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Ship features while you sleep with 'Ralph Wiggum' - Step 1: Write a detailed PRD (spend an HOUR on this) - Step 2: Convert it to small, atomic user stories - Step 3: Add clear acceptance criteria for each - Step 4: Loop your AI agent through each story... show more

The Startup Ideas Podcast (SIP) 🧃

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Here's how to actually start your solopreneur journey in 2026: Step 1: Pick a skill (design, copywriting, dev) Step 2: Freelance to learn what people pay for Step 3: Productize it ($5K for 5 app screens) Step 4: Spot the ONE feature clients keep asking for Step 5: Build a microSaaS around it Step 6: Use AI agents to fulfill delivery THE KEY: Each step compounds I'm rooting for you. Go ship something in 2026

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Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.

Most people trying to build a vertical AI business start by building the agent. That's the mistake. Here's the 5-step order that actually works: Step 1) Find a boring pain point - Pick a workflow you already know from your job. - That's your unfair advantage. Step 2) Map the workflow - Literally draw it out. - Every step, every handoff. Step 3) Do the job as a service - Manually. - For real clients. - Before any code. Step 4) Document the edge cases - This is where the real IP lives. - You only find them by doing the work. Step 5) Add vertical agents to replace each step - Now you have proprietary data and a workflow you own. Skip steps 1–4 and you're just building a demo.

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How to set up weekly SEO monitoring with Openclaw: Step 1: Write a custom skill Step 2: Configure cron Step 3: Wire up the Slack webhook Step 4: Debug it when it breaks ... How to set up weekly SEO monitoring with Helio: Step 1: Tell your AI teammate what you want ✓ Done. Reports go to Slack, email, wherever your team is. And it gets smarter every week. Open-source is for builders. Helio is for teams. Try Helio Now: Join Our Discord:

How to set up weekly SEO monitoring with Openclaw: Step 1: Write a custom skill Step 2: Configure cron Step 3: Wire up the Slack webhook Step 4: Debug it when it breaks ... How to set up weekly SEO monitoring with Helio: Step 1: Tell your AI teammate what you want ✓ Done. Reports go to Slack, email, wherever your team is. And it gets smarter every week. Open-source is for builders. Helio is for teams. Try Helio Now: Join Our Discord:

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Sleep while your AI assistant books appointments. This N8N automation will change your sales process forever. 🔥 What you'll learn: • How to build an AI voice agent for appointment confirmation • Step-by-step calendar to outbound dialer connection • Fast response configuration with GP4 mini realtime • Complete template for free download • Easy implementation guide for non-coders Save this video - it will automate your entire appointment booking process while you focus on closing deals! 💯 Want the SOP? DM me.

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PhD Students – How to automatically identify 90% of the issues in your research paper before you submit it to a journal? This is possible through manual or automated paper review. First, let’s understand the following. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰? Paper review is a process in which subject matter experts evaluate your paper based on the following criteria: 1. Significance – Is this research important? 2. Novelty – Is this research new? 3. Methodology – Is this research carried out in the correct way? 4. Verifiability – Can other researchers verify this research? 5. Presentation – Is the research presented in the right way? 𝐖𝐡𝐲 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧? ➟ Identify the critical issues in your paper ➟ Fix those issues to increase the chances of your paper acceptance 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 “𝐬𝐞𝐥𝐟-𝐫𝐞𝐯𝐢𝐞𝐰” 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫? Paperpal just launched an amazing feature – AI Review. Using this feature, you can get instant self-feedback. This feature will help you in the following ways. ➝ Check for gaps in your logic ➝ Get feedback on the structure and flow of your writing ➝ Review your research questions ➝ Identify opportunities to strengthen your paper ➝ Increase the chances of your paper acceptance Here is a step-by-step process for using AI Review feature. Step 1: Go to and login. Step 2: Open an existing document or make a new document Step 3: Go to the right-side bar and click on checks | AI Review. Step 4: For this feature to work there should be more than 150 words. Step 5: Copy and paste your paper. Step 6: Now go to the right side and check the prompts Step 7: With these prompts, you will evaluate your paper. Step 8: You will find various prompts e.g., suggest writing feedback, check flow and structure etc. Step 9: You can select a prompt from the existing prompts or write your custom prompt and execute Step 10: Paperpal will generate feedback as per the prompt. Step 11: Read through the feedback and save it for further use. Use other specific prompts for tailored feedback. Step 12: This way you can evaluate various aspects of your paper yourself. This is a very customized and efficient way of automatically reviewing your paper. You can also go one step further to work on the feedback and improve your paper based on suggestions. Please note that AI Review feature does not replace human or expert reviewers in any way. This feature only aims to provide you with quick self-feedback. Try the AI Review feature of Paperpal. Paperpal link:

PhD Students – How to automatically identify 90% of the issues in your research paper before you submit it to a journal? This is possible through manual or automated paper review. First, let’s understand the following. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰? Paper review is a process in which subject matter experts evaluate your paper based on the following criteria: 1. Significance – Is this research important? 2. Novelty – Is this research new? 3. Methodology – Is this research carried out in the correct way? 4. Verifiability – Can other researchers verify this research? 5. Presentation – Is the research presented in the right way? 𝐖𝐡𝐲 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧? ➟ Identify the critical issues in your paper ➟ Fix those issues to increase the chances of your paper acceptance 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 “𝐬𝐞𝐥𝐟-𝐫𝐞𝐯𝐢𝐞𝐰” 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫? Paperpal just launched an amazing feature – AI Review. Using this feature, you can get instant self-feedback. This feature will help you in the following ways. ➝ Check for gaps in your logic ➝ Get feedback on the structure and flow of your writing ➝ Review your research questions ➝ Identify opportunities to strengthen your paper ➝ Increase the chances of your paper acceptance Here is a step-by-step process for using AI Review feature. Step 1: Go to and login. Step 2: Open an existing document or make a new document Step 3: Go to the right-side bar and click on checks | AI Review. Step 4: For this feature to work there should be more than 150 words. Step 5: Copy and paste your paper. Step 6: Now go to the right side and check the prompts Step 7: With these prompts, you will evaluate your paper. Step 8: You will find various prompts e.g., suggest writing feedback, check flow and structure etc. Step 9: You can select a prompt from the existing prompts or write your custom prompt and execute Step 10: Paperpal will generate feedback as per the prompt. Step 11: Read through the feedback and save it for further use. Use other specific prompts for tailored feedback. Step 12: This way you can evaluate various aspects of your paper yourself. This is a very customized and efficient way of automatically reviewing your paper. You can also go one step further to work on the feedback and improve your paper based on suggestions. Please note that AI Review feature does not replace human or expert reviewers in any way. This feature only aims to provide you with quick self-feedback. Try the AI Review feature of Paperpal. Paperpal link:

Faheem Ullah

15,270 Aufrufe • vor 1 Jahr

Use this ai workflow to get 1000s of followers/day (Steps and tools included) Step 1: Find 3 posts that went massive Step 2: Ask Claude to break down WHY they worked Step 3: Extract the psychological patterns Step 4: Build prompts that interview YOU for context Tools: Claude 4.1 extended thinking (not basic GPT) Cost: $20/month Time: 10 min setup → infinite personalized content The KEY: Context is KING You have to feed it YOUR voice parameters You have to give it audience psychology You're one prompt away from automating your content creation like a pro Stop using garbage prompts Start reverse-engineering what works Use this playbook

Use this ai workflow to get 1000s of followers/day (Steps and tools included) Step 1: Find 3 posts that went massive Step 2: Ask Claude to break down WHY they worked Step 3: Extract the psychological patterns Step 4: Build prompts that interview YOU for context Tools: Claude 4.1 extended thinking (not basic GPT) Cost: $20/month Time: 10 min setup → infinite personalized content The KEY: Context is KING You have to feed it YOUR voice parameters You have to give it audience psychology You're one prompt away from automating your content creation like a pro Stop using garbage prompts Start reverse-engineering what works Use this playbook

The Startup Ideas Podcast (SIP) 🧃

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karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

GREG ISENBERG

435,933 Aufrufe • vor 4 Monaten

I've built a team of AI agents to run my business. Here's the exact order I follow: Step 1: Pick a role (executive assistant, head of marketing, etc.) Step 2: Open Claude Code and interview your way into context files "Ask me questions to build this out" Step 3: Build skills through daily use - Daily briefs - Meeting prep - Custom workflows Step 4: Get it all working reliably first. Step 5: THEN migrate to autonomous harnesses like OpenClaw. Most people jump straight to Step 5. That's why their agents break. Start with one agent. One role. Get it right. Then scale

I've built a team of AI agents to run my business. Here's the exact order I follow: Step 1: Pick a role (executive assistant, head of marketing, etc.) Step 2: Open Claude Code and interview your way into context files "Ask me questions to build this out" Step 3: Build skills through daily use - Daily briefs - Meeting prep - Custom workflows Step 4: Get it all working reliably first. Step 5: THEN migrate to autonomous harnesses like OpenClaw. Most people jump straight to Step 5. That's why their agents break. Start with one agent. One role. Get it right. Then scale

The Startup Ideas Podcast (SIP) 🧃

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RunwayML is not limited to 4sec! Here's how you can bypass the limitations 👇 Step 1: Generate your video with Runway Step 2: Get ffmpeg Step 3: Grab the last frame of your video using this command ffmpeg -sseof -3 -i [input_file_name.mp4] -update 1 -q:v 1 [output_file_name.jpg] Step 4 : Use the image output as the input for your next RunwayML generation Step 5: Stich them together in your favorite editing software Step 6: This is the most important step! Like, RT and follow for more good stuff, thanks! ♥️🤗 It's not perfect, but it's a start, I added 5 clips in total in the video below, just so you can see how it evolves over time. With a lot of generations eventually it can look decent for 2-3 clips. Voice by ElevenLabs Music and SFX by Epidemic Sound Sound mixing with Ableton

RunwayML is not limited to 4sec! Here's how you can bypass the limitations 👇 Step 1: Generate your video with Runway Step 2: Get ffmpeg Step 3: Grab the last frame of your video using this command ffmpeg -sseof -3 -i [input_file_name.mp4] -update 1 -q:v 1 [output_file_name.jpg] Step 4 : Use the image output as the input for your next RunwayML generation Step 5: Stich them together in your favorite editing software Step 6: This is the most important step! Like, RT and follow for more good stuff, thanks! ♥️🤗 It's not perfect, but it's a start, I added 5 clips in total in the video below, just so you can see how it evolves over time. With a lot of generations eventually it can look decent for 2-3 clips. Voice by ElevenLabs Music and SFX by Epidemic Sound Sound mixing with Ableton

Alex Patrascu

535,642 Aufrufe • vor 3 Jahren

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

Kimi CEO Zhilin Yang: "The biggest mistake in agent systems - people trying to build one smarter agent we didn't make one smarter - Kimi made a thousand: one brain, a thousand hands" here's how: step 1 → stop polishing one agent, split the task and run many in parallel - that's the real ceiling-break step 2 → give each agent a long context so it can run for days, not seconds - long context is what lets an agent run long step 3 → give each agent a strong prior, so agent-RL can search for a good solution instead of starting from zero step 4 → don't just add more data - make each token count. they hit the data wall, so they made every token 2x smarter step 5 → don't scale one brain, scale the swarm - many agents working in parallel beat one agent thinking harder he just dropped Kimi K3 - and this 40-min workflow is the best on how to actually use it watch & bookmark - then read the article on saving on agents ↓

codila

60,826 Aufrufe • vor 12 Tagen

This video will teach you how to build an AI trading bot that actually works. So simple, literally anyone can execute. 0:00 Intro 1:04 Trading bots explained 3:32 Why most bots fail (avoid these mistakes) 5:23 Your options for deploying a bot 9:12 Set up (Step 1) 12:46 Engineering a strategy (Step 2) 16:03 Giving your bot memory (Step 3) 17:48 Building cloud memory 19:04 Testing with paper money

This video will teach you how to build an AI trading bot that actually works. So simple, literally anyone can execute. 0:00 Intro 1:04 Trading bots explained 3:32 Why most bots fail (avoid these mistakes) 5:23 Your options for deploying a bot 9:12 Set up (Step 1) 12:46 Engineering a strategy (Step 2) 16:03 Giving your bot memory (Step 3) 17:48 Building cloud memory 19:04 Testing with paper money

Miles Deutscher

109,847 Aufrufe • vor 12 Tagen

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning for RAG / analytics - Chunking, metadata, indexing - Observability and data quality checks Step 2: Learn how model providers work - APIs, limits, retries, rate limits - Latency and cost trade-offs - Privacy and data handling constraints Step 3: Prompting as an engineering discipline - Prompt templates - Versioning + change logs - Structured outputs (JSON schemas) Step 4: Evaluation for generative systems - Golden sets - Automated checks (format, factuality signals, regressions) - Human review loops when it matters Step 5: Tool integration + agentic flows - Function/tool calling - Guardrails (allowed tools, timeouts, fallbacks) - Tracing what the agent did and why Step 6: Build 1-2 small projects end-to-end Examples: - RAG assistant over internal docs - Ticket triage bot with tool calls + evals For an experienced data engineer, this is usually not a multi-year shift. With focused effort + hands-on work, ~3-4 months can be enough to become interview-ready. If you're making this transition, what part feels most unclear: evals, prompting, or agents?

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning for RAG / analytics - Chunking, metadata, indexing - Observability and data quality checks Step 2: Learn how model providers work - APIs, limits, retries, rate limits - Latency and cost trade-offs - Privacy and data handling constraints Step 3: Prompting as an engineering discipline - Prompt templates - Versioning + change logs - Structured outputs (JSON schemas) Step 4: Evaluation for generative systems - Golden sets - Automated checks (format, factuality signals, regressions) - Human review loops when it matters Step 5: Tool integration + agentic flows - Function/tool calling - Guardrails (allowed tools, timeouts, fallbacks) - Tracing what the agent did and why Step 6: Build 1-2 small projects end-to-end Examples: - RAG assistant over internal docs - Ticket triage bot with tool calls + evals For an experienced data engineer, this is usually not a multi-year shift. With focused effort + hands-on work, ~3-4 months can be enough to become interview-ready. If you're making this transition, what part feels most unclear: evals, prompting, or agents?

Alexey Grigorev

13,694 Aufrufe • vor 4 Monaten

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → put everything that never changes at the top - tools, then system, then docs step 2 → mark where the static part ends. everything above it gets cached step 3 → one stray space breaks it - and you pay full price again step 4 → the cache dies in 5 min - every read resets the clock step 5 → cached tokens don't count against your rate limits. free headroom most people never touch this - it pays for itself on day one watch & bookmark - this 1-hour brilliant course ↓

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch: they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question the input dropped from 108,000 tokens to 11 here's how: step 1 → put everything that never changes at the top - tools, then system, then docs step 2 → mark where the static part ends. everything above it gets cached step 3 → one stray space breaks it - and you pay full price again step 4 → the cache dies in 5 min - every read resets the clock step 5 → cached tokens don't count against your rate limits. free headroom most people never touch this - it pays for itself on day one watch & bookmark - this 1-hour brilliant course ↓

codila

540,094 Aufrufe • vor 16 Tagen