Projects in HerzbergProjects in Herzberg
Cover image for Introducing PropertyPeak
Most estate templates are built
Introducing PropertyPeak Most estate templates are built for volume — endless grids, filter panels, cards that all look the same because nothing about them was ever looked at. PropertyPeak assumes the opposite. It is designed for a brokerage carrying seven or eight properties at a time, where each one deserves its own room on the page. Three things I spent the most time on: The stack. Featured listings stack on scroll with the title and price row pinned to the top of each card, so the ones underneath stay readable instead of becoming a sliver of roof. The numbers. Every price, floor area and yield uses tabular figures. Set two listings side by side and the digits line up. It sounds small until you see a column that doesn't. One motion system. A single easing curve runs through every scroll reveal, with offsets staggered by role — heading, rule, body, figure, button. Nothing was tuned per section, which is exactly why it feels tuned. Six CMS collections, ten pages, three breakpoints, five components. A district filter that actually filters. A contact form with real states. Demo content is a fictional Copenhagen agency, written at real sentence lengths so you can see how the layouts hold before you fill them. Fonts are Erode and Archivo, both free. Nothing is locked behind code components — remix it and go. https://noelmarketing.gumroad.com/l/propertypeak-framer-template
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Cover image for I built an automated German
I built an automated German AI publication with Astro — here are the early results Over the past few months, I’ve been working on a German-language publication focused on artificial intelligence, large language models, AI software, technical explanations, and industry developments. The project is built with Astro (https://noel.marketing/astro-themes/) and supported by a largely automated content and publishing workflow. I wanted to share some of the early performance data because the differences between Google Search, Bing, and AI-powered search have been especially interesting. Google Search Console Across the analyzed period, the website generated: -109 organic clicks -20,233 Google Search impressions -an average CTR of approximately 0.54% The monthly development looked like this: February: 7 clicks and 399 impressions March: 12 clicks and 1,662 impressions April: 16 clicks and 2,088 impressions May: 18 clicks and 4,039 impressions June: 41 clicks and 9,151 impressions July, partial month: 15 clicks and 2,894 impressions The most encouraging signal is the growth in visibility. Google impressions increased from 399 in February to 9,151 in June, which represents growth of more than 22 times within a few months. Clicks also increased from 7 in February to 41 in June. The CTR is still relatively low, but this appears to be mainly connected to ranking positions. Many pages are currently receiving impressions while ranking outside the top results, often between the lower first page and the following pages. That means the next major opportunity is not simply publishing more content. It is improving how existing pages perform. The most important areas are currently: -titles and search snippets -internal linking -search-intent alignment -topical authority -content differentiation -ranking positions -click-through rate Bing organic search Bing generated: -39 clicks -2,347 impressions -an average CTR of approximately 1.66% Bing currently provides much less total visibility than Google, but the click-through rate is noticeably higher. Google offers the larger traffic opportunity, while Bing already appears to send comparatively qualified visitors from a smaller number of impressions. Microsoft Copilot and Bing AI citations The most surprising part of the experiment has been the performance in Bing AI and Microsoft Copilot. AI citations developed as follows: March: 57 citations April: 165 citations May: 212 citations June: 384 citations July, partial month: 113 citations The number of pages cited by Bing AI also increased: March: 36 cited pages April: 72 cited pages May: 82 cited pages June: 115 cited pages By June, content from more than 100 pages had already been used as a source by Microsoft’s AI systems. This is especially interesting because some pages appear to receive AI citations before they achieve strong positions in traditional search. That suggests AI visibility may develop on a different timeline from classic organic rankings. Several individual pages also accumulated dozens or even hundreds of citations, particularly content with a strong practical or educational angle. The formats that currently appear to perform best include: -tutorials -technical explanations -glossary-style content -implementation guides -comparison-focused content -practical problem-solving articles I am intentionally not sharing the exact topic clusters yet, as part of the project is still experimental. The “Crawled — currently not indexed” category is the main area I am monitoring. These pages may need more time, stronger internal links, clearer differentiation, or better alignment with search demand. The 404 pages also need to be reviewed individually to ensure that removed URLs are handled correctly and are no longer referenced internally or included in the sitemap. Lighthouse and technical performance The Lighthouse results were: Performance: 98 Accessibility: 93 Best Practices: 100 SEO: 100 The measured performance metrics included: First Contentful Paint: 1.2 seconds Largest Contentful Paint: 1.5 seconds Total Blocking Time: 0 milliseconds Cumulative Layout Shift: 0 Speed Index: 4.0 seconds Astro has been a very strong fit for this type of content platform. The combination of static rendering, lightweight pages, and limited client-side JavaScript provides an excellent technical foundation. At this point, site speed is not the main bottleneck. The more important challenges are authority, rankings, CTR, content quality, and distribution. What I have learned so far The biggest lesson is that content volume alone is not enough. A scalable publishing system can create a large number of pages, but that does not automatically lead to traffic. The real work starts after publication. Another important observation is that traditional search and AI search do not behave in exactly the same way. Google appears to test many pages gradually before giving them stronger visibility. Microsoft Copilot, by contrast, has already cited a surprisingly broad selection of content. This may indicate that AI search visibility can emerge earlier than meaningful organic traffic. The project is still early, but the direction is encouraging. The strongest signals so far are: Google impressions increased more than 22 times from February to June Google clicks increased from 7 to 41 per month Bing organic CTR reached approximately 1.66% Bing AI citations increased from 57 in March to 384 in June the number of pages cited by Bing AI increased from 36 to 115 several individual pages accumulated a high number of AI citations Lighthouse Performance reached 98 SEO and Best Practices both reached 100 the technical foundation remains fast despite the growing amount of content The main question now is whether growing visibility and AI citations will translate into stronger rankings, more clicks, and sustainable revenue over the coming months. Would you like to see more insights into Astro, SEO, automated publishing, and AI search visibility? Follow me for future updates. You can also find high-quality Astro themes here: https://noel.marketing/astro-themes/
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