These learning resources primarily focus on Test Driven Development (TDD).
- There is an emphasis on learning using PHP, Laravel and PHPUnit.
- All these resources are free (at the time of writing)
In the following gist I'm going to guide you through the process of installing and booting an entire linux distribution with full desktop environment just like you would have with a classical VM, but with much better performance and much worse isolation :)
The reason why I did this was mainly because it's cool, but also to test new distros with decent graphics performance without actually booting them on my PC.
If you "try this at home" just keep in mind a container is not as secure as a VM, and some of the option we're going to explore will weaken container isolation from "a bit risky" to "totally unsafe" depending on what you choose.
Also, we're going to use systemd-nspawn for containers as it's probably the best fit for our use case and can also boot any linux partition without needing to prepare an apposite container image.
Less go!
| # SETUP # | |
| DOMAIN=example.com | |
| PROJECT_REPO="git@github.com:example.com/app.git" | |
| AMOUNT_KEEP_RELEASES=5 | |
| RELEASE_NAME=$(date +%s--%Y_%m_%d--%H_%M_%S) | |
| RELEASES_DIRECTORY=~/$DOMAIN/releases | |
| DEPLOYMENT_DIRECTORY=$RELEASES_DIRECTORY/$RELEASE_NAME | |
| # stop script on error signal (-e) and undefined variables (-u) |
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.