Macbook Neo (I'm on macOS Tahoe 26.5.1) cursor is lagging when the cursor is near the screen's edges or when it enters a Terminal window. [1][2][3][4][5]
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This PowerShell script allows you to bypass TPM 2.0, unsupported CPU, and memory checks enforced by the Windows 11 Upgrade Assistant and setup.exe from Windows installation media. It eliminates common upgrade blocks such as:
- This PC doesn't currently meet Windows 11 system requirements.
- TPM 2.0 must be supported and enabled on this PC.
- The processor isn't currently supported for Windows 11.
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.
YoraiLevi
/ machine-bound-secret-storage.md
Created
June 20, 2026 16:00
Built-in, machine-bound secret storage on Windows, Linux & macOS — a working engineer's guide (DPAPI, systemd-creds, Keychain/Secure Enclave, TPM, kernel keyring + the cross-platform layers): how each works, how to use it, how to verify a secret is really encrypted, and the honest threat model.
You need to keep a secret on a machine. A backup password, an API token a scheduled job uses at 3am, a key your service reads at startup. The secret has to sit on disk so the machine can use it without a human present — but it must not sit there as plaintext that anyone who copies the file can read.
The core idea every OS uses is the same: encrypt the secret with a key that lives only on this machine, so the encrypted blob is useless once it leaves. The differences are all in where the root key lives (a registry hive, a user's password hash, a TPM chip, a Secure Enclave), who is allowed to ask for decryption, and what survives a restore to new hardware. Get those three right and you understand the whole landscape.
Reach for built-in machine-bound storage when the secret belongs to this installation: a daemon's credential
GeneralSadaf
/ List of all bots owned by Discord.md
Last active
June 25, 2026 20:53
All official Discord bots I am aware of. This is an unofficial list and may not be kept completely up to date.
| Username | Description | ID | Invite |
|---|---|---|---|
| GearBoat#3958 | Used for moderation purposes, GearGoat is a custom instance of the public GearBot made by AEntherprise#4693 (106354106196570112) and used in Discord Townhall, Discord Developers, etc. Public instance can be found at: https://gearbot.rocks | 520953716610957312 | Invite |
| Discord DevBot#9425 | This bot is used in Discord Developers server, answers to frequently asked questions and has some public commands. | 545364944258990091 | Invite |
| Developers Role Bot#0397 | This bot is used in Discord Developers server to give out roles. | 976907566334484590 | Invite |
| DBug#8485 | This bot is |
jackjona
/ commonsensemedia.user.js
Last active
June 25, 2026 20:46
A user script that removes gate-wrapper and auto-clicks close button on CommonSenseMedia
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| // ==UserScript== | |
| // @name CommonSenseMedia Gate Remover + Auto Close | |
| // @namespace Violentmonkey Scripts | |
| // @match https://www.commonsensemedia.org/* | |
| // @grant none | |
| // @namespace https://gist.github.com/jackjona/e03ddd75d1308ba790f0221f1f6c935c | |
| // @homepageURL https://gist.github.com/jackjona/e03ddd75d1308ba790f0221f1f6c935c | |
| // @supportURL https://gist.github.com/jackjona/e03ddd75d1308ba790f0221f1f6c935c | |
| // @updateURL https://gist.githubusercontent.com/jackjona/e03ddd75d1308ba790f0221f1f6c935c/raw/commonsensemedia.user.js | |
| // @downloadURL https://gist.githubusercontent.com/jackjona/e03ddd75d1308ba790f0221f1f6c935c/raw/commonsensemedia.user.js |
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.