Creative Tools
Digital Tools for Creatives
Teaching AI to see Australian landscape, flora, and fauna—because generic algorithms default to Northern Hemisphere forests and someone needs to teach them eucalyptus.
The Problem
After years observing Dharawal Country—as a visual artist, as a firefighter, as someone documenting wildlife with a Canon R8—I kept noticing: AI image generators couldn't distinguish eucalyptus from oak trees. Couldn't render scribbly bark. Kept defaulting to redwoods and aspens.
This wasn't just an aesthetic problem. It was an attention problem.
The same practice that shapes The Wildline—observing closely, documenting honestly, building skill through constraint—applies to working with AI tools. If you can observe closely enough to understand how light actually moves across sandstone at golden hour, you can teach an algorithm to approximate it. If you understand fire ecology, you can prompt regeneration landscapes that reflect actual Australian bush.
These digital products emerged from that practice: 200+ hours of testing, refining, and teaching AI systems to pay attention the way photographers do.
They're not shortcuts. They're distillations of observation into language that machines can parse. Use them as starting points. Remix them. Make them yours.
Available Now
🎁 Free Sample Pack: 5 Atmospheric Australian Landscape Prompts
What it includes:
- 5 tested Midjourney prompts with sample images
- Companion Notion template for organizing generations
- Introduction to the methodology
Why start here: See if the approach works for you before committing. The free pack demonstrates the level of specificity needed to get AI past its Northern Hemisphere defaults.
🌿 Full Collection: 50 Atmospheric Australian Landscape Prompts
What it includes:
- 50 tested & refined prompts organized by mood and light
- Dawn, golden hour, moody atmospheric, post-fire regeneration, intimate macro
- Sample image for every prompt
- Technical tips and customization guidance
- Australian art references (Fred Williams, Hans Heysen, Bill Henson)
- Specific species and regional details
Why this matters: Stop wrestling with AI systems that default to redwoods and aspens. Each prompt includes the technical photography language, compositional guidance, and place-based specificity that produces authentic Australian imagery.
Created after 200+ test generations across 4 AI platforms. Informed by living near Dharawal National Park, volunteer firefighting experience, and MFA training in observation and materials.
$5 AUD | PDF delivery | Instant download
🦘 Coming Soon: Australian Flora & Fauna Prompt Pack
The problem: AI has even more trouble with Australian wildlife than landscape. Ask for a wombat, get a beaver. Ask for a kookaburra, get... something vaguely bird-shaped.
The solution (in testing): Species-specific prompts for native Australian fauna and flora in landscape context. Behavioral accuracy. Ecological relationships. The kind of detail that comes from actually encountering these creatures in the wild.
Expected launch: Mid-2026
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Why These Exist
This started as practical necessity. I needed Australian landscape reference imagery for shot planning and couldn't get AI to understand the difference between eucalyptus and oak. After months of testing, I had a working system.
Then I realized: other Australian artists, designers, photographers, and creatives are probably hitting the same wall.
These tools extend the same practice that drives The Wildline—observing closely, testing whether ideas work when confronted with reality. In this case, the reality is: AI systems are trained predominantly on Northern Hemisphere imagery. You have to teach them to see differently.
The connection:
- Observation as practice (phenomenology applied to AI prompting)
- Language shaping perception (how we describe affects what we see)
- Testing claims against material outcomes (does the generated image match the landscape I know?)
Connecting the Work
If you're here from The Wildline YouTube channel, you might be wondering how digital product development fits with wildlife photography and rural creative practice.
Here's the thread:
Both practices require paying attention in ways most people don't. Both involve testing language against material reality. Both demand iterative refinement when initial attempts fail. Both acknowledge that tools shape thinking—whether that tool is a camera lens or an AI prompt.
The Wildline documents wildlife encounters and landscape observations. These digital products test whether observation can be systematized into language that teaches machines to approximate what photographers see.
Different materials. Same underlying practice.
What's Next
Future digital products will emerge the same way these did—from actual problems encountered in practice, refined until they work, then shared.
Possibilities:
- Seasonal Australian landscape variations
- Composition & light workbooks for photographers
- Regional deep-dives (Dharawal/Illawarra specifically)
- Suno prompt libraries (music generation for soundtracks)
- Notion templates (content pipeline, gear tracking, wildlife encounter databases)
Nothing exists until it's been tested. Nothing gets released until it works.
If there's a specific tool that would help your practice, let me know. Resources develop based on what people actually need.
Technical Notes
What works with these prompts:
- Midjourney (primary testing platform)
- DALL-E 3
- Stable Diffusion (with adjustments)
- Most AI image generators with sufficient training data
What you need:
- Access to an AI image generation platform
- Willingness to iterate and refine
- Basic understanding of how prompts work (or willingness to learn)
Support: If you run into issues, email [your email]. I can't guarantee instant responses (wildlife documentation takes priority), but I'll help where I can.
Land Acknowledgment
This work is created on the traditional lands of the Dharawal people. I acknowledge their continuing connection to country and pay respects to Elders past, present, and emerging.
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Digital tools for photographers and creatives who actually observe.