Share your best AI workflow. We could show it to 2M+ people.

For too many years, I had always wanted to frame a series of photos of our favorite view of our cabin to capture it in all four seasons. But I never had the framing just right. So I simply uploaded the versions I had to Gemini's Nano Banana Pro along with an example of a painting style that I liked, and within a few iterations I got something that captured my vision perfectly. Try It: Start with the new super fast, and cheap Nano Banana 2 Lite to get several options to consider quickly. Then pass it off to Nano Banana Pro (Set to 4K - High Thinking) to get a file with enough resolution to send to a printer. Step-by-step: 1. I gathered the best photo I had for each season and chose a painting style reference that matched the look I wanted. 2. I uploaded the seasonal photos and style reference to Gemini and asked it to create one cohesive four-season composition. 3. I generated several fast variations with Nano Banana 2 Lite so I could compare framing and treatment without over-investing in the first idea. 4. I iterated on the strongest direction until the composition and style felt consistent across all four images. 5. I passed the final direction to Nano Banana Pro with 4K and High Thinking enabled to create a print-ready file.

I set up an AI assistant to run my entire writing business on autopilot. Every morning, it pulls RSS feeds from more than 30 AI and writing sources, deduplicates them against the previous day’s digest, curates the top items, and sends me a single Telegram message with numbered, linked items before I wake up. At 11 a.m. each day, it generates an original writing craft post. The topic comes from a rotation pool of more than 15 categories, and the assistant avoids anything used in the last 30 days. It also creates accompanying artwork in a rotating fine-art style, then cross-posts the content to Facebook, X, and my blog, including the featured-image upload to WordPress. Each week, it compiles and sends an email newsletter to my subscriber list through Brevo. It pulls from a curated candidates file that I approve before the newsletter goes out. Behind the scenes, the assistant manages a fleet of five servers, including servers for my wife, daughter, and two business colleagues. It handles daily backups, monitors costs across providers, and reminds me when context windows are becoming expensive. The key insight wasn’t the automation; it was the partnership model. My assistant has a persona file (`SOUL.md`) that defines how it communicates, a memory file (`MEMORY.md`) with everything it needs to know about my life and business, and a playbook of behavioral rules built from real mistakes over time. It pushes back on bad ideas, flags risks before executing, and has genuine opinions about craft and content. That shift—from “tool you talk to” to “colleague who has your back”—is what I wrote my book about. *Harnessing the Machine* is the field guide I wish I’d had when I started. It isn’t a tutorial, because the technology changes weekly; it’s a guide to building a working relationship with something that remembers yesterday. The tech stack is OpenClaw, GLM-5.2 as the primary model, DeepSeek V4 Pro as the fallback, and AWS Lightsail. The total monthly cost is under $30. The real cost was calibrating the assistant: teaching it what I care about, what “good” looks like, and when to ask versus when to act. That’s the part most people skip, and it’s why most “AI automation” posts feel like demos rather than relationships. Tools used: OpenClaw, GLM-5.2, DeepSeek, Telegram, WordPress, Brevo Step-by-step: 1. I configured OpenClaw with a persona file (`SOUL.md`), a memory file (`MEMORY.md`), and a behavioral playbook built from real mistakes. 2. I connected it to RSS feeds from more than 30 AI and writing sources and had it deduplicate, curate, and send a numbered Telegram digest each morning. 3. I created a rotation pool of more than 15 writing categories and instructed it to avoid topics used in the previous 30 days. 4. I scheduled it to generate a daily writing craft post, create artwork in a rotating fine-art style, and cross-post the result to Facebook, X, and my WordPress blog with a featured image. 5. I set up a weekly Brevo newsletter that pulls from a curated candidates file I approve before sending. 6. I connected the assistant to five servers, including servers for my wife, daughter, and two business colleagues, and had it manage daily backups, provider costs, and expensive context windows. 7. I configured GLM-5.2 as the primary model, DeepSeek V4 Pro as the fallback, and AWS Lightsail as the hosting environment. 8. I calibrated the assistant by teaching it my standards, what “good” looks like, and when to ask for approval versus acting on its own.

Nano Banana Pro has been a step change both for staying true to prompts and also being able to take its own creative liberties. The ability to talk through prompt brainstorming, image edits, and even providing context directly from our newsletter has made for some highly personalized new creative paths. Step-by-step: 1. I gave Nano Banana Pro the newsletter context and the creative goal for the image. 2. I used conversation to brainstorm several possible prompt directions before generating. 3. I created initial images and evaluated both prompt fidelity and the model’s useful creative choices. 4. I described the edits I wanted and kept the relevant newsletter context in the conversation. 5. I iterated toward a more personalized final creative rather than restarting from a generic prompt.