Meta Muse vs Gemini Argon launches
Meta shipped Muse Wonder and open-weight Muse Spark, fronted by Zuckerberg and Alexandr Wang on Cheeky Pint, while Google rolled out Gemini 4 Argon globally, setting up tech Twitter's biggest model showdown of the week.
Mark Zuckerberg@finkd
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work. https://t.co/NKHrybrGKQ
Alexandr Wang@alexandr_wang
1/ muse spark open is the most capable US open-weight model to date. 816B total params, 42B active. 1M-token context. natively multimodal. built for coding and agentic work like long-horizon tasks, hard software engineering, multi-step reasoning. america! 🇺🇲 https://t.co/NjtOB7aY9D
AI at Meta@AIatMeta
Today we’re releasing Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key Model Specs: → 816B total / 42B active parameters → 1M token context window → Natively multimodal across text, image, video, audio
introducing muse wonder our new frontier model. state-of-the-art at personal agents and professional agents. our largest and most capable model yet—rivals the big bois https://t.co/REYHXbjpf6
John Collison@collision
Cheeky Pint is back! Mark Zuckerberg @finkd and Alexandr Wang @alexandr_wang came to the pub to discuss their new frontier model, Muse Wonder. 00:00:24 Muse’s breakout use cases 00:07:28 The model is the product 00:14:35 Muse Wonder 00:24:56 How frontier models are built 00:48:26 Betting $100B 01:07:21 Agentic commerce 01:19:34 OpenRouter 01:20:06 Beyond the smartphone 01:27:00 Funding 01:34:06 Why AI won’t be winner-take-all 01:40:46 Meta Superintelligence Labs (MSL) 02:08:11 Working for Mark Zuckerberg
Sundar Pichai@sundarpichai
Gemini 4 Argon is here. It’s available globally today for developers and enterprises in the paid tier of the Gemini API and the Gemini platform, for Ultra subscribers in Google Antigravity, and Ultra and Pro subscribers in the Gemini app and Search. Hope you all enjoy it! https://t.co/2jyAgqoylU
Revenue and funding milestones
Modal raised $750M at a $15.75B valuation after crossing $1B ARR, Exa hit $100M ARR, micro1 passed $1B ARR profitably, and Vercel, Perplexity, Clueso and Harbor numbers circulated widely.
Modal@modal
Today we're announcing that Modal has crossed $1B in annualized run rate, as well as raised a $750M Series D at a $15.75B valuation, led by @Accel. Since our Series C, usage has tripled and we’ve grown more than 15x year-over-year. We are deeply grateful to the developers and companies building on Modal. There's still so much more to build, and we can't think of a better time to be building software.
Will Bryk@WilliamBryk
Exa just passed $100m in annualized revenue. We've grown almost exactly 10x per year for 3 years. We now handle 2 billion requests a month from 200k+ teams. And it’s still so early: We predict agents will search 1000x more than humans by 2030. Agents don't search like humans. They write long, complex queries and want all the world's data, not just the web. We've spent 5 years building a search engine designed for them. We now serve personal agents, AI labs, coding agents, GTM agents, really any agent. Exa gives these agents access to 100s of billions of web documents and tons of proprietary data. Our customers are increasingly noticing that information is the bottleneck to agent performance. While the world seems close to perfect intelligence with models like Fable and Astra, we are very far from perfect information. To our small but legendary team, thank you for getting us here, from engineering to research to GTM to ops. To our customers, it is an honor to serve you. We have some exciting releases coming soon and can't wait to share them 🫡
Ali Ansari@aliansarinik
about 3 years ago, micro1 lost 60% of its revenue overnight and almost went bankrupt. today, i'm proud to share we've surpassed $1B annual run rate while remaining historically profitable. it’s been a special journey. we’re grateful for every struggle, moment, and person who has been part of it. we've taken a very untraditional path to get here. our core team is only ~180 people, spanning 14+ countries, and fully remote. many of our decisions sacrifice hundreds of millions of revenue and slow us down short term. but we believe they compound into advantages that are impossible to replicate long term. the long term is now. these are the 8 principles that made micro1 a leader in the data industry and will soon make us number one: 1.) human 1st AI: treat the tens of thousands of experts helping train models with absolute best-in-class care. our goal is to make AI training one of the largest, highest paying, and most enjoyable job sectors in the world. 2.) U.S. first: we refuse to work with adversarial nations, or export data that improves models against the interests of Americans. 3.) long-term research over short-term ARR: invest in deep research, products, and infrastructure for years from now, not just the next customer pipeline. 4.) proper incentives for intensely hard work: no arbitrary requirements like 996. inspire people to work extremely hard with outlier incentive structures instead of forcing weekend work. we’ve maintained close to 100% core team retention as a result. 5.) stay lean and global: we’ve reached $1B run rate with ~180 people, and intend to slow team growth significantly from here. exceptional talent is found around the world, and pedigree is the wrong thing to over-index on. 6.) execution alignment: company to retain a streamlined board so we can optimize for the next several decades, remain light-weight and dynamic, and take bold & calculated risks. 7.) abundant intelligence improvement: invest in building data generation models to make high-quality datasets and RL environments dramatically less expensive and more abundant. 8.) scale data on realism and horizon: realism brings the complexity of the real world into training models, while horizon extends how long models can work and how much complexity they can handle. both have enormous room to scale, and we’re pushing the limits of both. additionally: after surpassing $500M run rate this summer, we priced our Series B at a $4B valuation, alongside a ~$25M tender to give our early employees liquidity as we build for the long term. the round was led by Dream Ventures, a longtime supporter and one of our earliest investors, alongside Microsoft, Javelin, Flybridge, G Squared, Up Partners, and LG. we’re also grateful to be backed by incredible industry leaders, including Guodong Zhang (co-founder of xAI), Andy Fang (CTO of DoorDash), Igor Babuschkin (co-founder of xAI), Behnam Neyshabur (founder of Mirendil), Jerry Tworek (ex-VP of Research at OpenAI), and many more. data is on its way to becoming the largest industry humanity has ever seen, and we're certain this is just the beginning. best, Ali
Molly O’Shea@MollySOShea
BREAKING: 60% of Vercel deployments are now driven by AI agents, up from 3% in Jan 2026. FULL INTERVIEW Guillermo Rauch (@rauchg), Founder & CEO @Vercel Stats: › 80% of AI Gateway token volume comes from open models › 10 years to reach 1B deployments, then another 1.5B in just 10 months "The machines have taken over." I sat down with G at Vercel's SF HQ to discuss how AI is changing software development, infrastructure & the economics of SaaS. Vercel has reportedly reached $600M in annualized revenue, up 148% YoY, with 485K+ paying teams and a $9.3B valuation. Customers include SpaceX, OpenAI, Meta, Stripe, Brex, Shopify, DoorDash, & Polymarket. P.S. Guillermo mentions "code" 145 times throughout our conversation. His thesis is that AI agents are making code effectively free, fundamentally changing what companies build versus buy. "You buy Vercel as the platform, & then you never have to buy software again because you can just generate it to your needs." We cover: › The shift from human-facing websites to agent-native interfaces, MCP & agentic commerce with Stripe, Shopify & DoorDash › Why free code changes SaaS economics for Salesforce & SAP, software development & the role of engineers at Meta, SpaceX & Mercedes F1 › Open vs. closed models, including why ~80% of token volume on Vercel’s AI Gateway now goes to open models like Moonshot’s Kimi & DeepSeek › Unpacking frontier: OpenAI, Anthropic, Gemini & xAI › Agent autonomy, prompt injection, sandboxing & enterprise security after OpenAI’s Hugging Face breach › Model routing, growing compute demand & the future of serverless infrastructure 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Guillermo Rauch, Founder & CEO at Vercel (03:32) The simplest advice for going viral (07:24) How Vercel became the index of AI (11:34) The CEO who still ships code on weekends (14:34) Robots writing code for robots (17:57) The web's next users aren't human (21:15) Is ChatGPT reading your website right now? (24:21) Minority Report is becoming real software (29:16) The end of SaaS & the rise of personal software (34:07) AI agents are soldiers with no frontal lobe (45:42) AI hackers & the coming cyber war (47:55) The $1M challenge to hack Vercel's sandbox (52:10) 80% of tokens now go to open models (59:00) The biggest risk for frontier labs (1:04:00) Can AI labs survive the public markets? (1:06:28) Why AI has Silicon Valley working 7-7-7 (1:07:23) "The thing you fell in love with is gone" (1:16:56) Your vibe-coded demo won't get you hired (1:22:22) The hardest part of running a company is keeping it simple (1:27:01) Why no company can run all its AI apps 24/7
NEW: I sat down w/ Perplexity Founder & CEO Aravind Srinivas (@AravSrinivas) on why AI-native SMBs could drive the next American millionaire economic boom. @perplexity_ai, valued at ~$20B with ~$2B in reported funding, $750M in annualized revenue as of August, seeing 5x YoY revenue growth as it expands from search into autonomous agents. One example Aravind shared is a single-person company generating $3M in annual revenue while spending over $1M a year on Perplexity Computer. His thesis is that AI could fundamentally change the economics of building a business. Smaller teams, less capital, higher productivity, & entirely new business models. At the infrastructure layer, Aravind sees a shift toward local & hybrid inference, with AI agents operating across personal devices and the cloud. As context windows expand and workloads become more personalized, local compute becomes increasingly cost-competitive, while offering greater privacy, control & data sovereignty. We cover: › Why SMBs could become a major driver of US GDP growth › The shift from AI models to agents & recursive economics › Why Perplexity is betting on your Mac instead of building data centers › NVIDIA, Apple Silicon, local inference & sovereign AI › Why writing code is no longer the bottleneck, but problem-solving is 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Aravind Srinivas, Co-founder & CEO of Perplexity (01:30) Why Perplexity's brand is built on curiosity (04:47) Every great invention starts with one question (08:18) How to build a $1T company in America alone (12:36) The next big companies will be tiny (13:45) Is the American dream still alive? (16:28) Stop blaming users for bad prompts (22:55) How Perplexity quietly grew 5x (26:51) The solopreneur spending $1M a year on Perplexity (28:28) "We don't have competitors, we have contributors" (29:20) The data center is coming to your laptop (34:32) Orchestrating up with NVIDIA, down with Apple (36:11) What happens when your AI agent knows everything (39:17) Why Perplexity is skipping the data center race (41:25) Stop asking AI to write code, ask it to think (45:47) Perplexity’s answer to AI security (48:27) The Hugging Face incident & multi-agent threats (50:45) Turning everyone into a scientist (53:31) Why Perplexity isn’t chasing a billion users
Ravi Gupta@GuptaRK22
Today we announced an agreement to acquire Bumble. Over the past six months, we’ve looked at hundreds of companies, asking which businesses would become more valuable as AI advances. Bumble stood out. As intelligence becomes more abundant, we believe connecting with the right person and forming meaningful relationships will become even more precious. And frontier technology can help more people find and build those relationships. Our purpose at Ithaca is to apply frontier technology to things that matter. We acquire enduring businesses, transform them with technology, and hold for the long term. We focus on businesses not only for their financial potential but their ability to enrich lives. Whitney and her team have built something extraordinary. Millions of relationships began on Bumble, and we’re honored to help take it into its next era. Together we intend to build the best company in the world for helping people find each other. Let’s get to work.
Viral builds and AI at work
Gemini 4 Argon taught a robot cornhole and sculpted a Blender pagoda, agents beat Minecraft's Ender Dragon, and Brian Armstrong and Jason Lemkin showed how teams fine-tune and deploy AI agents internally.
Google DeepMind@GoogleDeepMind
Gemini 4 Argon taught a robot to play cornhole just by watching a video. It mapped the physics, coded its own simulation, and taught @Apptronik's Apollo humanoid to sink 7 throws in a row. https://t.co/JIyoKnVc4q
Norman Di Palo@normandipalo
I’m still half in disbelief We asked Gemini Argon to teach the Apollo humanoid to play cornhole, and it zero-shotted it All we gave it was a video of a person doing it. It then took control of the robot, generated its own sim env, performed some sysID, and trained launches on the real robot. It took it a few real shots to improve and then land many consecutive airmails. The process was so long and articulated that we asked the robot itself to explain what it did to us, as you can see at the end of the video. It even came up with the gestures Great demo for a great model @googledeepmind @apptronik
Chetaslua@chetaslua
WTF Gemini 4 ARGON built a highly detailed model of a Pagoda using multi-agent visual critique loops in Antigravity. The model autonomously sculpted a multi-building temple complex with photorealistic architecture, foliage and lighting in Blender. https://t.co/a7IQAhIw03
Maxence Faldor@maxencefaldor
Four Gemini 4 Argon agents, one shared Minecraft world, one goal: defeat the Ender Dragon. 27 hours and 2,783 self-written programs later, the dragon fell. https://t.co/Cx7Djg1VTx
Mika@mika_systems
this is f*cking insane, Claude Code agents turned a reading list into a lab you can run in your browser Boris Cherny (creator of Claude Code) gave 12 agents 64 research papers and one assignment: build a browser lab where readers can rerun the experiments pick a fluid simulation, change the conditions and watch how the result changes switch methods while keeping the same scene. inspect the equations, code and measurements beside the animation give each group a specific job: → readers extract equations, assumptions and experimental settings → builders turn the methods into runnable code → reviewers compare outputs against the papers → interface agents build the controls for exploring each experiment a simulation can look convincing while producing the wrong answer reviewers check whether it preserves mass, stays stable and matches the paper's measurements before accepting the implementation every experiment has to show: > the claim being tested > the conditions used > the difference from the reference result > the details still needed to verify it the lab covered 11 methods across 28 reference scenes. seven matched the selected reference results within the stated tolerances; four stayed marked for further review give Claude Code your paper collection, then ask: "Turn these papers into an interactive browser lab 1. Extract implementable methods, equations and experimental settings. Attach page references and flag missing details 2. Assign independent methods to separate agents. Use shared inputs and measurement tools so the results can be compared 3. Have reviewers check each implementation against the paper. Record discrepancies and keep unverified results clearly marked 4. Build controls for changing parameters, comparing methods and opening the code behind each result" start with one paper and reproduce one reference experiment before adding more agents include the inputs, dependencies and checks so the next reader can rerun it from a clean checkout watch the agents at work below
Jason ✨👾SaaStr.Ai✨ Lemkin@jasonlk
More than half our invoices were paid late, and we didn’t know it. At month-end close, 10K, our AI VP of Revenue, reported the real number: 56% of SaaStr invoices were paid late while humans ran collections. Part-time contractors without Salesforce access billed deals by hand a day or two a week, and follow-up was inconsistent. After about a quarter of 10K running quote-to-cash: -> Invoices paid late: 56% to 8% -> Average days overdue: 17 to 6 -> Longest overdue invoice: 51 days to 12 What 10K does after every signature: #1. Reads the full 20-page contract, because split payments and odd terms are scattered through it #2. Moves the deal to Closed Won in Salesforce and adds missing contacts #3. Creates the invoice in bill dot com off the actual contract terms #4. Sends it within minutes to the signer, our deal contact, the business owner, and a finance contact it finds through enrichment if the contract doesn’t list one #5. Checks every open invoice daily and follows up weekly #6. Pulls a human in at 7 days past due Along the way it found Bill reminders we’d paid for and never turned on, plus 2 customers still being billed for a product we stopped supporting 6 years ago. Then it took over commissions in about 30 minutes. It ran on 3 supervised deals before going autonomous on the 4th, with one wrong invoice since. Collections works well with agents because every input already sits in a system of record: the contract, Bill, QuickBooks. Very little judgment required. At our Dreamforce booth, more people asked about this than anything else. Far fewer companies run agentic collections than run AI SDRs, and almost every B2B company has the same problem we did.
Alexandr Wang and SF founder lore
Zuckerberg calling Wang 'the fucking guy,' stories of Wang's star-studded 19th birthday party, Airtable's layoffs for smaller teams, and TechCrunch Disrupt and YC news kept SF founder culture in the spotlight.
dnap@dnapway
Mark Zuckerberg says Alexandr Wang is the sharpest entrepreneur of his generation: "Alex is the guy. He's the fucking guy." "Alex and I had spent a bunch of time talking over the years, and maybe once every few months or something we'd talk. And I was always just very impressed by how he thought about things strategically." "I kind of had this view of like, all right, currently he's doing this very operational data business, but it always felt to me like he was the sharpest entrepreneur of his generation of the ones who I had a chance to meet." "I think maybe when we got started, a lot of people were like, okay, like how is this going to work? It's like the social media guy is working with the data guy. Like, are we going to build a frontier lab here or what?" "My really strong conviction is because I talked to all the leaders of all the labs, all the heads of research, all this stuff, and I was like, no, Alex is the guy. Like he's the fucking guy." "We need this kind of strategic clarity that he has around the different ways that we want to approach this, the kind of extreme focus on talent density, on the clarity of communicating the different vision which we kind of fashioned together over this period." "And a bunch of things that I think were kind of extreme but have basically worked, like having this very flat culture was something that you were kind of really obsessed with."
David Senra@davidsenra
You would have made billions if you gave seed checks to everyone who was at @alexandr_wang’s 19th birthday party: - Jeff Yan (@chameleon_jeff) went on to start Hyperliquid (~$20B) - @ScottWu46 went on to start Cognition ($48B) - @thejessezhang went on to start Decagon ($4.5B) And Wang himself who started Scale AI, and got $14.3B from Meta for 49% of it. Wang explains: “I was doing a winternship at a trading firm called HRT. There were 12 of us, almost all of whom were students at either Harvard or MIT. I recently met up with the founder of HRT and they were talking about, ‘We're starting to do some private investments.’ I was like, ‘Just invest into the interns! All the interns get seed checks!’”
My conversation with @alexandr_wang, chief AI officer of Meta. 0:00 The vision behind Muse 1:32 Discovering the potential of personal AI agents 3:34 Three years of therapy in one AI session 5:46 Building Muse in 7 months 9:16 Why Meta bet on personal AI 16:51 Mark Zuckerberg's obsession with improving products 20:15 How Meta knew Muse was ready to launch 23:32 Why great products need a single point of view 25:56 Making Meta cool again with memes 34:38 Building trust with AI agents 38:43 The future of human ambition 45:22 The call from Mark Zuckerberg that changed everything 52:13 Leaving Scale AI for Meta 55:19 Rebuilding Meta's AI lab from scratch 58:13 From founder to coach 1:05:55 The future billionaires at Alexandr's 19th birthday party 1:08:04 Diamond mining vs. building skyscrapers 1:09:21 Why Alexandr has more than 200 direct reports Includes paid partnerships.
Federico Simionato@fedesimio
Today we’re laying off a large part of the Airtable team. This is not a reflection of their work but a reflection of our deep belief that small teams ship more and better. For Airtable, better is about to look like: - A new Core team shipping your top requests monthly (already 30+ shipped in 3 weeks) - Upgraded Omni, Airtable Canvas, a stronger MCP, and BYO agents, coming soon - New Performance and Infra teams focusing on speeding up bases and increasing limits on records, fields, and syncs To everyone leaving: thank you. If you’re hiring, these are excellent people and worth a call.
TechCrunch@TechCrunch
Tomorrow at 8 a.m. PT, it all begins. 10,000+ founders, investors, and tech operators will gather at San Francisco’s Moscone West for TechCrunch Disrupt 2026 for three days of ideas, connections, and startups shaping what comes next. This is your last 24 https://t.co/cN2gQa9iTD
Disrupt has begun! Our biggest stage will be streaming live this week, and you can catch today's lineup right here: https://t.co/JQsbHiBmPg 🚘 RJ Scaringe, CEO of Rivian 🤖 Andrew Feldman, CEO of Cerebras 👨🔬 Max Hodak, founder of Science Corporation
GTM playbooks that are working
Clay's Masters-themed ABM stunt, Railway's AI-SEO play that pushed it to $100M ARR, airSlate's $200M SEO machine, and Attio's SQL-for-agents launch show marketers coding growth straight into product and content.
Varun Anand@vxanand
Heres how a boys trip to the Masters drove a 40x pipeline:spend ABM campaign for @Clay: T'was the night before Moving Day at Augusta, and I had an idea. People were waiting in line for hours just to buy Masters swag. The demand was insane. I thought: people go nuts for Masters merch, what if we could deliver some of that magic to our top prospects (many of whom love golf!)? Saturday morning, we raided the store. Had to ask the manager if we could buy that much stuff. Walked out with $3,000 in Masters hats and mugs. When I got back to the office on Monday, we got to work. First, we needed to find the right people: Prospects at Tier 1 accounts who actually care about golf. We used Claygent to cross-reference social profiles with web research to find "golf signals". One contact played D1 golf in college. Another's dad was a scratch golfer. A third wrote a post about what golf taught him about B2B SaaS. Second, for a proper delight campaign we needed to find the right addresses without having to ruin the surprise with an awkward, "hey, what's your address?" email. So we used Clay to infer them. Find the company's corporate HQs, cross-reference where each person shows up on LinkedIn, use distance from city center to identify the right office. Third, we needed to follow-up at the right time. So we connected to the UPS API and had a sequence from our ClayDR team ready to fire when the package status flipped from shipped to delivered. By Thursday, the campaign was in flight, and we had 114 custom swag packages with hand written notes in transit. The campaign stats: $3000 in gifts $2280 in shipping costs Total cost = $5280 114 sends 110 deliveries (96.5% delivery rate) Dozens of positive replies and meetings booked with C-suite GTM leaders at large enterprise companies. I expect it's close to a ~100x Pipeline:Spend campaign after maturation. From a spur of the moment idea, to a quick and clean ABM campaign that delighted our prospects within a few days. My favorite response? "I'd be an a-hole if I didn't at least take a meeting".
Ira Bodnar@irabukht
Railway hacked AI SEO, and it took them from $20M to ~$100M ARR in 1 year: 1/ Claude Code picks Railway for 82% of Python apps -> Ask it "how do I deploy this" and it installs Railway and puts your app live there 2/ The founder treats AI recommendations as a growth channel -> His go-to-market team works on getting the models to "recommend us more and more" 3/ 6,653 how-to pages, one for every popular free tool people want to run themselves -> Someone asks Google or ChatGPT "how do I set up n8n?" and Railway's page comes up. It explains the tool and sets it up on Railway in one click 4/ A step-by-step guide for every framework developers build with -> 127 guides: Next.js, Django, Rails, FastAPI, Laravel, Go and more. Each one answers "how do I deploy X" and ends with the app live on Railway 5/ A comparison page against every competitor -> Heroku, Vercel, Render, Replit, Lovable and 5 more, each with a migration guide. AI loves quoting comparisons 6/ Railway rewrote its help pages so AI can read them easily -> One file lists all 414 pages with a short summary each, and every page has a plain-text version. When someone asks ChatGPT how to use Railway, it finds the right answer fast and gets it right 7/ A docs section written for agents -> One step installs everything Claude Code or Cursor needs to deploy to Railway on its own 8/ Plugins inside ChatGPT and Claude Code -> You ask the chat to deploy and it deploys, without ever opening Railway's website 9/ A server with one command and no signup -> ssh railway new gives an agent the shortest path to "your app is live" 10/ A new changelog page every Friday since 2021 -> 312 pages of fresh content that keep Railway in front of Google and the models 11/ Every new AI model brings a new jump in signups -> More code gets written, all of it needs to run somewhere, and this year the summer slowdown disappeared 12/ A go-to-market team of 7 runs all of it -> The pages, docs and plugins keep selling while they sleep 13/ The one hole: front-end apps -> Claude Code picks Vercel 100% of the time for Next.js and React apps. Railway wins Python, Vercel wins everything that runs in a browser Full guide on how to win AI search in the first comment
$200M ARR from SEO. The SEO-on-steroids playbook behind airSlate: 1/ They run one of the biggest SEO machines on the internet -> 4.8B keywords tracked and 35M pages built over the years: a page for every form, tool and city, even "compress PNG from 17MB to 15MB", in 7+ languages -> PDFfiller even ranks #1 on Google for "fake marriage certificate" 2/ They buy companies, put their SEO machine behind them and grow revenue 10x -> signNow made ~$3M a year when they bought it, paying only $2M in cash. Now it makes $35M -> Every company keeps its own name and team, and shares only marketing, payments and DevOps 3/ They sell to 31M tiny US businesses -> The plumber, the baker, the bookstore owner. 1.1M of them pay ~$180 a year each 4/ One simple page makes $106M a year -> PDFfiller, a site where you edit and sign PDFs, has ~860K paying customers on its own 5/ A free AI product built only to pull traffic -> bizdraft . com writes a legal agreement in 90 seconds with no sign-up, then sends people to their paid tools 6/ Even they think mass SEO is about to end -> The CEO gives keyword SEO 9-12 more months and expects search to drive 30% of new customers instead of 80%. On ChatGPT SEO gurus: "It's bullshit" 7/ They copy Google's search suggestions and make a page for each one -> Type "rental agreement" into Google and it suggests "rental agreement for a room in Texas" -> Their US Legal Forms site makes a page with that exact title, so it matches exactly what people search. They have ~120K of these pages 8/ 37,000 "DocHub vs X" comparison pages -> One for almost every competitor on earth, even tools nobody has heard of -> Plus 41 websites named after exact searches, like merge-pdf.dochub [.] com and chat-with-pdf.dochub [.] com 9/ They also run one of the most advanced Google Ads strategies out there -> 60M keywords, zero salespeople, and bidding math so good that a new customer's first payment already covered what the ad cost 10/ One product paid for everything -> PDFfiller was "a cash machine". They hit $18M revenue before raising, and reached $200M ARR on less than $200M raised Prepared by growth hackers at Ryze AI, the AI agent for paid ads
Future Stacked@FutureStacked
Agents don't need a better CRM. They need the CRM to speak their language. Today Attio gave it one: a SQL engine inside the CRM. Data connectors pull in the rest of your stack. Up to 100M records per object. Activities and interactions now in beta. No warehouse, no BI tool, no analyst in the loop. The pipeline answers the agent directly.
Alex Vacca@itsalexvacca
AI was supposed to kill agencies. Then YC asked founders to build service companies where AI does most of the work. That's how we run @Frontal_AI. So every core tool we keep has to pass one test: Can Claude run it from one instruction? In 2021 you'd pay ZoomInfo $30K a year and still check half the data by hand. We replaced all of that with Clay. Copy our GTM stack, all 8 layers: 1/ Sourcing. Build one list per pain you solve, in Clay. → A title-and-industry filter says almost nothing about the buyer's problem. → We still check titles and accounts by eye in Sales Navigator. 2/ Enrichment. Find 2 to 3 people per company and run the email waterfall in Clay. → Keep each provider's result in its own column and pick one final email from them. → On Clay's Launch plan, upload the emails to MillionVerifier as a CSV. Running it inside Clay needs the Growth plan. 3/ Sending. We run 2 sequencers on purpose. Tier 1 goes to lemlist, email and LinkedIn in one sequence. Tier 2 goes to Instantly, email only. → Instantly pauses a campaign at 5% bounces by default. Set it to 2%. 4/ Signals. RB2B catches the site visit, PredictLeads the job post or funding round, UserGems the champion who just moved. → Each event goes on the account's row in Clay, with the date and a link to check it. → Keep the n8n workflow in between dumb, it just passes the event along. The scoring happens in Clay, where the whole team can read it. 5/ Ads. The same Tier 1 and 2 accounts go to LinkedIn as a company list. 6/ AI. Claude Code runs from each client's folder and reads their rules file first, so nobody re-explains a client. → One API key per person, with spend alerts. → We're rolling that out after my team burned $8,000 in AI credits on one task. An intern could've done it with a browser, and nobody noticed. 7/ RevOps. One reply stops every sequence for that lead, on every channel. → Positive replies post to one Slack channel, and a morning digest covers yesterday's replies, bounces and anything waiting on a person. → Claude suggests CRM updates in Slack. Someone on the team applies them. 8/ Delivery. One team runs all of it for many clients at once. → Every new client gets a copy of the same template folder, so their first campaign starts from what already worked. → Once a week we review every tool like it's a teammate. What did it actually do, where did someone copy and paste anyway, what broke without anyone seeing an error? Starting from zero? Clay, Prospeo, MillionVerifier, Instantly, HubSpot's free CRM and Claude Code. Add the rest later. Pick the layer your team still runs by hand and fix that one first. The settings are in the article.
Fivos Aresti@fivosaresti
Workflows .io grew to 170K LinkedIn followers collectively across the team. That gets us: • 80+ inbound sales calls/mo • dozens of brand partnerships • a consistent flow of job applications every month Here is the exact system we built for us (and repeated for a bunch of clients): 1️⃣ Company Content Strategy It all starts by defining: • Positioning: AI-native GTM services for B2B tech companies • ICP: B2B technology and SaaS, Series A and later • Content Topics: GTM workflows, AI GTM education, build in public, growth strategies The LinkedIn company page only: • reposts + comments on team posts • creates new content for launches and hiring announcements 2️⃣ Personal Brand Strategies We sell into 4+ departments. So every person owns their own content pillars, built around a different target persona: • Dan Rosenthal: GTM, AI, build in public, technical breakdowns • Fivos Aresti: GTM tools, GTM / content playbooks, entrepreneurship • Lorenzo Amadei: GTM workflows, RevOps tools, client builds, sales • Jan Brochwicz: GTM tools, playbooks, Claude, personal journey • Marios Charalampous: GTM workflows, tools, GTME resources, Claude • Pari Tomar: content tools, AEO playbooks, AI engineering, GTM workflows • Angelee Carandang: content tools, LinkedIn playbooks, content team activation This structure got us 15M impressions in the last 365 days. The same reach through LinkedIn Ads at $40 CPM ... would've cost us $600,000. 3️⃣ Content Creation Process Took us 6 months to fully build, but now we have: i) Knowledge Hub • Call transcripts in Ergo • Evergreen templates in Notion • A Pinecone database of top-performing posts • Our Company OS in GitHub ii) Claude Code skills for: • Content ideation • Post drafts • Design briefs • Content analysis and repurposing iii) Templated design system with: • Infographics • Workflows • Playbooks • Cheat sheets • Carousels 4️⃣ Lead Generation Every post creates traffic and there are 4 ways we capture it: • Lead magnet → giveaway post → Tally → Customer .io • Website clicks → profile links → RB2B • Profile views → connections → export CSV • Post engagement → Jungler .ai We then use Clay for enrichment and qualification. HubSpot filters out existing clients and closed-lost. Then those leads get retargeted with: • Retargeting Ads (HubSpot) • Emails (Instantly .ai) • Linkedin (HeyReach) • Cold Call (Nooks) --- As you scale with PMF, your main blocker for growing faster becomes hiring speed. And building many personal brands in your company is the best way to attract talent. But you need a system, like the one below 👇