See also:
| Service | Type | RAM | Storage | Limitations |
|---|---|---|---|---|
| π Adaptable | PaaS | 256 MB | Non-persistent? (1 GB database storage available) | |
| AWS EC2 | IaaS | 1 GB |
The following is taken from https://gist.github.com/akihikodaki/87df4149e7ca87f18dc56807ec5a1bc5 with modifications to introduce Vulkan support. Since we need custom patches to QEMU, virglrenderer, MoltenVK, and libepoxy, the provided script will pull the right refs and build everything with the right params. Feel free to inspect the script to find the source for all the modifications. We are actively working to upstream everything so please do not build anything long-term with these patches.
| # RydMike LINTER Preferences v2.6.0 | |
| # | |
| # Get this file here: https://gist.github.com/rydmike/fdb53ddd933c37d20e6f3188a936cd4c | |
| # | |
| # We include and activate all_lint_rules, then below we disable the not used or desired ones. | |
| # You can find a list of all lint rules to put in your all_lint_rules.yaml file here: | |
| # https://dart.dev/tools/linter-rules/all | |
| # | |
| # This version is updated for Flutter 3.38 and Dart 3.10. | |
| # |
A reverse-engineered system design of Slack's web application, built from live network traffic analysis of the authenticated Enterprise Grid experience. 200+ API calls captured across boot, search, messaging, reactions, and navigation. Every backend service named.
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β BROWSER (Gantry v2 SPA) β
β β
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.
| Hi All! | |
| I've recently launched a tool that wraps many of the commands here with a user interface. This desktop application is currently available for macOS. There's a roadmap outlining planned features for the near future. | |
| Feel free to request any features you'd like to see, and I'll prioritize them accordingly. | |
| One of the most important aspects of this application is that every command executed behind the scenes is displayed in a special log section. This allows you to see exactly whatβs happening and learn from it. | |
| Here's the link to the repository: https://github.com/Pulimet/ADBugger | |
| App Description: | |
| ADBugger is a desktop tool designed for debugging and QA of Android devices and emulators. It simplifies testing, debugging, and performance analysis by offering device management, automated testing, log analysis, and remote control capabilities. This ensures smooth app performance across various setups. |