how to build an autonomous landing page engine that self improves from competitor churn: 1/ set up the @firecrawl MCP with cron/routine to run scheduled scrapes. pull from G2. Trustpilot. Reddit. forums. extract EXACT cancellation phrases into a living churn database refreshed weekly. 2/ run multi agent analysis in claude. cluster complaints by purchase friction. run 5 synthetic buyer panels to score candidate copy against current PostHog baselines. test the skeptic. the price checker. the comparison shopper. 3/ auto generate modular page component packages. → a pain inversion headline using an exact 4 word churn phrase → an uncropped UI screenshot showing the specific fix → an objection-handling block tailored to that competitor's flaw. 4/ deploy Next.js edge middleware. personalize the page in real time. match the variant to the incoming ad creative ID. UTM parameters. search intent. PostHog user traits. 5/ connect the @posthog MCP to run HogQL power calculations. set a hard volume floor ie 400 conversions per variant to call a valid 20% lift. default stance is underpowered until the math clears. 6/ auto promote winning variants via feature flags. route 80% of ad spend to the winner. feed the winning copy back into your Meta ad pipeline. treat CRO like a continuous data engineering loop

Last week @sebgoddijn shared Glass, Ramp's internal AI productivity tool, with the world. Nearly a million views later, everyone's asking the same thing: how did you actually build this? Read the full story here: nitter.net/buchan_sm/status/20445… The short of it: we built an app that builds itself A small team of us pretty much vibe coded the entire thing in about a month. The trick was teaching Glass how to improve its own codebase. Turns out if you discipline your AI agents well enough, they grow up to be high-functioning, self-sufficient adults in no time.

Don't hire a finance team. Build an AI CFO instead. Watch Mike Dion build a fully functional AI CFO in N8n that: → Routes questions to the right specialist (FP&A, Accounting, Treasury) → Uses GPT-4.0 for reasoning, cheaper models for execution (saves 30-40%) → Answers in real time with proper context and recommendations → Takes 45 minutes to set up → Costs $5/month to run No coding required. AI writes your system prompts. Full setup tutorial below.