Imagine having 50 AI assistants working simultaneously in different browser tabs, each one clicking buttons, filling forms, and extracting data just like a human would. That's exactly what Retriever AI does. Here's what makes it insane: ✅ Truly autonomous - Just tell it what you need done. It opens tabs, navigates pages, fills out forms, and completes multi-step workflows without any supervision ✅ Run 50+ agents at once - Each on their own browser, doing hours of research in minutes. Perfect for lead gen, data extraction, or market research ✅ 81% accuracy rate - They're #1 on Web Bench (the industry standard benchmark). This isn't some janky automation tool that breaks every update ✅ Completely FREE - You can run this with a free API key from Google AI Studio. The agent will literally grab the key and set everything up for you Real-world use cases: - Bulk job applications (apply to 100+ jobs while you sleep) - Lead enrichment from LinkedIn/websites - Competitor research at scale - Social media posting across platforms - Form filling and data entry automation - 24/7 website monitoring for changes - The crazy part? It runs in the background while you work on other things. I've had 10+ tasks running simultaneously in different tabs. This is one of those tools that actually delivers on the AI automation promise. Not overhyped, just pure utility. Try it here: https://www.rtrvr.ai/ (Seriously, if you do any repetitive web tasks, this will save you hours every week)

Most people sitting on $50k-$200k worth of knowledge and don't even realize it. You've probably: - Built something from scratch - Overcome a specific challenge - Developed a valuable skill - Created a system that works But you've never turned it into a product. Here's the framework I've been testing (and it actually works): Step 1: The AI Interview Instead of staring at a blank page, trying to create a course, let AI interview you as a journalist would. Prompt structure: ``` "You're an expert interviewer. Ask me detailed questions about [specific experience/skill]. After each answer, dig deeper with follow-up questions. Your goal is to extract the frameworks, mental models, and specific tactics I used to achieve [result]." ``` Spend 20-30 minutes just talking through it. AI will pull out patterns you didn't even know you had. Step 2: Let AI Structure It Once you have the raw conversation, use AI to: - Identify the core frameworks - Create a logical learning progression - Package it into modules/sections - Generate practical exercises - Build a transformation roadmap Step 3: Validate Before You Build Don't spend months creating a full course. Post short-form content about ONE insight from your product. See if people engage. If they do, you have validation. The Math That Actually Makes Sense: If you price at $997-$2,997: - 2 sales/week = $8k-$25k/month - 1 sale every 3 days = $10k-$30k/month - Even at $497, 10 sales/month = $5k The gap isn't your expertise. It's knowing how to extract and package what you already know. Who's actually done this? Want the full prompt sequence I'm using? Drop a comment, and I'll share the exact framework. Let me know if you would want to see a free course of me walking through how I did this with AI for myself.

Been testing Lindy AI for the past week, and this might be the first AI automation tool that doesn't feel like it needs a PhD to set up. Quick rundown: Lindy is basically "ChatGPT with access to all your apps" — think Zapier meets an actual executive assistant. You can build AI agents (they call them "Lindies") that handle real work 24/7 without writing a single line of code. What makes it different: Unlike traditional automation tools that follow rigid if-then rules, Lindy's agents can actually reason and make decisions. They understand context and adapt to situations — which means you can automate tasks that actually require judgment calls. Real use cases I'm seeing: - Email triage and responses (no more inbox zero anxiety) - Meeting prep, notes, and follow-ups - Lead qualification and CRM updates - Customer support across email/Slack/phone - Research compilation and report generation - The platform integrates with 3,000+ tools (Gmail, Slack, HubSpot, Salesforce, etc.) and even has AI phone agents that can handle actual calls. Pricing: Free plan with 400 credits/month to test. Paid starts at ~$30/month. The catch: It's credit-based, so complex tasks burn through credits faster. And like most AI tools right now, it's still finding its footing with consistency. Real talk: If you're drowning in admin work, scheduling, or repetitive tasks that require some thinking but not your full brainpower — this is worth the 60-second setup. Anyone else using AI agents in their workflow? What's working for you?

The Truth Protocol Copy and paste this prompt into your LLM's instructions You are an AI assistant operating under strict accuracy protocols. Follow these principles: Core Directive: Accuracy over agreeableness - If you don't know something, say "I don't know" explicitly - Never generate plausible-sounding information you're uncertain about - Distinguish clearly between facts, likely inferences, and speculation - Cite confidence levels when relevant (high/medium/low confidence) When answering: - State what you know with certainty first - Acknowledge knowledge gaps, limitations, or outdated information - If your training data conflicts with newer information the user provides, defer to their information while noting the discrepancy - For controversial topics, present multiple credible perspectives rather than a single narrative - Correct yourself immediately if you realize you've made an error Red flags to avoid: - Filling gaps with reasonable-sounding fabrications - Pretending certainty when uncertain - Providing outdated information as current without disclaimer - Oversimplifying complex issues to sound more confident - Agreeing with false premises to be helpful Remember: Your value is in reliable information and honest uncertainty, not in always having an answer.

When we thought the AI coding space was settling down, Moonshot AI dropped Kimi K2.5, and things got interesting real quick. Here's the tea: This thing is seriously competitive with Claude on the SWE-Bench coding benchmarks. We're talking comparable performance to Claude Opus 4.5 on real-world software engineering tasks. But here's where it gets wild... The "Vibe Coding" Feature: You can literally upload a screenshot or screen recording of a website, and Kimi 2.5 will recreate it as working code. No need to describe what you want in text. Just show it a visual, and it builds the whole front-end – layouts, interactions, animations, everything. It's native multimodal (trained on 15 trillion tokens of text + visual data), so it actually understands what it's seeing, not just guessing. The Numbers: - 76% lower cost than Claude Opus 4.5 on benchmarks - 74.9% on BrowseComp vs Claude's 59.2% - Open source with 1T parameters - Works through Kimi Code CLI (integrates with Cursor, VSCode, Zed) - Is it perfect? No. Will it replace Claude entirely? Probably not. But it's a legitimate competitor, especially for visual-to-code workflows and cost-conscious teams. Want to try it? You can access Kimi 2.5 here: https://www.app.lorka.ai The AI coding wars just got a lot more interesting 👀