Discover gists
| $subscriptionId = "xxx" | |
| $resourceGroup = "xxx" | |
| $azfwPolicyName = "xxx" | |
| function GetIpGroupName { | |
| param ( [string] $resourceId ) | |
| $resourceSubscriptionId = (Get-AzResource -ResourceId $resourceId).SubscriptionId | |
| $context = Get-AzContext |
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.
| # Ford security access seed-key algorithms, reversed from libfnvfsa.so on the ECG2 GWM | |
| # | |
| # Algorithm 0: legacy 24-bit Galois LFSR cipher (3 byte seed, 5 fixed bytes) | |
| # Described in https://flaviodgarcia.com/publications/BtB.pdf | |
| # Algorithms 1/2: modern HMAC-SHA1 (16 byte seed, 12 fixed bytes) | |
| # | |
| # "seed bytes" are the challenge bytes sent by the ECU | |
| # "fixed bytes" are the secret bytes which differ per ECU model and security level | |
| import argparse |
name: the writing whip description: >- Kill all AI writing tropes and behaviours at the source: generation. Focus on quelling behaviour-driven triggers. Load before writing, especially for anything longer than a paragraph and re-check output against it, extremely important for communications, documentation, outbound, client-facing copy and for your own replies to the user. metadata: category: writing author: Ossama Chaib
| #EXTM3U | |
| #EXTINF:-1 tvg-id="" tvg-name="CRO: VIP MAX SPORT 1" tvg-logo="https://docdog.top/logo/countries/croatia/maxsport1.png" group-title="EU | CROATIA VIP",CRO: VIP MAX SPORT 1 | |
| http://vipgerretger.top:8080/VIP01291755248523551/9feb67bc364a/34146 | |
| #EXTINF:-1 tvg-id="" tvg-name="CRO: VIP MAX SPORT 2" tvg-logo="https://docdog.top/logo/countries/croatia/maxsport2.png" group-title="EU | CROATIA VIP",CRO: VIP MAX SPORT 2 | |
| http://vipgerretger.top:8080/VIP01291755248523551/9feb67bc364a/37641 | |
| #EXTINF:-1 tvg-id="" tvg-name="CRO: ARENA SPORT 1" tvg-logo="https://docdog.top/logo/countries/croatia/arenasport1.png" group-title="EU | CROATIA VIP",CRO: ARENA SPORT 1 | |
| http://vipgerretger.top:8080/VIP01291755248523551/9feb67bc364a/2991 | |
| #EXTINF:-1 tvg-id="" tvg-name="CRO: ARENA SPORT 2" tvg-logo="https://docdog.top/logo/countries/croatia/arenasport2.png" group-title="EU | CROATIA VIP",CRO: ARENA SPORT 2 | |
| http://vipgerretger.top:8080/VIP01291755248523551/9feb67bc364a/2992 |
| #!/usr/bin/env python3 | |
| """ | |
| QE Admissions — Standalone Desktop App (with Live Monitor + Mock Sync/Backup) | |
| Build aims | |
| - Desktop CRM for Enquiries, Applications, People, Households, Agencies, Agents, Campaigns, Activities | |
| - Live “connected” feel via simulated network metrics + mock sync service | |
| - Rolling SQLite backups with rotation | |
| - Thread-safe DB (single connection + global RLock) | |
| - No external packages • Python 3.13+ |