Working with ADOX products: http://www.adox.de .
- Water at 20°C
- ADONAL: Developer which equals to Rodinal
| KEY=XXXXXXXXXXXX | |
| HOST="https://metrics.crisidev.org" | |
| mkdir -p dashboards && for dash in $(curl -k -H "Authorization: Bearer $KEY" $HOST/api/search\?query\=\& |tr ']' '\n' |cut -d "," -f 5 |grep slug |cut -d\" -f 4); do | |
| curl -k -H "Authorization: Bearer $KEY" $HOST/api/dashboards/db/$dash > dashboards/$dash.json | |
| done |
| #!/bin/sh | |
| ## | |
| ## Create a CISCO AnyConnect VPN Client *.deb from anyconnect-predeploy-linux-*.tar | |
| ## Copyright (c) SATOH Fumiyasu @ OSS Technology Corp., Japan | |
| ## | |
| ## License: GNU General Public License version 3 | |
| ## | |
| set -u | |
| set -e |
| node { | |
| echo 'Results included as an inline comment exactly how they are returned as of Jenkins 2.121, with $BUILD_NUMBER = 1' | |
| echo 'No quotes, pipeline command in single quotes' | |
| sh 'echo $BUILD_NUMBER' // 1 | |
| echo 'Double quotes are silently dropped' | |
| sh 'echo "$BUILD_NUMBER"' // 1 | |
| echo 'Even escaped with a single backslash they are dropped' | |
| sh 'echo \"$BUILD_NUMBER\"' // 1 | |
| echo 'Using two backslashes, the quotes are preserved' | |
| sh 'echo \\"$BUILD_NUMBER\\"' // "1" |
| module Fastlane | |
| module Actions | |
| module SharedValues | |
| INSERT_BUILD_PHASES_CUSTOM_VALUE = :INSERT_BUILD_PHASES_CUSTOM_VALUE | |
| end | |
| class InsertBuildPhasesAction < Action | |
| def self.run(params) | |
| require 'xcodeproj' |
| # install Roswell: https://github.com/roswell/roswell | |
| # and Qlot: https://github.com/fukamachi/qlot | |
| # they are "must have" tools if you are Common Lisp professional :) | |
| git clone [email protected]:nEXT-Browser/nEXT.git | |
| cd nEXT | |
| echo 'ql :all :latest' > qlfile | |
| qlot install | |
| ros use ccl-bin |
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.