The First Numbers Are Coming In
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The gist
The stream documents preparations for a Shopify app intended to improve merchants’ product visibility in AI answers, with practical work on help-center-grounded support, onboarding emails, scan-flow clarity, progress states, and pre-merge review. Much of the session focuses on launch readiness: developing static and video ads with tools such as Codex, Remotion, and Higgsfield, then using audience feedback to prevent the creative from being mistaken for a shoe ad and to explain AEO within seconds. Luqman discusses testing multiple angles and formats across Meta and Shopify rather than predicting CTR, CPC, or CPI without data; comparing a direct-install link with a scan-and-email funnel; and using predefined success criteria and a unified analytics dashboard. Ad underdelivery, attribution, incomplete data integrations, target-market selection, pricing, and campaign structure remain unresolved or subject to testing. Other discussions cover reaching Gmail’s Primary tab, the ethics of reviews and UGC, avoiding unsupported marketing promises, and personal—not benchmarked—comparisons of AI tools. Audience questions also explore learning programming and marketing through practice, repository-level specialist agents, changing software roles, and building skills or projects with limited resources. No launch success is claimed: the session is presented as an evolving work record whose market results will require weeks of measurement.
171 moments from the stream
- 0:40:22
Reviewing the Visual Composition of an AI-Generated Ad
The speaker discusses how repeated AI logos may shape an ad for shoes, finds the image too muted and in need of cleanup, and proposes trying a similar concept with another generation tool to compare the result.
- 0:41:37
Teaching AEO Through the Ad Itself
The speaker argues that many Shopify merchants understand SEO better than AEO, so the product must educate its market. He explains how an ad can ask merchants to search for their own brand in AI platforms, simultaneously introducing the concept and exposing a visibility problem, while testing different hooks and angles instead of relying on guesses.
- 0:47:39
Testing Ads and Expanding the Winning Ideas
The speaker stresses the importance of creative work and says that finding what resonates with a target market requires testing multiple ideas and angles, then reading the metrics. Strong performers can inspire further variations, while competitor research remains useful but does not replace testing.
- 0:49:11
UGC Authenticity and AI Avatars
The speaker raises an ethical concern about UGC that implies someone has used a product when they have not. He then discusses the shift toward AI avatars and the possibility of using his own likeness and voice to present a written script in a generated setting.
- 0:50:29
Testing Ads Incrementally Without Sacrificing Quality
The speaker describes starting with an informed guess about how customers will receive an ad, generating some initial sales, and then expanding testing. Early versions should still be high quality and free of distracting elements, with feedback guiding refinements or a broader rethink.
- 0:51:56
Using UGC to Educate a Market
The speaker argues that user-generated content can help explain an emerging category as awareness and Shopify competition were increasing ahead of Q4. He also contrasts the quick turnaround of UGC with the coordination time of studio production, presenting this as practical opinion rather than a conclusive test.
- 0:55:16
When Emails Land in Promotions Instead of Primary
The speaker discusses follow-up emails landing in the Promotions tab rather than the primary inbox and worries that users may not find them. He compares this with competitors whose messages reportedly reach the regular inbox and treats discoverability as an important part of the follow-up experience.
- 0:57:58
Reviewing an AI-Generated Promotional Video
The discussion combines audience sensitivity to AI-generated video with concrete production feedback. The speaker says acceptance depends on how familiar the audience is with the technique, asks for a visual artifact on a shoe to be fixed and the vertical scanning line to remain consistent, and recommends locking the current version before experimenting with another tool.
- 1:03:43
Choosing a Target Market Before Launch
The discussion begins by asking whether the intended audience is in the Arab region, Europe, or North America, and notes that the choice had not yet been settled. The speaker suggests considering several English-speaking markets with relatively high purchasing power, treating purchasing power as one factor in targeting.
- 1:11:17
Question About Net Profit in Mizan
While configuring the business side of the Mizan app, the speaker noticed that the displayed net-profit value fluctuated and appeared incorrect, then asked what the metric represented. No confirmed explanation or fix appears on this page.
- 1:12:07
Cleaning Email Sequences and a Click-Tracking Issue
The speaker discussed refining an ad-creative workflow and cleaning up email sequences after messages landed in the Promotions tab, then encountered an interface with no visible save button when disabling click tracking. No confirmed solution was shown.
- 1:16:25
Explaining a Merchant AI-Visibility Project
The speaker described a project for merchants that aims to improve their visibility in AI systems within the AEO space. He said the website should make the product understandable within the first two or three minutes; otherwise, its explanation likely needs improvement.
- 1:18:02
Automating Lead Collection with Scraping Tools
An audience contribution described a workflow for collecting Instagram leads with scraping tools, filters, and AI assistance, while the speaker noted that similar approaches may extend to sources such as Apollo. Based on what he had heard, he also mentioned an unidentified open-source plugin or repository that might assist with scraping and said he planned to explore the area later, without claiming to have tested it.
- 1:22:33
Using Competitor Teardowns to Build a Stronger Product
The speaker explains that installing and closely examining a competing app helped him understand the market and identify weaknesses to avoid in his own product. He presents this as a way to build on existing work, while acknowledging that no product can eliminate every mistake and that further versions depend on results that justify continued investment.
- 1:25:28
Improving the Visibility of AI Engine Logos
Design feedback calls for making the five AI-engine logos at the top of the interface more prominent through a white background and stronger contrast, including displaying the ChatGPT logo in black rather than blue.
- 1:26:38
Pursuing the Work Without Clinging to the Outcome
The speaker discusses focusing on sustained effort while accepting that outcomes are not guaranteed, combining persistence with resilience against disappointment. He recommends preparing a contingency plan in advance in case the primary approach does not work.
- 1:35:47
Do Meta Video Ads Need Multiple Sizes?
A practical question asks whether one video-ad size is sufficient for Meta or whether supplying multiple sizes is best practice; this page does not contain a conclusive answer.
- 1:37:12
Email and SMS Marketing as Specialized Disciplines
The discussion notes that email and SMS marketing form a broad, specialized area within digital marketing.
- 1:41:49
A Task Map for Preparing a Meta Campaign Launch
A launch workflow uses a small map of essential tasks, tracking ad creatives and email marketing while checking completed items before starting a Meta campaign.
- 1:42:57
Reviewing Overnight Development Work Before Merging
A request is made to inspect overnight development work carefully, identify any issues, and determine whether the pull requests are ready before merging.
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