GymPro

A workout tracker with an AI coach, in English and Traditional Chinese. It has three AI features, and an eval suite decides what ships in each:

• Ronnie: a coaching agent that works on the user's own training data.
• Coach G: a routine planner.
• The weekly report: a training review.

Live: gympro.chichi.mom. I log every workout in it.

Highlights

• An agent with guardrails. Ronnie is a Claude tool-use agent with 14 tools over the user's data and a knowledge base of 151 papers.
  • Reads are free. A change to today's workout that the user names is made at once. A change Ronnie picks, and any change to a routine, is shown as a card, and code applies it when the user taps it.
  • Code writes every line that states a fact: memory receipts, plan summaries, corrections.
  • A reply that contradicts today's log is caught before it's shown.
• Evals decide what ships.
  • 99 scripted conversations against a fixture user, plus 146 search queries and 44 report scenarios.
  • Graded by code checks and an LLM judge.
  • Every run is recorded in the repo, and the model for each feature was chosen by eval.
  • Mistakes found in evals are replayed in CI on every pull request, at no cost.
• Facts in code, words from the model.
  • The weekly report's numbers and advice come from a rule table in code, backed by 27 cited papers whose DOIs are checked in CI. Code writes what to do next session on each lift; the model only phrases the rest.
  • Coach G's plan is built by code; a small model only reads the user's free text.
• Measured, then tuned.
  • Each page reports where its server time goes.
  • Tab switches went from 0.4–0.6 s to 0.16–0.33 s. The old and new data loaders were compared on 1,720 runs before the change shipped.
  • A CI check makes every write count, so a page never shows data from before the user's own change.
• Production infrastructure in Terraform.
  • Vercel and Supabase, with AWS for the report pipeline and the daily reminder: SQS, Lambda and EventBridge Scheduler.
  • No long-lived AWS keys.
  • 15 CloudWatch alarms. Every AI call is traced with its tokens and cost.
  • Every model call asks a spend limit first: one per user, and one for all users together.

What it does

Log	One set table for strength and cardio, with 8 log types, from weight × reps to treadmill and rower. Units are per exercise (kg or lb, km or mi). Each lift shows today's target, by the weekly report's own rule. There is also a daily note and body weight.
Plan	Routines, and a training cycle of 1 to 28 days that puts one on each day. A month calendar shows what's planned and what was done, and a day ahead can take another routine or become a rest day.
Ronnie	A chat coach. "My elbow hurts, what instead of pushdowns?", "What's left today?", "Plan me a 4-day week.", "Will creatine make my hair fall out?" It remembers injuries, likes and dislikes, equipment and schedule across conversations, and answers fitness questions from research notes, with the papers listed under the reply.
Coach G	The user answers a few questions and gets a week of routines built from a 190-exercise library, rated for muscles and joint load.
Weekly report	Made when the user's week ends, Monday to Sunday in their time zone. It shows the goal's progress, the key numbers and follow-up on last week's advice, then what to do next session on each lift, with the research behind each piece. A week under 10 working sets gets its numbers only, with no model call.
Reminder	At 9 PM in the user's time zone, a push notification asks "Did you train today?", except on a rest day. It is turned on in Settings.
Layouts	Separate phone and desktop layouts. On iPhone it installs to the home screen.

The screenshots are from a demo account with made-up training (supabase/seed/demo_account.sql). The report and Ronnie's replies in them are real model output.

Architecture

flowchart LR
    user["Browser / home-screen app"] --> app["Next.js 16 on Vercel"]
    app -->|"queries under row-level security"| db[("Supabase Postgres + pgvector")]
    app -->|"Ronnie, Coach G"| claude["Claude API"]
    app -->|"Vercel OIDC → IAM role"| bedrock["Bedrock: Cohere Embed v4"]
    app -->|"Vercel OIDC → IAM role"| sqs["SQS report queue"]
    sched["EventBridge Scheduler, hourly"] --> schedfn["Scheduler Lambda"]
    schedfn --> sqs
    sqs --> worker["Report worker Lambda"]
    worker --> db
    worker -->|"report text"| claude
    schedfn -->|"AI health metrics"| cw["CloudWatch alarms → SNS email"]
    daily["EventBridge Scheduler, daily + Monday"] --> reviewfn["Review Lambda"]
    reviewfn -->|"each ended day of Ronnie chats"| db
    reviewfn -->|"review"| claude
    reviewfn -->|"weekly digest"| mail["SNS email"]
    hourly["EventBridge Scheduler, hourly"] --> remindfn["Reminder Lambda"]
    remindfn --> db
    remindfn -->|"Web Push, VAPID"| push["Push services → the user's device"]

• App: Next.js 16 on Vercel, in English and Traditional Chinese.
• Data: Supabase Postgres, with row-level security on every user table, and pgvector for search.
• Reports: an hourly scheduler queues each user's report when their week ends in their own time zone. A Lambda worker builds it, with retries and a dead-letter queue.
• Reminders: an hourly Lambda sends a Web Push to each user whose local time is 21:00.
• AWS access: Vercel assumes IAM roles through OIDC, and keys live in SSM Parameter Store. Everything AWS-side is in infra/terraform.

This is the beginning of the README. Read the rest at the original. Read the full README ↗