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CODEX SKILL THAT TURNS CUSTOMER FEEDBACK INTO A ROADMAP! Most feedback analysis stops at positive or negative. I made a Codex skill that turns support tickets, interviews, surveys, reviews, sales calls, and churn notes into evidence-backed product priorities and credible customer proof. Give it your feedback files and Codex...

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Kappaemme

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CODEX SKILL THAT FINDS YOUR STARTUP’S FIRST CUSTOMERS! I made a Codex skill that analyzes your startup and finds potential customers from real public signals. Paste your startup URL while Codex defines your ideal customer, searches public discussions, qualifies each prospect, and generates a polished report with personalized outreach openers. -> ideal customer profile analysis -> public pain + buying signal research -> evidence-backed prospect shortlist -> fit, timing + reachability scores -> original source links for every prospect -> personalized outreach openers -> polished HTML report -> one-command install Install: npx --yes codex-first-customer-finder-skill 100% open source. Repo in Bio.
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Most companies don't know what their customers are actually frustrated about. So I built an AI agent called **Customer Pain Point Miner**. Type a company name, and it automatically finds: • Top customer complaints • Features users keep requesting • Recurring frustrations • Product improvement opportunities It scans reviews, Reddit threads, forums, and blogs, then turns thousands of discussions into a sourced report. No surveys. No guessing. Just real customer feedback from across the web. Built it with Nimble Their APIs made it surprisingly easy to search, extract, and structure live web data into something an AI agent can actually reason about. The best roadmap isn't hidden in another brainstorming session. It's already sitting in your customers' conversations.

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🚨 THIS 100% FREE GITHUB REPO JUST MADE EVERY AI NOTE-TAKING APP POINTLESS. Drop any source in article, PDF, YouTube transcript, or Journal entry. Claude reads it, links it to everything in your vault, and files it into a connected knowledge graph on your own machine. Most tools save your notes. This one connects them. Every source gets linked to everything you already know. Karpathy described the idea - 15 skills. Four vault methods. Works with Claude Code, Codex, and Gemini. Most people have years of knowledge buried in folders they open twice a year. This turns all of it into a second brain that works while you sleep. MY ARTICLE COVERS THE EXACT METHOD I BUILT TO DO THE SAME THING. Guide below!
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🚨 THIS 100% FREE GITHUB REPO JUST MADE EVERY AI NOTE-TAKING APP POINTLESS. Drop any source in article, PDF, YouTube transcript, or Journal entry. Claude reads it, links it to everything in your vault, and files it into a connected knowledge graph on your own machine. Most tools save your notes. This one connects them. Every source gets linked to everything you already know. Karpathy described the idea - 15 skills. Four vault methods. Works with Claude Code, Codex, and Gemini. Most people have years of knowledge buried in folders they open twice a year. This turns all of it into a second brain that works while you sleep. MY ARTICLE COVERS THE EXACT METHOD I BUILT TO DO THE SAME THING. Guide below!

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I just built a Reddit research agent in Claude Code that turns real customer complaints into ad angles 🤯 It scrapes the threads where people actually rant about your category, reads every post and comment, and hands you back a dashboard of pain points, exact customer phrases, and ready-to-test ad angles. All inside Claude Code. Perfect for DTC brands and agencies who want ad angles pulled from real customer language, not guessed in a vacuum. Reddit is the most honest focus group on earth. People say things there they'd never put in a survey or a review with a brand watching. So if you're writing hooks off your own assumptions — or digging through Reddit by hand, opening tab after tab, pasting quotes into a doc... This agent runs the entire loop: → Give it a product or category + a couple of subreddits → It searches Reddit for the threads where people actually discuss the problem → Pulls every post and comment → Claude reads all of it and finds the patterns → Builds a dashboard: pain points, desires, objections, and swipe-worthy phrases No manual scrolling. No pasting quotes into docs. No guessing what your customers actually care about. What you get: → Ranked customer pain points, each with a real verbatim quote → The exact language your customers use, ready to drop into ad copy → Objections and buying triggers pulled straight from the threads → 8-10 ready-to-test ad angles built from all of it Built 100% in Claude Code. Want the skill file for free? > Like this post > Comment "REDDIT" And I'll send it over (must be following so I can DM)
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I just built a Reddit research agent in Claude Code that turns real customer complaints into ad angles 🤯 It scrapes the threads where people actually rant about your category, reads every post and comment, and hands you back a dashboard of pain points, exact customer phrases, and ready-to-test ad angles. All inside Claude Code. Perfect for DTC brands and agencies who want ad angles pulled from real customer language, not guessed in a vacuum. Reddit is the most honest focus group on earth. People say things there they'd never put in a survey or a review with a brand watching. So if you're writing hooks off your own assumptions — or digging through Reddit by hand, opening tab after tab, pasting quotes into a doc... This agent runs the entire loop: → Give it a product or category + a couple of subreddits → It searches Reddit for the threads where people actually discuss the problem → Pulls every post and comment → Claude reads all of it and finds the patterns → Builds a dashboard: pain points, desires, objections, and swipe-worthy phrases No manual scrolling. No pasting quotes into docs. No guessing what your customers actually care about. What you get: → Ranked customer pain points, each with a real verbatim quote → The exact language your customers use, ready to drop into ad copy → Objections and buying triggers pulled straight from the threads → 8-10 ready-to-test ad angles built from all of it Built 100% in Claude Code. Want the skill file for free? > Like this post > Comment "REDDIT" And I'll send it over (must be following so I can DM)

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Open-sourcing GenOffice, the world's first full-featured open-source AI Office for PC and Mac. GenOffice is free for everyone, ad-free. All the editing tools you'd expect, no strings attached. How it started? One engineer, one week, $10,000 in tokens. That's what it took to build the GenOffice Alpha. Help shape the AI office experience We invite you to build the rest of it with us. Join the GenOffice group chat on GenTeam and bring your requests straight to the agent. Not by writing code, but by telling it what you want. You say what you need, and every piece of your feedback becomes part of the product. What's in GenOffice? Docs, Sheets, Slides, and PDF, with all the editing tools you already know. And with Genspark Super Agent built in, powered by top-tier models, it digs into research, crunches your data, then writes the doc or builds the deck for you, using your Genspark credits. This is an Alpha, and we mean it. Come tell us what's broken and what's missing. Everyone who jumps in with real feedback gets 1,000+ Genspark credits as a thank you. GitHub 👉 Download it now 👉

Genspark

488,070 Aufrufe • vor 4 Tagen

how to find a UNIQUE startup idea that'll actually make you $10k/month: go to g2, capterra or trustpilot right now pick any popular software company in a niche you care about filter by 1-2 star reviews now start reading. you're looking for patterns. when 10+ people complain about the exact same thing, you've found the problem you're going to solve. why? because these are paying customers. they already spend money on solutions. they're just not happy with what exists. that's the easiest market to enter. here's what i did: > pulled up jira and clickup reviews > hundreds of users saying the same things "too bloated" "impossible to onboard my team" "paying for features i'll never use" "too complex for small teams" that's not just a review. that's a feature spec for your next product. every negative review tells you: > what to build > what to charge > what features to prioritize > who your customer is why this approach works: > validated demand - people already pay for solutions > clear problem - they tell you exactly what's wrong > existing market - no need to create demand > pricing benchmark - see what they currently pay > feature roadmap - they list what's missing this is how i found my current startup 450+ paying customers later and it started with reading 1-star reviews you don't need a genius idea you need a frustrated customer they're everywhere. you just have to look.
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how to find a UNIQUE startup idea that'll actually make you $10k/month: go to g2, capterra or trustpilot right now pick any popular software company in a niche you care about filter by 1-2 star reviews now start reading. you're looking for patterns. when 10+ people complain about the exact same thing, you've found the problem you're going to solve. why? because these are paying customers. they already spend money on solutions. they're just not happy with what exists. that's the easiest market to enter. here's what i did: > pulled up jira and clickup reviews > hundreds of users saying the same things "too bloated" "impossible to onboard my team" "paying for features i'll never use" "too complex for small teams" that's not just a review. that's a feature spec for your next product. every negative review tells you: > what to build > what to charge > what features to prioritize > who your customer is why this approach works: > validated demand - people already pay for solutions > clear problem - they tell you exactly what's wrong > existing market - no need to create demand > pricing benchmark - see what they currently pay > feature roadmap - they list what's missing this is how i found my current startup 450+ paying customers later and it started with reading 1-star reviews you don't need a genius idea you need a frustrated customer they're everywhere. you just have to look.

Om Patel

13,572 Aufrufe • vor 6 Monaten

I found a skill that turns Claude into an AI ad creative maker. It’s called Goose Ads by Gooseworks, and it works directly inside Claude, Claude Code, Cowork, or Codex. You can now create ad creatives for your brand with one command. How it works: → Install Gooseworks npx gooseworks install --all → Run this prompt /goose-ads create ads for my brand → Optional Pick templates you like on the platform Goose Ads finds creatives that companies are already spending money on, studies what is working, and generates new ad creatives for your brand. It also keeps everything aligned with your website, messaging, logo, assets, and positioning, so the final output feels brand-ready instead of generic. And this is bigger than one ad skill. Gooseworks has 100+ open-source growth skills for ads, content, competitor research, GTM, SEO, lead generation, and more. Claude is slowly becoming more than a chatbot. It is becoming a growth operator. Comment 'Goose' and I’ll DM you the full open-source skill library.
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I found a skill that turns Claude into an AI ad creative maker. It’s called Goose Ads by Gooseworks, and it works directly inside Claude, Claude Code, Cowork, or Codex. You can now create ad creatives for your brand with one command. How it works: → Install Gooseworks npx gooseworks install --all → Run this prompt /goose-ads create ads for my brand → Optional Pick templates you like on the platform Goose Ads finds creatives that companies are already spending money on, studies what is working, and generates new ad creatives for your brand. It also keeps everything aligned with your website, messaging, logo, assets, and positioning, so the final output feels brand-ready instead of generic. And this is bigger than one ad skill. Gooseworks has 100+ open-source growth skills for ads, content, competitor research, GTM, SEO, lead generation, and more. Claude is slowly becoming more than a chatbot. It is becoming a growth operator. Comment 'Goose' and I’ll DM you the full open-source skill library.

Jaynit Makwana

53,937 Aufrufe • vor 1 Monat

This guy literally automated his entire social media with one free AI agent. No team. No scheduling tools. No VA. Here's exactly how he did it 👇 The agent is called Hermes. It's free and open source. Here's what it's running on autopilot right now: → Scans Reddit every 24 hours for trending topics → Writes posts based on breaking news → Generates custom images with Nano Banana 2 → Posts directly to X, TikTok, and Instagram with captions and hashtags → Pulls daily analytics and flags what's working → Researches competitors and shows what's getting engagement One post already hit 1,000 views. He didn't write it. He didn't post it. He didn't even check it. Here's the part most people skip: Day one it won't be good. You give it feedback. It writes notes on what went wrong. It improves the skill file. By day 7 it's better. By day 30 it knows your voice. It's called a closed learning loop. Every task makes the next one sharper. The setup takes an afternoon: Install Hermes from GitHub. Connect a free Qwen 3.6 API key from OpenRouter. Add Firecrawl for web research. Create your first skill. Give it feedback. Let it run. That's the whole system. Free. Open source. Gets better every single day. Save this. Then go install it.
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This guy literally automated his entire social media with one free AI agent. No team. No scheduling tools. No VA. Here's exactly how he did it 👇 The agent is called Hermes. It's free and open source. Here's what it's running on autopilot right now: → Scans Reddit every 24 hours for trending topics → Writes posts based on breaking news → Generates custom images with Nano Banana 2 → Posts directly to X, TikTok, and Instagram with captions and hashtags → Pulls daily analytics and flags what's working → Researches competitors and shows what's getting engagement One post already hit 1,000 views. He didn't write it. He didn't post it. He didn't even check it. Here's the part most people skip: Day one it won't be good. You give it feedback. It writes notes on what went wrong. It improves the skill file. By day 7 it's better. By day 30 it knows your voice. It's called a closed learning loop. Every task makes the next one sharper. The setup takes an afternoon: Install Hermes from GitHub. Connect a free Qwen 3.6 API key from OpenRouter. Add Firecrawl for web research. Create your first skill. Give it feedback. Let it run. That's the whole system. Free. Open source. Gets better every single day. Save this. Then go install it.

Julian Goldie SEO

27,164 Aufrufe • vor 4 Monaten

I just built a Meta Ad Comment Spy in Claude Code that turns every comment on your ads into a voice-of-customer goldmine 🤯 Point Comment Spy at your ad account → it pulls every comment and reply, clusters them into objections, questions, and proof, and writes your next 5 ad angles from real customer language. All inside Claude Code. Perfect for DTC brands and agencies who want ad copy that's already written, buried in the comments on their own ads. If your sharpest hooks come from how customers actually talk, but mining them means scrolling your ad comments by hand, copy-pasting the good ones into a doc, and missing the objections quietly killing your conversion rate... Comment Spy kills the entire loop: → Ranks your ads by spend, so you mine the ones that matter → Pulls every comment AND reply thread through the Meta API → Clusters them: objections, buying questions, desires, proof → Surfaces the exact phrases to drop into your next ad → Flags the conversion leaks hiding in your comments — dead links, broken lead-magnet delivery → Renders the whole thing as a dashboard No manual scrolling. No missed objections. No $99/mo "ad intelligence" subscription. What you get: → Every comment off your top ads, pulled automatically → Clustered into objections, questions, proof, and desires → 5 ad angles written from your real customer language → A dark-mode dashboard, every quote traced to the ad that drew it Built 100% in Claude Code, on the Meta API. I put together a step-by-step playbook showing you how to build Comment Spy yourself: the Meta API setup, the exact prompts, the whole pipeline. Want the playbook for free? > Like this post > Comment "SPY" And I'll send it over (must be following so I can DM)
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I just built a Meta Ad Comment Spy in Claude Code that turns every comment on your ads into a voice-of-customer goldmine 🤯 Point Comment Spy at your ad account → it pulls every comment and reply, clusters them into objections, questions, and proof, and writes your next 5 ad angles from real customer language. All inside Claude Code. Perfect for DTC brands and agencies who want ad copy that's already written, buried in the comments on their own ads. If your sharpest hooks come from how customers actually talk, but mining them means scrolling your ad comments by hand, copy-pasting the good ones into a doc, and missing the objections quietly killing your conversion rate... Comment Spy kills the entire loop: → Ranks your ads by spend, so you mine the ones that matter → Pulls every comment AND reply thread through the Meta API → Clusters them: objections, buying questions, desires, proof → Surfaces the exact phrases to drop into your next ad → Flags the conversion leaks hiding in your comments — dead links, broken lead-magnet delivery → Renders the whole thing as a dashboard No manual scrolling. No missed objections. No $99/mo "ad intelligence" subscription. What you get: → Every comment off your top ads, pulled automatically → Clustered into objections, questions, proof, and desires → 5 ad angles written from your real customer language → A dark-mode dashboard, every quote traced to the ad that drew it Built 100% in Claude Code, on the Meta API. I put together a step-by-step playbook showing you how to build Comment Spy yourself: the Meta API setup, the exact prompts, the whole pipeline. Want the playbook for free? > Like this post > Comment "SPY" And I'll send it over (must be following so I can DM)

Mike Futia

14,397 Aufrufe • vor 1 Monat

Chris Camillo reacts to Grant Cardone saying you can make $1,000,000/year as an AI consultant “I said the same thing on your show and got 4,000,000 views in two days on X.” “He stole it from me. I think he stole it from me. Because what I said was that you can make $500,000 a year basically doing exactly that. I said you go into a company and you say, 'Give me one problem that you have.' And the problem could be as simple as, 'We're missing phone calls every night and because we're missing those phone calls because we close at five or six, they call one of our competitors.' Create an AI widget to answer those calls, figure out what the issue is, immediately send responses out, inform the entire company, so the next morning you already have a lead sheet, you're already engaging with that customer, and you never lose that customer. You could potentially increase a company's revenue by 10 or 15%. Small businesses, HVAC companies, plumbing companies, doesn't matter what it is. You can just go company to company and say, 'I'm going to fix simple problems like that for you.' Put them on a monthly retainer and make half a million dollars a year just having"
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Chris Camillo reacts to Grant Cardone saying you can make $1,000,000/year as an AI consultant “I said the same thing on your show and got 4,000,000 views in two days on X.” “He stole it from me. I think he stole it from me. Because what I said was that you can make $500,000 a year basically doing exactly that. I said you go into a company and you say, 'Give me one problem that you have.' And the problem could be as simple as, 'We're missing phone calls every night and because we're missing those phone calls because we close at five or six, they call one of our competitors.' Create an AI widget to answer those calls, figure out what the issue is, immediately send responses out, inform the entire company, so the next morning you already have a lead sheet, you're already engaging with that customer, and you never lose that customer. You could potentially increase a company's revenue by 10 or 15%. Small businesses, HVAC companies, plumbing companies, doesn't matter what it is. You can just go company to company and say, 'I'm going to fix simple problems like that for you.' Put them on a monthly retainer and make half a million dollars a year just having"

The Iced Coffee Hour

58,940 Aufrufe • vor 2 Monaten

I just built a skill called 'Explain This'. It explains anything to you, tailored to how you learn. It's inspired by Geoffrey Litt's talk at AI Engineer: 'Understanding is the New Bottleneck'. AI can generate infinite code and content, and the scarce thing now is a human who actually understands. How often have you realized you read something but retained none of it? I built '/explain-this' to solve that. How it works: 🧠 It interviews you first. Ten minutes on what you know deeply, how explanations land for you, and what you're aiming to become. That generates your LEARNER.md profile. 📄 Then point your agent at any paper, PDF, or blog post and say "explain this." ✍️ It writes an explainer doc straight into your notes. Obsidian, OpenKnowledge, wherever you think, anchored to what you already know, skipping what you don't need to learn again. ✅ Every explanation ends with a quiz, and it grades your answers. Mastery learning means you don't move on until you prove to yourself you understood. 🔁 Your misses become spaced-repetition cards. Say "quiz me" next week and it re-teaches what didn't stick. Over time it learns your actual weak spots from evidence. Works in Claude Code or any agent that supports skills. Free and open source. Link to GitHub below. 👇
1:09

I just built a skill called 'Explain This'. It explains anything to you, tailored to how you learn. It's inspired by Geoffrey Litt's talk at AI Engineer: 'Understanding is the New Bottleneck'. AI can generate infinite code and content, and the scarce thing now is a human who actually understands. How often have you realized you read something but retained none of it? I built '/explain-this' to solve that. How it works: 🧠 It interviews you first. Ten minutes on what you know deeply, how explanations land for you, and what you're aiming to become. That generates your LEARNER.md profile. 📄 Then point your agent at any paper, PDF, or blog post and say "explain this." ✍️ It writes an explainer doc straight into your notes. Obsidian, OpenKnowledge, wherever you think, anchored to what you already know, skipping what you don't need to learn again. ✅ Every explanation ends with a quiz, and it grades your answers. Mastery learning means you don't move on until you prove to yourself you understood. 🔁 Your misses become spaced-repetition cards. Say "quiz me" next week and it re-teaches what didn't stick. Over time it learns your actual weak spots from evidence. Works in Claude Code or any agent that supports skills. Free and open source. Link to GitHub below. 👇

Dan McAteer

27,296 Aufrufe • vor 21 Tagen

ANTHROPIC OPEN-SOURCED A FREE REPO THAT TURNS CLAUDE INTO A SALES REP, A MARKETER AND A FINANCE LEAD. PEOPLE ARE ALREADY SELLING THIS AS A SERVICE You're not prompting from scratch. You're hiring a department How to set it up: 1. Open Cowork. In the Claude desktop app, open Cowork and go to Customize. You'll see a Browse Plugins button. That's the marketplace 2. Pick your first role. Inside you'll find sales, marketing, finance, legal, data, customer support, product management and more. Each plugin has the skills for that role already baked in, plus the connectors it would use. Install the one you need most 3. Put it to work standalone. It works day one with no setup. Trigger a workflow with a slash command like /sales:call-prep or /data:write-query, paste your notes, and it already knows what good output looks like 4. Connect its tools to supercharge it. Authorize the tools that role uses, your CRM, your analytics, your data warehouse. Standalone is the intern. Connected is the senior hire 5. Build the rest of the team. Install more roles and they work together in one session. Data pulls the numbers, finance reconciles them, marketing turns it into a report. One operator, a full cross-functional team, no payroll This is the same base layer Claude for Legal and Claude for Financial Services are built on. You're getting it for free Full breakdown in my article below. Bookmark this
0:30

ANTHROPIC OPEN-SOURCED A FREE REPO THAT TURNS CLAUDE INTO A SALES REP, A MARKETER AND A FINANCE LEAD. PEOPLE ARE ALREADY SELLING THIS AS A SERVICE You're not prompting from scratch. You're hiring a department How to set it up: 1. Open Cowork. In the Claude desktop app, open Cowork and go to Customize. You'll see a Browse Plugins button. That's the marketplace 2. Pick your first role. Inside you'll find sales, marketing, finance, legal, data, customer support, product management and more. Each plugin has the skills for that role already baked in, plus the connectors it would use. Install the one you need most 3. Put it to work standalone. It works day one with no setup. Trigger a workflow with a slash command like /sales:call-prep or /data:write-query, paste your notes, and it already knows what good output looks like 4. Connect its tools to supercharge it. Authorize the tools that role uses, your CRM, your analytics, your data warehouse. Standalone is the intern. Connected is the senior hire 5. Build the rest of the team. Install more roles and they work together in one session. Data pulls the numbers, finance reconciles them, marketing turns it into a report. One operator, a full cross-functional team, no payroll This is the same base layer Claude for Legal and Claude for Financial Services are built on. You're getting it for free Full breakdown in my article below. Bookmark this

Yarchi

31,798 Aufrufe • vor 2 Monaten

OpenAI member of product staff Alexander Embiricos describes the evolution of "Lord Bottleneck," an internal Codex loop developed by a single staff member that ultimately ended up creating a tight feedback and improvement loop for new user experiences: "This person on the growth team needed to figure out what experiments to run. And they needed to write code to run the experiment. Then they needed to analyze the experiment." "They started using Codex for each separate thing. So they had it run a bunch of analyses, interrogate the data, talk to Codex about the data. Then they would pick an experiment, and ask Codex to write the code. Then they would run the experiment, then ask Codex what the results of the experiment were. Then they would produce a deck." "All steps they were doing individually. They didn't start by saying, 'I'm going to automate this entire thing,' because that's hard and scary. They just started with using Codex to accelerate themselves." "Then, they started connecting all these things together into a giant skill. And one day, they just said [to Codex], 'Why don't you do this every morning?'" "They gave it a name: 'Lord Bottleneck.' Because it's solving the bottlenecks of friction for new users." "Now, every morning, Lord Bottleneck evaluates past experiments, looks at data, proposes some [new] experiments, and offers to the team to run the experiments. The team picks [what experiments to do]. Then Lord Bottleneck is like, 'Ok cool. Here's some code or whatever config that needs to be done,' runs the experiment, and they go and do the same loop the next day." "It's really serious value. I forget the numbers, but it's produced significant company value automatically through Codex."
1:42

OpenAI member of product staff Alexander Embiricos describes the evolution of "Lord Bottleneck," an internal Codex loop developed by a single staff member that ultimately ended up creating a tight feedback and improvement loop for new user experiences: "This person on the growth team needed to figure out what experiments to run. And they needed to write code to run the experiment. Then they needed to analyze the experiment." "They started using Codex for each separate thing. So they had it run a bunch of analyses, interrogate the data, talk to Codex about the data. Then they would pick an experiment, and ask Codex to write the code. Then they would run the experiment, then ask Codex what the results of the experiment were. Then they would produce a deck." "All steps they were doing individually. They didn't start by saying, 'I'm going to automate this entire thing,' because that's hard and scary. They just started with using Codex to accelerate themselves." "Then, they started connecting all these things together into a giant skill. And one day, they just said [to Codex], 'Why don't you do this every morning?'" "They gave it a name: 'Lord Bottleneck.' Because it's solving the bottlenecks of friction for new users." "Now, every morning, Lord Bottleneck evaluates past experiments, looks at data, proposes some [new] experiments, and offers to the team to run the experiments. The team picks [what experiments to do]. Then Lord Bottleneck is like, 'Ok cool. Here's some code or whatever config that needs to be done,' runs the experiment, and they go and do the same loop the next day." "It's really serious value. I forget the numbers, but it's produced significant company value automatically through Codex."

TBPN

80,403 Aufrufe • vor 3 Monaten

I've long believed that the traditional MVP model, "launch fast with the basics," is outdated. I was delighted to see Tuomas Artman, co-founder of Linear, agrees! MVPs work in blue-ocean markets where even the basic tenets of the business concept need validation. The reality is, the vast majority of us are not building in blue oceans. In today’s ecosystem, where established players crowd nearly every sector, "MVPs are dead." You have to make a first impression that not only meets the market's needs but also dazzles from the start. We’re calling it the Minimum Delightful Product - MDP. 🎯 Focus on a Narrow, Well-Defined Market Tailor your product to meet specific customer needs, enhancing its relevance and significantly improving user feedback. ✨ Know What Delivers a Magical Feeling I told a story on the podcast of my YC experience all the way back from 2007. 🤙 Conduct In-depth Customer Interviews Understand potential users' true needs with broad, open-ended discussions, refining your product concept with increasing specificity. ✅ Validate Based on Riskiest Assumptions Identify critical assumptions that could derail your product. For instance, with LinkedIn Sales Navigator, we used pre-sales to validate pricing strategies. 🔒 Refine Through Closed Betas Use closed beta periods to refine the product with detailed feedback from a select group of users, and make sure they are in your target audience. 🚀 Leverage Waitlists Strategically Use waitlists not just for hype but to align your product with an audience that will benefit most from and appreciate your offering, ensuring relevant feedback and strong initial advocacy. Find the link to the full podcast episode below 👇
1:28

I've long believed that the traditional MVP model, "launch fast with the basics," is outdated. I was delighted to see Tuomas Artman, co-founder of Linear, agrees! MVPs work in blue-ocean markets where even the basic tenets of the business concept need validation. The reality is, the vast majority of us are not building in blue oceans. In today’s ecosystem, where established players crowd nearly every sector, "MVPs are dead." You have to make a first impression that not only meets the market's needs but also dazzles from the start. We’re calling it the Minimum Delightful Product - MDP. 🎯 Focus on a Narrow, Well-Defined Market Tailor your product to meet specific customer needs, enhancing its relevance and significantly improving user feedback. ✨ Know What Delivers a Magical Feeling I told a story on the podcast of my YC experience all the way back from 2007. 🤙 Conduct In-depth Customer Interviews Understand potential users' true needs with broad, open-ended discussions, refining your product concept with increasing specificity. ✅ Validate Based on Riskiest Assumptions Identify critical assumptions that could derail your product. For instance, with LinkedIn Sales Navigator, we used pre-sales to validate pricing strategies. 🔒 Refine Through Closed Betas Use closed beta periods to refine the product with detailed feedback from a select group of users, and make sure they are in your target audience. 🚀 Leverage Waitlists Strategically Use waitlists not just for hype but to align your product with an audience that will benefit most from and appreciate your offering, ensuring relevant feedback and strong initial advocacy. Find the link to the full podcast episode below 👇

Sachin Rekhi

58,057 Aufrufe • vor 2 Jahren

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.
5:13

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

The Peel

209,752 Aufrufe • vor 1 Jahr

When I got my first job at a restaurant in Bode Thomas, Surulere, Lagos, I truly understood what it meant to work. I had no clearly defined role. You simply did whatever needed to be done. One minute you were attending to customers, the next you were cleaning, serving, taking orders, or helping in another department. And all of that was for a salary of ₦21,000. At that time, it was necessary for my survival, so I embraced it. Looking back now, I realise that season gave me much more than a paycheck. It taught me discipline, humility, attention to detail, and most importantly, customer relations. It taught me how to treat people, how to solve problems, and how to serve with excellence. Many of the things I know about business and customer experience today were first learnt in that small restaurant in Surulere. Sometimes, the jobs and seasons we are eager to outgrow are quietly preparing us for the future we cannot yet see. #Tadé
4:03

When I got my first job at a restaurant in Bode Thomas, Surulere, Lagos, I truly understood what it meant to work. I had no clearly defined role. You simply did whatever needed to be done. One minute you were attending to customers, the next you were cleaning, serving, taking orders, or helping in another department. And all of that was for a salary of ₦21,000. At that time, it was necessary for my survival, so I embraced it. Looking back now, I realise that season gave me much more than a paycheck. It taught me discipline, humility, attention to detail, and most importantly, customer relations. It taught me how to treat people, how to solve problems, and how to serve with excellence. Many of the things I know about business and customer experience today were first learnt in that small restaurant in Surulere. Sometimes, the jobs and seasons we are eager to outgrow are quietly preparing us for the future we cannot yet see. #Tadé

Tadé Makinwa

93,402 Aufrufe • vor 1 Monat

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
0:24

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,186 Aufrufe • vor 2 Monaten

Scott Belsky on the most common mistake founders make when building a product “Every product has what we call a ‘first-mile experience’, which is the part of your product that the most customers will see. And it’s all drop-off from there. What gets people through the first-mile experience? First, you have to empathize with where that customer is at — whether they’re a consumer or an enterprise customer, in the first 30 seconds of that first mile of your product, I guarantee you, they’re lazy, vain, and selfish. They want to get through it fast. They want to look good to their boss or their friends or feel good about themselves. There needs to be some quick hit of feeling successful in that first mile for them to engage further.” Ironically, the first-mile experience is the last thing many companies and product people will focus on. “Typically it’s the final mile before you launch where you’re like, ‘Oh wait, what should the onboarding be?’ or ‘What should the defaults be?’ That’s like a happenstance conversation towards the end of shipping when in fact that’s the only thing that every customer will ever see. So why not nail that?” Scott takes this point even further: “If you can really just nail the first-mile experience of your product — even if after that it’s all kind of crappy — you’re probably in the top 1% of products out there.” Another interesting point Scott makes here is that optimizing the first-mile experience is something that you’ll have to continually work on because your customers change over time. “Your first cohort of customers that used your product, those were early adopters — and your first-mile experience was nailed for them. But [as you’ve grown] this new cohort of customers that started using your product are no longer early adopters. They’re pragmatists. They’re not coming because they like to test and try new products. They’re coming because their boss told them they had to or they read some blog that said this was the best product in the space, and the first-mile needs to be different for them.” Scott sums his point up as follows: “Spend consistent time, forever on that first-mile experience of your product.” Video source: South Park Commons (2025)
3:25

Scott Belsky on the most common mistake founders make when building a product “Every product has what we call a ‘first-mile experience’, which is the part of your product that the most customers will see. And it’s all drop-off from there. What gets people through the first-mile experience? First, you have to empathize with where that customer is at — whether they’re a consumer or an enterprise customer, in the first 30 seconds of that first mile of your product, I guarantee you, they’re lazy, vain, and selfish. They want to get through it fast. They want to look good to their boss or their friends or feel good about themselves. There needs to be some quick hit of feeling successful in that first mile for them to engage further.” Ironically, the first-mile experience is the last thing many companies and product people will focus on. “Typically it’s the final mile before you launch where you’re like, ‘Oh wait, what should the onboarding be?’ or ‘What should the defaults be?’ That’s like a happenstance conversation towards the end of shipping when in fact that’s the only thing that every customer will ever see. So why not nail that?” Scott takes this point even further: “If you can really just nail the first-mile experience of your product — even if after that it’s all kind of crappy — you’re probably in the top 1% of products out there.” Another interesting point Scott makes here is that optimizing the first-mile experience is something that you’ll have to continually work on because your customers change over time. “Your first cohort of customers that used your product, those were early adopters — and your first-mile experience was nailed for them. But [as you’ve grown] this new cohort of customers that started using your product are no longer early adopters. They’re pragmatists. They’re not coming because they like to test and try new products. They’re coming because their boss told them they had to or they read some blog that said this was the best product in the space, and the first-mile needs to be different for them.” Scott sums his point up as follows: “Spend consistent time, forever on that first-mile experience of your product.” Video source: South Park Commons (2025)

Startup Archive

32,450 Aufrufe • vor 1 Jahr