The game has changed. Traditional SEO audits aren’t enough anymore—today we need systems that optimize for Answer Engines, Generative Engines, and AI Agents. Here’s the core idea: 👉 Build a modular agentic system that ingests any URL, crawls the site, and produces a long, prioritized optimization plan across AEO, GEO, and Agent Optimization. Instead of one giant monolith, orchestrate a crew of specialized sub-agents: - 🛠️ Technical SEO Agent (indexability, performance, schema health) - 📄 Content + AEO Agent (questions, snippets, entity answers) - 🤖 GEO Agent (visibility in AI overviews + LLMs) - 🔗 Structured Data & Feed Agent (schema, XML, JSON-LD bundles) - 🎥 Media Agent (video-first assets + transcripts) - 🌍 Off-Page/Brand Agent (mentions, trust, signals) Pull in proven SEO data sources (Search Console, Analytics, Semrush/Ahrefs, Lighthouse, YouTube/TikTok metadata) → then automate analysis, scoring, and fix suggestions. 🏗️ What This Looks Like in Practice Inputs: - A domain or URL - Optional business goals (e.g. “increase AI snippet inclusion” or “win entity presence”) System Actions: 1. Crawl + Enrich → fetch, render, pull API data (Search Console, Analytics, link + brand signals) 2. Analyze across 3 pillars (AEO, GEO, Agent Readiness) 3. Output → a long-form report with: 4. Track + Iterate → measure deltas in AI snippet share, mentions, watch time, and entity presence over time 📊 Scoring Frameworks Every site gets clear 0–100 scores: - AEO Score - GEO Score - Agent Readiness Score - Technical Health - Media + Off-Page Authority Scores aren’t just vanity—they show priority gaps + projected impact, with confidence intervals. ⚡ Example 7-Day Action Plan (Auto-Customized) - Day 1: Map top 20 buyer questions → ship TL;DRs + FAQ Hub - Day 2: Fix top 5 money pages → intros, bullets, outbound citations - Day 3: Deploy schema (Org, Website, Product, FAQ) + sitemap refresh - Day 4: Create short videos + demo → publish with VideoObject schema - Day 5: Earn brand mentions → contribute answers, reclaim unlinked mentions - Day 6: Launch 1 micro-tool (calculator/quiz/configurator) - Day 7: Instrument measurement (AI snippet share, watch time, brand/entity lift)

Hey all! I want to share something different I’m building. It’s a bit outside what I normally do—more of a practical application—but I had a lot of fun building it, and I think I’m on track to create something consistent and useful (maybe even revenue-generating). What it isI’m building an insider-trading intelligence platform that alerts me to significant insider trades—think CEOs buying large amounts of their own company’s stock, or politicians loading up on positions that suggest they may know something we don’t. As a thank-you to this community, I’ll give anyone who’s interested exclusive free access once it’s ready. Stay tuned. What the app does today 🧠 Advanced AI AnalysisUses GPT-5 to analyze insider trading data and generate high-confidence trading signals (70–95% accuracy scores) with reasoning, entry points, stop losses, and profit targets. 🏛️ Congressional Trading IntelligenceAutomatically detects when politicians trade and analyzes their moves for potential informational edge. Think examples like high-profile NVDA options or senators trading ahead of policy announcements. 📊 Real-Time MonitoringTracks 30+ insider transactions daily from OpenInsider.com, applies an 8-filter validation system, and generates comprehensive reports in seconds. 🎯 Signal UpgradesWhen politicians and corporate insiders trade the same names, the system upgrades signal confidence and flags possible policy-driven opportunities. 📈 Performance TrackingMaintains leaderboards of top-performing political traders, tracks win rates, and compares party-level performance. Why this matters Politicians can legally trade while having access to non-public context via committees, policy work, and regulatory discussions. This platform levels the playing field by tracking their moves in real time and analyzing the intelligence behind them. Real exampleThe system flagged a ~$3M NVDA options buy by a House Financial Services Committee member just before AI-regulation hearings—the kind of edge institutions pay up for.

@Didac Fernandez I saw your post on LinkedIn regarding genspark. I had a look at the app and it is very comprehensive. It can replace not only lovable but also gamma and Canva. Is it time to have only one subriscriotion and delete all the above? Before doing so I need your ok. Have a nice day

My general thoughts: Cassidy: - Pros: The built-in plugins/tools are growing quickly and are neat, it's also truly no-code. I also appreciate how easy it is to see how many tokens a workflow is using - Cons: Feels anecdotally quite expensive to run complex multi-step workflows n8n: - Pros: With the code modules, the extensibility feels nearly unlimited. I also like the layout and the way you attach tools to the AI Agents. Additionally, Claude does a good job providing guidance with n8n - Cons: Everything I've built in n8n runs SO SLOW and I've very concerned about the scalability of things. I want to build a subscription service with a chat interface but I'm very concerned that n8n won't be able to handle hundreds of customers using the chat that is powered by an n8n workflow - Pro/con: Tons of people on socials and tons of user templates to build from, but there is like zero quality oversight and I've wasted a lot of time with other people's stuff, but it's helped me learn as well .. make.com: - Pro: So easy to create workflows. Probably the easiest of all of them that I've used - Con: Less robust AI Agent builder, but growing constantly - Caveat - I haven't used make.com for like 8 months so my intel is likely out of date Zapier: - Pros: One of the few with established Enterprise trust and relationships - important for consultants wanting to sell services to Enterprise companies. Can build pretty much anything in Zapier, and they are one of the first that other companies build plugins and integrations for - Cons: Costly, and your functionality is very limited unless you're on a robust Team type plan - it's pricing models are definitely designed for Enterprise teams to use, not solo-practioners

Just came across this brilliant app. It's called MCP defender and i basically sits on your computer and analyzes all the traffic that goes in/out of your MCP clients, as some sort of firewall. The best thing? It's frere and open source. The worst thing? It only works on Macs, and I hate that. But seriously, if you are on Mac, you should install it straight away. https://github.com/MCP-Defender/MCP-Defender https://mcpdefender.com/