I fed Claude my sweat-test data, and it formulated a DIY high-carb cycling mix tuned to my sweat chemistry. It also scaled the batch to match my available supplies and generated print-ready labels and batch sheets. Step-by-step: 1. I provided Claude with my sweat-test data. 2. I used Claude to formulate a DIY high-carb cycling mix tuned to my sweat chemistry. 3. I had Claude scale the batch to match my available supplies. 4. I had Claude generate print-ready labels and batch sheets.

I used Claude Code to find scientific seminars hosted by colleges and universities across the US and collate them into a single, searchable list. Researchers can use it to find talks that match their interests near them. The site, seminarsearch.org, runs mostly on free-tier SaaS services, costs me $5 per month to operate, and currently supports a few thousand users. The same pattern could be adapted to collate all 5K runs in the US, tennis matches, craft shows, quilting bees, or any other type of event that interests you. I started by explaining to Claude what I wanted to build. We then spent a few hours exploring where and how to identify seminar calendars at universities across the US, which was arguably the hardest part. We developed a pluggable framework with scraping plugins for the common CMSs used by universities, along with iCal parsers, HTML parsers, and JavaScript interpreters to handle the complexity of normalizing content from hundreds of departmental websites. Once we had the seminar-scraping infrastructure, we built a database schema in Supabase and simple APIs to create, read, update, and delete records. The APIs are written in Python and hosted on Railway for $5 per month. We used Vercel to deploy a simple React-based frontend for presenting the data. Now I can ask Claude to implement a feature or add a new university to the list of sources. It figures out where the data is hosted, creates entries for the university and its departments, finds their seminar feeds, collects the seminar data, puts it into the database, and runs GitHub Actions to send alerts to registered users. The scraping job runs once a week to collect new data, and it also runs through GitHub Actions. I wrote zero code during this process and have never even looked at the code. I only had to paste a few error messages from Railway into Claude so it could debug them, and there were only a few errors. Since building seminarsearch.org, I have cloned the infrastructure and codebase and modified it to build a site that collects road bike rides and group mountain bike rides across the country. The system is highly extensible to new areas of interest and is useful when traveling or living in a large metropolitan area where it is difficult to keep track of all the opportunities available for your interests. Step-by-step: 1. I explained to Claude Code that I wanted to build a searchable directory of scientific seminars hosted by colleges and universities across the US. 2. We explored where and how to identify university seminar calendars, focusing on the challenge of finding sources across many institutions. 3. We built a pluggable scraping framework with plugins for common university CMSs, plus iCal parsers, HTML parsers, and JavaScript interpreters. 4. We created a Supabase database schema and simple Python APIs for creating, reading, updating, and deleting seminar records. 5. We hosted the APIs on Railway for $5 per month and deployed a React-based frontend on Vercel. 6. We configured Claude to add universities and implement features by finding source locations, creating university and department records, locating seminar feeds, collecting seminar data, and adding it to the database. 7. We used GitHub Actions to run the weekly scraping job and send alerts to registered users. 8. When Railway returned errors, I pasted a few error messages into Claude so it could debug them. 9. I cloned and modified the infrastructure and codebase to create additional sites for road bike rides and group mountain bike rides across the country.

As a hobby/non-work-related creative outlet, I've started to write and create YouTube videos about retro football (soccer) stories. Part of the visuals are vintage stickers of the players mentioned. This used to be a time-intensive Google/eBay search when I was looking for 20+ players at a time. So I used Claude to build a local "Sticker Search" tool. It lets me enter the player, season, and team, find their sticker from that year, and download it to my working folder with one click. It's made my hobby way more fun and sped up the tiresome bit. P.S. I used Billy's guide on Manus to create the animated logo for the videos! Step-by-step: 1. I listed the inputs I needed for each search: player, team, and season. 2. I used Claude to build the local Sticker Search interface and search logic. 3. I added recent-search shortcuts so I could repeat or refine searches quickly. 4. I made each result downloadable with one click to my working folder. 5. I used Billy's Manus guide to create the animated logo for the videos.

A while ago, my dad told me he wanted to use AI to analyze World Cup matches. He had never coded before, and his AI experience was mostly limited to occasionally asking Gemini a question. I installed Claude Code for him and gave him a few prompting tips. He ended up building a full football analytics site himself and deploying it on Vercel so he could show it to his friends. Getting started with AI is easier than people assume. Sometimes, you just need a project you genuinely want to make. Now that the Premier League season has kicked off, he’s already reworking the site for it. Step-by-step: 1. I helped my dad choose a football analytics project he genuinely wanted to build for analyzing World Cup matches. 2. I installed Claude Code for him, since he had never coded before. 3. I gave him a few prompting tips and let him use AI to help build the site. 4. He built a full football analytics site himself. 5. He deployed the site on Vercel so he could show it to his friends. 6. After the Premier League season kicked off, he began reworking the site for the new competition.

This World Cup 2026 poetry generator scraped the web for match information and called LLMs to help users generate match-specific poems. 1. A daily schedule triggered web scraping to collect match information. 2. When a user requested a poem, the generator used that match information to create a new match-specific poem with help from LLMs.

I used /goal in OpenAI's Codex to build a Magic: The Gathering app so my brother and I can play asynchronously without needing to coordinate a call or awkwardly play over FaceTime. The idea is to let each of us take turns when we have time, track the board state cleanly, and keep a game going over days instead of trying to line up schedules. The command allowed Codex to continue running until everything was done, basically one-shotting exactly what I was looking for without any intervention. Step-by-step: 1. I defined the core problem: two people needed to play asynchronously without coordinating a call. 2. I described the app’s essential behavior, including turn-taking, persistent board state, and games that continue over multiple days. 3. I used /goal in Codex so the build could continue autonomously toward the finished result. 4. I let Codex implement the app and resolve the work without requiring step-by-step intervention. 5. I tested the finished flow to make sure each player could return later and continue from the correct state.

My fantasy baseball auction draft is around the corner, and I decided to try Claude Cowork for the planning and research efforts this year. I uploaded my league's settings, the players being kept across the league, and my own rambling of strategies and thoughts on my current players. Claude then performed a deep analysis of my current options and their projected values, provided strong recs on who I should prioritize, and gave me a detailed list of draft targets around the league, perfectly tailored to my needs and league format — also scouring the web for tons of articles that saved me the tedious manual searching. Step-by-step: 1. I uploaded the fantasy league settings and the full list of players being kept by other teams. 2. I added my own current players, draft strategy, and rough thoughts about possible approaches. 3. I asked Claude Cowork to analyze the available options and projected player values. 4. I had it search the web for relevant articles and incorporate that research into the recommendations. 5. I turned the result into a tailored list of priorities and draft targets for the auction.