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| #!/usr/bin/env bash | |
| # Download VMware Workstation Linux bundles from Internet Archive and GitHub. | |
| # Requires Bash, curl, Python 3, and standard Unix tools. Does not install VMware. | |
| set -euo pipefail | |
| export LC_ALL=C | |
| ARCHIVE_ITEM="vmwareworkstationarchive" | |
| GITHUB_REPO="201853910/VMwareWorkstation" | |
| DOWNLOAD_DIR="${HOME}/Downloads" | |
| SOURCE="auto" |
| # Please Note, I am sharing this, hoping this will be used for good public use, such as data analysis, penetration testing etc | |
| # These links are already available all over the internet | |
| # Also Note, Trying to login into someone else's account without their permission is unethical and illegal | |
| # Collection 1 | |
| magnet:?xt=urn:btih:B39C603C7E18DB8262067C5926E7D5EA5D20E12E&dn=Collection%201&tr=udp%3a%2f%2ftracker.coppersurfer.tk%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.leechers-paradise.org%3a6969%2fannounce&tr=http%3a%2f%2ft.nyaatracker.com%3a80%2fannounce&tr=http%3a%2f%2fopentracker.xyz%3a80%2fannounce | |
| # Collection 2-5 & Antipublic | |
| magnet:?xt=urn:btih:D136B1ADDE531F38311FBF43FB96FC26DF1A34CD&dn=Collection%20%232-%235%20%26%20Antipublic&tr=udp%3a%2f%2ftracker.coppersurfer.tk%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.leechers-paradise.org%3a6969%2fannounce&tr=http%3a%2f%2ft.nyaatracker.com%3a80%2fannounce&tr=http%3a%2f%2fopentracker.xyz%3a80%2fannounce |
| // Types for the result object with discriminated union | |
| type Success<T> = { | |
| data: T; | |
| error: null; | |
| }; | |
| type Failure<E> = { | |
| data: null; | |
| error: E; | |
| }; |
A small setup so a Claude Code agent can start a long process, keep working (or wait cheaply), and answer "how's it going?" in one tool call, with a real percentage and a time estimate instead of guesses. Written for other Claude Code agents to copy: everything is two stdlib-only Python scripts, one skill file and a few lines of instructions.
Long jobs (data runs, scrapes, builds) used to be handled with nohup ... & plus until grep ...; do sleep loops or repeated
polling. That burned turns and context, hit the Bash tool's 10-minute limit, and gave no sense of progress. The fix:
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.
| CACHE_INFO: 127.0.0.1 | |
| CF_CONNECTING_IP: 127.0.0.1 | |
| CF-Connecting-IP: 127.0.0.1 | |
| CLIENT_IP: 127.0.0.1 | |
| Client-IP: 127.0.0.1 | |
| COMING_FROM: 127.0.0.1 | |
| CONNECT_VIA_IP: 127.0.0.1 | |
| FORWARD_FOR: 127.0.0.1 | |
| FORWARD-FOR: 127.0.0.1 | |
| FORWARDED_FOR_IP: 127.0.0.1 |
| LGOROWLENGTH 12 | |
| ORIGINATOR "ColorCheckerSG - November2014 edition and newer" | |
| MANUFACTURER "X-Rite - http://www.xrite.com" | |
| 5/11/2015 # Time: 16:08 | |
| "i1Pro 2 ; Serial number 1000691" | |
| "MeasurementCondition=M0 Filter=no" | |
| NUMBER_OF_FIELDS 4 |