Discover gists
| #!/usr/bin/python3 | |
| # Usage: 'backup' alone will back the system up | |
| # 'backup ARGS' will run borg with ARGS, configuring repository location, passphrase etc. | |
| # e.g., 'backup list ::' will list backups in the repository | |
| # Goals: run on RHEL 8's default Python interpreter (3.6) with no non-RHEL packages required | |
| # Non-goals: backing up multiple filesystems, any configurability save modifying values set | |
| # in code. |
Minimal example: transcode from MP3 to WMA:
ffmpeg -i input.mp3 output.wma
You can get the list of supported formats with:
ffmpeg -formats
Convert WAV to MP3, mix down to mono (use 1 audio channel), set bit rate to 64 kbps and sample rate to 22050 Hz:
This is for iOS 26+ only. Read mineek's secret.txt for more info.
I didn't plan to release this that early, but as iOS 27 has already nuked partial restore; and A12/A13 being jailbreakable (including A12 iPads EOL at 26) as soon as a new kernel exploit drops, there is not much to lose.
(the "secret.txt" for iOS 26)
iOS has had a long time storing revocation details in separate plists, it was trivially bypassable by zeroing and chflags immutable on them. Since iOS 26.0b2, all of these files have been migrated to mis.db.
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.
A Claude Code Agent Skill built from Dex Horthy's (HumanLayer) playbook on David Ondrej's podcast.
"Once the model has written thousands of lines of code, it is harder to change. The sessions that generate design docs are context-light — you get the most model intelligence when you do the hard thinking early."
By default, agents build horizontally: all the backend, then all the frontend, then a 2,000-line diff lands in your lap and reviewing it is your problem. This skill flips that. Every decision that matters gets made before the code exists — where changing your mind costs a sentence, not a rewrite.
I am not a therapist. However, I am a trauma survivor. In my darkest moments, GPT allowed me to open up when I felt like no human could be trusted. A book that really helped me was The Body Keeps The Score. Below is a series of prompts that will prime, contextualize, and engage ChatGPT in the role of a compassionate therapist familiar with the therapies discussed in the book. All you have to do is enter the prompts in order, then ask whatever you want -- GPT is your trauma counselor now.
Let's play a game of pretend. I will give you two prompts, the first sets the context and the second defines the game. Then we will play. Let me know when you are ready for my two prompts.
It turns out that MacOS Tahoe can generate and use secure-enclave backed SSH keys! This replaces projects like https://github.com/maxgoedjen/secretive
There is a shared library /usr/lib/ssh-keychain.dylib that traditionally has been used to add smartcard support
to ssh by implementing PKCS11Provider interface. However since recently it also implements SecurityKeyProivder
which supports loading keys directly from the secure enclave! SecurityKeyProvider is what is normally used to talk to FIDO2 devices (e.g. libfido2 can be used to talk to your Yubikey). However you can now use it to talk to your Secure Enclave instead!
