If your laptop’s CPU is running very hot and you’ve tried to undervolt it, you’ve probably discovered there’s no easy way to do so—especially on laptops whose BIOS doesn’t expose those controls. I ran into the same issue with my Ryzen 7 5800H, despite numerous attempts, I couldn’t undervolt because the firmware simply wouldn’t allow it. While it may seem drastic, disabling Turbo Boost is one workaround—and you might be surprised how little real-world performance you lose by turning it off.
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How to download from Quark Pan (pan.quark.cn / 夸克网盘) without an account or a Chinese phone number (2026)
✅ Working method (October 2026): baidufast.com
Paste the pan.quark.cn/s/… link (and its code, if any) → see the real size and price → get a direct download link. No Quark account, no +86 number, no Quark app. Files under 150 MB are free. Or post your link in the comments and I'll fetch it for you.
Full disclosure: BaiduFast is my own service. It fetches the file for you at full speed and hands you a plain HTTPS link. This page is a how-to, not an independent review.
✅ Working method (October 2026): baidufast.com
Paste a Baidu Netdisk (百度网盘), Quark Pan (夸克网盘), 123pan (123云盘) or Xunlei (迅雷云盘) share link → see the real size and price → get a direct download link. No account, no Chinese phone number, no app, no 50 KB/s. Files under 150 MB are free. Or post your link in the comments and I'll fetch it for you.
🔄 Last update 24 Oct 2026: Xunlei links are now automatic, and UC Drive and CSDN links are supported too — paste, see size and price, pay, download. PanDownload,
baidu.kinh.cc, userscripts and Telegram bots are dead — see Old methods below.
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.
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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.
- x2 x86 instances (2core/1g)
- x2 ampere instances (with 4core/24g spread between them)
- 200GB total boot volume space across all intances (minimum of 50G per instance)
[!WARNING]
| #!/bin/bash | |
| HTB_DATA_DIR=~/Documents/HTB | |
| if [[ $1 ]]; then | |
| a=1 | |
| else | |
| echo "Usage: $0 <Box name>" | |
| exit 1 | |
| fi |
- To easily list files giving more information -
ll
alias ll='ls -alhF'- First, install
xclipin your system. This will let you copy output of a command directly to your clipboard. -cat file.txt|c
alias c='xclip -selection clipboard'What I changed
- Wrong or misleading statements fixed:
config show/config dump(dump is JSON, andconfig redactedis the share-safe one).rclone dudoesn't exist (it'sncdu).rclone check --checksumisn't how check works.- The multi-thread default is 256Mi, not 250Mi.
--use-mmapis about releasing memory back to the OS, not paging.