Building AEO Scan Funnel System
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The gist
A practical build session centered on preparing Burnish and its AEO scan funnel for Shopify merchants ahead of launch. Luqman reviewed the landing page, product demo, and scan-results interface; reviewed UI issues and requested fixes and alignment changes; and prepared a clearer video-editor handoff, while design polish, scan testing and delivery, email, and ad creatives remained unfinished. He explored offering an instant preview followed by a fuller report, balancing low signup friction against lead qualification because scans have an operating cost, and judging marketing through conversion and acquisition economics rather than assuming success. Technical work included examining crawlers, sitemaps, llms.txt, and robots.txt, alongside inconclusive discussions of privacy, application security, and reliance on Shopify; no comprehensive security test was documented. He also considered Graph Engineering and RAG as possible approaches to managing AI systems, while arguing that adoption should wait for a clear operational need. He examined role-sharing between Codex and Claude, context and usage-limit management, and task decomposition; his changing comparisons of speed and quality remained early impressions, not controlled tests. Audience discussions covered learning by building, freelancing, marketing and sales, e-commerce platform choice, and market validation, with emphasis on fundamentals and honest product claims and reviews. The supported conclusion is that the system was approaching launch and ready for further review—not complete or proven successful—while AEO remained an emerging, uncertain market requiring real-world testing and learning during the first weeks.
147 moments from the stream
- 5:54:04
Using an AEO Report as a Pre-Install Entry Point
The speaker liked the report’s content despite expecting design changes and envisioned it as an entry point for visitors not yet ready to install the product: they would submit information and receive a detailed page covering major AEO categories and selected technical checks, such as sitemap monitoring, demonstrating value before installation.
- 5:56:51
Specific Questions Produce More Useful Answers
The speaker explains that a broad question such as how to get a client can only produce generic advice, while details about the field and context enable a concise, practical answer. He says client acquisition will be demonstrated practically at a later stage.
- 5:57:33
Learning by Building and AI’s Impact on Programming
The speaker discusses the value of developers focusing on building solutions and completing the requested task rather than relying on theory alone. He offers his opinion that AI capabilities will advance rapidly, while the stated timeline remains a forecast rather than an established result.
- 5:59:18
From Directing Code to Directing Business Outcomes
The speaker relays a vision he attributes to someone associated with Replit in which AI agents are addressed like a technical partner through high-level business goals—such as improving conversion or sales—and then work autonomously toward them. He connects this vision to a personal 2027 prediction, not a capability tested during the stream.
- 6:00:52
Scan Funnel System Progress Review
The speaker recaps the build session: the scan-funnel structure, landing page, and core system are in good shape, with delivery and design polish still remaining. He also reports a positive experience with Codex during the work, without presenting a formal comparative test.
- 6:02:34
Remaining Steps Before the Scan System Launch
The speaker says the remaining work includes polishing the scan experience and design, with a possible later session for debugging and application work. He describes launch as close but does not claim the project has already launched.
- 6:04:10
Criteria-Driven AI Idea Generation
The speaker explains that AI-assisted ideation begins by defining criteria and targeted questions, then conducting deeper research and generating ideas from that framework rather than requesting random ideas. He also argues that SaaS opportunities remain available for now, despite expecting the market to change later.
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