best free tools I use to get a 70% reply rate from cold outreach read the article first, then steal this stack: 1. finding the right person @boardyai. tell it what you’re building and it’ll find someone relevant from 240k+ people it has talked to. if you can get a warm intro, take it @TheOrgCom. free org charts with reporting lines, titles, and team sizes. really useful when you know the company but have no idea who you should actually message 2. searching X X advanced search. completely free and still insanely underused. use operators like min_faves:500, since: and from: to find people already talking about the problem you solve. also great for researching someone before reaching out 3. finding contacts @useapolloio. 900 credits a year, granted monthly @prospeo_io. 100 credits a month. searches are free, you only use a credit when you reveal a contact both are enough if you’re doing targeted outreach instead of blasting thousands of people 4. finding the email @EmailHunter. 50 credits a month. domain search helps find the company’s email pattern, then you can verify the address before sending anything 5. making sure your emails actually land CanISendThis by @jessethanley. send it an email and it checks SPF, DKIM, DMARC, unsubscribe headers, and deliverability across Google, Yahoo, and Outlook. takes around 30 seconds and can save you from wondering why nobody is replying when your emails are just going to spam 6. researching the person @clay. 500 actions a month. Claygent can research someone across the web and pull together the useful context F5Bot. free alerts whenever someone or something you’re tracking gets mentioned on Reddit or Hacker News Google News RSS. add /rss to a Google News search and you’ve got a free live feed for that person or company. fundraises, launches, new hires, interviews. basically all the context that can give you a natural reason to reach out 7. finding where they’ll actually reply @whatsmynameproj. put in one username and it checks 700+ platforms. people reuse usernames everywhere. sometimes finding someone’s X, Telegram, or another personal account is much more useful than sending another. email to an inbox with 500 unread messages 8. writing the message no tool. seriously. use AI for research. use it to critique your draft. use it to find things you missed but write the final message yourself. people are getting very good at recognizing AI outreach the tools can find the right person and give you all the context. you still need to think

if you’re raising, this skill will dramatically improve your investor conversion it finds the right investors, potential conflicts, the right partner, warm intro paths, and writes the outreach weeks of research in one run here’s what it found for a seed-stage AI company: $24k MRR 5 paying customers ~30% MoM growth raising $1.5–3M the founder wanted to get into a16z Speedrun the skill ran four research agents in parallel first thing it found: Speedrun already has a company in the current cohort building almost the same product then it went through the rest of the founder’s investor list and found six more funds that had already invested in companies in the same space this doesn’t mean you should remove all of them some VCs invest around a thesis and are totally fine backing multiple companies in the same market but you want to know this before asking for an intro the skill checks the overlap, gives you the context, and helps you decide which investors are worth prioritizing then it researched who actually fits the company for each fund, it found the specific partner to reach, relevant investments they made, things they’ve written or said about the space, and the best way to get to them then it wrote the first messages and the blurb your mutual connection can forward it also caught something the founder could’ve easily missed: the priority application window for the next Speedrun cohort closes November 1 so instead of spending weeks researching funds one by one, you can get most of the work done before sending your first message you know who to prioritize, who might have a conflict, which partner you need, why they could be interested, how to reach them, and what to say and you don’t waste your best intros blindly one line from the report was especially good: “don’t take investor intros before checking conflicts. that includes the investors your founder friend offers” it also lists everything it couldn’t verify, so you know what still needs to be checked manually if you’re raising, run this before you start asking people for intros you only get one first intro to each investor send this to Claude Code, Codex, or any other agent with terminal access: run npx skills add paulklayvc/skills --skill investor-warm-intros and set it up for this agent

revenue doesn’t mean as much in VC decision-making as most founders think let me explain why when a VC looks at a startup, they’re trying to understand why this company could become huge why this team? why this market? why now? what makes you different? what makes you hard to compete with? then comes another question: even if this is a good company, is this the best deal they can invest in right now? VCs see opportunities all the time, but they can’t invest in everything so you’re not only competing against companies in your market you’re competing against every other deal they could put that money into and there’s another thing that matters a lot in venture: nobody wants to be the VC who passed on an amazing company early that’s why a great team with a great product can still raise pre-revenue if there are already enough reasons to believe this team is going to win, revenue isn’t necessary to prove it and getting in before that proof exists usually means more upside for the investor so if VCs keep passing, it doesn’t necessarily mean you need more revenue maybe you’re talking to investors with a different thesis or priorities or maybe you simply haven’t given them enough reasons to believe you’re going to win and that’s where revenue becomes important if you don’t have other strong signals an exceptional team unique insights real defensibility a product people clearly want distribution advantages or something else that makes your success feel much more likely then revenue becomes one of the clearest ways to prove yourself you’re basically saying: “you don’t have to believe the story anymore. people are already paying us” so it’s not that VCs don’t care about revenue revenue is just one form of evidence

Proud to be your investor @xmemory_ai

i’m 22, Venture Partner and 4x founder currently in Barcelona, moving to London soon looking to connect with experienced founders, angels and VCs

Gianni Infantino carefully explains to Donald Trump that he is not part of the spanish national team

My son Lev is showing @Arsenal and @_DeclanRice how to score from corners.

everybody calm down. i got this.

sir...they just dropped a new LLM with 12 MILLION context window and 10x cheaper than Opus 4.7

Even in their celebrations, you can see the instincts of Arteta’s team. First, maximum compactness off the ball. Second, the gravitation toward the corner flag.

Had a Jane Street interview in 2019. Round 8. Interviewer texts: 'Equinox Brookfield. 6 AM. Bring a calculator you won't use.' I show up. He's on the StairMaster reading a printout of the CBOE VIX term structure. Doesn't get off. Nods at the machine next to him. 'You see that guy on the rower? Goldman MD. Comes here every morning at 6:04. Leaves at 6:38. What's the implied vol on his arrival time?' 'I don't know his variance.' 'Sample size of one year, 250 sessions. Standard deviation is 90 seconds. Annualize it.' I do the math in my head. '90 seconds times sqrt(250). About 24 minutes annualized.' 'Wrong. You annualized like it's a return. Time-of-arrival doesn't compound. It's a Poisson process with drift. The correct answer is his arrival is more punctual than the 6 train. Now price me an option on whether he shows up tomorrow.' I think for a second. 'If he's been here 250 days in a row, base rate is 99.6%. But you have to adjust for his vacation schedule and probability of injury, call it 96%.' 'Strike?' '$10 if he shows, $0 if he doesn't.' 'I'll sell you that option for $9.40.' I think about it. 'No. Expected value is $9.60. You're underpricing by 20 cents.' 'Correct. Now why am I selling it to you?' I freeze. 'Because I just saw him limp on the way in. You're buying my information for 20 cents. You overpaid.' We get off the machines. Walk to the smoothie bar. He orders a $19 smoothie, doesn't drink it. 'Last question. The girl behind the counter makes 200 smoothies per morning. She has perfect information on who's actually here and who's faking it. Citadel guys leak their attendance to her every day for the price of a tip. If I gave you $50,000 to set up a market on which Citadel PM gets fired this quarter, what's your bid-ask?' 'Insider trading.' 'Wrong answer. There's no public security. Try again.' I think. 'I'd quote 8 to 12 percent on any given PM. Spread of 4 points to cover adverse selection. Tighten the spread for PMs I have data on.' 'Where do you get the data?' 'The smoothie girl.' 'Good. How much do you pay her?' '10% of P&L.' 'Wrong. You pay her a flat $200 a week. If you pay her on P&L she becomes your counterparty. Right now she's your data source. Don't conflate edges.' He hands me the untouched smoothie. 'Throw this out on Vesey Street, not in the building. The staff knows what gets wasted. Outside, it was consumed. Same smoothie, different signal.' I do it. Thursday I get the email. 'Offer rescinded. Your bid-ask on the PM market was too tight. 4 points doesn't cover the tail. The girl is a single point of failure and you didn't price her counterparty risk. Also you held the smoothie in your right hand. Right-handers throw with their right hand. Camera saw the hesitation.'

🏆 Champions for the second time in our history. #YCFC 🔴🔵

A mathematician who shared an office with Claude Shannon at Bell Labs gave one lecture in 1986 that explains why some people win Nobel Prizes and other equally smart people spend their whole lives doing forgettable work. His name was Richard Hamming. He won the Turing Award. He invented error-correcting codes that made modern computing possible. And he spent 30 years at Bell Labs sitting in a cafeteria at lunch watching which scientists became legendary and which ones faded into nothing. In March 1986, he walked into a Bellcore auditorium in front of 200 researchers and told them exactly what he had seen. Here's the framework that has been quoted by every serious scientist for the last 40 years. His opening line landed like a punch. He said most scientists he worked with at Bell Labs were just as smart as the Nobel Prize winners. Just as hardworking. Just as credentialed. And yet at the end of a 40-year career, one group had changed entire fields and the other group was forgotten by the time they retired. He wanted to know what the difference actually was. And he said it wasn't luck. It wasn't IQ. It was a specific set of habits that almost nobody is willing to follow. The first habit was the one that hurts the most to hear. He said most scientists deliberately avoid the most important problem in their field because the odds of failure are too high. They pick a safe adjacent problem, solve it cleanly, publish it, and move on. And because they never swing at the hard problem, they never hit it. He said if you do not work on an important problem, it is unlikely you will do important work. That is not a motivational line. That is a logical one. The second habit was about doors. Literal doors. He noticed that the scientists at Bell Labs who kept their office doors closed got more done in the short term because they had no interruptions. But the scientists who kept their doors open got more done over a career. The open-door scientists were interrupted constantly. They also absorbed every new idea passing through the hallway. Ten years in, they were working on problems the closed-door scientists did not even know existed. The third habit was inversion. When Bell Labs refused to give him the team of programmers he wanted, Hamming sat with the rejection for weeks. Then he flipped the question. Instead of asking for programmers to write the programs, he asked why machines could not write the programs themselves. That single inversion pushed him into the frontier of computer science. He said the pattern repeats everywhere. What looks like a defect, if you flip it correctly, becomes the exact thing that pushes you ahead of everyone else. The fourth habit was the one that hit me the hardest. He said knowledge and productivity compound like interest. Someone who works 10 percent harder than you does not produce 10 percent more over a career. They produce twice as much. The gap doesn't add. It multiplies. And it compounds silently for years before anyone notices. He finished the lecture with a line I have never been able to shake. He said Pasteur's famous quote is right. Luck favors the prepared mind. But he meant it literally. You don't hope for luck. You engineer the conditions where luck can land on you. Open doors. Important problems. Inverted questions. Compounded hours. Those are not traits. Those are choices you make every single day. The transcript has been sitting on the University of Virginia's computer science website for almost 30 years. The video is free on YouTube. Stripe Press reprinted the full lectures as a book in 2020 and Bret Victor wrote the foreword. Hamming died in 1998. He gave his final lecture a few weeks before. He was 82. The lecture that explains why some careers become legendary and others disappear is still free. Most people who could benefit from it will never open it.

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AI has gone too far. Harry Potter "6 7" platform. Credit: Unhindered Studios

In honor of 50 years of Apple, we're sharing - for the first time ever - Don Valentine's original 1977 memo for Sequoia's investment into Apple Computer. #Apple50

EBITDA = Earnings before Iran & Donald Trump Announcements