$ pacman -S percona-server-clients percona-server
| https://lemmy.bolha.one/ | |
| https://o.opnxng.com/ | |
| # single instance mode # | |
| https://old.lemmings.world/ | |
| https://old.kerala.party/ | |
| https://oldlemmy.justin.rs/ | |
| https://old.ttrpg.network/ | |
| https://old.lemmyonline.com/ |
| ./tokenizer_checklist.chk 50 | |
| ./tokenizer.model 499723 | |
| ./7B/checklist.chk 100 | |
| ./7B/consolidated.00.pth 13476939516 | |
| ./7B/params.json 101 | |
| ./13B/checklist.chk 154 | |
| ./13B/consolidated.00.pth 13016334699 | |
| ./13B/consolidated.01.pth 13016334699 | |
| ./13B/params.json 101 | |
| ./30B/checklist.chk 262 |
This guide explains how to migrate your existing Claude Code configuration—both personal and project-based—into Antigravity's .agent format.
Antigravity shares many concepts with Claude Code, making migration straightforward. We provide a script, migrate_claude_to_antigravity.sh, to automate this process.
tl;dr: Wayland is not "the future", it is merely an incompatible alternative to the established standard with a different set of priorities and goals.
Wayland breaks everything! It is binary incompatible, provides no clear transition path with 1:1 replacements for everything in X11, and is even philosophically incompatible with X11. Hence, if you are interested in existing applications to "just work" without the need for adjustments, then you may be better off avoiding Wayland.
Wayland solves no issues I have but breaks almost everything I need. Even the most basic, most simple things (like xkill) - in this case with no obvious replacement. And usually it stays broken, because the Wayland folks mostly seem to care about Automotive, Gnome, maybe KDE - and alienating e
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.