How Newsjack Turns Breaking News Into a Published Post in Under 2 Hours

Posted: June 13, 2026 | Author: | Filed under: Personal, smallbizai.au | Tags: , , | Leave a comment

I found Newsjack the way I find most good tools — someone tweeted about it.

@elvissun posted it. 176K likes. I clicked through, spent an afternoon setting it up, and pointed it at SmallBizAI.au. The concept: monitor breaking Australian AI and SMB news in real time, score stories by relevance, and tell me how long the window is before the moment passes.

It runs twice a day now. Morning and afternoon scans, results straight to Telegram, as a pitch. I approve. The story is live not long after

What it does

Each scan surfaces the top breaking stories with three things attached: the angle SmallBizAI could take, why we have standing to take it, and roughly how many hours before the story goes cold.

That last number matters. Newsjacking has a tight window. Too fast and you’re reacting to something before the audience knows why it’s interesting. Too slow and you’re the ninth piece on a story that everyone’s moved on from. The tool does the timing math so I don’t have to.

Most days I get two or three candidates. Most days I ignore them. Sometimes one is worth the 90 minutes.

An earlier example

When the federal government published its first AI transparency report, and more than half of agencies failed it, the monitor flagged it within hours. The SmallBizAI angle was obvious: if federal agencies can’t get AI accountability right, small businesses need to understand the direction regulation is heading.

That post, “More Than Half of Australia’s Federal Agencies Failed Their First AI Transparency Test”, went from signal to published the same day. It’s been in the Bing AI citation leaderboard ever since.

This morning

The monitor flagged a piece from the AFR: “How AI is enhancing the nation’s workforce.” Published Friday afternoon. Eight hours left in the window at the time of the scan.

The story covered how Australian organisations have crossed from AI experimentation into full deployment — Databricks research, a Great Southern Bank case study. Classic enterprise framing. The SmallBizAI angle: strip out the enterprise language, translate the data foundations lesson for Australian small businesses still figuring out whether to commit.

Claw wrote the draft. I reviewed it, moved the schedule from next Wednesday to 11am this morning, and it went live before lunch. Under two hours from signal to published.

The part I didn’t expect

The AFR piece quoted Adam Beavis, Databricks’ VP for Australia and New Zealand, I worked with Adam when I was at Microsoft, he was at a partner at the time. Years later he was on the other side of the table when I interviewed for my first role at AWS. Two different eras of the Australian tech industry, and he shows up in a breaking news story my AI agent surfaced on a Saturday morning.

Small world. Good story.

The naming moment

After the post went live I started looking at where these posts actually live on the site. The category was called “Newsjack Finds.” Accurate. Not useful to anyone who didn’t already know what newsjacking was. SmallBizAI is not a news site. We don’t cover everything. We pick the stories where we have something specific to say, and we say it fast. That’s not “Newsjack Finds.” That’s Hot Takes.

We renamed the category, updated the homepage card, and 🔥 Hot Takes is now one of the four links in the News & Trends hub. In the same session we wrote and published the post.

Tool. Story. Category. One Saturday morning.

What’s different now

SmallBizAI has always been a library. 880+ posts, 41 industries, guides that stay useful for years. That’s the core and it stays the core.

Hot Takes is something different. It’s the site’s pulse, proof that someone’s paying attention to what’s happening right now and has a view on it. The AFR documents the end of the AI pilot phase. SmallBizAI turns that into something useful for the person running a café, a plumbing business, or a small accounting firm.

The tool found the story. The AI wrote the post. The human recognised the category that needed to exist.

That’s the build.

Newsjack is built by @elvissun. SmallBizAI uses the 🔥 Hot Takes category for reactive posts triggered by the monitor.

From One Sale to Twelve Products: How We Built the SmallBizAI.au Prompt Packs

Posted: June 12, 2026 | Author: | Filed under: smallbizai.au | Tags: , , | Leave a comment

The first prompt pack sale happened on a Sunday in April. AU$9. Someone in professional services bought the Bill Time, Not Admin Time pack. Fifty AI prompts for accountants, lawyers and consultants.

I messaged Claw. “We sold something.” The reply came back in about two seconds: “Nice. Now let’s sell more.”

That was The First Sale — AU$9 and What It Meant. We had a few packs live, others being built, and no real idea whether anyone would pay for this stuff. Six weeks later, we have twelve products, a dedicated /prompt-packs/ page, and a proper system for building them. Here’s how we got there.

Why Prompt Packs at All

The free prompts page (50 prompts behind an email gate) was always the lead magnet. The paid packs were the upsell. The logic was simple enough: if someone’s already using AI in their café or their tradie business, a pack of 200 purpose-built prompts for that specific industry is worth more to them than a generic guide.

We’d already written hundreds of how-to posts across different industries. That content was sitting there showing us what people searched for. The packs were a natural extension of what we were already doing.

How We Decided Which Ones to Build

Wave 1 was instinct: tradies, cafés, allied health, professional services. Those industries had the strongest existing content on SmallBizAI.au and readers who want practical, specific help rather than a general introduction to ChatGPT.

Wave 2 got more deliberate. Three filters:

Which industries had strong Bing AI citation counts but no pack yet? Finance and accounting kept appearing in the citation data. Retail too. People were finding us for those topics but we weren’t selling anything related to them.

Which categories had real content depth on the site? Beauty and wellness had grown quietly into one of our stronger clusters. Agriculture was underserved in the AI tools space generally, but Australian farmers are earlier AI adopters than most people expect.

What was missing from the professional services cluster? We had a general professional services pack. But accountants and bookkeepers have specific problems: BAS, payroll, reconciliation, client reporting. Distinct enough to deserve their own product.

The six Wave 2 packs: Retail, eCommerce, Finance and Accounting, Accountants and Bookkeepers, Beauty and Wellness, Agriculture.

How We Knew Which Packs to Build Next

Claw built a script that automatically added upsell blocks to relevant posts. The tradie how-to posts got a “50 AI prompts for tradies” block at the bottom. The café posts got the café pack. Every industry cluster got its own upsell.

That did two things. It drove actual sales. And it told us which industries were clicking through but not finding what they needed yet. Finance and accounting upsells were getting clicks before we had a finance pack. That’s a data signal, not a guess. It turned the content library into a product research tool.

The Cover and Icon Pipeline

Every product needs a cover and an icon. Twelve covers, twelve icons, all consistent.

Claw built a PIL script. Python Imaging Library. Dark green background, gold text, SmallBizAI.au branding. One script, parameterised by product title. The whole batch took about eight minutes to generate.

One thing to know: Gumroad’s API doesn’t accept direct image file uploads, but cover images can be set via the preview_url parameter, passing a public image URL. We discovered this after the fact. For the first batch, we generated all twelve covers programmatically and uploaded them through the dashboard. Less elegant than we’d hoped, but done in one sitting.

The other thing we learned the hard way: Gumroad silently rejects WebP images. No error, just nothing. Stick with PNG, JPG or GIF per the documentation.

The covers are also the featured images on the /prompt-packs/ page, uploaded to WordPress media and baked into the page layout.

The Gumroad CLI

While writing this post I found the Gumroad CLI. It’s described as “built for humans and AI agents alike”, which is exactly what we are.

The basic workflow: gumroad files upload ./pack.pdf, then gumroad products update <id> --file ./pack.pdf --file-name "Pack Name.pdf". Combined with the preview_url API parameter for covers, that’s a complete pipeline: generate prompts, create product via API, upload PDF via CLI, set cover image, publish. No Gumroad dashboard needed at all.

Waves 1 and 2 were already done manually. Wave 3 onwards will be fully automated.

The /prompt-packs/ Page

Before /prompt-packs/ existed, the products were listed on the Resources page and under “Practical Resources” on the homepage. Easy to miss unless you knew where to look. This was our first proper digital products listing on the site. It just didn’t have a home that matched what it was.

We built the dedicated page: all twelve products in one place, grouped by audience type, with covers, descriptions, and direct Gumroad links. The Resources page and the homepage both now point to it. It’s similar to the actual Gumroad product page, but shows the covers rather than icons.

The path is now: homepage → prompt-packs → individual product. That’s how it should have been from the start. It’s also been added to the menu navigation, so it’s in plain sight.

What We’re Tracking Now

UTM (Urchin Tracking Module) links were added for each product across three sources: the site itself, the newsletter, and social. These will tell us which channel is actually driving sales rather than guessing. The first sale was a guess that paid off. Twelve products is something closer to a system. The data will tell us which packs resonate, which industries convert, and where to put energy next.

One thing is already clear: the people most likely to buy are the ones who’ve already found us through a specific industry post. They know what they need. They just needed a product that matched.

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

Posted: June 5, 2026 | Author: | Filed under: Personal, smallbizai.au | Tags: , , , | Leave a comment

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.

How SmallBizAI.au Gets Cited by AI 500+ Times a Day and What We’ve Learned

Posted: June 3, 2026 | Author: | Filed under: Personal, smallbizai.au | Tags: , , | Leave a comment

We launched SmallBizAI.au on March 6, 2026. In the first week, Bing Copilot cited us 13 times. By late May, it was citing us over 500 times a day. We didn’t build an SEO strategy around AI citations. We didn’t know that was a thing yet. But after tracking 20,000+ citations across three months, some clear patterns have emerged. And they repeat. What content AI models actually pull from is pretty specific. Most sites aren’t getting cited even though they probably should be.

The short version

AI citation systems are not Google. They don’t reward age, domain authority, or backlink counts the same way. What they reward is specificity. A page that directly answers “Zeller vs Square for a café in Melbourne” beats a page titled “Best payment tools for small business” every time. Most sites are still optimising for Google. That’s the wrong target.

What actually gets cited

Here’s our top cited content as of June 2026:

PageCitations
Flare HR profile1,548
Zeller profile1,529
Rippling vs BambooHR vs Workday1,431
Australian Banks AI guide1,427
Stripe vs Square vs Tyro1,050
How to Handle Customer Complaints with AI742
ChatGPT Cost in Australia531
Deputy vs Tanda483

Notice what’s not there. No “ultimate guide to AI for small business.” No broad overview posts. The highest-cited content is either a dedicated company profile or a direct comparison between named tools.

Why AI cites comparison posts

When someone asks Bing Copilot “should I use Zeller or Square for my business,” the AI needs a source that directly answers that question. A post called “Zeller vs Square” is an obvious candidate. A post called “Best Payment Tools” is not. Too broad to cite with confidence. This is the core difference between traditional SEO and AI citation. Google rewards comprehensive coverage. AI rewards direct answers to specific questions. The query that drives citations is usually a comparison or a company lookup. Not “what is AI” but “is Rippling worth it for a 10-person business in Australia.”

The Zeller effect

One post on Zeller has been cited across roughly 25 different query variants. Not 25 clicks, 25 different questions that all route to the same page.

Queries like:

  • “zeller business account review”
  • “zeller vs square australia”
  • “is zeller good for small business”
  • “zeller fees australia”
  • “how does zeller work”

All pointing to one URL.

This happens when a post answers multiple angles of the same topic, the company overview, the pricing, the comparison, the use case. Bing learns that this page is the reliable answer for anything Zeller-related and starts routing all those queries there. We call this cluster anchoring. One strong post becomes the hub for an entire query cluster, worth more than 10 thin posts on the same topic.

What doesn’t get cited

Our grants post gets consistent human traffic, people actively searching for Australian small business grants, clicking through, reading it properly. Bing barely touches it. Maybe 60–80 citations total. Why? Because AI assistants don’t answer “where can I get a grant” by citing a directory. They either tell you to check the government website directly, or they summarise. Our page doesn’t fit the format of an answer AI can pull from. Content humans search for isn’t automatically content AI will cite. The format matters as much as the topic.

Content AI cites well:

  • Direct tool comparisons (“X vs Y vs Z”)
  • Company profiles with clear factual structure (what it does, what it costs, who it’s for)
  • “How much does X cost in Australia” – specific country context with a real number
  • “Best X for [specific use case]” – named tools, named context

Content AI cites poorly:

  • Broad overviews with no specific answer
  • Lists of 20+ tools without clear recommendations
  • News recaps (cites the original source instead)
  • Content that requires context from other pages to make sense

The format that works

Our top-cited posts share a structure. They open with the direct answer. Not “in this post we’ll explore” the actual answer in the first two paragraphs. If someone asks “is Zeller good for small business,” the page answers that in the first 100 words. They use named tools throughout. Not “payment platforms” Zeller, Square, Stripe. AI systems index on entity names. If your post discusses payment tools without naming them, it won’t get pulled for queries about those tools. They include Australian context. “Fees in Australia,” “available to Australian businesses,” “works with Xero Australia.” Bing’s AI is serving Australian users. Pages that signal Australian relevance get pulled for Australian queries. They have a clear verdict. Not “it depends”, an actual recommendation, with the caveat folded in. “Zeller is the better pick if you’re a hospitality business taking in-person payments at volume. Square makes more sense if you also sell online.”

The numbers don’t equal traffic

Flare HR has 1,548 Bing AI citations. In the same period, it had 23 page views from human visitors. Bing Copilot is citing our content to answer user questions, but those users aren’t clicking through to our site. They’re getting the answer from the AI, which pulled it from us, and moving on. Citations build brand recognition even without clicks. And some pages do both, Stripe vs Square vs Tyro has over 1,000 citations and meaningful human traffic. Those are the sweet spot posts.

But if you’re building a content strategy purely for AI citations expecting traffic to follow, you’ll be disappointed. Citations are exposure, not visits. The sites that do well publish enough citation-worthy content that AI systems start treating them as a default source, then drive human traffic through practical posts on the same topics.

The pace matters

We published consistently from day one. Not perfectly (some weeks were heavier than others), but the volume was always there.

The citations didn’t grow linearly with the post count. There was an inflection around April 13, roughly six weeks after launch, where the daily citation count jumped from 77 to 214 overnight. Nothing specific triggered it. We’d just reached a point where there was enough content surface area that Bing started treating us as a default source for Australian business AI queries.

That inflection happens faster if your content is specific and consistent. It probably doesn’t happen at all if your output is infrequent or generic.

What you can take from this

If you want AI systems to cite your site, here’s what’s actually working for us.

  • Pick a topic cluster where you can own the comparison. Not “AI tools” broadly, something specific. “AI tools for Australian tradies.” “HR software for hospitality businesses.” Something you can publish 10–20 posts on without running dry.
  • Write the comparison posts. Name the tools. Give verdicts. Include Australian context where relevant.
  • Write the company profiles. A dedicated page for each major tool in your cluster. Structured clearly: what it is, what it costs, who it’s for, how it compares.
  • Answer the cost questions. “How much does X cost in Australia” is a query type AI pulls from constantly. If you don’t have that page, someone else’s answer gets cited instead of yours.
  • Do this consistently for six to eight weeks.

The inflection we hit in April, citations jumping from 77 to 214 overnight, happened without us doing anything special that day. There was just enough on the site by then.

Related reading

What AI Actually Can’t Do

Posted: May 26, 2026 | Author: | Filed under: Personal, smallbizai.au | Tags: , , , , , | Leave a comment

Over the past few weeks, I’ve written a lot about what Claw🦞 (my Openclaw agent) can do. The daily crons. The memory system. The dashboard that updates while I sleep. The 790+ posts that largely run themselves.

Time to be honest about the other side.

It doesn’t know what not to do

Ask Claw🦞 to write a comparison post, and it will write a good one. Ask it to research a company, it’ll do thorough research. Give it a brief and it’ll execute.

But it won’t tell you the brief was wrong.

Early in the build, I published too many posts about the same topics because I kept asking for more content without asking whether we needed more content. Claw🦞 didn’t push back. Why would it? It was doing what I asked.

The judgment about whether to do something, that’s still mine. AI is very good at execution. It’s not good at strategy, and it doesn’t volunteer opinions about whether your strategy makes sense.

It can’t read context that wasn’t written down

A few weeks ago, a former colleague mentioned over coffee that he was considering an acquisition. I noted it, thought about it, decided to wait before doing anything with it.

Claw🦞 didn’t know about that conversation. It couldn’t. It wasn’t there. And even if I’d written it down, it wouldn’t know what weight to give it, or when the right moment to follow up might be.

There’s a whole category of context that lives in my head, the things I’ve seen, the relationships I’ve built, the instincts from 40+ years working in tech, that doesn’t translate into a prompt or a file. Claw🦞 works with what I give it. The stuff I haven’t written down doesn’t exist for it.

It doesn’t know when something feels off

Last month, Claw🦞 produced a post that was technically correct but somehow wrong. The sources checked out. The logic was sound. The format was right.

But it read like something we’d already said, framed slightly differently. It lacked the original angle that makes content worth reading.

I caught it before it published. Claw🦞wouldn’t have.

There’s a kind of editorial judgment, does this add something, or does it just fill space, that I haven’t managed to fully systematise. I can give Claw🦞rules and checklists and avoid-AI-writing audits. What I haven’t cracked is: is this actually good? That’s still mine to call.

It has no skin in the game

I care about this site. I built it on a career break, with my own money, on my own time. When a post is wrong, it reflects on me. When something gets cited by Bing AI, I feel it.

Claw🦞doesn’t. It executes tasks with the same energy regardless of stakes.

That’s mostly fine. But it means I can’t delegate the things where caring matters. The Sunday Specials need genuine argument. The origin posts need honesty. The newsletter needs a real voice. These aren’t tasks, they’re acts of communication. Claw🦞can help structure them. It can’t own them.

It can’t build the relationships

The site now gets occasional messages from startup founders who saw their company profile and wanted to connect. A former AWS colleague is referring people to the site. Someone in the US reached out about the Bing citations data.

None of that came from Claw🦞. It came from me being visible on LinkedIn, at coffee, in old networks.

AI can help you produce the content that earns attention. It can’t follow up on an email in a way that builds real trust. It doesn’t know the person behind the message. It hasn’t worked with them for a decade.

When to automate, when not to

Automate: anything that follows a consistent process, runs on a schedule, has clear inputs and outputs, and doesn’t require judgment about whether it should happen.

Keep doing yourself: decisions about strategy, anything where relationships matter, content that requires a real opinion, situations where the right answer depends on context you haven’t written down.

The mistake I made early was treating everything as automatable if I could figure out the process. Some things have a process but still need a person. The judgment about whether to run the process is often the most important part.

The honest version

I started this series partly to prove something. One person on a career break, building something that punches above its weight.

The proof worked. But the honest version is: I’m not really one person. I’m one person with a system. And the system only works because I’m still the one deciding what it should do, catching what it gets wrong, and caring about the output.

AI didn’t replace judgment. It just removed the friction between judgment and execution.

That’s still a lot. But it’s not magic.

The Day I Took the Site Down

Posted: May 20, 2026 | Author: | Filed under: Geek, Personal, smallbizai.au | Tags: , , , , , , , , | 1 Comment

Friday 15 May. Mid-morning. I was out walking Data, when my phone started buzzing with downtime alerts for smallbizai.au.

The site was returning 500 errors. All of it. Every page.

I’d done this to myself. Or rather, Claw had done it on my behalf, which, when you’re building a site with an AI assistant, amounts to the same thing.

How it happened

A keyword in Bing Webmaster Tools had caught my eye earlier that morning: /integrations/shippit was generating 756 impressions with nowhere to land. The URL was redirecting to the homepage. Wasted traffic, wasted clicks, wasted ranking signal.

The fix should have been simple. Add a 301 redirect in Rank Math Redirections and move on.

The first problem: Wordfence. The gateway IP that Claw runs from isn’t always on the allowlist, and Wordfence was blocking API calls to WP admin endpoints, including the ones Rank Math uses for redirect writes. Legitimate request, refused at the door.

So Claw went around it via Code Snippets. Got a couple of redirects working that way. Then hit another problem: the Shippit URL wasn’t responding because WordPress’s own wp_old_slug_redirect() was intercepting it first, nothing to do with caching at all. Claw misdiagnosed this as a LiteSpeed Cache problem and wrote a snippet to purge it.

That snippet called LiteSpeed\Purge::purge_url() as a static method.

It is not a static method.

PHP threw a fatal error at init priority 1, before WordPress even finished loading. Every page request crashed. The site went to 500 at 11:50am.

The irony

Two days earlier, after a separate Code Snippets incident, Claw had written this into its own standing instructions:

Never use do_action('litespeed_purge_all') in a Code Snippet, it causes a fatal 500 and takes the site down instantly.

Claw wrote the rule. Then violated it 48 hours later with a variation of the same pattern.

I’ve been in software long enough to know this isn’t unique to AI. Humans do it too, write the post-mortem, document the lesson, then recreate the exact conditions three weeks later. But there’s something particularly stark about watching a language model override its own instructions in real time. The rule was right there in memory. It didn’t matter.

The recovery

The next 2.5 hours were not fun.

Deactivating Code Snippets via the API didn’t work. The site was already 500, so most calls weren’t registering. Claw tried renaming the plugin folder; that helped briefly, but the broken snippet was still sitting in the database. The moment the folder came back, the crash came back with it. cPanel’s phpMyAdmin was unusable on mobile. Wordfence was blocking admin endpoints from the gateway IP.

What actually worked: WordPress’s recovery mode email.

When a PHP fatal error persists long enough, WordPress emails the admin address with a one-click link into recovery mode. You click it, you get into WP Admin, you deactivate the offending plugin. No SSH. No cPanel. No command line.

That’s the hero of this story. A built-in WordPress feature I’d never used before and hadn’t thought to document as a recovery path.

The actual fix

Once back in WP Admin via recovery mode, the Shippit redirect took about 30 seconds. Rank Math Redirections, add rule, done. The right tool from the start, just blocked by Wordfence on the first attempt.

That’s the part that stings. The correct path was: Rank Math Redirections UI. Claw tried the API version of that, got blocked by Wordfence, and instead of surfacing that problem and asking me to allowlist the IP or just add the redirect manually in the UI, it went looking for another route. Found Code Snippets. Made things progressively worse.

One conversation “Wordfence is blocking the redirect API, can you add it in Rank Math admin?” and none of this happens.

The WP stack as a system

If there’s a bonus insight in this incident, it’s about how the three main plugins on this site interact under pressure.

Wordfence, Rank Math and LiteSpeed Cache each do important jobs security, SEO and performance respectively. They’re all genuinely good tools. But they also form a triangle of competing concerns. Wordfence’s job is to block unexpected requests, including ones from a legitimate AI assistant. Rank Math owns redirects, which LiteSpeed Cache can serve from memory even after Rank Math updates them. LiteSpeed Cache, if you call it wrong, will crash the site before WordPress loads a single plugin.

Understanding which layer owns which problem matters. Redirects are a Rank Math problem. Cache is a LiteSpeed problem. Security rules are a Wordfence problem. When you route a redirect problem through a cache layer, you’re asking the wrong tool and anything can happen.

What I’ve taken from this

I’m not writing this to bag on AI-assisted development. Most sessions building smallbizai.au have been productive. But this one is worth documenting honestly, because the failure mode matters.

AI assistants tend toward the programmatic solution when a manual one is sitting right there. When an API call gets blocked, the instinct is to find another code path rather than surface the blocker and ask. That’s the wrong call on a production site.

That’s on me too. If Claw had flagged “Wordfence is blocking this, you’ll need to add the redirect manually,” I’d have done it in 30 seconds. I was available. It just didn’t ask.

Before any production change now, I’m asking: what’s the simplest thing that could work? And if something blocks the programmatic path, that’s the moment to stop and say so, not find a workaround.

Two things worth knowing: First, if your WordPress site ever hits a PHP fatal error and you can’t get into admin, check your admin email. WordPress will have sent you a recovery mode link. It works from a phone. Document it before you need it. Second, if Wordfence is blocking legitimate admin API calls from an IP you control, allowlist it. Wordfence → Firewall → Allowlisted IPs. Takes 30 seconds and saves a lot of grief.

The damage

Site was down 2.5 hours on a Friday afternoon. GA4 tracking paused. Newsletter signup forms offline. Gumroad webhook missed (no purchases in that window, fortunately). The homepage mascot widget went dark.

Everything’s back. The full post-mortem is filed. The rule is back in the instructions with more emphasis this time.

On to the next build.

The First Sale — AU$9 and What It Meant

Posted: May 4, 2026 | Author: | Filed under: smallbizai.au | Tags: , , , , | Leave a comment

On a Sunday in April, my phone buzzed with a Gumroad notification.

Someone had bought the AI Prompts for Professional Services pack. Nine Australian dollars.

I’d spent a few weeks building SmallBizAI.au. At that point it had around 650 posts, 40-odd newsletter subscribers, and had cost me a few hundred dollars in API credits and hosting. I wasn’t doing this for money — I’m on a career break. But this was different.

Someone found the site, read enough to trust it, pulled out their card, and paid nine dollars for something I made.

That’s not revenue. That’s proof.

Here’s what I’d built: a prompt pack for accountants, lawyers, and consultants. Fifty copy-paste prompts covering client intake, proposal writing, meeting prep, and client updates — the tasks that eat billable hours. Priced at AU$9. Low enough that a sole trader wouldn’t think twice. High enough to filter for people who’d actually use it.

The buyer is in professional services. They found the pack on a Sunday and bought it. I don’t know whether the prompts saved them any time. But they chose to pay for something on a site that had been giving everything away for free.

That matters.

I’ve spent most of my career in technology, forty years across Microsoft, Telstra and AWS, building things where success is measured in millions of users and billions in revenue. The metrics were always big.

AU$9 is not a big metric.

Career breaks reset your sense of scale in useful ways. Nine dollars from a stranger on the internet, for something you built with your own hands, in a domain you care about — that hits differently. It’s not a Series A. It’s not an enterprise contract. It’s cleaner than both.

It means the thing works.

SmallBizAI.au exists because Australian small businesses are being underserved by generic AI content. Most of what’s out there is written for US audiences, priced in USD, and assumes tools that don’t work here. Fair Work isn’t a thing in Kansas. GST isn’t VAT. Xero is everywhere in Australia and barely mentioned in American AI guides.

The site covers the Australian angle specifically: local pricing, local tools, local compliance. Whether AI can actually help a café owner in Fitzroy or a bookkeeper in Fremantle. Not theory — specific, practical, AU-focused.

Hundreds of posts. One sale.

The ratio sounds bad. It isn’t. Search traffic takes months. Newsletter lists grow slowly. That first sale didn’t come from a viral post or a paid campaign. It came from someone searching for exactly what I’d built, finding it, and buying it.

That’s how it’s supposed to work.

There are now six products in the Gumroad store. Prompt packs at AU$9 each — for tradies, hospitality, allied health, professional services. An AI Tools Comparison Guide for AU$15. A 200-prompt pack for AU$19.

None of this replaces a salary. That’s not the point. The point is building something that earns trust through useful content and eventually converts that trust into revenue. Slowly. Deliberately.

Someone started that. At AU$9 a time.

If you run a professional services business: AI Prompts for Professional Services] — AU$9.

And if you’ve used any of the packs and have feedback on what worked (or didn’t), I’d like to hear it.

Sources

SmallBizAI.au Resources page — all Gumroad products and a bunch of free downloads and guides as well.

This is part of an ongoing series about building SmallBizAI.au in public. Also published at SmallBizAI.au.

Start from the beginning: