A beginner-friendly guide to understanding and fixing AMD GPU crashes, freezes, and instability on Linux.
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| // ==UserScript== | |
| // @name BookWalker Downloader | |
| // @namespace http://tampermonkey.net/ | |
| // @version 2.51 | |
| // @description Download full and preview books from BookWalker | |
| // @author GolyBidoof | |
| // @match https://viewer.bookwalker.jp/* | |
| // @match https://viewer-trial.bookwalker.jp/* | |
| // @icon https://www.google.com/s2/favicons?sz=64&domain=bookwalker.jp | |
| // @require https://cdnjs.cloudflare.com/ajax/libs/jszip/3.10.1/jszip.min.js |
Wanderburg Trainer 2026 for Windows with the current 10-option set: God Mode, Instant Skill Cooldown, Add Nitro On Use, Unlimited Silver, Gold Multiplier, Instant Kill, Damage Multiplier, Stop Wave Timer, Game Speed, and Movement Speed Multiplier.
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.
| diff --git a/builddefs/common_features.mk b/builddefs/common_features.mk | |
| index 18f8b0bbfc..4ef3e230e4 100644 | |
| --- a/builddefs/common_features.mk | |
| +++ b/builddefs/common_features.mk | |
| @@ -878,6 +878,10 @@ ifeq ($(strip $(JOYSTICK_ENABLE)), yes) | |
| endif | |
| endif | |
| +ifeq ($(strip $(APPLE_FN_ENABLE)), yes) | |
| + OPT_DEFS += -DAPPLE_FN_ENABLE |
CONTROL Resonant Trainer 2026 for Windows — launch-ready single-player utility concept built around Dylan Faden's paranormal combat, extraordinary powers, health/energy profiles, ability cooldown presets, resource and artifact utility, movement/traversal, game speed, FOV, hotkeys, and saved configs.
| (?i)((access_key|access_token|admin_pass|admin_user|algolia_admin_key|algolia_api_key|alias_pass|alicloud_access_key|amazon_secret_access_key|amazonaws|ansible_vault_password|aos_key|api_key|api_key_secret|api_key_sid|api_secret|api.googlemaps AIza|apidocs|apikey|apiSecret|app_debug|app_id|app_key|app_log_level|app_secret|appkey|appkeysecret|application_key|appsecret|appspot|auth_token|authorizationToken|authsecret|aws_access|aws_access_key_id|aws_bucket|aws_key|aws_secret|aws_secret_key|aws_token|AWSSecretKey|b2_app_key|bashrc password|bintray_apikey|bintray_gpg_password|bintray_key|bintraykey|bluemix_api_key|bluemix_pass|browserstack_access_key|bucket_password|bucketeer_aws_access_key_id|bucketeer_aws_secret_access_key|built_branch_deploy_key|bx_password|cache_driver|cache_s3_secret_key|cattle_access_key|cattle_secret_key|certificate_password|ci_deploy_password|client_secret|client_zpk_secret_key|clojars_password|cloud_api_key|cloud_watch_aws_access_key|cloudant_password|cloudflare_api_key|cloudflare_auth_k |
Compiled from awesome-lists (restyler/awesome-sandbox, webcoyote/awesome-AI-sandbox, bureado/awesome-agent-runtime-security) and a survey of vendor blogs / field guides published through 2026. Grouped by isolation primitive and then by deployment model.
These rely on kernel/userland features to constrain a normal host process. Lowest overhead, weakest boundary.
- macOS Seatbelt /
sandbox-exec— Apple's TrustedBSD-based MAC framework. Used directly by Codex CLI, Gemini CLI, and underneath Anthropic'ssrt. - Linux Landlock — Unprivileged filesystem/network LSM; default backend for Codex CLI on Linux.
