and bring it to paper. 4. Find the Truth A journalist also has to find the truth before he publishes a story. Same goes for an investor. It could be fatal if he makes a decision before he knows “the truth” about a company. Thus, he has to avoid all sorts of biases and misleading influences. 5. Commitment Bias One of these biases is the commitment bias. To avoid this one, Li Lu rarely agrees to public appearances. The more you talk about investments, the more you talk yourself into them. The perceived knowledge about a company increases for no reason. 6. ROIC Just like Charlie Munger, Li Lu emphasizes the importance of ROIC as a metric for superior performance and competitive advantages. The longer your holding period, the more your return will equal the ROIC of the underlying company. 7. Volatility As explained before, stock prices are a lot more volatile than the business behind that stock. Investors, therefore, should pay attention to slow, long-term changes in the business instead of stock prices. 8. Self Defense To Li Lu, the Margin of Safety is a concept of self-defense. Even if the company is more valuable than the market gives it credit for, the management could destroy this advantage. This possibility is something investors have to look out for. 9. Uninvestable Some industries are impossible to value. Li Lu gives the example of restaurants. Even if the business is great, there are little to no durable advantages. Investors shouldn’t try the impossible and just focus on what can be valued. You can find the whole Lecture on YouTube. I highly recommend watching it!

A clip from Li Lu's greatest-ever public speech. The Columbia Lecture for Bruce Greenwald's 2006 Columbia Class. Here are 9 Learnings from Li Lu's Investment Strategy: 1. Why Value Investing Works The market isn’t built for value investors. It is built in a way that increases the urge to speculate. That’s why businesses are so often misprized in the short term. Value investors can benefit from this circumstance. 2. Understand Who You Are You’ll be more interested in some industries/topics than in others. And in investing, you can choose in what industries you’ll look for opportunities. Investors should use this advantage and be sure about their circle of competence. 3. Be a Journalist Being an investor is a lot like being a research journalist. You have to dig into the company on a level that journalists do when they research their stories. You also need to clearly articulate your thesis and research and bring it to paper. 4. Find the Truth A journalist also has to find the truth before he publishes a story. Same goes for an investor. It could be fatal if he makes a decision before he knows “the truth” about a company. Thus, he has to avoid all sorts of biases and misleading influences. 5. Commitment Bias One of these biases is the commitment bias. To avoid this one, Li Lu rarely agrees to public appearances. The more you talk about investments, the more you talk yourself into them. The perceived knowledge about a company increases for no reason. 6. ROIC Just like Charlie Munger, Li Lu emphasizes the importance of ROIC as a metric for superior performance and competitive advantages. The longer your holding period, the more your return will equal the ROIC of the underlying company. 7. Volatility As explained before, stock prices are a lot more volatile than the business behind that stock. Investors, therefore, should pay attention to slow, long-term changes in the business instead of stock prices. 8. Self Defense To Li Lu, the Margin of Safety is a concept of self-defense. Even if the company is more valuable than the market gives it credit for, the management could destroy this advantage. This possibility is something investors have to look out for. 9. Uninvestable Some industries are impossible to value. Li Lu gives the example of restaurants. Even if the business is great, there are little to no durable advantages. Investors shouldn’t try the impossible and just focus on what can be valued. You can find the whole Lecture on YouTube. I highly recommend watching it!

Daniel Mahncke

244,913 Aufrufe • vor 2 Jahren

5 AI agents are walking to a war room right now to vote on how to make me more money. It looks like a video game. It isn’t. Every agent lives in a village I built on top of OpenClaw, running off 1 VPS in the back end. Matt is the manager. Late to the meeting again. He delegates every task to the other 4. Dennis never leaves the research lab. Today he pitched 3 ideas: a candle line, an agentic observability tool, a website shop for local businesses. I click approve. Matt assigns the work. Bunk walks to the factory and designs the product. Luke heads to the media studio, shoots the photos, posts the TikToks. Etsy. Fiverr. Local service sites. All running while I watch little pixel men commute to work. Then there’s Maverick. He lives alone at a camp up top. I hand him cash and 30 days to triple it. No instructions. 1 dashboard tracks token spend, today’s briefing, and when Matt wakes up next. 1 task needs my attention. That’s the only thing I have to do today. I built a business. Then I built a town so I could watch it run itself. The agents think they’re working. I’m just playing a game.

5 AI agents are walking to a war room right now to vote on how to make me more money. It looks like a video game. It isn’t. Every agent lives in a village I built on top of OpenClaw, running off 1 VPS in the back end. Matt is the manager. Late to the meeting again. He delegates every task to the other 4. Dennis never leaves the research lab. Today he pitched 3 ideas: a candle line, an agentic observability tool, a website shop for local businesses. I click approve. Matt assigns the work. Bunk walks to the factory and designs the product. Luke heads to the media studio, shoots the photos, posts the TikToks. Etsy. Fiverr. Local service sites. All running while I watch little pixel men commute to work. Then there’s Maverick. He lives alone at a camp up top. I hand him cash and 30 days to triple it. No instructions. 1 dashboard tracks token spend, today’s briefing, and when Matt wakes up next. 1 task needs my attention. That’s the only thing I have to do today. I built a business. Then I built a town so I could watch it run itself. The agents think they’re working. I’m just playing a game.

Spike 1%

15,581 Aufrufe • vor 1 Monat

New Andrej Karpathy interview Says AI agent failures stem from user skill, not model capability. Poor instructions cause errors. He suggests delegating 20-minute macro actions like coding and research to parallel agents and reviewing their work. --- "I think everything, like so many things, even if they don't work, I think to a large extent you feel like it's a skill issue. It's not that the capability is not there; it's that you just haven't found a way to string together what's available. Like, I didn't give good enough instructions to the agents in the file, or whatever it may be. I don't have a nice enough memory tool that I put in there, or something like that. So, it all kind of feels like a skill issue when it doesn't work to some extent. You want to see how you can parallelize them, and you want to be a 'Pierce tender,' basically. Pierce famously has a funny photo where he's in front of lots of these Codex agents behind the monitor. They all take about 20 minutes if you run them correctly and use high effort. You have multiple—you know, 10 or 20—pull requests checked out. It's just like you can do much larger macro actions. It's not just, 'Here's a line of code, here's a new function.' It's like, 'Here's a new functionality, delegate it to agent one. Here's a new functionality that's not going to interfere with the other one, give it to agent two.' Then, you try to review their work as best as you can, depending on how much you care about that code. You look for these macro actions that you can manipulate your software repository by. Another agent is doing some research, another agent is writing code, another one is coming up with a plan for some new implementation. Everything just happens in these macro actions over your repository. You're just trying to become really good at it and develop a muscle memory for it. It's very rewarding when it actually works, but it's also a new thing to learn. Hence, the psychosis." --- From No Priors YT channel (link in comment)

New Andrej Karpathy interview Says AI agent failures stem from user skill, not model capability. Poor instructions cause errors. He suggests delegating 20-minute macro actions like coding and research to parallel agents and reviewing their work. --- "I think everything, like so many things, even if they don't work, I think to a large extent you feel like it's a skill issue. It's not that the capability is not there; it's that you just haven't found a way to string together what's available. Like, I didn't give good enough instructions to the agents in the file, or whatever it may be. I don't have a nice enough memory tool that I put in there, or something like that. So, it all kind of feels like a skill issue when it doesn't work to some extent. You want to see how you can parallelize them, and you want to be a 'Pierce tender,' basically. Pierce famously has a funny photo where he's in front of lots of these Codex agents behind the monitor. They all take about 20 minutes if you run them correctly and use high effort. You have multiple—you know, 10 or 20—pull requests checked out. It's just like you can do much larger macro actions. It's not just, 'Here's a line of code, here's a new function.' It's like, 'Here's a new functionality, delegate it to agent one. Here's a new functionality that's not going to interfere with the other one, give it to agent two.' Then, you try to review their work as best as you can, depending on how much you care about that code. You look for these macro actions that you can manipulate your software repository by. Another agent is doing some research, another agent is writing code, another one is coming up with a plan for some new implementation. Everything just happens in these macro actions over your repository. You're just trying to become really good at it and develop a muscle memory for it. It's very rewarding when it actually works, but it's also a new thing to learn. Hence, the psychosis." --- From No Priors YT channel (link in comment)

Rohan Paul

23,090 Aufrufe • vor 3 Monaten

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Today, we are launching the first publicly available AI Scientist, via the FutureHouse Platform. Our AI Scientist agents can perform a wide variety of scientific tasks better than humans. By chaining them together, we've already started to discover new biology really fast. With the platform, we are bringing these capabilities to the wider community. Watch our long-form video, in the comments below, to learn more about how the platform works and how you can use it to make new discoveries, and go to our website or see the comments below to access the platform. We are releasing three superhuman AI Scientist agents today, each with their own specialization: A general-purpose agent (Crow); An agent to automate literature reviews (Falcon); and An agent to answer the question “Has anyone done X before” (Owl). We are also releasing an experimental agent, Phoenix, that has access to a wide variety of tools for planning experiments in chemistry. More on that below. The three literature search agents (Crow, Falcon, and Owl) have benchmarked superhuman performance. They also have access to a large corpus of full scientific texts, which means that you can ask them more detailed questions about experimental protocols and study limitations that general-purpose web search agents, which usually only have access to abstracts, might miss. Our agents also use a variety of factors to distinguish source quality, so that they don’t end up relying on low-quality papers or pop-science sources. Finally, and critically, we have an API, which is intended to allow researchers to integrate our agents into their workflows. Phoenix is an experimental project we put together recently just to demonstrate what can happen if you give the agents access to lots of scientific tools. It is not better than humans at planning experiments yet, and it makes a lot more mistakes than Crow, Falcon, or Owl. We want to see all the ways you can break it! The agents we are releasing today cannot yet do all (or even most!) aspects of scientific research autonomously. However, as we show in the video, you can already use them to generate and evaluate new hypotheses and plan new experiments way faster than before. Internally, we also have dedicated agents for data analysis, hypothesis generation, protein engineering, and more, and we plan to launch these on the platform in the coming months as well. Within a year or two, it is easy to imagine that the vast majority of desk work that scientists do today will be accelerated with the help of AI agents like the ones we are releasing today. The platform is currently free-to-use. Over time, depending on how people use it, we may implement pricing plans. If you want higher rate limits, especially for research projects, get in touch. Michael Skarlinski, Andrew White 🐦‍⬛, Tyler Nadolski, Remo Storni, James Braza, Ludovico Mitchener, Michaela Hinks, as well as Jason Carman and his team for making such fantastic videos of us!

Sam Rodriques

724,665 Aufrufe • vor 1 Jahr

We're launching the best way to sell with AI. Sales reps are drowning in grunt work. CRM updates. Follow-ups. Research. Meeting prep. We built a superagent that connects across your entire GTM stack and just gets things done. Give it to your reps → watch them claim 50% of their time back. But here's what sets us apart: You can move work from chat into autopilot agent teams - triggered by signals, backed by evals, self-improving. We're the only platform that truly decouples growth from headcount. The four levels of autonomy: > Level 1 - Assisted: Superagent eradicates grunt work Teams move 10x faster > Level 2 - Copilot: Agents run playbooks with oversight Reps get blocks of time back > Level 3 - Autopilot: Agent teams automate entire functions Growth decouples from headcount > Level 4 - Self-Driving: AI workforce self-improves and builds new agents Business grows at the pace of ideas In closed beta for enterprise customers. Book a demo:

We're launching the best way to sell with AI. Sales reps are drowning in grunt work. CRM updates. Follow-ups. Research. Meeting prep. We built a superagent that connects across your entire GTM stack and just gets things done. Give it to your reps → watch them claim 50% of their time back. But here's what sets us apart: You can move work from chat into autopilot agent teams - triggered by signals, backed by evals, self-improving. We're the only platform that truly decouples growth from headcount. The four levels of autonomy: > Level 1 - Assisted: Superagent eradicates grunt work Teams move 10x faster > Level 2 - Copilot: Agents run playbooks with oversight Reps get blocks of time back > Level 3 - Autopilot: Agent teams automate entire functions Growth decouples from headcount > Level 4 - Self-Driving: AI workforce self-improves and builds new agents Business grows at the pace of ideas In closed beta for enterprise customers. Book a demo:

Relevance AI

18,687 Aufrufe • vor 4 Monaten