Yesterday, I re-implemented my custom URL shortener glez.me from scratch. I have a Claude Pro plan with Claude.ai, and just a week or so ago, they announced that it includes access to Claude Code as well, which is Anthropic’s agentic AI coding tool, so I decided to let Claude Code do it for me. I was done in less than a day (including walking the dog multiple times, fixing lunch and some other household chores).

Here are a few observations:

The project

Maybe “URL shortener” is a bit of an exaggeration, because the architecture is super simple: each short URL is an Amazon S3 redirect object, and the URL shortener itself is a CLI tool that generates these URLs. At the very beginning of my conversation with Claude, I emphasized that simplicity is the most important principle here. That’s why I chose this S3/CloudFront/CLI architecture. Initially, it was only S3/CLI, but then we realized that CloudFront is necessary for adding HTTPS support to the architecture.

The full project scope turned out to be a bit more elaborate:

An AWS CDK (Python) stack for managing the S3 bucket, a CloudFront distribution on top of that, and an AWS ACM certificate for supporting HTTPS.
The CLI tool (a Python tool with Click) that lists and generates new short URLs as needed.
A migration of my previous (similar, but old and stale) short URLs that used to live in a similar S3 bucket scheme.
All of the necessary project scaffolding, including documentation, Python project/virtual env/package management using uv, etc.

The architecture scales completely automatically, thanks to how S3 and CloudFront are built. Certainly much more scalable than I would ever need for my humble little URL shortener. Also, the cost side is negligible: Storage cost on S3 is minimal, even if I had hundreds of thousands of URLs stored there. Data transfer to CloudFront is free, and since CloudFront is a caching service, transaction costs for GET requests are minimal as well. The only cost that matters are the CloudFront costs, which offers the first TB of data transfer out for free, and less than 10 cents for the next 9 TB. Similarly, the first 10 million HTTP(S) requests are free, and then it’s less than a cent per 10K HTTP(S) requests. So, negligible for a personal blog.

The most important thing: planning

The biggest amount of time I spent was actually chatting back and forth with Claude Code in order to get the details planned out properly. And that’s my biggest advice when coding with AI: start with a good plan

I started basically dumping my ideas for how to set this up (S3, CloudFront, migration requirements, CDK, Python, uv, etc.) into its chat interface, and explicitly told it to not implement anything but instead to ask me questions and help me identify blind spots instead. Which proved to be critical. Things we came up with included:

CloudFront or not?: Initially, it felt to me like adding CloudFront would be overkill, but then Claude reminded me that it’s required to also support HTTPS. So CloudFront became part of the scope.
URL shortening algorithm: In the beginning, I wanted something simple, like start with very short alphanumeric strings for short URLs. Over time, we agreed that we want to avoid ambiguous characters (the letter “O” vs. the number “0”, but also “Z” vs. “7” or “b” vs. “6”, which may look too similar in some fonts). I also realized that generating random or sequential alphanumeric combinations might accidentally create awkward or offensive words, so we came up with a scheme where short URLs are made up of triplets, each of which starts with a digit, then two letters. We can then concatenate triplets and cut them to a desired length. This scheme almost guarantees that no words are formed at all.
Migration approach: Claude initially proposed an extra migration command for the CLI. However, I felt that it would be overkill to add a whole subcommand for something that is likely going to be done only once. Instead, thinking more about it, I realized that by breaking down the migration process into separate bulk export/import steps makes it more useful. Now we have bulk export, which enables backups, and bulk import, for cases where I may want to add many URLs at once.
CLI user experience: Thinking more about the day to day use of the URL shortener and talking to Claude about it also helped me streamline the CLI experience.
Default pages: I also realized that it would be good to have some default and user-friendly index.html and 404.html pages, in case people try out the domain only or mistype any short URLs. Claude helped me create quite nice versions of those, too.

After this detailed conversation, I asked Claude to create a NOTES.md and a TODO.md file to document our decisions and to serve as context for future implementations.

Christian Bonzelet, who is a real software engineer, agrees. He wrote:

I start every feature with a conversation. But this isn’t just any conversation—it’s a structured engineering discussion using proven prompt frameworks to write a technical specification.

And that is the most important thing: developing code with AI is not about the code writing itself, it is about careful planning. I actually spent more than an hour chatting with Claude Code before we had an initial plan, and then we added a couple of iterations to narrow down some details, before a single line of code was written. This aligns with a key lesson I learned at Amazon: good writing is clear thinking.

The actual implementation

The actual implementation was almost boring: Claude wrote a working CDK stack for the infrastructure and proceeded with testing and deploying it. It’s funny to see how it runs into the same little mishaps that we humans tend to encounter, like forgetting to execute the CDK CLI with uv run to get the right environment, or realizing that S3 buckets need to be deleted by hand after the CDK stack is rolled back, etc. The classic mistake? I forgot to tell Claude that I prefer the eu-central-1 (Frankfurt) region, so we had to tear down everything down from the default us-east-1 region and deploy it all over in Frankfurt again.

Bug fixing

Despite careful planning, there were inevitable bugs, but Claude is great at testing, diagnosing and fixing. It’s quite nice to watch it thinking and trying out things, while it’s meticulously fixing stuff. After we were about 90% done, my Pro usage limits kicked in (I also had a different conversation running on the Claude.ai app, which was eating into those limits as well), so I used a few on-demand tokens with Claude inside Zed’s agent to finish everything.

Which revealed a more obscure bug: The original architecture used Amazon S3 as an S3 native origin for CloudFront. It turns out that in this configuration, CloudFront does not support HTTP redirects. Claude correctly identified this as an issue (after I told it that a few example redirects didn’t work), and it changed the CDK stack to configure S3 in website mode instead. Then everything worked as expected.

Conclusion

It’s not perfect yet, but does it have to be? It works, my old URLs are successfully migrated, I have a useful CLI, and some ideas in NOTES.md and TODO.md for some future additions, like analytics, etc. The super simple and static S3/CloudFront architecture is secure, so no worries there as well, so, success!

If you’re planning to build something with AI assistance, here’s my biggest takeaway: invest heavily in the planning phase. Don’t rush to code. Spend time articulating your requirements, exploring edge cases, and thinking through the architecture. The planning conversation is where the real value lies—that’s where you and the AI can collaborate most effectively to create something that truly meets your needs. The coding part? That’s just execution.

P. S.: If you’d like to try it out: This post has this short URL: https://glez.me/3aa3

P. P. S.: I’ve extracted the Initial part of my conversation with Claude Code using claude-conversation-extractor and manually added the “(Thinking)” bit at the beginning (because I answered some of the questions there). If you’re interested, you can see it below:

Claude Conversation Log

Date: 2025-06-18 08:51:02

👤 User

🤖 Claude

I’ll help you log in. Let me first check what’s in your current directory to understand the project structure.

🤖 Claude

I can see this is a new project with just a LICENSE file. To help you with login functionality, I need to understand what type of application you’re building and what login system you want to implement. Could you provide more details about:

What type of project this is (web app, mobile app, etc.)
What authentication method you prefer (email/password, OAuth, etc.)
What technology stack you want to use

👤 User

Thanks! Please ignore the initial “login” prompt, which was intended to log me into my Claude.ai account.

🤖 Claude

Got it! No problem - I understand that was for logging into Claude.ai, not related to your project.

Is there anything you’d like help with for your glez.me project?

👤 User

Yes, this is a brand new project. Before we start implementing anything, I want us to think this through in terms of overall goals, scope, architecture, design decisions, etc. Then our output should be a comperehnsive NOTES.txt file that records everything we need to know about this project so we can hit the ground running in future sessions. In this project, I want to implement my own URL shortener. It should be as simple as possible — simplicity is our top design principle here. My current ideas are:

Use Amazon S3 for hosting shortened links as S3 redirection objects.
Should I use Amazon CloudFront on top of that for further scalability and performance? Or would this be overkill for a URL shortener? I think we should start with an S3 bucket only and add CloudFront later if we feel that traffic is becoming significant.
Use AWS CDK in Python (my preferred language at the moment, Typescript gives me headaches…) to manage the infrastructure.
I already have an Amazon S3 bucket called “glez.me” with redirection objects. The CDK stack should import it seamlessly.
Although we already have an S3 bucket “glez.me” with a number of short links, this is going to be a complete re-implementation of this URL shortener. However, whatever we do should leave any existing redirects unmodified and work around their existance.
There’s also a bucket called “glez-me-logs” configured for logging access to glez.me. We can use it to perform analytics later. However, our CDK stack should also import this bucket and keep the logging settings for glez.me to continue storing logs there.
That should cover our infrastructure. Next is the way we’ll use to create and manage those short URLs.
Let’s write a CLI tool called “glezme” in Python using CLick that manages everything about our short URLs for glez.me (but not the infrastructure — that is handled by the CDK stack described above).
Our initial version of glezme should support creating a new short URL. Then doing so:
- it should support an optional second argument for the user to specify the desired new URL.
- if no desired short URL is given, it should generate the shortest URL possible that hasn’t been generated yet.
- URLs should remain easy to type as a human. Therefore, they should be alphanumeric only with upper and lowercase letters, but no special characters.
- Let’s also avoid ambiguous characters: Digits should always be digits, so no “O” or “o” in the alpha part, and no “l”, because it could be confounded with “1”. Add other potential ambiguity filters to this as necessary.
- It should always check if the short URL exists already, and move on to the next one to avoid collisions until it found a suitable one.
- Let’s keep a local cache of existing URLs for speed in ~/.config/glezme/. If necessary, we should also keep a config.toml file there.
- Once we find a non-existing, new short URL, the tool should ask the user for confirmation, then (if given), go ahead and create it. If no confirmation is given, the user should have the option of generating a new, automatic one, or specifying their own short URL choice instead. If the user chooses a short URL that exists already, then the tool should give them a descriptive error message.
- For all of our Python development (CDK stack and CLI tool), let’s use uv as a Python package, environment and project manager.
- In addition to creating a new URL, the tool should list existing URLs with their destination. Let’s implement a paging mode, sorted alphanumerically, to help with large numbers of URLs.

I think this covers my initial thoughts. Please read through this and think with me. Be honest (no BS) and focus on simplicity, small implementation chunks, and fast iteration: What are the pros and cons of this approach? What are blind spots or what may I be missing? What else should I consider?

🤖 Claude

(Thinking) The user wants me to help them think through a URL shortener project design and create a comprehensive NOTES.txt file. This is a planning and design discussion, not an implementation task yet. Let me break down their proposal and think through it critically:

Their proposed architecture:

Amazon S3 for hosting shortened links as redirection objects
Maybe CloudFront later for performance
AWS CDK in Python for infrastructure management
CLI tool "glezme" in Python using Click
Local caching for performance
uv for Python package management

Key design decisions:

Simplicity as top principle
Start with S3 only, add CloudFront later
Import existing "glez.me" bucket and "glez-me-logs" bucket
Alphanumeric URLs only, avoiding ambiguous characters
Local cache in ~/.config/glezme/
Confirmation workflow for URL creation

Let me think through pros, cons, and potential issues:

Pros:

Very simple architecture
Leverages existing S3 infrastructure
S3 redirect objects are perfect for this use case
CDK provides good infrastructure as code
CLI tool gives programmatic control
Local caching reduces API calls

Cons and potential issues:

S3 consistency: S3 has eventual consistency for some operations. Newly created objects might not be immediately available for reads.
Collision detection: Checking if a URL exists by trying to access it via S3 API could be slow and expensive if done frequently. The local cache helps but needs to be kept in sync.
Concurrent access: What happens if multiple users are creating URLs simultaneously? The local cache won’t help with this.
URL generation strategy: Starting with shortest possible URLs (single character) might not be the best approach - these are very valuable "real estate" and might be better reserved for important links.
Analytics complexity: While S3 access logs are useful, parsing them for analytics might be cumbersome compared to having a database.

I see potential risks with data persistence, scalability, and multi-user support. The current design needs robust mechanisms for handling cache failures, team collaboration, and performance challenges. Key areas to address include developing a more sophisticated URL generation approach, implementing comprehensive error handling, and creating a strategy for managing concurrent URL creations across different users.

The design requires careful consideration of backup processes, performance optimization, and cost-effective S3 usage. A more nuanced approach to URL generation and validation will be crucial for long-term reliability and usability.

Critical next steps involve establishing secure authentication, configuring domain routing, ensuring HTTPS compatibility, and implementing monitoring systems to track service health and prevent potential abuse.

I’ll document the comprehensive design strategy, capturing key architectural considerations and potential implementation pathways for this lightweight URL shortening service.

Great idea to think this through comprehensively first! Let me analyze your approach and create a detailed NOTES.txt file. Your design is solid with a clear focus on simplicity.

👤 User

[Request interrupted by user for tool use]

👤 User

Thank you! Sorry, I meant to say let’s create a NOTES.md file. I prefer Markdown.

Thank you for pointing out your concerns! Let me address these:

I am the only user of this URL shortener, so we can ignore race conditions and other multi-user issues for now. S3 recently added some consistency features we may use in the future, but for now, that would be overkill.
Great point about the very short URLs being valuable real estate! Let’s protect them so I can use them intentionally. That means that the automatic URL generation algorithm should start with a minimum number of digits. Let’s pick 4, which I feel is a good middle ground.
To avoid cache inconsistencies and (unexpected) collisions, the tool should first check its cache, then check the S3 HEAD API to see if it exists already before asking the user. When performing the final creation, the tool should check once again using S3 HEAD before actual creation. That should be enough, given I’m the only user after all.
Yes, let’s optimize the character set up front.
Good point about HTTPS. I absolutely do want HTTPS, so I guess we need to add CloudFront from the beginning after all.
In a future version, we might want to add caching in the form of index objects on S3: We could periodically scan the whole bucket, then dump the data as /data/index_<starting charachter>.json objects. For now, I’m anticipating less than 1000 URLs per year, so not a big concern.
This reminds me of another point: What if we unintentionally generate embarrasing, inappropriate or awkward words as short URLs? There’s a layer of filtering with requiring the user to approve, but I’d like a mechanism to prevent that. We could use a dictionary with words to avoid, but that leaves some blind spots open and is cumbersome to add. Perhaps we can use a URL scheme that is guaranteed to avoid words, by requiring each URL to have at least one or two digits? What are your ideas here?
The glez.me bucket is configured with versioning enables, this should handle our backup needs. Perhaps we can add a local backup (for GitHub check-ins) to the CLI tool as well.

Please think through these additional points and feel free to ask questions and run ideas by me, before starting to write the NOTES.md document.

🤖 Claude

Excellent additional points! Let me think through these with you before writing the NOTES.md:

Character Set & Word Avoidance Strategy: Your digit requirement is brilliant - it elegantly solves the embarrassing words problem. A few options:

At least 1 digit required (your suggestion) - cleanest approach
At least 2 digits - even safer but reduces available space
Specific patterns like digit-letter-letter-digit

For character set, let’s be more systematic about ambiguous chars:

Definitely exclude: 0, O, o, 1, l, I
Maybe also: 5/S, 6/b, 2/Z in some fonts?
What’s your take on this level of filtering?

CloudFront Questions: Since we need HTTPS from the start:

Should we set up ACM certificate for glez.me domain?
Any specific caching behavior preferences for redirects?
Do you already have the domain registered/configured?

URL Generation Strategy: With 4-char minimum + 1 digit requirement:

Should digits be allowed anywhere, or specific positions?
Priority order: try 4-char first, then 5-char, etc.?

Cache Strategy Clarification: The double-check approach (cache → S3 HEAD → confirm → S3 HEAD → create) is solid. Should we:

Cache negative results too (URLs we know don’t exist)?
How often should we refresh the full cache?

Backup Strategy: For local Git backup - should this be:

Automatic on each operation?
Manual command like glezme backup?
Just the URL mappings, or include logs/analytics?

Let me know your thoughts on these details and I’ll create a comprehensive NOTES.md!

👤 User

Thanks! To answer your questions:

At least 2 digits sounds good. That still leaves us with >10K 4-character short URLs before we need to go with 5 characters. Let’s break down the short URL into a triplet scheme: Short URLs are composed of digit-letter-letter triplets that we generate in sequence, then we assemble those triplets and cut to the current lengths (starting with 4). This ensures we never have 3 letters in a row, guaranteeing no awkward words. There’s still a very minor chance that the result may spell out something undesired in L33t spelling, but I’ll leave that for the user to filter out.
Yes, also add the maybes to the letters to avoid, and always prefer digits.
Yes, glez.me is already configured in my AWS account in Route53, and it currently points to the existing glez.me S3 bucket. We need to plan for a 2-step migration: 1. set up the infrastructure, including CloudFront, etc. but don’t tell CF that it’s associated with our domain, 2. unhook the domain from the glez.me bucket, 3. update the CDK stack so CF knows it belongs to our domain, and 4. Update R53 (I’ll do this by hand, as a final manual verification step).
I think it’s ok to only cache existing, known URLs. Candidate verification over HTTP HEAD should be fast enough.
I’d like the CLI to be as stateless as possible, with the local cache only being optional.
I’d like the GitHub backups to be manual at first, which will be part of this project. So let’s add them as a subcommand to the CLI tool.
Also, let’s leave the cache uploads to S3 for the future as well. I’ll think of a good scheme later.
Let’s also treat analytics separately — we can add a glezme analytics command later as well.

What do you think?

🤖 Claude

Perfect! Your triplet scheme is brilliant - it’s algorithmic rather than blacklist-based, which is much more robust. The digit-letter-letter pattern guarantees no embarrassing words while keeping URLs readable.

Your migration strategy is also very sensible - the 4-step approach minimizes risk and gives you manual control over the final DNS cutover.

Let me create the NOTES.md with all these decisions incorporated:

🤖 Claude

Perfect! I’ve created a comprehensive NOTES.md file that captures all our discussion and design decisions. The document covers:

Triplet scheme for URL generation (digit-letter-letter pattern)
Refined character set excluding ambiguous characters
4-step migration plan for safely adding CloudFront
Stateless CLI design with optional caching
Implementation phases from infrastructure to enhanced features
Cost analysis and security considerations

The triplet approach is particularly elegant - it algorithmically prevents awkward words without needing blacklists, while your 4-step migration plan minimizes risk during the CloudFront transition.

You now have a solid foundation to start implementation. Ready to begin with Phase 1 (infrastructure setup) or would you like to refine anything in the plan first?

👤 User

Thank you! Please create an additional TODO.md document with the todos resulting from NOTES.md. Please format it into 3 sections: Minimum requirements for initial migration, useful additions that deliver value in the short term, and unstrutured ideas for the future.

🤖 Claude

Perfect! I’ve created a comprehensive TODO.md file with three clear sections:

🎯 Minimum Requirements - Essential tasks to get your URL shortener working with HTTPS migration (17 core tasks)

🚀 Short-Term Additions - High-value features that enhance usability without complexity (20+ practical improvements)

💭 Future Ideas - Unstructured brainstorming for advanced features when you want to expand (50+ possibilities)

The structure prioritizes getting a working system first, then adding polish and features that deliver immediate value. The future ideas section captures all the “what if” possibilities without cluttering the immediate roadmap.

You now have both the architectural foundation (NOTES.md) and a clear implementation path (TODO.md) to start building. Ready to dive into the first tasks?

👤 User

Thank you! Let’s check this in to GitHub as our initial planning step. Please add a nice commit message, with a bit of fun, because, why not?

🤖 Claude

Perfect! Your planning documents are now committed with a fun message that captures the essence of turning long URLs into “digital haikus” 🎯. The commit includes both your comprehensive architecture notes and structured implementation roadmap.

Ready to start tackling those minimum requirements for the initial migration?

👤 User

Actually, I have one change: Since we’re using CloudFront now, there’s no requirement for the underlying S3 bucket to have the name of the domain. So we can start with a fresh bucket, keep the existing one as a legacy backup, then copy over the old short URLs. This means we’ll need a new logging bucket as well, but we can use that to put CloudFront logs in there (no need to keep S3 logs as well). What do you think?

🤖 Claude

Excellent point! That’s actually a much cleaner approach. Since CloudFront handles the domain association, we can:

Start fresh with a new S3 bucket (e.g., glez-me-urls)
Keep existing glez.me bucket as legacy backup
Copy URLs when ready (gives us a clean migration path)
New logging bucket for CloudFront logs (better for analytics anyway)

This simplifies the migration significantly - no complex bucket imports, cleaner separation, and easier rollback if needed. CloudFront logs are also more useful for analytics than S3 access logs.

Let me update the NOTES.md and TODO.md to reflect this better approach:

🤖 Claude

Perfect! I’ve updated both NOTES.md and TODO.md to reflect your much better approach. The key changes:

Infrastructure:

Fresh glez-me-urls bucket instead of importing existing glez.me
New glez-me-cf-logs bucket for CloudFront logs
Keep legacy bucket as backup

Migration:

Now 5-step plan with URL copying phase
Added glezme migrate command
Much cleaner separation and easier rollback

This approach eliminates the complexity of bucket imports while providing better testing and rollback capabilities. Plus CloudFront logs will be more useful for future analytics than S3 access logs.

Ready to start building this simpler, more robust system?

👤 User

Thanks! Now, let’s think through the migration strategy in more detail:

We will be migrating only once.
A “migration” really means backing up all redirects from one bucket (glez.me initialy, but that could become our new bucket in the future, for making backups from there, too) into a single file (backups/<bucket_name>_<ISO-date>.json). This could be handled via a glezme backup subcommand, with an optional bucket name as argument (the default being our new bucket).
Then, the next step would be to push the backup into the new bucket. However, this is equivalent to creating redirects in bulk, so we could implement a glezme push command that takes a JSON file (like we created during glezme backup from the legacy bucket) and pushes all redirect in it into the current production bucket.
Unrelated, but thinking about subcommands, let’s make the default command glezme create and since it’s a default, running glezme <URL> should simply offer a new short URL to the user, while glezme <URL> <short_URL> should attempt to generate this. Finally, glezme alone should ask the user for which URL to generate a short one for.

What do you think?

🤖 Claude

Excellent refinements! This is much more elegant and flexible:

Migration Strategy Benefits:

Reusable: backup/push works for any future migrations, not just one-time
Version controlled: JSON backups can live in Git
Flexible source: Can backup from any bucket (legacy, production, etc.)
Testable: Can validate JSON before pushing

CLI UX is much more intuitive:

glezme → interactive prompt
glezme <URL> → quick shortening
glezme <URL> <short> → custom short URL
Very natural workflow!

Updated migration flow:

glezme backup glez.me → creates backups/glez.me_2025-06-18.json
Test new infrastructure
glezme push backups/glez.me_2025-06-18.json → bulk create in new bucket
Switch DNS

This also means we can easily:

Backup production regularly for disaster recovery
Test migrations in dev environments
Restore from any historical backup

Let me update the planning docs to reflect this much better approach:

🤖 Claude

Perfect! I’ve updated both planning documents with your excellent refinements:

🔄 Migration Strategy:

glezme backup [bucket] → creates versioned JSON files in ./backups/
glezme push <json_file> → bulk creates from backup
Reusable for future migrations & disaster recovery

⚡ CLI UX:

glezme → interactive prompt
glezme <url> → quick shortening
glezme <url> <short> → custom short URL

📁 Structure:

Backup files in Git for version control
5-phase migration with clear separation
Enhanced backup format with metadata

This approach is much more flexible and maintainable - you can now backup/restore between any buckets, test migrations safely, and the CLI feels natural to use. Ready to start building this refined system!

👤 User

Thank you! Now, before we start, one last thing: After reaching a reasonable level of maturity, I’d like to open up this repository on GitHub for public use. Since all of the content of a URL shortener is public by default, I don’t see any security concerns with making this public, correct? How should I handle configuration variables for the CDK stack (like the names of the bucket, CDK state, etc.). Is it dangerous to keep this in a public repository? Or should we put them into a config management system like AWS SSM Parameter store to be sure?

🤖 Claude

Great question! You’re right that URL redirects are inherently public, but there are some considerations for open-sourcing:

✅ Safe to keep in public repo:

Bucket names (they’re in DNS anyway: glez-me-urls.s3.amazonaws.com)
CloudFront distribution settings
Domain names (already public)
CDK stack configuration
Most infrastructure settings

⚠️ Keep private/configurable:

AWS Account ID (can be inferred but better not to hardcode)
Region preferences (users will want different regions)
Personal domain names (other users need their own)

🎯 Recommended approach:

Make it configurable - Use a config.toml that users customize
Template approach - Provide config.toml.example in repo
Environment variables - For sensitive values like AWS Account ID
Keep it simple - AWS SSM is overkill for this use case

Example structure:

config.toml.example     # Committed to Git
config.toml            # Gitignored, user creates locally

This way:

Users can easily customize for their domain/preferences
No secrets in public repo
Simple setup process
CDK can read from local config file

Security concerns are minimal since:

S3 bucket names are discoverable anyway
CloudFront configs are visible via headers
No credentials stored in infrastructure config

Want me to update the planning docs to reflect this open-source friendly configuration approach?

👤 User

Ok, got it. Let’s go with this. Then, how can I persist my personal config.toml if it’s not checked in?

🤖 Claude

Great question! Here are several good options for persisting your personal config.toml:

🏆 Best approach: Multiple config layers

# Check these in order of priority:
1. ./config.toml           # Local project config (gitignored)
2. ~/.config/glezme/config.toml  # User global config
3. Environment variables   # CI/CD or temporary overrides
4. config.toml.example     # Fallback defaults

📁 Recommended setup:

Your personal config: Store in ~/.config/glezme/config.toml (permanent)
Project overrides: Use ./config.toml for project-specific tweaks (gitignored)
Backup your config: Include it in your personal dotfiles repo or backup system

🔄 For your use case specifically: Since you’re the primary user, you could:

Keep your config in ~/.config/glezme/config.toml
Back it up to your personal dotfiles/backup system
The CLI tool reads from there by default
Other users create their own when they install

💡 Alternative: Private config branch

Keep a private config-personal branch with your config
Merge public changes into it
Never push that branch to public repo

The global config approach (~/.config/glezme/) is cleanest - it’s where CLI tools typically store user settings, and it’s automatically backed up with your home directory.

Want me to update the planning docs to reflect this config hierarchy approach?

👤 User

Ok, let’s got with the global config approach then! Yes, please update the planning docs, then we can start with the CDK part.

🤖 Claude

Perfect! I’ve updated the planning docs with the open-source friendly configuration approach:

🔧 Configuration System:

Global config: ~/.config/glezme/config.toml (your personal, not in Git)
Template: config.toml.example (in Git for other users)
Priority: local override > global > env vars > template defaults

🔒 Security:

Personal AWS settings stay private
Template shows structure without real values
Standard CLI tool location (~/.config/)

🌟 Open-source ready:

Clean separation of code and personal config
Easy setup for new users
Your personal config persists across updates

Now let’s start building! Ready to create the CDK stack with the configurable infrastructure?

👤 User

Yes! BTW, since we’re gitignoring the user-specific config.toml file, we should probably also gitignore the backups directory, so the public repo remains clean. I’ll have to find a better way to persist the backups outside of this repo. Anyway, let’s start with the CDK stack!

🤖 Claude

Good point about gitignoring backups! You’re right - personal backups shouldn’t be in the public repo. You could store them in your dotfiles, a separate private repo, or just backup to S3 periodically.

Let’s start with the CDK stack!

👤 User

[Request interrupted by user for tool use]

👤 User

There’s already a .gitignore file in place from GitHub, with Python specific rules.

🤖 Claude

Perfect! Let me add our project-specific items to the existing .gitignore:

🤖 Claude

Now let’s create the config template: …