TimeToPost
AI SEO and GEO to outrank your competitors
AI & Machine Learning·Social·Productivity·Marketing·Communication
About
TimeToPost is an AI SEO and GEO platform that helps you get found in Google and in AI-generated answers. It brings your whole growth stack into one workflow: keyword research to find real demand, Google rank tracking to measure progress, backlinks and outreach to build authority, AI-mention monitoring (GEO) to see whether ChatGPT, Perplexity, and other engines cite your brand, and the AutoSEO blog engine that plans, drafts, and auto-publishes search-ready articles to your own site on a controlled cadence with approval on by default. It also publishes and schedules social posts, with X live today and more platforms coming. What makes TimeToPost different is that you can drive the entire thing from a dashboard or from an MCP integration, so an AI agent can run your SEO and content for you, while you keep a human approval gate before anything ships. Built for solo founders, marketers, and small teams who want to compete on search and AI visibility without hiring an agency or juggling four separate subscriptions.
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FounderPlaybooks.
What other founders did to grow.
2722 dispatches from hundreds of founders, pulled from the week's best podcasts.
we can try to build ML models and then use that to predict users willing to pay and then we can surface the best product they might most likely to buy
ML predicts willingness to pay and serves each user their best-fit SKU
Like Netflix's top picks eliminating scroll paralysis, Tinder's ML dynamically matched each user to the subscription tier most likely to convert. The team replaced one universal paywall with personalized offers — the resulting revenue lift was measured in the multi-millions annually. Shawn frames this as the industry shift from stale to dynamic pricing.
A lot of people despise Google's products for that reason but as we were kind of pointing out it's like it's all trade-offs. You trade off some of that control for reach for the high intent things like that.
UAC trades targeting control for scale — accepting that trade-off is the key mindset shift
Google UAC's biggest drawback is its black-box nature: advertisers cannot force video ads to run in specific placements, and traffic distribution across Play Store search, YouTube, AdMob (in-app network), and mobile web display is opaque. Ashley Black reframes this not as a flaw but as a designed trade-off — you sacrifice placement-level control in exchange for Google's algorithm optimizing across massive scale inventory. Fighting the algorithm by manipulating bids to force traffic to specific placements usually backfires.
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