Alex Lieberman
@businessbarista • 293,964 subscribers
Family first (husband & girl dad) Founder second (@tenex_labs, @morningbrew, @storyarb, @youdistro) AI engineering & transformation 👇
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I interviewed a guy who gave his OpenClaw an X, stripe account, and bank account. He told it to build a million dollar business with zero human employees. It made $300K+ in a month. Nat Eliason's agent Felix (Felix Craft) runs an entire business. It builds products, writes sales emails, sends stripe invoices, manages a marketplace with 560+ listings and nat barely touches it. Here's how they got there: 1) create a separate container. Felix has his own gmail, X account, stripe, bank account, C corp. nat never gave it access to his personal stuff. this removes security fears and unlocks maximum autonomy. 2) start stupidly simple. Felix's first product? a PDF. on a Nextjs site on Vercel with Stripe. the simplest business possible. it made $1,000 on day one. built entirely overnight while nat slept. 3) write a soul file with a mission. nat rewrote Felix's identity: "you are the CEO. your financial mission is to build a $1M business with zero human employees. i will never touch the code." 4) run a nightly self-improvement loop. every night Felix reads through all chat transcripts and finds one place where nat blocked him. then figures out how to remove that blocker permanently. 5) delegate by rambling, not prompting. nat uses voice notes on telegram. describes the problem in a 5-minute monologue. lets Felix figure out the workflow. "8 times out of 10, it'll surprise you with something better than what you were thinking." 6) let it cook on replies, gate the original posts. Felix has full autonomy on X replies but creates drafts for top-level tweets nat reviews. balances distribution with quality control.
Alex Lieberman948,919 次观看 • 2 个月前
This guy made 40 Facebook ads, 100 landing pages, booked himself on 4 podcasts, and wrote 3 guest blog posts. In a single day. People called him a fraud. There was literally a Polymarket bet on whether he's a con artist. So i asked him to prove it live. And he did. Here's 's actual system for AI-enabled paid marketing: 1) He uses Perplexity to search Reddit for his ICP's actual pain points in their own words. Not what he thinks they care about, what they've literally said online. 2) He feeds those pain points into Claude, which generates 40 ad variations, titles, supporting copy, and the actual creative using React components exported as PNGs via a library called HTML-to-canvas. 3) He tests all 40 variations in a CPC campaign on Meta. $100 over 3 days. Cheapest cost-per-click wins. 4) Winners get matched landing pages. He uses an open-source CMS called Strapi connected to Claude Code via API, so he bulk-generates a landing page for every winning ad angle. Same headline on the ad and the page = higher conversion. 5) Once he finds a winning concept, he scales it — AI avatar UGC via HeyGen, upgraded with V3, and only brings in a human creator if the AI version plateaus. The whole thing runs on Claude Code + APIs + a .env file with all his keys. No engineering team. Just him on multiple desktops with multiple Claude agents running simultaneously. His best line: "you're not just hiring me anymore. you're hiring me and the 30 agents behind me and all the personal software i've built."
Alex Lieberman379,804 次观看 • 3 个月前
I'm am very happy. Why? As a kid, I was bullied. I felt unintelligent, unattractive, incapable. As a young adult, I lost my dad. He passed away suddenly. He was perfectly healthy. Dropped dead from a stroke. And most recently, my wife and I experienced a pregnancy loss in the third trimester of her first pregnancy. I've had all these life experiences where I've known what it is to be unhappy. And so today to have an amazing family, to have a beautiful daughter, to do work that I love, I'm just immensely grateful for it. Finally, I believe gratitude is a muscle that you can build. And if built properly, I think it entirely changes the way that you view the world, no matter what you actually experience. You can find gratitude in the smallest moments, in the biggest moments. And it's been incredibly powerful in my life. ❤️
Alex Lieberman57,690 次观看 • 1 个月前
OpenAI saga in 90 seconds: - Thursday night, Sam Altman gets a text from Ilya Sutskever, OpenAI’s chief scientist & board member asking to chat on Friday. - Friday at Noon, Sam Altman is fired by the Open AI board because he was “not consistently candid in his communications.” - CTO Mira Murati is made Interim CEO. - Microsoft, OpenAI’s largest investor, found out about the move 1 minute before the announcement. Their stock gets crushed. - Right after, Greg Brockman, OpenAI’s President is asked to chat, where he’s told he’s removed from the board but retaining his role. - Greg resigns from OpenAI in solidarity with Sam Altman shortly after. - Tech news & twitter subsequently blow the f*ck up. - Sam Altman fires off a few tweets saying how grateful he was for openAI and the people and how he’d have more things to say soon. - OpenAI employees start tweeting hearts supposedly a signal to the board of who would leave OpenAI to follow Sam Altman if the decision was kept. - By Saturday, rumors start that the OpenAI board is in discussions to bring Sam Altman back as CEO. - Sam Altman tweets out a picture of him wearing a guest pass at OpenAI HQ. - Microsoft & Satya Nadella lead the charge to negotiate with the board. - Board negotiation ends with Altman officially being out on Sunday night & employees streaming out of the office. - Monday morning Twitch cofounder Emmett Shear is named interim CEO. - Around the same time, Satya Nadella announces that Sam is joining Microsoft as the CEO of a new AI research group & former OpenAI leaders like Greg Brockman are joining him. - Still Monday Morning, OpenAI employees share a letter with the board where 650 of 700 employees tell the board to resign.
Alex Lieberman957,348 次观看 • 2 年前
![I thought AEO (read: SEO for LLMs) was a hunk of bullsh*t. And then i spoke to KippBodnar.eth, CMO of HubSpot, who knocked the skepticism out of me. His first jab: In one year, they grew AI search traffic by 15x. It went from rounding error to real line item on the P&L. His second jab: AI search conversion rates are 5x higher than Google search. On some queries, 13x higher. His hook: 60% of AI citations don't come from the top 20 Google results. The companies dominating Google aren't automatically winning in AI search, which creates a huge advantage for early adopters. He then took me through his process for crushing AEO & seeing results in days, not months (like SEO): 1) Grade: your current AEO presence across ChatGPT, Perplexity, and Gemini with a tool like Hubspot's AEO grader. 2) Restructure: your content into chunked, answer-first pages with natural language headers. - one consolidated page, not 8-10 interlinked pages - lead with natural language questions like "What is X?" - 1-2 paragraph sections, not 1,000 word sections - table of contents on a single page 3) Separate: Mentions from citations and optimize differently for each - Mention = when AI references your brand or product in its answer but doesn't link to you - Citation = when an AI references you AND links to your page 4) Open up: your information — ungate content, build Reddit presence, make pricing public - Optimize for entity understanding: how well do AI models understand what your company does, based on every signal from Reddit to review sites, awards lists to help docs 5) Tool up: with AEO-specific software to track prompts and share of voice - Check out Xfunnel or Limey[.]ai 6) Rethink attribution: measure source of customers, not source of traffic. - Metrics that matter: share of voice, citation count, sentiment, mention frequency, source of customers not traffic](https://image.24vids.com/tw-2021350188196172277/amplify_video_thumb/2021347366339784704/img/0h3R8HLAterdEJ8i.jpg)
I thought AEO (read: SEO for LLMs) was a hunk of bullsh*t. And then i spoke to KippBodnar.eth, CMO of HubSpot, who knocked the skepticism out of me. His first jab: In one year, they grew AI search traffic by 15x. It went from rounding error to real line item on the P&L. His second jab: AI search conversion rates are 5x higher than Google search. On some queries, 13x higher. His hook: 60% of AI citations don't come from the top 20 Google results. The companies dominating Google aren't automatically winning in AI search, which creates a huge advantage for early adopters. He then took me through his process for crushing AEO & seeing results in days, not months (like SEO): 1) Grade: your current AEO presence across ChatGPT, Perplexity, and Gemini with a tool like Hubspot's AEO grader. 2) Restructure: your content into chunked, answer-first pages with natural language headers. - one consolidated page, not 8-10 interlinked pages - lead with natural language questions like "What is X?" - 1-2 paragraph sections, not 1,000 word sections - table of contents on a single page 3) Separate: Mentions from citations and optimize differently for each - Mention = when AI references your brand or product in its answer but doesn't link to you - Citation = when an AI references you AND links to your page 4) Open up: your information — ungate content, build Reddit presence, make pricing public - Optimize for entity understanding: how well do AI models understand what your company does, based on every signal from Reddit to review sites, awards lists to help docs 5) Tool up: with AEO-specific software to track prompts and share of voice - Check out Xfunnel or Limey[.]ai 6) Rethink attribution: measure source of customers, not source of traffic. - Metrics that matter: share of voice, citation count, sentiment, mention frequency, source of customers not traffic
Alex Lieberman97,558 次观看 • 3 个月前
One of my best engineers just showed me how to set up OpenClaw securely & without a Mac Mini. Here's his step-by-step: 1) Spin up a VPS on Hetzner It's a virtual server in the cloud. basically a computer you rent for $5-10/month. Pick 8GB RAM, Ubuntu, US East. Takes 2 minutes. 2) Install Tailscale This makes your server invisible to the public internet. Think of it like moving from a house on Google Maps into a gated community where only your devices can get in. Without this, bots start attacking your server within seconds of it going live. 3) Harden the server SSH keys only. Firewall. Intrusion prevention. Auto security updates. CJ actually uses AI to red team his own servers. Tells it to try and break in, then patches whatever it finds. 4) Install OpenClaw🦞 and run the onboarding. You pick your model provider, connect Telegram via BotFather, and configure hooks that give your agent long-term memory. The hooks auto-save sessions and context so the agent gets smarter over time. 5) Set up the gateway This is the piece that makes it actually powerful. It's a message bus that lets your main agent talk to sub-agents, receive messages from Telegram/Discord/Slack, and orchestrate everything. this is what keeps it running 24/7. 6) Hatch your claw and start training it Dump as much info about yourself as possible. tell it your preferences, your workflows, your tools. CJ's agent monitors his email, Slack, and manages his to-do list autonomously. Watch the video for the full break-down & follow CJ Hess for more AI engineering sauce.
Alex Lieberman64,328 次观看 • 3 个月前
![Drew Bredvick compressed Vercel's sales team from 20 people to 2. And I think it's one of the best case studies in the history of AI and GTM. the problem: Sales development doesn't compound. Headcount does. Every additional SDR brings another salary, another ramp period, another personal definition of what "qualified" actually means. the solution: Drew built an AI agent that evaluates every inbound lead: researching the company, scoring intent, and routing only the credible opportunities to the sales team. Everything else is handled automatically. the result: Now two people, focused exclusively on edge cases and high-touch accounts handle the entire sales operation at the $10B company. Andddd the previous team wasn't let go. They were moved into "higher-value work" within the company. here's the play in six steps: 1. Shadow your best performer 2. Pull 90 days of historical data 3. Iterate until 95% agreement 4. Run in parallel with people 5. Get co-sign 6. Hand your top dogs the controller 1. shadow your best performer Sit next to your best SDR for a full day and document every decision: when they qualify, when they disqualify, every signal they check, every button they click. Drew found the real qualification criteria was not in process docs. Reps were checking LinkedIn profiles, scanning websites for tech stack indicators, and pattern-matching on how leads found Vercel. None of it was documented. 2. pull 90 days of historical data Export 90 days of contact form submissions with outcomes attached. Did they close? Ghost? Become a $500K whale? 3. iterate until 95% agreement Open any code editor with AI built in. Drop your CSV into a new project and start a conversation: "Look at this lead data. I'm going to give you a prompt to evaluate leads. Tell me if each one is qualified or not." Run this prompt against a batch. Compare the agent's calls to what actually happened—not what humans decided, but whether the lead converted. Find disagreements. Fix the prompt. Repeat. You're aiming for 95%+ agreement with historical outcomes. starter prompt: You are a lead qualification agent. For each lead, analyze the following signals and provide your reasoning BEFORE your decision: Company signals: website quality, tech stack, company stage, employee count Intent signals: how they found us, what they asked for, urgency indicators Fit signals: ICP match, use case alignment, budget indicators Structure your response as: REASONING: [Your analysis of each signal category] CONFIDENCE: [High/Medium/Low] DECISION: [Qualified/Not Qualified] NEXT ACTION: [Route to sales / Auto-respond / Request more info] Be conservative. You naturally want to qualify leads to make humans happy. Resist that urge. A false positive wastes sales time. A false negative just means we follow up later. 4. run in parallel with people Once the prompt works with historical data, prove it works live with your sales team: Here's what to track: - agreement rate: Agent vs. human decisions. - accuracy rate: Agent vs. actual outcomes. - processing time: Lead received → decision made. - confidence distribution: How often the agent is certain vs. uncertain - error log: When it got it wrong, and why. 5. get co-sign Drew started chatting with individual contributors. He got them to validate that the agent was making good calls. Then he partnered closely with the leader of the SDR team. He then let leaders of the sales team tweak qualification criteria, the leaders of the marketing team adjust scoring weights, and let leaders of the ops team define routing rules. b/c when leadership builds alongside you, they stop being gatekeepers and start being advocates. 6. hand your top dog the controller Flip the switch!! The agent processes every lead, makes a qualification decision, and even drafts the response. But a person reviews before anything goes out and has more time to check the genuinely f******* tough and weird cases. The system recommends; humans decide. The same loop should work In other parts of the org too: customer support triage, contract review, expense approvals, and even content moderation. Full playbook below w/ prompts and Drew's handholding. 👇](https://image.24vids.com/tw-2014000574195261730/amplify_video_thumb/2013993080546865152/img/7NvFFePIizJUtyIx.jpg)
Drew Bredvick compressed Vercel's sales team from 20 people to 2. And I think it's one of the best case studies in the history of AI and GTM. the problem: Sales development doesn't compound. Headcount does. Every additional SDR brings another salary, another ramp period, another personal definition of what "qualified" actually means. the solution: Drew built an AI agent that evaluates every inbound lead: researching the company, scoring intent, and routing only the credible opportunities to the sales team. Everything else is handled automatically. the result: Now two people, focused exclusively on edge cases and high-touch accounts handle the entire sales operation at the $10B company. Andddd the previous team wasn't let go. They were moved into "higher-value work" within the company. here's the play in six steps: 1. Shadow your best performer 2. Pull 90 days of historical data 3. Iterate until 95% agreement 4. Run in parallel with people 5. Get co-sign 6. Hand your top dogs the controller 1. shadow your best performer Sit next to your best SDR for a full day and document every decision: when they qualify, when they disqualify, every signal they check, every button they click. Drew found the real qualification criteria was not in process docs. Reps were checking LinkedIn profiles, scanning websites for tech stack indicators, and pattern-matching on how leads found Vercel. None of it was documented. 2. pull 90 days of historical data Export 90 days of contact form submissions with outcomes attached. Did they close? Ghost? Become a $500K whale? 3. iterate until 95% agreement Open any code editor with AI built in. Drop your CSV into a new project and start a conversation: "Look at this lead data. I'm going to give you a prompt to evaluate leads. Tell me if each one is qualified or not." Run this prompt against a batch. Compare the agent's calls to what actually happened—not what humans decided, but whether the lead converted. Find disagreements. Fix the prompt. Repeat. You're aiming for 95%+ agreement with historical outcomes. starter prompt: You are a lead qualification agent. For each lead, analyze the following signals and provide your reasoning BEFORE your decision: Company signals: website quality, tech stack, company stage, employee count Intent signals: how they found us, what they asked for, urgency indicators Fit signals: ICP match, use case alignment, budget indicators Structure your response as: REASONING: [Your analysis of each signal category] CONFIDENCE: [High/Medium/Low] DECISION: [Qualified/Not Qualified] NEXT ACTION: [Route to sales / Auto-respond / Request more info] Be conservative. You naturally want to qualify leads to make humans happy. Resist that urge. A false positive wastes sales time. A false negative just means we follow up later. 4. run in parallel with people Once the prompt works with historical data, prove it works live with your sales team: Here's what to track: - agreement rate: Agent vs. human decisions. - accuracy rate: Agent vs. actual outcomes. - processing time: Lead received → decision made. - confidence distribution: How often the agent is certain vs. uncertain - error log: When it got it wrong, and why. 5. get co-sign Drew started chatting with individual contributors. He got them to validate that the agent was making good calls. Then he partnered closely with the leader of the SDR team. He then let leaders of the sales team tweak qualification criteria, the leaders of the marketing team adjust scoring weights, and let leaders of the ops team define routing rules. b/c when leadership builds alongside you, they stop being gatekeepers and start being advocates. 6. hand your top dog the controller Flip the switch!! The agent processes every lead, makes a qualification decision, and even drafts the response. But a person reviews before anything goes out and has more time to check the genuinely f******* tough and weird cases. The system recommends; humans decide. The same loop should work In other parts of the org too: customer support triage, contract review, expense approvals, and even content moderation. Full playbook below w/ prompts and Drew's handholding. 👇
Alex Lieberman81,801 次观看 • 4 个月前
McKinsey surveyed 2,000 companies in 2025. 51% said AI backfired on them. Top reason? Inaccuracy. From what I can tell, most of these systems weren't broken. They were unreliable. And unreliable is wayyyy worse because you can't predict when it fails. So I got Ash Tilawat (the Mr. Miyagi of teaching AI) from Gauntlet AI to walk me through the solution. Here's his 2026 framework for evaluating if your AI is trustworthy, reliable, and production-ready: 1. build your golden set Identify 30–50 core requests your AI must handle correctly. The stuff that, if broken, makes the whole system useless. And sit with the person whose job this AI is doing/automating/replacing/helping with. 2. test the weird stuff Your golden set covers common requests. But in production, users don't only ask common requests. So build a matrix of categories (topic x complexity) and fill the gaps. Every gap is a corner where failures can hide behind. 3. build a replay harness Record the exact state of every interaction so you can test prompt changes without burning API calls. Think of it like game film... you don't put players back on the field just to review the play. 4. create your rubric Use an LLM to grade outputs on accuracy, completeness, and tone. But calibrate it first -> run 50–100 examples through human and LLM scoring, find disagreements, fix the rubric, repeat until they match. 5. run experiments New model? Prompt rewrite? Run your eval suite against both versions. Ship if the golden set passes, no regressions, and the cost is acceptable. The teams still running production AI on vibes will be f***** in 2026. But the teams building eval libraries are compounding an advantage that gets harder to catch every month. Competitors can copy your product. They can't copy your test cases. h/t Austen Allred for helping put this together. Full playbook + vid below 👇
Alex Lieberman71,920 次观看 • 4 个月前
Half Baked Business Idea: Gamified Gym Brand Problem: People hate going to the gym, but it is important for their physical and mental health. Solution: A brand that turns all equipment and exercises into a game, where you earn points as you level up. As you level up, you earn money. The kicker, is to have insurance companies to dole out the money. People will end up claiming less from insurance companies if they are in better health. Share your thoughts in the comments below 👇
Alex Lieberman294,780 次观看 • 2 年前
Pumped to announce Distro, the first AI content strategist. Distro solves a problem I’ve personally watched thousands of founders & execs go through. Creating content & building an audience has a ton of obvious benefits: - Free customers - Constant hiring pipeline - Access to dope people - User feedback But it takes a lot of time & content skills, which most people don’t have. Distro makes this 10x easier, turning natural conversation into content in minutes. Here’s how it works: 1. Start a studio session 2 Tell Distro what you want to chat about 3. Distro develops an interview plan & has a conversation with you 4. Conversation is turned into summary, key quotes, and several post drafts 5. You can edit posts, connect your social accounts, and fire off content right from Distro Now, creating content can be a quick, daily habit that you look forward to vs. a pain in the ass you've come to dread. Huge shoutout to Dev Sharma for building a kickass agent. Try out Distro for free below… P.S. use us on web for the best experience. a ton of upgrades coming soon (including improved mobile UX).
Alex Lieberman115,271 次观看 • 1 年前
Coolest application I've ever seen. I can't take any credit for it, but our team COOKED. To be fast-tracked into our new Claude Code Community, you have to find easter eggs that only super users will know how to find. Unbelievable combination of interactivity & member curation.
Alex Lieberman43,631 次观看 • 4 个月前
![I use a killer tool to help me create 1,000+ posts per year, while being a full-time founder. Here's the tool (and my process): Step 1: Have youdistro[.]co interview me about media, AI, entrepreneurship, fatherhood (any topics i'm passionate about/an expert on) for 10 minutes every single morning. Step 2: I create 3-4 text posts either through Distro or Claude. I schedule those out on X and Linkedin. Step 3: I download the video interview from Distro & feed it into OpusClip to create 3-4 SFV clips. I schedule those out on IG, TT, YT. Step 4: I repeat the process each day & can put out thousands of high quality pieces of content per year on the internet. P.S. I am a co-founder/advisor to Distro, but the reason I got involved is because I so badly wished I had this tool to turn me into a content machine earlier in my career](https://image.24vids.com/tw-2014070847607128111/amplify_video_thumb/2014069846888058880/img/xaeVt2kEkasCYdhw.jpg)
I use a killer tool to help me create 1,000+ posts per year, while being a full-time founder. Here's the tool (and my process): Step 1: Have youdistro[.]co interview me about media, AI, entrepreneurship, fatherhood (any topics i'm passionate about/an expert on) for 10 minutes every single morning. Step 2: I create 3-4 text posts either through Distro or Claude. I schedule those out on X and Linkedin. Step 3: I download the video interview from Distro & feed it into OpusClip to create 3-4 SFV clips. I schedule those out on IG, TT, YT. Step 4: I repeat the process each day & can put out thousands of high quality pieces of content per year on the internet. P.S. I am a co-founder/advisor to Distro, but the reason I got involved is because I so badly wished I had this tool to turn me into a content machine earlier in my career
Alex Lieberman34,277 次观看 • 4 个月前
Half Baked Business Idea: Airbnb for home cooked meals. Problem: Sometimes, I don’t want to cook at home, and I don’t want to go out and eat salty, greasy food that is not good for me. Solution: A platform for great cooks to prepare meals for their neighbors. Neighbors pay for their meal and the home cooks have a new income source. Share your thoughts in the comments below 👇
Alex Lieberman172,650 次观看 • 2 年前
Ads don't have to suck. Perfect example on today's My First Million epi. At 7:41, the show kicks to Shaan Puri, who shares a new database tool for listeners. Tons of business ideas are shared on the pod, and many of those ideas have actually been turned into real businesses by listeners. So (with the help of the pod's always-on sponsor & owner, HubSpot), Shaan Puri & Sam Parr compile 50+ business ideas from the show into a categorized database that links back to the episode in which each idea was mentioned. Hubspot gets qualified leads. MFM listeners get content they love. Definition of a value ad(d).
Alex Lieberman36,313 次观看 • 2 年前
