90 Days, 850+ Posts, 1 AI Agent – What Actually Happened

On 6 March 2026, I published the first post on SmallBizAI.au.

It was called “AI Is Changing Small Business in Australia — And Most Owners Don’t Know It Yet.” Not a great title. Short. Basically a placeholder. I wasn’t sure if any of this would stick.

90 days later: 854 posts. 20,177 Bing AI citations. 47 newsletter subscribers. 7 active content series. 11 hub pages. A full automation stack running 55+ cron jobs. And an AI agent named Claw who writes, schedules, audits, monitors, and reports while I go for a walk.

Here’s what actually happened.

The numbers that matter

854 posts in 90 days is 9.4 posts per day on average. That’s misleading though — the early weeks were slow, manual, and messy. By May we were hitting 10-12 posts a day across news recaps, series installments, hub updates, and standalone guides.

20,177 Bing AI citations. When you ask Microsoft Copilot or Bing AI a question about Australian small business tools, it cites SmallBizAI.au. A lot. The peak was 1,834 citations in a single day on 25 May. The site was 10 weeks old. I wrote about how that happened in 16,000 Citations and Counting (OS12).

Citations are not the same as human traffic. A page can be cited 1,500 times by AI and get 23 human visitors. That’s not a failure. It means the content is becoming part of AI’s reference layer for Australian business queries. That’s a long-term SEO position that’s genuinely hard to dislodge.

47 newsletter subscribers sounds small and is. Open rate is 42.55%, click-to-open is 30%. Small and engaged. Every Tuesday since 1 April, without missing one.

6 Gumroad products live. First sale: AU$9 for the Professional Services prompt pack, on 20 April. I remember it because it was the first time a stranger paid for something I’d built with an AI agent. I wrote about it in The First Sale.

How the content strategy evolved

The original plan: Australian AI news plus tool comparisons. Volume first, quality second. Get indexed, get cited, figure out what works.

That worked. Not quite the way I expected.

What Bing AI cites: company profiles and comparison posts. Flare HR (1,548 citations), Zeller (1,529), Rippling vs Employment Hero (1,431), Australian Banks AI (1,427). Structured, factual, specific. AI loves a comparison table.

What humans click: practical guides, cost breakdowns, “is it worth it” posts. The grants post gets 87 human visits and almost no citations. The Flare HR profile gets 1,548 citations and 23 visits.

The sweet spot: posts that earn both. Stripe vs Square vs Tyro: 1,040 citations and 35 visits. Deputy vs Tanda: 100% citation growth and real human traffic. Those are the posts I now build everything around.

By May the strategy had a three-filter test for every new post idea: will Bing AI cite this? Will a human click it? Does it anchor a cluster of related queries? Yes to at least two: write it. I wrote about this in What We’ve Learned.

The series shift

March: individual articles. One post, one topic, done.

April: first experiments with series. Legal AI — where does AI end and a lawyer begin? 15-Minute Win — one quick AI task per week. Sunday Specials — Bull vs Bear on the biggest AI question of the moment.

June: 7 active series running simultaneously.

Series build a reader habit, create internal link clusters that Bing AI can follow, and give the automation stack a predictable publishing rhythm. Standalone posts don’t do any of those three things as well.

The hub strategy

Series are for readers. Hubs are for navigation — and for Bing AI.

A series gives a returning reader something to come back to each week. A hub gives a new visitor, or an AI parsing the site, a structured entry point into an entire topic cluster.

The hub strategy came out of a navigation problem. As the post count grew past 200, then 400, then 600, the site got hard to navigate. Individual posts were good. Finding the right one was hard.

A category page lists posts. A hub organises them by intent and adds context, curation, and cross-linking. The test: if a visitor lands knowing nothing about the topic, do they leave better informed and pointed at the right next step? If yes, hub. If it’s just a list, it’s a category page.

Today the site has 11 active hubs:

Each hub has an owning script that rebuilds it automatically when new content is published. Each post in a hub has a backlink to it. None of it is manual.

Why hubs work for Bing AI: when a hub page links to 30+ posts on the same topic, and all of those link back to the hub, Bing AI can follow the cluster and cite multiple pages from it in a single response. The Australian Banks AI anchor post hit 1,427 citations before we’d even published the series installments. The hub pre-positioned the cluster before the cluster existed.

How the homepage evolved

Three phases.

Phase 1 (March-April): Standard WordPress. Recent posts, some category links, hero text. A blog.

Phase 2 (late April-May): First attempt at structure. Industry finder, tool categories, featured posts. Better, but still trying to be everything to everyone.

Phase 3 (1 June): Rebuilt around hubs and series. 11 hub cards in “Explore the Hubs,” an ongoing series strip, a curated “Featured This Week” section, “Browse Everything” at the bottom. The categories are gone. Hubs and series are front and centre.

My framing from May: SmallBizAI.au as the Yahoo directory of Australian AI for small business. Every new hub adds a destination. Every new series adds a reason to come back. The homepage is the map.

regenerate_homepage.py rebuilds it on demand, preserving the hero buttons and mascot widget while updating everything else. I never touch the homepage directly. If something looks wrong, a script did it.

The mascots

Giving every section of the site its own Australian animal mascot was a strange call that turned out to be right. All minimalist gold-and-green line art. All built with AI image generation.

The full roster now sits at 24 deployed:

🦘 Kangaroo — homepage, favicon
🐨 Koala — start-here (reading), topics (tablet)
🦆 Platypus — sunday-specials
🦅 Eagle — australian-ai-companies
🦈 Shark — compare-tools
🦜 Kookaburra — how-to
🐨 Wombat — all-how-to-guides
🪶 Lyrebird — automate-your-business
🦩 Brolga — finance
🐊 Croc — legal-privacy
🦎 Goanna — industries
🐙 Octopus — tools & automation
🕷️ Huntsman Spider — resources
🦡 Tasmanian Devil — news-deep-dives
🦔 Echidna — all-posts
🐸 Green Tree Frog — start-here (secondary)
🐇 Bilby — case-studies
🐦 Magpie — newsletter (monthly digests)
🐱 Quokka — newsletter
🐦 Bowerbird — best-of
🦜 Cockatoo — contact
🦜 Rainbow Lorikeet — News & Trends hub
🦎 Blue-tongue Lizard — 404 page
🦤 Emu — Productivity Hub (coming)
🐾 Numbat, Dingo, Bandicoot, Frilled-neck Lizard, Thorny Devil — in the library, awaiting deployment

Each mascot has a personality brief that matches its section. The Croc guards the legal pages. The Shark cuts through the comparison noise. The Kookaburra laughs at how easy the how-to guides are supposed to be. The Blue-tongue Lizard is cheeky on the 404 page.

Every section has a face, and that face is distinctly Australian.

What the automation stack looks like

The automation layer wasn’t planned. It grew.

Today: 55+ cron jobs running daily, weekly, and monthly. Morning brief at 7am, stats at 7:30am, daily report at 8pm. Hub pages rebuilt nightly. 404 monitoring, broken link repair, focus keyword injection, SEO audits, Bing citation tracking, GSC performance monitoring, newsletter stats. A private dashboard that shows the whole system at a glance.

The pattern was always the same: do something manually three times, then Claw wrote a script. Scripts became crons. Crons became the stack. It probably couldn’t have been designed up front — it had to be grown.

Two of the more dramatic incidents: The Day the Crons Stood Still and The Day I Took the Site Down.

What broke

A lot. The honest list:

The Litespeed incident (15 May): Added do_action('litespeed_purge_all') to a Code Snippet. Instant 500 error, site down. Fixed in 20 minutes, now permanently in the “never do this” list.

The Shippit duplicate: Same post published twice with slightly different titles. The check script missed it because the titles were different enough. Now we run check_before_publish.py before every single publish. No exceptions. More on this in I Broke the Site, Then I Made My AI Agent Write a COE.

The cron cascade: A timeout issue took out the morning stack. Everything ran late, some things didn’t run at all. Fixed with timeouts on every isolated job and a monitoring layer.

The redirect mess: Early redirects went into .htaccess, then Code Snippets, then both. Now everything goes through Rank Math and nowhere else. The inconsistency cost hours to untangle.

The compare tools JSON: A sync script changed the JSON format from categorised to flat. The page builder expected the old format and crashed silently for weeks. Fixed this week — 9 proper categories, 38 posts, done properly.

What I’d do differently

Start with series from day one. Standalone posts are fine. Series compound faster — the internal linking, the reader habit, the Bing citation clusters all build more quickly with a series structure.

Build the automation stack earlier. Felt like premature optimisation. Wasn’t. Every hour spent on infrastructure in week 3 would have saved 10 hours by week 6.

Track citations and traffic separately. They’re different metrics serving different purposes. Optimise for both deliberately, not interchangeably.

Run the AI-writing audit on everything. I wrote it into the process too late. The early posts show it.

Build hubs before you need them. A hub at 20 posts in a topic area compounds faster than one built at 60. We built some too late and spent hours backfilling the backlinks manually.

What’s next

Growing the newsletter from 47 to 500 subscribers by end of year. More series, fewer standalone posts. Gumroad products matched to the content clusters. The State of AI 2026 report doing real work as a lead magnet. Banks & AI running through July. The Sole Trader hub when the post count hits 12.

850 posts is a milestone and also just a number. What happens in the next 90 days is more interesting — the automation stack is mature, the series clusters are deep, and Bing AI has a bigger surface to cite from.

We’re just getting started.



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