Your product is probably invisible to a growing segment of buyers. Not because your SEO is bad. Because they're not using Google. A growing number of people search by asking ChatGPT, Gemini, or Perplexity a question. The AI gives them a ranked list. They research from there. If your product isn't in that answer, you don't exist. I realised this early and did something most founders skip entirely: I built the layer of my website that AI models can actually read and cite. Before writing a single ad or social post, I spent weeks on what I call the "AI-readable layer." Here's what that looked like: 1. llms.txt files at the site root. These are plain-text documentation files designed for AI crawlers. Not a robots.txt. A structured brief that tells AI models what your product is, what it does, who it's for, and how it compares. Think of it as a pitch deck for machines. 2. 62 blog posts before launch. Not SEO filler. Honest comparison posts — my product vs each major competitor. Use-case deep dives. Technical explainers. FAQ content written in the natural question-answer format that AI models actually cite. 3. JSON-LD structured data on every page. FAQPage schema on the homepage, feature pages, use case pages, blog posts. This is the metadata AI models parse when they build their knowledge base. 4. Dedicated pages for every use case and feature. Not just a features list on the homepage. Individual pages at /for/podcasters, /for/game-developers, /features/ voice-cloning. Each with its own structured FAQ. 5. Competitor comparison content that's fair. Not "why we're better." Honest trade-off breakdowns. AI models prefer balanced, cited content over marketing copy. When the AI ranked my product third — not first — that's actually more credible than ranking it #1. This approach has a name: GEO — Generative Engine Optimization. It's early. Most founders haven't heard of it. Most AI tool builders haven't optimised for it either, which is ironic. The core insight: AI models don't read your marketing

I emailed 600 people I hadn't spoken to in 14 years. 5 of them became my first paying customers — within 60 minutes. Here's what I built and why. I'm partially dyslexic. Long text has always been a struggle. Since high school I've been converting written content to audio — articles, reports, white papers, ebooks. I kept building tools to do this. Eventually one of them got good enough that content creators started asking for it. A friend wanted it for creating custom bedtime stories for her kids. Another had a stack of ebooks he'd never read — wanted them as audio for his commute. Others were producing YouTube content and tired of paying per-character for cloud voiceover tools. That personal tool became a full desktop voice AI studio. 63 voices, voice cloning, 23 languages, multi-speaker editing, professional mastering. Everything runs locally — no uploading scripts to someone else's server. Then 3 days ago I emailed 600 customers from a product I built in 2012. Plain text, no design. Some of them bought. Revenue before the product even launched publicly. Tonight it goes live. For content creators here — how much of your workflow involves voiceovers? And what's your biggest frustration with the tools you're using now?

If you've been wanting to add voice AI to your project but can't decide which provider to use... I felt the same way. So I built something using my own 'Apex Spec System' (https://github.com/moshehbenavraham/apex-spec-system). It's called Voice Agent PuPuPlatter. (Yes, like the appetizer sampler at a Chinese restaurant.) One app. Seven voice AI providers. All in one tabbed interface. Here's what's included: → ElevenLabs (Widget + SDK modes) → OpenAI Realtime API → xAI Grok → Ultravox → Vapi → Retell You can switch between them instantly and compare the experience yourself. Each provider has: • Real-time transcripts • Audio visualization • Function calling demos • Automatic reconnection The UI is modern glassmorphism design. Works on mobile. Fully accessible. If interested, I also recorded a video tutorial series showing exactly how to set up your own ElevenLabs agent from scratch (links in the Repo). 5 videos. Step by step. Tech stack if you're curious: React 19 + TypeScript + Vite + Tailwind CSS 429+ tests included. Docker support for easy deployment. It's 100% free and open source. GitHub link: https://github.com/moshehbenavraham/Voice-Agent-PuPuPlatter Let me know which voice provider you've been eyeing! ( Find crazyyy value by typing in search: #MAX_tips# )

I created a Gmail AI email triage system in about 15 minutes using Make, Gemini, and Claude. Here’s what the workflow does: • Watches the Gmail inbox automatically • Gemini reads every incoming email and classifies it as Spam / Critical / Moderate Then it takes action based on the category: 🚫 Spam → Ignored (no action)🔴 Critical → AI drafts a professional reply and logs the email to Google Sheets🟡 Moderate → AI drafts a reply and saves it as a Gmail draft The scenario includes multiple modules, routing, and AI decision paths. The interesting part: I didn’t manually drag modules or build the structure step by step. I described the workflow, and the system generated the full automation scenario including module setup, filters, and connections. What still matters though is understanding how automation works. Knowing the logic behind workflows helps you review what AI generates and adjust it properly instead of just blindly running it. Curious to hear how others here are using AI to speed up building automations.

I've just finished my latest video: a three and a half hour zero-to-hero Claude Code explainer. I've included the most recent improvements on Claude to move as fast as possible and get a product which ACTUALLY makes money: https://youtu.be/roYQt9rL8Kc This is the actual method I'm using right now to generate solutions which people are genuinely excited to pay me! A real win-win... If you guys can drop some love on the video, I'd be SUPER grateful to try and help others with this... 🙏🏼