Alex Lieberman
@businessbarista • 314,850 subscribers
Family first (husband & girl dad) Founder second (@tenex_labs, @morningbrew, @storyarb, @youdistro) AI engineering & transformation 👇
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This intern 6x'd her company's website traffic with an AI content machine called BlogEO. The big f*cking problem - Blog had almost no real measurement - Insights lived in ~5 places (GSC, Semrush, PostHog, Sanity, prior run snapshots) so nobody joined the data - ~70% of traffic came from 14 posts; ~50% from just 5 - Top SEO posts were often invisible to LLMs (AEO gap) Step 1: Run the AI audit - Pulls those five sources into one view - Content hygiene scan first: broken links, positioning drift, deprecated products - Broken-link fixes use Browserbase's fetch API to find the right replacement - Broken links + missing SEO fields can auto-publish (no human gate) - Scores every post into a ranking / opportunity queue - Flags the top ~15 for deeper fact-check + SEO verification - Drafts surgical edits (small, intentional) and stores each suggestion - Edits stay small on purpose. Most posts were handwritten; don’t paste AI voice over human voice - High-leverage changes: SEO title, meta description, swap a link - 28-day cooldown after changes so GSC has time to catch up (no thrashing the same post) Step 1b: Score post opportunities • Unit of opportunity = clicks • Click recovery: if clicks fell hard vs the last ~28 days, that lost volume is opportunity. Real click loss beats any estimate and jumps the queue. • CTR gap: compare your CTR at a given position to what page-one / peers get at that same spot. Gap × impressions ≈ clicks you’re leaving on the table. (Alex’s example: position 9 averages ~5% CTR; you’re at 1% → the 4-pt delta is the opportunity.) • Rank upside: if you’re on page 2/3, estimate clicks if you moved to page 1 / to the average for that better position. (Her example: post at 9.4 with a pink-dot underperform → ~7,400 click opportunity if it hit the average for that position.) • Queue, then spend: every post gets a cheap score; only the top ~15 get the expensive fact-check / deep SEO pass (token control). Near-invisible / irrelevant queries don’t score high on purpose. Step 3: Avoid AI slop - New posts aren’t a firehose ideas surface as drafts, ~once a week - Ignore / skip is a first-class option if the query isn’t worth owning - Quality gate before anything ships - Slack card shows who approved and who published — accountability stays with a person - Human pride > “we shipped another AI blog” Step 4: Run the full blog machine - Weekly cadence in Slack: Monday = content generation, Tuesday = audit (plus on-demand “audit this page now”) - Agent (BB) has skills: audit, strategy, writing, generate...always drafts suggestions first - Agent has no write path to live content until a human clicks - Slack cards: Approve / Edit / Skip / Discard → only then does it hit the CMS - Approved edits + outcomes land in internal DBs (including what never shipped) - Near-miss queries (show up in search, no targeted page) feed the generator side The results Search impressions: +5.8x Page-one queries: +9.8x Avg blog position: page 2 → page 1 Massive shoutout to harsehaj ⋆˙⟡ for the amazing internship project & masterclass in AI-powered AEO/SEO!
Alex Lieberman31,176 Aufrufe • vor 2 Tagen
One of the most cracked engineers I know believes that local development is (mostly) dead. He walked me through why + his setup for using cloud agents in parallel... The big fucking problem: - You've got one codebase on your computer - Worktrees are supposed to let you run agents in parallel. On a real stack they don't (database contention, port conflicts, a dev server that only works in one tree) - So people flip back to one agent at a time and become the bottleneck themselves - Or they run multiple agents on the same machine: one writes a change, another writes over it, and they're fighting - Then you have to poke around, find everything each agent did, and test it independently - That's too much mental clutter - Locally you're operating like a CPU. You get an instruction, you build it, you queue a backlog The solution: - Stop sharing the computer. Give every agent its own - CJ Hess' setup is Amp "orbs": a full computer in the cloud that clones the repo, starts the dev server, and runs one agent with nothing else contending - A "portal" is a live URL into that running app so you can click around from any device before you trust it - Isolated like an actual team, each person on their own machine The payoff: - It feels like a GPU, not a CPU. A list of 10 tasks, all started, real progress, loop closed - Smaller, contained diffs hit main faster and create fewer conflicts, not more - Favorite prompt: "give me irrefutable evidence that this works." On his laptop he would never run something so heavy he can't use Slack and Chrome. In an orb he will (millions of simulated DB writes, full demo videos) - Smaller diffs, no mixed concerns, higher confidence, ship more often - Mental clutter is gone. One thread, one set of changes, one computer. He always knows the state. That's what lets him do more in parallel, not less - The last things he still does locally: read production logs, set a sensitive secret. He is not spinning up the app on his laptop
Alex Lieberman98,687 Aufrufe • vor 17 Tagen
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 Lieberman953,872 Aufrufe • vor 6 Monaten
One of our ambitions at Tenex is to make NYC the AI capital of America. We know it’s a bit insane, but we believe it’s possible & we’re gonna try our hardest to make it inevitable. Just to show how committed we are, I’m pumped to announce Tenex’s new HQ. - Coming Jan 2027 - Will be home to 200 AI engineers, strategists, creators, etc - 20,000 sqft - 2 floors including penthouse for events - Podcast/video studios - Rooftop overlooking Madison Square Park Come join us:
Alex Lieberman87,979 Aufrufe • vor 28 Tagen
Introducing the Content Machine! This was the first time I walked through the mechanics of our anti-slop content system & how we drive 10,000,000+ impressions at tenex with a marketing team of...2. Thanks to claire vo 🖤 for having me on her show to share. Full-writeup & interview below... What is the Content Machine? A directory of daisy-chained skills that turn what you already say into publish-ready content. This is the key way to avoid turning into a slop cannon. It mines the places you already talk (Slack, Notion, Gmail, Linear, GitHub) plus what the internet is saying, finds the ideas worth writing, interviews you to extract the story, drafts it, and edits it to a 9/10 bar before you post. What are the principles of this system? 1) It is not a "write me a post" prompt. The core belief baked into it: the raw material must come from you. 2) The machine never invents your voice and never fabricates your insight. It does the research, the structure, and the editing, so your time goes only to the part only you can do. It focuses the human on the first & final mile of the content process. How is it structured? A two-layer split between the process & the person - The process layer is generic and shared: the pipeline, the content-type specs, the copywriting references, the onboarding flow. That is what lives in git and what gets shipped to other people. - The personal layer is yours and never leaves your machine: content-machine.config.md, creators/ / (profile, style guide, lessons), projects/, published/, oracle-reports/. All gitignored, and the desktop build script refuses to package any of it. What is the 10-step pipeline? 1) Creator Select. Multi-creator by design. It figures out who this run is for and loads their profile, style guide, and content lessons. Everything downstream is scoped to that person. - Onboarding (first run only). Scaffolds the workspace, auto-creates your Notion Vault, connects your sources, and builds your voice guide one of three ways: import a guide you already have, feed it writing samples, or sit for a short voice interview. 2) The Oracle. Two idea engines running in parallel: - Oracle scans what you wrote in the last 7 days across Slack, Notion, Gmail, Linear, and Git, hunting for "spikes," moments where you naturally said something worth expanding. - The Internet Reader scans what the world is saying, pulled only from the source list in your profile (handles, labs, outlets, keyword watchlist), plus a social sweep across Reddit, X, YouTube, Hacker News, and more (thanks /last30days & Matt Van Horn). - Every idea is scored 0 to 10 (POV strength 25%, story potential 25%, emotional intensity 20%, lesson/framework 20%, depth 10%). Everything qualifying gets written to The Vault, a Notion database that is the durable idea bank. 2.5) Research. Before you get interviewed, a research agent builds a sourced brief: - key facts with links - current developments - what has already been said in-market - contrarian angles - open questions only you can answer. 3) Interview Panel. Six interviewer personas (Ferriss, Rogan, Larry King, Stern, Barbaro, Barbara Walters) ask you one question at a time, each chasing a different dimension: tactics, story, core truth, the hidden thing, clarity, emotional depth. It pushes back on vague answers and will not advance until it has 2 to 3 specific stories with real details. 4) Production. Your interview becomes a raw markdown file: stories, core insights, quotable moments, the emotional anchor, surprising reveals, the "so what." Your exact words are preserved. This file is the source of truth for everything that follows. 5) Refinement. Now it drafts, and only now. It must read your style guide, your content lessons, and the spec for the chosen format (LinkedIn post, X thread, long post, playbook, podcast promo, reaction post, article, and so on). 6) Writer's Council. Six reviewers score the draft: Morgan Housel (will this matter in 10 years), Tim Urban (is it confusing), Shaan Puri (would I stop scrolling, plus three alternate hooks), Greg Isenberg (what can someone steal), David Perell (is it personal, observational, playful), and a Slop Detector hunting AI tells. Each gives what's working, what needs work, a fix, and a score. 7) Revision Loop. Under 9/10 goes back around. The smart part: fixes get sorted into editorial (the machine rewrites it itself) and information gaps (only you have the answer), and information gaps route back to the Interview Panel with targeted questions rather than letting the machine make something up. Max 3 editorial cycles. At 9/10 the piece becomes the anchor. 8) Repurposing Engine. One anchor fans out into 10+ derivatives: X article, LinkedIn article, short X posts, short LinkedIn posts, a playbook if there is a framework in it. Each one is written native to its platform with a fresh hook, not cross-posted, and each runs the full council and revision loop to 9/10 on its own. This is the multiplier. 9) Distribution (optional, off by default). UTM tagging, a scheduled publishing queue, CRM capture of every touchpoint, attribution reporting back to pipeline, and marking the Vault row as Published. 10) The Learning Loop, always running. After you approve a piece it diffs your first draft against the final, extracts the pattern, and asks you to confirm it. Confirmed lessons go into content-lessons.md and override the style guide. Once a lesson proves out across a few projects it graduates into the style guide itself. Your first drafts get better over time instead of you re-explaining preferences. P.S. i'm thinking about opensourcing this. should i do it?
Alex Lieberman115,336 Aufrufe • vor 1 Monat
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 Lieberman381,129 Aufrufe • vor 6 Monaten
One of the most productive engineers I know cannot read code. Matt Van Horn is not an engineer by training. He co-founded June, has 44,000 GitHub stars, and got contributions merged into Go & Python. I had him build live for an hour & walk me through his process. My fav lessons & quotes from the convo: 1) He never reads code. He does not have an IDE installed. In his words, "I fundamentally believe that very soon no humans should ever write any code and that no humans should ever read any code." 2) Every project starts with the agent writing itself a plan.md file using Compound Engineering. Agents are lazy, he says, and the plan keeps them honest. He never reads the plan either. "Plans are for agents, you silly human." 3) His CLIs leave notes for themselves. Each run records what it learned in a markdown file, so the next run starts where the last one ended. He calls them self-healing. 4) His agent has an email address. From Telegram on his phone, he sends a task. It emails his Mac, authenticated, and the work starts while he is at his kids' soccer practice. 5) He feeds whole transcripts, not summaries. After a two-hour meeting with a Google Ventures researcher, his agent read the researcher's entire book, wrote itself a report on every chapter, and turned it all into a plan for his business. 6) Him yapping to his agents is like nerd ASMR. "Go agent go" is how he likes to finish telling the agent what to do. 7) How he thinks about this next chapter of building: "Every generation of tools moves the engineer's job up a level. The code was never the point. The problem was."
Alex Lieberman78,422 Aufrufe • vor 1 Monat
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 Lieberman957,503 Aufrufe • vor 2 Jahren
People made fun of Alex Finn for buying three Mac Studios to run AI at home. Then Fable got banned for a week, GLM 5.2 dropped, and those exact Mac Studios started reselling for 4x what he paid. He showed me how he built his home AI lab from scratch. Here's the playbook: 1) The hardware. three 512GB Mac Studios, an NVIDIA DGX Spark, a custom RTX 5090 build, and a few Mac Minis. ~$30k all in. 2) The buying framework... - Mac Studio: huge memory, runs GLM 5.2 (open weights, near Opus 4.8 on benchmarks), but slow. - DGX Spark ($4,800): the sweet spot for most people. - RTX 5090: smaller models at blazing speed (Qwen's 29B now hits Sonnet 4 level). 3) Tailscale networks every machine into one private network with root access to each other. Only one machine is plugged into a monitor. 4) A Nous Research Hermes agent is his IT guy. New model drops? It SSHs into the right box, loads 5 candidates, runs evals overnight, and reports back which task belongs on which machine. Alex has literally never loaded a model himself. 5) The whole point: achieving "ambient intelligence." Always-on jobs that would bankrupt you on per-token billing. A security sweep of his API endpoints every hour. Code optimization every 20 minutes. Database anomaly & churn detection. Hourly scraping of X, Reddit & Hacker News for business opportunities. 6) Running those workloads on frontier models would cost thousands a month. His actual cost: ~$60 more in electricity. 7) Btw he's not anti-frontier. He still maxes out his Claude plan. The way he sees it: frontier is for hard thinking, local is for the foot soldiers that never sleep. 8) "We own everything except for the intelligence. Why can't we own the intelligence?" 9) He thinks frontier-level intelligence runs on consumer hardware within 6 months.
Alex Lieberman57,764 Aufrufe • vor 2 Monaten
![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 Lieberman98,635 Aufrufe • vor 7 Monaten
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 Lieberman57,843 Aufrufe • vor 4 Monaten
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 Lieberman84,725 Aufrufe • vor 7 Monaten
![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 Aufrufe • vor 7 Monaten
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 Lieberman294,814 Aufrufe • vor 3 Jahren
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 Lieberman64,999 Aufrufe • vor 6 Monaten
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 Lieberman115,373 Aufrufe • vor 1 Jahr
Loved this 22-minute talk on continual learning for AI agents. Must watch for anyone looking to get agents performant and into production. Credit: Soheil Feizi at AI Engineer • Agent learning can happen at three layers: the model (weights), the harness (prompts, tools, skills, code, workflows), and memory (session or persistent). • Two fundamental challenges: (1) getting feedback, meaning how do we know if the agent did well and what it should have done instead, and (2) acting on that feedback, meaning deciding which layer or component to change and how. • Feedback sources differ by stage: In development you have benchmarks with evaluators that score pass/fail. In production you only have logs, which can be judged either automatically (LLMs or code analyzing the log, which is scalable) or by human experts (low volume but critical domain knowledge). • Logs plus feedback aren't enough because they're not testable: A single log with feedback is one observation of what happened. You need to lift it into a replayable learning environment, a simulation with tools, users, and defined evaluators, so candidate fixes can be run, verified, and compared. • Three ways to optimize the agent, with tradeoffs: Model-layer updates (SFT, RL post-training like DPO/GRPO, LoRA) are expensive and need benchmarks and evaluators. Harness updates (trace-to-harness coding agents, prompt search like GEPA) are flexible but either untestable and "vibe-based" or benchmark-dependent. Memory updates (fact storage like Letta/Mem0, skill distillation) are cheapest and fastest but usually unverified. • A good learning engine makes "the smallest durable change at the right layer" of the agent. • Verifiable continual learning (VCL): Improve an agent from its own experience where every fix is proven to help and proven to break nothing that already worked. It requires an executable test (replayable failure), a measured delta (score before and after), and regression tests (prior tests still pass). • Four principles of practical VCL: Replayability (turn one-off failures into rerunnable tests), holisticness (one failure can have causes in memory, prompts, tools, workflow, or model, so route the fix to the right layer), lifelongness (fix new failures subject to no regression on past environments, with regression handled inside the optimization loop rather than post-hoc), and efficiency (the loop must run frequently and cheaply, without scaling linearly as past environments accumulate). • Three takeaways: (1) Agent continual learning isn't necessarily fine-tuning; many useful updates live in the harness and memory layers. (2) Production logs are not learning environments and must be transformed into replayable ones. (3) The frontier is regression-aware improvement: fixing new failures while verifying you don't break old ones.
Alex Lieberman20,417 Aufrufe • vor 2 Monaten
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 Lieberman172,662 Aufrufe • vor 3 Jahren