Thanks for sharing this, I wasn't even aware of Versity S3 from my searches and discussions here. I recently migrated my projects from MinIO to Garage, but this seems like another viable option to consider.

I usually provide the initial context by describing the app that I'm working on (language, framework, etc) as well as the feature I want to build, and then add the files (either snippets or upload) that are relevant to build the feature (any includes or other files it will be integrating with).

This keeps the chat context focused, and the LLM still has access to the code it needs to build out the feature without having access to the full code base. If it needs more context (sometimes I'll ask the LLM if they want access to other files), I'll provide additional code until it feels like it has enough to work with to provide a solution.

It's a little tedious, but once I have the context set up, it works well to provide solutions that are (mostly) bug free and integrate well with the rest of my code.

I primarily work with Perplexity Pro so that I have access to and can switch between all pro level models (Claude, ChatGPT, Grok, etc) plus Google search results for the most up-to-date information.

> Still, I wouldn’t trust Claude, or any AI agent, to touch production code without close human oversight.

My experience has been similar, and it's why I prefer to keep LLMs separate from my code base. It may take longer than providing direct access, but I find it leads to less hidden/obscure bugs that can take hours (and result in a lot of frustration) to fix.

From my experience, it's much easier to get an LLM to generate code for a React/Tailwind CSS web app than a mobile app, and that's why we're seeing so many of these apps showing up in the SaaS space.

One of the features I'm most excited about is access to Podman 5.4.2, and the ability to use Podman Quadlets. It'll be nice to start transitioning my systemd service units over to the new format for my containers.

- Making Postgres slower (https://news.ycombinator.com/item?id=44704736)

Taking this approach seems to be effective at surfacing issues that otherwise wouldn't show up in regular testing. I could see this being useful if it was standardized to help identify issues before they hit production.

I think the better long-term approach though is to use systemd user units for deployment, or the more modern approach of using Podman Quadlets. There's a bit of a learning curve, but these approaches are more native to the Podman platform, and learning how systemd services work is a great skill to have.

It may seem obvious, but Sam Altman also recently emphasized that the information you share with ChatGPT is not confidential, and could potentially be used against you in court.

[1] https://www.pcmag.com/news/altman-your-chatgpt-conversations...

[2] https://techcrunch.com/2025/07/25/sam-altman-warns-theres-no...