Audit this entire codebase for materially useful simplifications in its data structures, state representation, control flow, algorithms, and ownership.
This is an audit-only exercise. Do not edit files, run tests, implement recommendations, commit, or push. Read-only inspection commands are allowed.
You are the coordinator. Continue until the complete codebase has been reviewed and the final audit is validated.
Inspect the repository and inventory every identifiable subsystem.
Hello and welcome to my Shortcuts Catalog!
This is a public resource designed to help people get started with Siri Shortcuts and the Shortcuts app.
It’s made up of the Shortcuts Library, a collection of over 125+ shortcuts grouped into folders, and the Action Directory, a documentation of 125+ of the actions in the Shortcuts app used to build shortcuts.
Enjoy!
TLDR: JWTs should not be used for keeping your user logged in. They are not designed for this purpose, they are not secure, and there is a much better tool which is designed for it: regular cookie sessions.
If you've got a bit of time to watch a presentation on it, I highly recommend this talk: https://www.youtube.com/watch?v=pYeekwv3vC4 (Note that other topics are largely skimmed over, such as CSRF protection. You should learn about other topics from other sources. Also note that "valid" usecases for JWTs at the end of the video can also be easily handled by other, better, and more secure tools. Specifically, PASETO.)
A related topic: Don't use localStorage (or sessionStorage) for authentication credentials, including JWT tokens: https://www.rdegges.com/2018/please-stop-using-local-storage/
The reason to avoid JWTs comes down to a couple different points:
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how to leverage oracle's temping offers
The limits of the free tier say that you can create up to 4 instances.
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A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| Act like a Linear product teammate, not a request-taking assistant. Linear is a AI Supported issue tracking/project management/product building tool. All analyzing should take that in to account. | |
| For every input, start by identifying the underlying problem instead of accepting the proposed solution at face value. Treat customer requests as signals about unmet needs, not instructions to implement literally. Infer what is unsaid, look for patterns across feedback, and explain the deeper need in clear language. | |
| Before suggesting work, evaluate: | |
| what problem is actually being expressed | |
| who is affected |
