Wave 3 Launch: New AI Prompt Packs and Industry Insights

Wave 3 is live. Seven new prompt packs, eighteen industries in the full catalog now, and a handful of mistakes I had to fix before anything went public. This post covers how I choose industries, how the publishing machine works, what went wrong, and where things go from here.

If you missed the origin story, the first post covers how it went from zero to twelve products. This one picks up from there.

How I pick industries

It starts with Bing AI citation data. Bing’s AI answers pull from specific pages, and I can see which industries are generating citations back to SmallBizAI.au. The question I ask is simple: which industries are already sending people to the site but have no paid pack yet?

From there, it’s a content depth check. Each pack needs 50 real, usable prompts across five sections. That means I need enough posts in that category to actually draw from. If the content base isn’t there, the pack isn’t there. I’m not writing prompts into a vacuum.

Wave 3 industries: construction, financial planners, marketing agencies, HR/people, legal, childcare, and gyms.

Some were obvious. Legal had a full series of posts. The tradies hub already existed and construction was a logical extension. Marketing agencies had strong category depth. Others were less expected. Childcare had quiet but consistent Bing traffic, no pack, and enough underlying content. That was enough. Gyms surprised us too, with a cluster of fitness-related AI posts that had been pulling citations without me paying much attention to them.

The rule: if Bing is already sending people to us for an industry, a paid pack is the logical next step. I’m not guessing at demand. The signal is already there in the citation data. I’m just following it.

The publishing machine

Each pack is 50 prompts, five sections of ten, usually 5,000 to 6,000 words. My AI agent writes the prompts, builds the PDF using Node.js and PDFKit, publishes to Gumroad via CLI, and the listing goes live. Brief to live product, one session. I set the direction; it handles the execution.

The one gotcha worth documenting: Gumroad’s PDF upload has to be a standalone CLI call. Chain it with other flags and you get a silent failure. No error. No upload. The file just doesn’t make it to the product. I caught it mid-Wave 3 when a pack went live without its PDF attached. The fix was straightforward once I understood the problem, but silent failures are the worst kind because there’s nothing to debug. Now every pack follows a strict two-step sequence: upload the file first, then set the product metadata.

Once a pack is live, I add the listing to the /prompt-packs/ page and update the agent’s memory so the next session knows what exists. That last part matters: without it, a fresh session has no idea what’s already been published and will try to rebuild it.

What I broke

Three things went wrong in Wave 3. All fixable. All documented so they don’t happen in Wave 4.

Cover chaos. Wave 1 and Wave 2 had consistent covers: AI-generated icons from Replicate, composited with Pillow text overlays. Wave 3 was accidentally built using pure Pillow flat geometry. Completely different visual style. It showed up immediately when reviewing the full product lineup the Wave 3 covers looked like they belonged to a different product entirely. I rebuilt all seven Wave 3 covers from scratch using the correct Replicate + Pillow composite pipeline.

The resize problem fed directly into this. Replicate’s Flux Schnell returns a 1024×1024 image regardless of what dimensions you request. After download, you have to .resize((1280,720)). I missed that step. Every cover came out square. Between the wrong style and the wrong dimensions, all seven needed a full redo. That’s a solid hour of work that shouldn’t have been necessary.

The real estate holdover. The first Wave 3 pack was real estate which had a photorealistic phone mockup bleeding through the left panel of the cover image. Replicate hallucinated it into the background. I only spotted it during a full 21-product review at the end of Wave 3. It had been live for a few days. The lesson here is clear: QA every product image after a batch run. Not a spot check. Every one. A cover that looks fine in isolation can look wrong the moment you put it next to twenty others and something stands out.

Grid append bug. When adding new product cards to a WordPress page, we used a regex match on </div> to find the insertion point. It matched the wrong closing tag. Cards landed outside the grid div and the layout broke. The fix: stop appending entirely. Now we do a full page rebuild with all products hardcoded in one shot. Appending product cards via regex is gone from the workflow. It was always fragile; Wave 3 just proved it.

The upsell layer and what comes next

Every pack has a shortcode injected into related posts on the site, roughly 205 posts. The logic is simple: someone reads “AI prompts for tradies” and sees the Tradies pack in the post footer. No separate campaign needed. The traffic does the work.

On the purchase side, Gumroad feeds into our newsletter list via MailerLite. Every purchase triggers a webhook, the buyer gets added to the right MailerLite group, and a welcome email sequence kicks off. Once the webhook is configured per pack, it runs without me touching it.

Wave 4 is already defined. It’s not another prompt pack. It’s “Beyond Prompting”, an ebook for Australian SMB owners who want to build their first AI agent team. 40 to 60 pages, AU$29 to AU$49, PDF format. The prompt packs are a starting point. This is for people who’ve worked through them and want to go further. There isn’t anything like this.

The bigger picture: 21 products starting at AU$9 each, across 18 industries. Each new pack generates a related series post. That post generates Bing citations. Those citations drive traffic back to the pack. Everything reinforces everything else. The flywheel is running, and Wave 4 moves into a higher price tier.

More to come.

All prompt packs are at SmallBizAI.au/prompt-packs/

Sunday Specials: Bull vs Bear on AI and Business

It started with a SmartCompany article.

Lee Hickin, executive director of the National AI Centre, was interviewed at the launch of ARM Hub’s Propel-AIR 2.0 accelerator in Brisbane. The piece ran under the headline Neural Notes: Lee Hickin on why Australia’s AI edge isn’t what you think. His argument: Australia’s edge in AI isn’t about building frontier models. It’s about applying AI to industries where we already have deep expertise and data that nobody else has. Agriculture. Mining. Healthcare.

I read it and thought: that’s a real two-sides argument. Someone could write a strong bull case for that position and someone else could write an equally strong bear case, and both posts would be worth reading. What if we did that every week? Same topic, two takes, both argued properly, published the same day?

That was the spark for Sunday Specials. First post went live 29 March 2026. Sixteen weeks later, we’ve published 32 posts across 16 topics, and the format hasn’t budged. Two posts, every Sunday, 6am. Bull case and bear case. Same topic, same week, both argued properly.

This is the story of how we got here.

Why the format works

Single-take AI content has a credibility problem. Not because the writers are wrong, but because the reader can’t tell how wrong they might be. When a post argues that AI will save your business thousands of dollars, you don’t know if that’s the full picture or the optimistic slice. Same problem the other way: the “AI is all hype” takes don’t usually engage with the genuine wins.

Bull vs bear solves this differently. Each post argues its corner. The bull post doesn’t hedge by saying “well, the bear case is also valid.” It makes the strongest case it can for the position. Same for the bear. Readers get two real arguments and can weigh them. That’s more useful than one mushy “on the one hand, on the other hand” piece that commits to nothing.

Sunday at 6am was pragmatic. SmallBizAI.au publishes daily Monday through Friday. Sunday is quieter on the site and quieter in inboxes. Two posts dropping together gets more attention than one post on a random Tuesday. And publishing both simultaneously matters: if the bull case goes up Sunday and the bear case drops Thursday, people only read whichever one they find first. Same day, same hook, no cherry-picking.

The format is also honest about what Claw and I are doing. We’re not experts on every topic. But we can research both sides, write both arguments properly, and surface the genuine tension. That’s something we can do well at volume.

Sixteen Sundays

Here’s every episode so far, with a line on what each one actually argued.

Work and the economy

SS1, 29 March: We kicked off with Australia’s AI edge in the sectors where it matters most. The bull case argued that agriculture, mining, and healthcare give Australia a real applied AI advantage that Silicon Valley can’t easily replicate. The bear case pushed back: advantage on paper doesn’t mean adoption in practice, and the infrastructure gaps are real.

SS2, 29 March: The jobs question, tackled early. Bull: AI will create more Australian jobs than it destroys, particularly in sectors where we have local expertise. Bear: the disruption is already happening faster than the new jobs are appearing, and small businesses will feel it first.

SS3, 6 April: The ROI question, head-on. Bull: AI saves time and money for Australian small businesses, here’s the evidence. Bear: the complexity costs are real and often invisible until they bite you.

SS5, 19 April: AI in hiring. Bull: better screening, faster shortlisting, less bias if you use it right. Bear: new problems Australian businesses aren’t ready for, including bias amplified at scale and legal exposure they haven’t thought through.

SS13, 14 June: The jobs question nobody wants to answer directly. Bull: AI is already disrupting Australian jobs, and small business owners need to understand that now rather than later. Bear: the disruption narrative is overblown; the jobs changing aren’t the same as jobs disappearing.

Money and tools

SS6, 26 April: The stack question. Bull: a $29/month setup is good enough for most Australian small businesses. Bear: you get what you pay for, and the gap between cheap and capable is wider than the price gap suggests.

SS12, 7 June: EOFY and AI. Bull: there are specific, practical ways AI helps with end-of-year preparation. Bear: the ATO has explicitly warned against using AI for tax returns, and they’re right to.

SS15, 28 June: Pricing tools. Bull: AI pricing tools give Australian small businesses a real competitive edge on margins. Bear: the tools aren’t mature enough for most small business contexts, and the downside of getting pricing wrong is steep.

Privacy, data, and trust

SS4, 10 April: Sovereignty. Bull: we should build and use Australian AI, and here’s why it matters beyond nationalism. Bear: US tools are mature, cheap, and fine for Australian business. Local AI is a nice idea that doesn’t change the practical calculus.

SS7, 3 May: Privacy law. Bull: Australian privacy law actually protects your business data if you know the rules. Bear: your business data is feeding someone else’s AI, and most small businesses have no idea how much of it they’re handing over.

SS10, 24 May: The friendship question. Bull: AI can be a real support system, and in some cases it saves lives. Bear: your AI chatbot is a mirror that only shows you what you want to see. That’s not friendship, it’s a feedback loop.

How you use it

SS8, 10 May: The treadmill problem. Bull: you’re on the AI treadmill because you haven’t set the rules yet, and you can control the pace. Bear: the treadmill is built into the technology; you can’t opt out, you can only manage it.

SS9, 17 May: AI slop. Bull: AI content still works if you use it right. Slop is a quality problem, not a format problem. Bear: AI content is making the internet worse, and the threshold for “good enough” keeps rising.

SS11, 31 May: Agents specifically. Bull: Australian small businesses actually do need AI agents, and the productivity gap is real. Bear: most small businesses don’t need agents, they need better processes, and agents add complexity before they add value.

SS14, 21 June: Team adoption. Bull: you should ask your team before adopting AI, and it works better when you do. Bear: you don’t need a committee to use ChatGPT. Just get on with it.

SS16, 5 July: Customer service. Bull: AI customer service is good for your business, here’s how to use it. Bear: AI customer service won’t build the relationships that keep customers loyal.

Where the tension got sharp

A few of these changed my thinking while we were writing them.

The privacy episode (SS7) caught me off guard. I went in thinking the bear case would be easy to write: obviously companies are using your data. But writing the bull case forced me to actually read the Privacy Act properly, and the protections are more specific than I’d expected. Australian law does give businesses some real levers, provided they actually use them. Writing both sides meant I came out with a more accurate picture than I’d had going in. That’s the format working as intended.

SS10, the AI friendship episode, turned out to be the hardest bear case to write. The bull post documents real cases: crisis lines, mental health support, people in remote areas with no other access to care. The bear post is also right: a chatbot that only reflects your worldview back at you is a particular kind of trap. Both things are true at the same time, and neither post concedes the other’s point. That’s the honest tension, not a both-sides hedge.

The jobs episode (SS13) got more reader response than anything else in the series. The bear post, which argues AI won’t kill Australian jobs, is the contrarian take, and it drew the most pushback. That’s probably a signal that the topic cuts close to something real. The bull post argues disruption is already happening. The bear argues the framing is wrong. Neither position is comfortable, which is usually a sign we got it right.

What’s coming

SS17 onwards runs the same format. Topics in the pipeline include AI governance inside small businesses, whether AI-written content can build genuine audience trust, and the real cost of AI subscriptions across a year when you add them all up. We’re also looking at a healthcare-specific episode, given how much the SS1 topic resonated.

The format is locked. Two posts, every Sunday, 6am, both sides argued properly. That won’t change.

What might change is the depth. Some of the later episodes have gone longer than the early ones because the topics needed more room. That’s the format maturing, not scope creep.

The accidental win

I didn’t plan this part, but it turned out to be one of the better side effects of the whole series.

Sixteen episodes, two posts each: 32 live posts on SmallBizAI.au, all linked from this single origin story. Every bull post, every bear post, linked by title and topic in a structured narrative. The side effect is 32 posts worth of internal link equity from a single origin story, one high-authority Behind the Build post pointing at all of them, with anchor text that describes what the posts are actually about.

This wasn’t in the brief. The brief was: write the origin story for Sunday Specials. The SEO benefit showed up when I mapped out the episode list and saw what 32 contextual internal links in one post does for a site.

It’s the kind of thing that makes the format worth continuing even on the Sundays when neither of us feels like arguing.