I Let Google's Free AI Code for Me for a Week

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Google Jules is an autonomous coding agent that works 24/7, handles 60 tasks simultaneously and costs nothing. I gave it my messiest GitHub repo and a list of 50 bugs.

Here's what happened. Monday, 9 AM. I connected Jules to my personal project, a fitness tracking app I'd been meaning to fix for months.

The issues list was embarrassing: broken authentication, mobile UI glitches, database queries timing out, missing test coverage.

50 issues total. Most would take me 30-60 minutes each.

I wrote one prompt: "Fix all issues labeled 'bug' in priority order. Write tests for each fix. Create pull requests with detailed explanations."

Then I went to get coffee.

When I came back 6 hours later, Jules had completed 12 tasks.

By Friday, the count was 47 completed, 2 failed, 1 still in progress.

My total hands-on time: 3 hours reviewing pull requests and approving merges.

Jules' time: 168 hours (working continuously).

This isn't a demo. This is what actually happened.

What Jules Actually Does (And Why It's Different)

Jules isn't GitHub Copilot. Copilot suggests code as you type. You're still writing, Jules is assisting.

Jules is autonomous. You give it a task. It completes the entire task. Without you.

Here's what that looks like:

Traditional coding workflow:

1. Read issue description

2. Locate relevant code files

3. Understand existing implementation

4. Write fix

5. Write tests

6. Run tests

7. Debug failures

8. Commit changes

9. Create Pull Request (PR)

10. Write PR description

Time per issue: 30-90 minutes

Jules workflow:

1. Tell Jules to fix the issue

2. Review the pull request later

Time per issue: 5 minutes (just review)

The difference: Jules does steps 1-10 autonomously.

The 47 Tasks That Actually Worked

Let me show you specific examples from my week.

Task 1: Fix broken authentication

The issue: Login working on desktop, failing on mobile. Token storage problem.

What I would've done: Spent 2 hours debugging localStorage vs sessionStorage differences across browsers. Trial and error. Stack Overflow searches.

What Jules did:

• Analyzed authentication flow across 4 files

• Identified the mobile Safari localStorage bug

• Implemented fallback to IndexedDB

• Added detection for Safari

• Wrote tests for both scenarios

• Submitted PR with explanation

Time saved: 1 hour 45 minutes

Task 2: Database query optimization

The issue: Dashboard loading 8+ seconds. Unacceptable.

What I would've done: Profiled queries, added indexes one by one, tested each change. 3-4 hours minimum.

What Jules did:

• Analyzed all dashboard queries

• Identified N+1 query problem

• Implemented eager loading

• Added database indexes

• Reduced load time to 1.2 seconds

• Wrote performance tests

Time saved: 3 hours

Task 3: Add test coverage

The issue: 23% test coverage. Need 80% minimum.

What I would've done: Procrastinated for weeks because writing tests is boring.

What Jules did:

• Analyzed entire codebase

• Generated tests for all untested functions

• Achieved 84% coverage

• Identified 3 actual bugs through test failures

• Fixed those bugs too

Time saved: Infinite (I would never have done this)

The 2 Tasks That Failed (And Why That Matters)

Let's be honest about limitations.

Failure 1: Complex UI refactor

The task: Redesign the workout creation flow with drag-and-drop exercise ordering.

What went wrong: Jules generated code that compiled but the UI was unusable. Drag handles appeared in wrong places. Drop zones didn't register properly. The logic was technically correct but spatially broken.

Why it failed: Jules can't "see" the UI. It understands code structure but not visual layout. Complex interactions requiring spatial reasoning are beyond current capability.

My fix: Did it manually in 4 hours.

Failure 2: Third-party API integration

The task: Integrate with Strava API for importing workout data.

What went wrong: Jules read the API docs, wrote integration code, but couldn't test it properly. The code looked right but had subtle authentication flow errors that only appeared during actual API calls.

Why it failed: Jules can't execute code that requires external credentials or make real API calls. It can write integration code but can't verify it works.

My fix: Manually debugged and fixed in 2 hours.

What This Means Practically

Jules excels at:

• Bug fixes (80%+ success rate in my testing)

• Writing tests (95% success rate)

• Code refactoring (70% success rate)

• Documentation (near 100% success rate)

• Database optimization (75% success rate)

Jules struggles with:

• Complex UI work requiring visual judgment

• Features requiring extensive user interaction design

• Code needing external API testing

• Anything requiring "taste" or aesthetic decisions

The pattern: Jules is excellent at mechanical coding work. Still needs humans for creative or visual work.

How It Actually Works (Without the Buzzwords)

Step 1: Connect to GitHub

Jules clones your repository into a Google Cloud VM. Secure, isolated, read-only initially.

Step 2: Understand your codebase

Jules reads every file. Builds a map of dependencies. Understands architecture. This takes 2-5 minutes for most projects.

Step 3: Process tasks

You assign issues. Jules:

• Reads issue description

• Locates relevant code

• Plans the fix

• Implements changes

• Writes tests

• Runs tests

• Fixes test failures

• Creates PR with detailed explanation

Step 4: You review and approve

Jules can't merge without permission. You review the PR like any code review. Approve or request changes.

The key: Jules can handle 60 tasks simultaneously. While fixing authentication, it's also writing tests, optimizing queries, and updating documentation. Parallel processing at scale.

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The Real Numbers (From One Week)

My project stats:

• Repository: 15,000 lines of code

• Issues assigned: 50

• Tasks completed: 47

• Tasks failed: 2

• Tasks in progress: 1

Time breakdown:

• My time reviewing PRs: 3 hours

• Equivalent manual coding time: 40-50 hours

• Time saved: ~45 hours

• Success rate: 94%

Quality metrics:

• PRs requiring revision: 8 (17%)

• PRs approved immediately: 39 (83%)

• Bugs introduced by Jules: 1 (caught in review)

• Tests written: 247

• Test coverage increase: 23% → 84%

Cost: $0 (currently free during beta)

What Other Developers Report

I'm not alone in testing this. Here's what others found:

Sarah (frontend developer): "Gave Jules 30 accessibility issues. It fixed 28, including proper ARIA labels, keyboard navigation, and screen reader support. The two failures were complex animation timing issues. Saved me a week."

Mike (startup CTO): "Had Jules write API documentation for our entire codebase. Perfect. Every endpoint documented with examples. Would've taken our team 3 days. Jules did it overnight."

Jennifer (solo indie developer): "I'm not a backend developer. Gave Jules some database performance issues I didn't know how to fix. It optimized queries, added indexes, improved response times by 70%. Game-changer for solo devs."

Pattern: Works exceptionally well for well-defined, mechanical tasks. Struggles with ambiguous requirements or creative decisions.

The Catches (Because There Are Always Catches)

1. It's in beta. Free now, likely paid later.

No pricing announced. Based on Google's other AI products, expect $20-50/month when it launches commercially. Still worth it for time saved.

2. Limited to GitHub repos.

Can't work with GitLab, Bitbucket, or local repositories. GitHub-only currently.

3. Requires clean issue descriptions.

Vague issues ("Make it better") don't work. Specific issues ("Fix login timeout on mobile Safari") work great. Garbage in, garbage out.

4. Can't access production systems.

Jules works in isolated sandbox. Can't connect to real databases, APIs with credentials, or production environments. Good for security, limiting for testing.

5. Review is mandatory.

You can't blindly trust Jules' code. Some PRs need revision. Always review before merging. The time savings comes from Jules doing 90% of the work, not 100%.

What You Should Actually Do

If you're a developer with GitHub repos:

Sign up for Jules beta immediately. Even if you only use it for writing tests, it's worth it. Test coverage alone saves debugging time later.

Start with:

• Writing tests (highest success rate)

• Fixing well-documented bugs

• Adding documentation

• Simple refactoring tasks

Avoid starting with:

• Complex UI work

• Features requiring user feedback

• Anything mission-critical without extensive testing

If you're a team lead:

Jules changes capacity planning. One developer + Jules can handle workload that previously required two developers. Not because Jules replaces humans, but because it eliminates mechanical work.

Practical use:

• Assign Jules all test-writing tasks

• Have Jules handle bug fixes first, humans review

• Use Jules for documentation (always behind otherwise)

• Free developers for architecture and design work

If you're learning to code:

Jules is a mixed blessing. Great for seeing solutions to problems. Terrible if you use it to avoid learning.

Recommendation: Solve problems yourself first. Then check Jules' solution. Compare approaches. Understand why Jules chose its approach.

The Uncomfortable Future

Here's what nobody's saying publicly but everyone's thinking privately:

Jules handles ~40-50% of typical developer work autonomously.

That's:

• Bug fixes

• Test writing

• Documentation

• Simple features

• Code maintenance

What's left for humans:

• Architecture decisions

• Complex problem-solving

• User experience design

• Creative feature development

• Strategic technical decisions

The implication: Junior developer positions doing mechanical work are endangered.

Not gone. Endangered. Companies will hire fewer junior devs and use Jules for entry-level work.

But senior developer roles become more valuable. Someone needs to architect systems, review code, make strategic decisions. Jules doesn't replace that. It amplifies it.

The skill shift: From "write code" to "design systems and review AI code."

Your Move

Jules is available now. Free during beta. Integrated with GitHub.

Two options:

Option 1: Ignore it. Watch competitors reduce development cycles by 40%. Fall behind while debating whether AI should write code.

Option 2: Test it this week. Pick your most annoying backlog items. Assign them to Jules. Review results. Make informed decision based on actual experience.

What I'm doing:

Jules is now part of my workflow. Not replacing my development. Augmenting it. I write new features. Jules handles maintenance, tests, and bugs.

My development capacity just increased by ~50%. For free. While I sleep.

The AI that codes for you isn't coming. It's here. It's free. It's working right now in thousands of GitHub repos.

The question isn't whether to use it. The question is whether you'll use it before or after your competition.

Full disclosure: I'm not paid by Google. I just really hate writing tests, and Jules is really good at it.

That’s all for today, folks!

I hope you enjoyed this issue and we can't wait to bring you even more exciting content soon. Look out for our next email.

Kira

Productivity Tech X.

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