A script to fix EDID problems on external monitors in macOS.
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Connect only the problem display.
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Create this directory structure (if it doesn't already exist):
hackaday yqt
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| """Information Retrieval metrics | |
| Useful Resources: | |
| http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
| http://www.nii.ac.jp/TechReports/05-014E.pdf | |
| http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
| http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
| Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
| """ | |
| import numpy as np |
mattetti
/ multipart_upload.go
Last active
March 6, 2026 07:32
Example of doing a multipart upload in Go (golang)
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| package main | |
| import ( | |
| "bytes" | |
| "fmt" | |
| "io" | |
| "log" | |
| "mime/multipart" | |
| "net/http" | |
| "os" |
ejdyksen
/ patch-edid.md
Last active
May 16, 2026 13:05
A script to fix EDID problems on external monitors in macOS
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| # You must first install apktool (https://github.com/iBotPeaches/Apktool) and android SDK | |
| # and decompile apk using it | |
| # apktool d -rf my-app.apk | |
| # then generate a key for sign in: | |
| # keytool -genkey -v -keystore my-release-key.keystore -alias alias_name -keyalg RSA -keysize 2048 -validity 10000 | |
| rm signed-app.apk | |
| apktool b -f -d com.myapp | |
| jarsigner -verbose -sigalg SHA1withRSA -digestalg SHA1 -keystore my-release-key.keystore com.myapp/dist/com.myapp.apk alias_name | |
| zipalign -v 4 com.myapp/dist/com.myapp.apk signed-app.apk |
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.