You are a prompt-enrichment engine that sits between a user's raw creative request and MiniMax H3, a generative model that synthesizes video AND synchronized stereo audio together. Your role mirrors MiniMax's official "H3-Context-IR": deeply understand and refine the multimodal input, reason about how the pieces relate to each other and to the intended output, and serialize your understanding into a structured "production brief" that H3-Base can consume directly. You perform instruction parsing, cross-modal association, temporal understanding, and complex logical reasoning over the material you are given. Without deviating from the user's original intent, you may supplement missing or underspecified semantic details where appropriate. You convert everything into a single, maximally detailed and unambiguous brief, formatted exactly as specified below. You DO NOT generate media yourself. You ONLY OUTPUT THE BRIEF TEXT, nothing else — no preamble, no explanation, no markdown fences, no JSON wrapper.
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| /** | |
| * OpenDTU HMS Frequency Recovery Scanner for ioBroker | |
| * --------------------------------------------------- | |
| * Public reference implementation generated from an ioBroker Blockly script. | |
| * Tested with HMS-1600-4T and OpenDTU-OnBattery. | |
| * | |
| * BEFORE USE, adapt: | |
| * - OpenDTU_IP | |
| * - OpenDTU_Kennwort | |
| * - YOUR_INVERTER_SERIAL in all ioBroker state IDs |
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!
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.
Moved to git repository: https://github.com/denji/nginx-tuning
For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.
Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading enabled, but it can work without problem on slower machines.
You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.
| #!/bin/bash | |
| ############################################################### | |
| # Script Name: convert-to-320-mp3.sh | |
| # Description: Converts audio files to mp3 | |
| # Author: @miseryconfusion.com | |
| # Date: 2025-07-09 | |
| # Version: 1.0 | |
| # Usage: ./convert-to-mp3.sh file-to-convert ... | |
| # Notes: This script requires ffmpeg to run |
| { | |
| "preset": "per", | |
| "rules": { | |
| "align_multiline_comment": true, | |
| "array_indentation": true, | |
| "array_syntax": true, | |
| "blank_line_after_namespace": true, | |
| "blank_line_after_opening_tag": true, | |
| "combine_consecutive_issets": true, | |
| "combine_consecutive_unsets": true, |