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Automate a Weekly Workout Log with Claude and Hevy

Every Friday at 10 a.m. ET, a scheduled Claude agent refreshes a living "Hevy Workout Log" document with that week's strength-training data, body measurements, and a progress photo. The automation runs fully unattended, with no human in the loop after the owner sets it up. The agent pulls information from three independent systems: the Hevy fitness-tracking API, a Google Drive photo folder, and the living document. It reconciles those sources against a date range, determines what's new versus already recorded, and writes structured, formatted updates in the correct places. This requires more judgment than a simple script or webhook. The scheduled task starts a fresh agent session every Friday at 10 a.m. ET with the full task specification and API key included in the prompt. The Hevy API is the source of truth for workouts from `GET /v1/workouts` and body measurements from `GET /v1/body_measurements`; both endpoints are paginated. Google Drive holds the "Progress Pics" folder, which the agent searches by file timestamp. Claude Docs, through a Claude Artifact, is the living document: it has one tab for each weekday and a Weekly Summary tab, and the agent edits it in place with structured operations rather than full rewrites. To embed a photo, the agent downloads the Drive file locally, uploads it to the Claude Artifact asset store, converts it to a document-native blob, and references it as a Markdown image. There is no custom backend, database, or glue code. The agent calls each system's API directly and keeps the orchestration logic in its reasoning for that run. Step-by-step: 1. Read the document's current state and find the most recent week already recorded in the Weekly Summary tab. 2. Call the Hevy API and determine which Monday–Friday week has just ended, making sure today's date is past that week's Friday. 3. Check whether that week's row is already in the Weekly Summary table, fully populated, and contains the latest matching photo. If everything is already recorded, there may be nothing to update, but the agent still checks for a newer photo that may have appeared in Drive after the data was written. 4. Pull per-set details for every workout that week, including the exercise, weight, and reps. Compute total daily lifted volume by summing weight × reps for each set on each day. 5. Search the Google Drive photos folder for images timestamped within that Monday–Sunday window. Select the single most recent image, replacing any older photo already embedded for that week. 6. Pull body measurements from the same week and calculate weekly averages for weight and body fat, along with the week's waist reading. Convert units as needed from kilograms to pounds and centimeters to inches. 7. Update the Weekly Summary tab with one row per week, including daily volumes, weekly averages, and the progress photo. 8. Update each weekday tab with a dated section containing the workout name, a per-exercise set-by-set table, and a Total row for summed volume and reps. If no workout was logged, add a note instead. 9. Send the human a plain-language summary every time, describing what changed or confirming that nothing changed, because nobody is watching the scheduled run live. The workflow is idempotent without a database. There is no job-tracking table; on each run, the agent reads the live document, compares dates, and checks whether cells are filled before deciding what to write. Progress photos are not tagged by week, so the agent matches them only by whether their Drive timestamp falls within the Monday–Sunday window. It explicitly selects the most recent match, allowing a better photo taken later in the week to replace an earlier embedded photo. The workflow also handles the multi-hop process required to embed an image in a live document: Drive file to local download, artifact asset upload, document-native blob conversion, and Markdown image reference. If the Hevy API key is revoked, the task stops cleanly and tells the owner to generate a new key and provide it, rather than failing silently or looping. Because the workflow runs on a schedule without anyone watching, its specification requires a notification every time, including when nothing changed. This keeps the automation auditable even when it performs no updates.

Bridget Overson
  • Claude Cowork
  • Google Drive
2

Build a Voice-Powered Family Memory App for Everyday Information

“Honey, do you remember where Ryan’s practice is? Do you remember our Wi-Fi password? Do you know my TSA PRE number? Remember who that painter was that we used last year?” If you have a family with children, you may hear questions like these all the time. In a large family, there always seem to be questions about the house, the kids, or technology that could be answered more easily. The information is usually stored in different places: password files, a Rolodex of business cards, or sheets of paper in junk drawers. I set out to build an app where anyone in my family could use voice to save something for the future or retrieve something they had already stored. I integrated AI to understand the meaning of each voice note. It might create a calendar event, store a password in a vault, or simply remember a phone number. Then anyone else in the family could access the information. The app uses multi-factor authentication for anything particularly secret. It is not intended to be a dedicated password protector; I would build in much more security if that were the goal. Instead, it is an everyday-life app for remembering the little things: How long is the warranty on this appliance? Which exact light bulbs did we use here last time? What was our daughter’s email address that we needed to set up on her phone? Being able to use voice to enter information or retrieve it was the key for me. I think that will save people time. As I started adding entries and validating the idea, my wife came up with the name. After a few weeks, I knew she was completely right, because I now use it about 10 times a day—and I hear that exact phrase every time. Step-by-step: 1. I identified recurring family questions about locations, passwords, identification numbers, contacts, warranties, and household items. 2. I built an app that lets family members use voice to save or retrieve information. 3. I integrated AI to interpret the meaning of each voice note and determine whether to create a calendar event, store a password in a vault, or remember a phone number. 4. I made the stored information accessible to other family members. 5. I added multi-factor authentication for information that is particularly secret, while keeping the app focused on everyday memory rather than dedicated password protection. 6. I added entries and validated the idea through regular use, eventually using the app about 10 times a day.

Thomas (Josh) Thephasdin
  • ChatGPT
9

Remember everything your friends recommend—restaurants, books, streaming series, jokes, and more

I first tried to build Rumbly.xyz about six months ago using Lovable, but I ran into several issues and kept getting errors, so I temporarily gave up. Then I discovered Replit. AI had also matured over those six months, which likely helped. I entered a three- or four-paragraph prompt describing exactly what I wanted, and about 75% of the app was built immediately. The problem I wanted to solve was simple: on an average Tuesday or Wednesday, my wife and I talk about where we want to eat. We know our friends have recommended countless places, and we keep saying we're going to try them, but we can only think of the two or three restaurants we always visit. So we end up going to the same places again. I built Rumbly.xyz as a simple web app. It lets you record a recommended restaurant, who recommended it, and what type of food it serves. Rumbly connects each restaurant to the Google Maps database, giving you the exact name and a map view, and you can add notes. Then I realized there was no reason to save only restaurant recommendations. People also recommend streaming series and books, and they tell jokes. I can never remember a joke two months later when someone asks, “Do you know any good jokes?”—even if I thought the last five were hilarious. Rumbly.xyz lets you save any of these recommendations from your friends and share them with friends who are already on Rumbly or have not joined yet. I connected it to email and enabled it to send mobile messages to your phone. I built all of this through Replit. Step-by-step: 1. I described the problem and the app I wanted in a three- or four-paragraph prompt in Replit. 2. I built a web app that records restaurant recommendations, the person who made each recommendation, the type of food, and additional notes. 3. I connected restaurant entries to the Google Maps database for exact names and a map view. 4. I expanded the app to save recommendations for streaming series, books, and jokes—not just restaurants. 5. I connected Rumbly.xyz to email and mobile messaging so recommendations can be shared with friends who are on Rumbly or have not joined yet.

Anon Ocelot
2

Research App

I’m a 71 year old retired cattleman and recently started learning about AI. After learning to build some simple apps I recently built a research tool using Replit. You type in any question or topic, and instead of just giving you a generic AI answer, it actually goes out and does real research — searches the web, pulls official SEC filings for company data, checks peer-reviewed medical journals, reads financial pages for live stock quotes, and even reviews documents you upload. It breaks your question into multiple sub-questions, researches each one separately, then writes a full report with cited sources. It has different modes depending on what you need — general research, company due diligence, market analysis, competitive intelligence, legal document review, and stock picks with full investment breakdowns. I've been using it to research stocks for my investment club, dig into companies before making decisions, and get up-to-speed on markets fast. It's like having a research analyst on call 24/7.

Anon Octopus
1

Created a restaurant recommendation site for the top 40 cities in the world

I first wrote a few paragraphs in ChatGPT explaining what I wanted and asked it to create a detailed prompt for Replit to build the site. Then I bought a domain through GoDaddy, and the site was live online less than an hour after I came up with the idea. Step-by-step: 1. I described my idea for a restaurant recommendation site featuring the top 40 cities in the world to ChatGPT. 2. I asked ChatGPT to create a detailed prompt for Replit. 3. I used the prompt in Replit to build the site. 4. I bought a domain through GoDaddy. 5. I launched the site online less than an hour after coming up with the idea.

Peter Vogel
  • ChatGPT
3

Build an AI Project Management App for a Church Renovation

I’m 71 years old, retired from owning a large cattle-feeding operation, and fairly new to AI and software development. I’ve discovered that I really enjoy using AI to build practical tools in Replit. My latest project is an app for our church to help us oversee a $13.5 million renovation of a 65,000-square-foot former movie theater into our new church facility. I’m not a programmer or a construction expert, so I’m using AI to help bridge both gaps. I built the app almost entirely by describing in plain English what I wanted it to do, testing what it built, and working back and forth with AI to improve it. The app uses AI to read meeting notes, emails, texts, and general project updates and identify decisions, action items, important dates, budget changes, and project events. Nothing is accepted automatically. I review, edit, approve, or reject what AI finds before it becomes part of the project record. The app also keeps our budgets, documents, meetings, and project history together. My goal isn’t to replace our project manager or construction software. I want to see whether someone my age, with no programming or construction background, can use AI to build a useful tool for helping an owner understand and manage a complicated real-world project. Step-by-step: 1. I described in plain English what I wanted the app to do. 2. I used AI in Replit to build the app based on those descriptions. 3. I tested what AI built and worked back and forth with it to improve the app. 4. I designed the app to read meeting notes, emails, texts, and general project updates. 5. I use it to identify decisions, action items, important dates, budget changes, and project events. 6. I review, edit, approve, or reject every item before it becomes part of the project record. 7. I use the app to keep our budgets, documents, meetings, and project history together.

Anon Octopus
  • ChatGPT
  • Claude Code
  • Gemini
2