I used an AI assistant to draft a volunteer management handbook for an arts organization. I wanted a complete starting document I could review and adapt, rather than building one from scratch. 1. I asked the AI to draft a volunteer handbook for the arts organization. 2. It pulled information from the organization's website. 3. It referenced relevant state laws. 4. It included the organization's current volunteer opportunities. 5. It reflected the organization's DEI policies. 6. It took the needs of the Board of Directors into account. 7. It wrote job descriptions and training opportunities for each volunteer role. The output was an 18-page volunteer management handbook with job descriptions and training for each role. The document is fabulous.

Mid- and end-of-year self-evaluations used to cost me a week or two of reconstructing a year of work from memory. I rarely recorded or remembered the “small things,” and I couldn’t easily explain how my daily work connected to our goals. People often forget their most impactful work months later, so I wanted continuous capture instead of annual reflection. I also wanted my work goals and development plan grounded in real evidence rather than guesswork. I built an “Achievement Collector” agent as a sub-agent of my Chief of Staff agent. When my Chief of Staff agent recognizes something important, it formats the accomplishment into an Achievement record, asks whether I want to record it, and sends it to the Achievement Collector agent. SharePoint is the system of record, with fields including Details, Business Impact, Key Insights, Related Goal, Significance, and a flag for accomplishments I’m especially proud of. A Power Automate flow lets me log an accomplishment conversationally instead of filling out a form. A Copilot Studio agent sits on top of the list and reads three work-goal documents—mine, my manager’s, and my director’s—so it can map each accomplishment up the chain. Its instructions tell it to prioritize what I’m proudest of, group work into themes, surface business impact, and never invent anything. The result is that my self-evaluation and goal-progress summaries are now queries against real records instead of writing projects. If you want to build one, start with the data-list fields: capturing impact and insight at the moment of the work is what makes everything downstream possible. The combination of my Chief of Staff agent and Achievement Collector has made work a lot more fun for me and turned me into a laser-focused employee. Step-by-step: 1. I created an “Achievement Collector” agent as a sub-agent of my Chief of Staff agent. 2. I configured the Chief of Staff agent to recognize important work, format it into an Achievement record, ask whether I want to save it, and send it to the Achievement Collector. 3. I created a SharePoint list as the system of record, with fields for Details, Business Impact, Key Insights, Related Goal, Significance, and a flag for accomplishments I’m especially proud of. 4. I used Power Automate to let me log accomplishments conversationally instead of filling out a form. 5. I placed a Copilot Studio agent over the SharePoint list and three work-goal documents: mine, my manager’s, and my director’s. 6. I instructed the Copilot Studio agent to map accomplishments up the goal hierarchy, prioritize what I’m proudest of, group work into themes, surface business impact, and never invent anything. 7. I now query the accumulated records when creating self-evaluations and goal-progress summaries instead of reconstructing my work from memory.

I use a Claude skill to reduce the manual work involved in my job search. It searches for the latest roles, assists with my applications, and tracks my application journey. I can fully automate the process with Claude scheduling. I download the SKILL file from my GitHub repository (https://github.com/petehawtree/job-search-tracker-skill) and launch it in Claude Desktop. Alternatively, I can download the entire repository and recreate it in my Claude skills directory. I schedule it to run each day or trigger it manually. Each run summarizes the latest activity and tracks it against previous runs. The workflow includes a dashboard for monitoring roles and applications, along with a daily digest that guides me through the new roles it finds. Step-by-step: 1. I download the SKILL file from https://github.com/petehawtree/job-search-tracker-skill. 2. I launch it in Claude Desktop, or download the entire repository and recreate it in my Claude skills directory. 3. I put the workflow on a schedule or trigger it manually each day. 4. I review the daily summary, which is tracked against previous runs. 5. I use the dashboard to monitor roles and applications. 6. I review the daily digest for guidance on the new roles the workflow finds.

In my ongoing mission to go paperless, I created a web app that runs on tablets and replaces paper sign-in sheets. The admin console gives HR instant visibility and detailed reporting on employee and guest attendance across all our branches from a single pane of glass. In Phase 2, I’m expanding the app to eliminate even more paperwork by allowing employees and managers to request, approve, and manage PTO. Department calendars show everyone’s PTO balance and minimum coverage requirements. Step-by-step: 1. I created a web app that runs on tablets to replace paper sign-in sheets. 2. I added an admin console so HR can view and report on employee and guest attendance across all branches. 3. In Phase 2, I expanded the app to support employee and manager PTO requests, approvals, and management. 4. I added department calendars showing PTO balances and minimum coverage requirements.

I built Lumi, an HR assistant in French, with Claude Code in about two hours. It was an experiment: could Claude Code build a working product from a plain brief while being left completely alone? Employees ask questions, and Lumi answers using their company’s own HR documents. Each company has its own space, and HR can see which questions the documents couldn’t answer. Step-by-step: 1. I set up one window with Claude Code on one side and the files appearing on the other. I didn’t write code, but I could always see what was being built. 2. I described the product in plain language. I dictated my brief out loud, typos included: who pays, who uses it, the problem, a demo client, keeping clients separate, the admin view, the language, and the price. 3. I let Claude Code build the product and make every call. Thirty-four minutes later, the first version had 47 files and 3,949 lines. It even invented a fictional client company with a full HR handbook so I could demo it immediately. 4. I put the product online. Claude Code wrote a setup guide for Supabase and Vercel. I followed it one step at a time and sent a screenshot whenever something broke. 5. I improved it with feedback. I told Claude Code that the design looked like it had been created by a junior designer. Eighteen minutes later, it came back with a name, a design system, pricing plans, and everything in French. 6. I checked security, although I had skipped this during the build. When I asked for a full security review eleven weeks later, it took less than six minutes and found three serious problems, including a database function that allowed anyone to read a company’s documents. Claude Code fixed all three. Run `/security-review` before sharing the link and before each new change. The full write-up, including the unedited prompts: https://www.theaithinker.com/p/how-to-build-a-saas-product-with

We're currently hiring for a Creative Strategist at The Rundown, so I gave Claude our job description and asked it to search LinkedIn for high-quality candidates. It took control of my browser, searched for key terms, and created a spreadsheet with 20 high-quality candidates. I then asked it to craft personalized messaging for each and do outreach for me. It took me about 15 minutes to review the spreadsheet and refine the messaging, but it saved me 2-3 hours of manual work! Step-by-step: 1. I gave Claude the Creative Strategist job description and the qualities that defined a strong candidate. 2. I asked it to control the browser and search LinkedIn using the most relevant terms. 3. I had it capture 20 qualified candidates in a spreadsheet so I could review them efficiently. 4. I asked Claude to draft personalized outreach for each person based on their background. 5. I spent about 15 minutes reviewing the list and refining the messaging before allowing the outreach to proceed.

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 had a bad workplace experience, so I built Doris: a saucy but loving anonymous AI aunt who remembers the tea, protects storytellers, and warns others. I built Spill Tea with Doris, an anonymous AI workplace confessional for conversations people cannot really have on LinkedIn: the bad manager, the inexplicable reorg, the coworker who somehow survives every layoff, and the meeting that should probably be entered into evidence. Doris does something more interesting than simply listen. She remembers the tea—and, carefully, spills it. The problem I wanted to solve was not really “chat with an AI.” Most AI conversations are disposable, but workplace stories are not. They accumulate companies, people, reorganizations, layoffs, recurring behaviors, management decisions, and institutional weirdness. At the same time, people are understandably reluctant to talk openly about their employers because a sufficiently specific story can identify its author. I designed Doris around a different idea: retain the knowledge without retaining the storyteller’s identity. Someone visits [spillteawithdoris.com](https://spillteawithdoris.com) and tells Doris what happened at work. The application is built in Next.js and deployed through Vercel. The conversation goes to an AI model with Doris’s personality and behavioral rules. Redis handles temporary conversational context, while Neon Postgres and Prisma maintain the structured, longer-lived pieces of the story—companies, people, events, and their relationships. Rather than treating every conversation as one giant transcript, the application extracts useful information and connects it to the larger story of a company. That creates the second half of the experience. When another visitor asks Doris, “Have you heard anything about working at Company X?”, she can draw upon what previous visitors have told her. But she does not simply retrieve someone’s confession and repeat it. The system separates what is useful about a story from what could identify the person who told it. Names, exact teams, precise dates, unusual job titles, and other unnecessarily identifying details do not need to travel with the underlying observation. Doris can instead recognize that she has heard several stories involving reorganizations, unusual management turnover, or a particular cultural complaint. Then Doris tells the story herself, in Doris’s voice. She might say that she’s “heard some tea” about a company, explain the general pattern, distinguish something she’s heard once from something that appears repeatedly, and avoid pretending anonymous reports are established facts. Visitors get useful institutional memory without being handed the breadcrumbs needed to identify an individual employee. Public information can provide a second layer of context. If appropriate, Doris can search for publicly available information about a company and compare it with what people have privately described. Those sources remain conceptually separate: what Doris can verify publicly, what Doris has heard privately, and what Doris herself infers should never become the same thing. The result is deliberately a little strange. It is an anti-LinkedIn. LinkedIn is where thousands of individual experiences are polished until every company sounds wonderful and every departure is an exciting new chapter. Doris works in the opposite direction. One anonymous story may just be a story. Ten people independently telling Doris versions of the same story start to describe a workplace. And Doris remembers. She just doesn’t need to remember who told her. Step-by-step: 1. I built a Next.js application and deployed it through Vercel at spillteawithdoris.com. 2. I defined Doris’s personality and behavioral rules for the AI model. 3. I added Redis to manage temporary conversational context. 4. I created a Neon Postgres database and used Prisma to model Company, Person, Story, and Event relationships. 5. I built an extraction layer that converts conversations into structured observations, removes unnecessary identifying information, and associates the knowledge with the appropriate company. 6. I built retrieval so Doris can find relevant prior observations when someone asks about a company. 7. I had the AI synthesize those observations in Doris’s voice instead of quoting or exposing the original submissions. 8. When appropriate, I let Doris search publicly available company information while keeping public sources, private reports, and Doris’s inferences conceptually separate.

I built a job-offer net-net calculator to determine what a proposed job is actually worth to the employee’s household after taxes, employment-related expenses, benefit changes, commuting costs, and the economic value of commuting time. Gross salary should not be treated as household value. The calculator should interview me first and ask only for information that materially affects the calculation. If I do not know a number, it should identify it as an estimate and use a clearly labeled reasonable placeholder only with my permission. Step-by-step: 1. Collect the proposed annual gross salary. 2. Collect the employee’s state and locality, tax filing status, and other household taxable income when needed to estimate the incremental federal tax burden accurately. 3. Record the work arrangement: fully remote, hybrid, or on-site. 4. Record the number of commuting days per week and working weeks per year. 5. Collect the home location and job location, or the known one-way mileage. 6. Collect the vehicle make, model, year, drivetrain, and approximate MPG when available, along with current annual vehicle mileage if relevant. 7. Collect the gasoline price or ask permission to obtain a current local price. 8. Record tolls, parking, and the expected change in automobile insurance. If the insurance change is unknown, keep it as a clearly labeled placeholder rather than presenting it as fact. 9. Record employee medical, dental, and vision premiums under the proposed job, along with current household medical, dental, and vision expenses that would disappear if the employee accepts the job. 10. Record the employee’s current or previous annual compensation, or another hourly value to use for valuing commuting time. 11. Record any other recurring costs caused by accepting the job and any recurring household expenses that would disappear because of accepting it. 12. Estimate after-tax pay as follows: - Gross salary - Federal income tax - State and local income tax - Social Security - Medicare - Estimated after-tax salary Use current tax rules when web access is available, and distinguish estimated withholding from actual tax liability when relevant. 13. Calculate annual commute miles: - Round-trip miles × commuting days per week × working weeks per year 14. Estimate the incremental cash cost caused by commuting, including fuel, maintenance, oil and service, tires, repairs, tolls, parking, and the incremental auto-insurance premium when applicable. Do not double-count expenses. 15. Separately estimate additional depreciation and other mileage-related economic costs that were not already included. If using an all-in per-mile vehicle-cost benchmark, do not also add its constituent costs separately. Explicitly state which method was used. 16. Calculate annual commuting hours: - Estimated minutes each direction × 2 × commuting days per week × working weeks per year ÷ 60 17. Calculate the employee’s implied hourly value: - Annual benchmark compensation ÷ 2,080 Multiply annual commuting hours by the hourly value to calculate the economic value of commuting time. Treat this as an economic cost, not a cash expense. 18. Calculate the employee’s annual out-of-pocket premiums for medical, dental, vision, and other mandatory benefits. Separately calculate any current household expense that would disappear because the new employer provides that benefit. 19. Calculate the change from the current household baseline. Do not subtract an existing expense and then add the same expense back in the same scenario. 20. Calculate cash net as: - Estimated after-tax salary - Incremental cash commuting costs - Incremental insurance costs - Employee-paid benefit premiums - Other new cash expenses + Current household expenses eliminated by taking the job This represents the approximate improvement in household cash flow attributable to the job. 21. Calculate economic net-net as: - Cash net - Value of commuting time - Incremental vehicle depreciation or other non-cash economic costs not already counted This represents the broader economic value of the job. 22. Start the final output with only these numbers: - GROSS SALARY: $____ - ESTIMATED AFTER-TAX PAY: $____ - ANNUAL CASH NET: $____ - MONTHLY CASH NET: $____ - ANNUAL ECONOMIC NET-NET: $____ - MONTHLY ECONOMIC NET-NET: $____ 23. Then provide the calculation table. 24. Finally, provide the three largest cost items, the three assumptions with the greatest uncertainty, the break-even gross salary at which accepting the job would produce the target household net value I specify, and any information I should verify before making the decision.