I built a job application workflow that goes beyond finding vacancies and generating generic CVs. First, GPT helps me scan for vacancies that match my practical requirements, interests, skills, and preferred types of work. The more useful part starts when a vacancy looks genuinely promising. I connected GPT to Canva and created several “base CVs” for the sectors and roles I was interested in. Instead of using one universal template, I designed each CV to fit the visual and cultural tone of a particular type of work. For example, a front-desk role, a back-office administrative position, and an assistant role in a creative company may involve overlapping skills, but they communicate very different expectations. Each base CV therefore uses different layout choices, colour use, visual tone, and photo selection. For every version, I review several possible photos and choose the one that best matches the role and the impression I want to convey. When I apply for a specific vacancy, GPT helps turn the relevant base CV into a more personalized version. Instead of rewriting my entire work history, we emphasize the experience, tasks, strengths, and values that are genuinely most relevant to that role. The same applies to motivation letters. Rather than generating a generic corporate letter, GPT uses a tone of voice shaped through months of conversation with me. The goal is for the application to sound recognizably like me while still matching the language, priorities, and culture of the vacancy. Before building this workflow, I did everything manually. For a vacancy that felt worth applying to, I typically spent around two hours refining the CV and motivation letter alone, not including the time spent searching for the vacancy. Even a small typo in the final application email could undermine hours of careful work. Now, once I decide a vacancy is a good fit, the full personalization process takes around twenty minutes on average. That includes selecting the right base CV, adapting the emphasis, refining the letter, checking the tone, and preparing the final application. The workflow works across several layers: vacancy discovery, role and sector matching, base CV selection, adapting experience and values, adjusting visual tone, personalizing the letter, final review, and application. What I like about this approach is that personalization is not limited to inserting keywords from a vacancy. It includes content, visual identity, emphasis, tone, and context. The result is a small family of CVs rather than one document being stretched awkwardly across every possible job. Each version keeps the same underlying career history while presenting the parts that matter most for a particular type of role. The biggest improvement is not only speed but consistency. The workflow reduces repetitive manual rewriting while keeping each application specific, personal, and carefully matched to the role. It turns roughly two hours of manual polishing per strong vacancy into about twenty minutes of collaborative refinement, with fewer opportunities for small final-stage errors to spoil an otherwise strong application. Step-by-step: 1. I use GPT to scan for vacancies that match my practical requirements, interests, skills, and preferred types of work. 2. When a vacancy looks promising, I identify the relevant sector, role, and type of impression I want to convey. 3. I use Canva and GPT to create and maintain several base CVs, each with its own layout, colour use, visual tone, and photo selection. 4. For each base CV, I review several possible photos and choose the one that best fits the role and the impression I want to convey. 5. I select the base CV that best matches the vacancy. 6. I adapt the CV by emphasizing the experience, tasks, strengths, and values that are genuinely most relevant instead of rewriting my entire work history. 7. I use GPT to personalize the motivation letter in a tone shaped through months of conversation with me, while matching the vacancy’s language, priorities, and culture. 8. I check the tone, review the application for small errors such as typos, and prepare the final application. 9. I complete the personalization process in around twenty minutes on average instead of spending roughly two hours on manual polishing for a strong vacancy.

I wanted to turn two characters I had already developed, Fant the elephant and Viper the nurse snake, into a children's book about illness, sadness, and friendship. I had story ideas and character art, and I wanted to know whether AI could help me build a real book without losing their identity. This workflow is for anyone who wants to use an AI book generator as a storyboard and voice test, then develop the final art separately. 1. I used my conversation-archive plugin, Archiefvis, to revisit character and series concepts from a year ago. That context helped us choose Fant's fear that his friends only like him when he's entertaining. 2. I ran Kids Book Maker with my original drawings and a page-by-page brief. It produced nine illustrated screens, including the cover, plus Dutch narration and dialogue I liked. The pacing and compositions were useful, but the art drifted toward soft pastel baby-animal illustrations instead of our bold retro cartoons. 3. I kept the page order, camera angles, warm settings, and some dialogue, which I also edited. My original artwork became the character and style reference, and the generator's pages served only as composition references. 4. I rebuilt one illustration at a time and approved useful versions as I went. Fant needed wide-open, expressive eyes because he was performing exaggerated cheerfulness. We corrected his gray skin tone and removed an unwanted tattoo. This is the prompt I adapted for those edits: «Keep the scene composition, camera angle, props, and atmosphere from image A. Redraw the characters to match the approved designs in image B: fixed proportions, expressive faces, bold dark outlines, saturated warm colors, and subtle retro-print texture. Preserve approved elements; change only [specific detail]. Add no unrequested lettering, tattoos, or limbs.» ! An extra elephant leg kept surviving my edits. Precise erasures may need a local masked edit rather than another full regeneration. 6. I made a clean, text-free cover illustration (rthag was No good) , so used an old Version and then added the title over the messed up text in Canva, because AI-generated lettering looked generic and then overly glossy. The result is several approved scenes, a much clearer visual style, and a story that holds together. The full print-ready book was finished by hand in Canva.

I use Claude Desktop on my Mac, connected to Canva and Metricool, to build and schedule social media posts. I used ChatGPT to help me set everything up because it has more history and access to my company knowledge documents. ChatGPT created instruction files for Claude that provide our company context and guidelines. I pointed Claude to our website, which contains many articles, and connected our Canva and Metricool accounts. We discuss suitable topics to post about. Claude reviews the website for relevant articles or landing pages to link to, then suggests a series of Facebook and Instagram posts. It checks the designs in our Canva account, adjusts them as needed, or asks me for photos and other artwork. Claude then builds the posts in Metricool and saves them as drafts for me to review, following our working rules. I review the drafts and move them to pending. Claude checks that they will publish automatically on the correct date and at the correct time, and it creates links with meaningful UTM parameters. After publishing, I can ask Claude to analyze the results, identify what is working and resonating with my market, and help us test different approaches, content, and topics. Metricool captures the data, and Claude uses it to inform the next round of posts. This saves me hours every week, gives me time to work on other things while Claude handles the process, and produces results that are improving each week. Step-by-step: 1. I used ChatGPT to help configure Claude Desktop and create instruction files containing our company context and guidelines. 2. I connected Claude to our website, Canva account, and Metricool account. 3. I discuss potential topics with Claude, and it finds relevant website articles or landing pages to link to. 4. Claude suggests a series of posts for Facebook and Instagram. 5. Claude reviews our Canva designs, adjusts them when necessary, or asks me for photos and other artwork. 6. Claude builds the posts in Metricool and saves them as drafts for me to review according to our working rules. 7. I review the drafts and move them to pending. 8. Claude checks the publishing date and time and creates links with meaningful UTM parameters. 9. After publication, I ask Claude to analyze the results and identify what is resonating with my market. 10. I use the Metricool data and Claude’s analysis to test different approaches, content, and topics in the next round of posts.

Faceplant is a real full-stack app, not a mockup. It uses a FastAPI and PostgreSQL backend, a React and MUI frontend, and the Anthropic API (Claude) to power bot replies. The core is the reaction engine. When a human posts, the backend schedules two timed waves of reaction jobs. A background scheduler built with APScheduler polls for due jobs, calls Claude for an in-persona reply, and writes that bot’s comment and like. The 56 personas are stored as data in a roster file. Adding a voice requires only one new entry, so the crowd can scale without additional code. A subset of the personas are GIF-first bots: they ask the model for a caption and search tag, then pull a matching GIF from Giphy. The part I’m proudest of is the honesty layer. Every Claude call is metered and priced, and “The Meter” rolls the data up live with the cost per post, the dollar-per-minute burn rate, and a “spent on nobody” line for bot-to-bot chatter with no human at either end. A “% human” badge drains toward “dead internet” for each thread. The dead-internet loop—bots posting and replying to one another with no human present—is disabled by default and protected by three guardrails: generation decay, a per-thread cap, and a global spend kill switch. Optional cost controls include the Message Batches API at half price and prompt caching for a shared house-style prompt. The whole project is a working demonstration that manufactured engagement is cheap to produce and expensive to mean anything. Step-by-step: 1. I built the app with a FastAPI and PostgreSQL backend, a React and MUI frontend, and Claude replies powered by the Anthropic API. 2. When a human posts, the backend schedules two timed waves of reaction jobs. 3. APScheduler polls for due jobs and triggers Claude to generate an in-persona reply before writing the bot’s comment and like. 4. I keep the 56 personas in a roster file so adding a voice requires only one new data entry. 5. For GIF-first bots, I have the model generate a caption and search tag, then use that tag to pull a matching GIF from Giphy. 6. I meter and price every Claude call, then display live cost per post, dollar-per-minute burn, “spent on nobody” costs, and the “% human” status for each thread. 7. I keep bot-to-bot activity disabled by default and limit it with generation decay, a per-thread cap, and a global spend kill switch. 8. I can reduce costs further with the Message Batches API at half price and prompt caching on a shared house-style prompt.

I use AI to create personalized tools and social stories that help therapists and group-home staff better understand my son’s complex disability and support his independence as an adult. Step-by-step: 1. I use AI to create personalized tools for my son. 2. I create social stories that help therapists and group-home staff better understand his complex disability. 3. I use these resources to support his independence as an adult.

I wanted to see what Awish would automate first if I gave it a real client business instead of a predefined workflow. I used Martolia Marble, one of my previous clients, and gave Awish the website. I asked it to analyze the business, suggest automations that could support growth, and build them only after I approved them. Awish analyzed the website and proposed several opportunities. I chose two: a lead generation and email outreach workflow, and an Instagram workflow that tracks relevant trends, plans content, creates posts, and publishes them. Step-by-step: 1. I gave Awish the Martolia Marble website and asked it to analyze the business. 2. Awish identified areas where automation could support growth and suggested workflows. 3. I approved a lead generation workflow that finds relevant prospects and prepares personalized email outreach. 4. Awish selected the required apps, I connected the accounts, and the agents built the workflow. 5. I also approved an Instagram workflow that analyzes relevant trends, plans upcoming content, creates the posts, and publishes them. 6. Once the accounts were connected, both automations were ready to run without me manually building each step. What I liked most was starting with the business rather than with an automation idea. I gave Awish the website, reviewed its suggestions, and approved only the workflows that made sense for the client.