Onimusha: Way of the Sword Trainer PC utility featuring Player Controls, Resource Controls, Damage Settings, Enemy Controls, Game Speed, Quick Actions, reusable profiles, quick actions, and Windows configuration management.
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Local AI Voice Studio for Windows — create voice projects offline with text-to-speech, voice presets, narration workflows, script management, project folders, batch export and private local processing.
Offline-first design: this project is organized around local processing on the user's Windows PC. The intended workflow does not require mandatory cloud upload.
| blueprint: | |
| name: ⚠️ Repeating Alert Notifications by LTek | |
| description: | | |
| **Repeating Alert Notifications ideal for doors, windows, locks, etc** | |
| 🚀 Version 2026.07.30.39 | |
| - FIXED: two leftover `rendered_title: !input notification_title` variables (one in the main | |
| trigger path, one in the restart-persistence-restore path) were dead code — never referenced |
- First, you need to open cmd in the admin mode, then run all commands below one by one.
- For x86 and x64
cd /d %ProgramFiles(x86)%\Microsoft Office\Office16
cd /d %ProgramFiles%\Microsoft Office\Office16
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.
List of freely available resources to study computer graphics programming.
Table of Contents
- https://dancres.github.io/Pages/
- https://ferd.ca/a-distributed-systems-reading-list.html
- http://the-paper-trail.org/blog/distributed-systems-theory-for-the-distributed-systems-engineer/
- https://github.com/palvaro/CMPS290S-Winter16/blob/master/readings.md
- http://muratbuffalo.blogspot.com/2015/12/my-distributed-systems-seminars-reading.html
- http://christophermeiklejohn.com/distributed/systems/2013/07/12/readings-in-distributed-systems.html
- http://michaelrbernste.in/2013/11/06/distributed-systems-archaeology-works-cited.html
- http://rxin.github.io/db-readings/
- http://research.microsoft.com/en-us/um/people/lamport/pubs/pubs.html
- http://pdos.csail.mit.edu/dsrg/papers/
Audit this Go codebase and aggressively simplify AI-generated, defensive, overly abstract, or unnecessarily generic code.
The goal is to make the code look like it was written by an experienced Go engineer:
- simple
- explicit
- strongly typed
- boring
| # -*- coding: utf-8 -*- | |
| """ | |
| draw_bbox_clip_dtm.py | |
| ===================== | |
| Da incollare nella Console Python di QGIS. | |
| Attiva uno strumento sul map canvas: si traccia un rettangolo trascinando il | |
| mouse, e al rilascio viene scaricato SOLO il DTM contenuto nella bbox disegnata, | |
| via GDAL /vsicurl/. Il ritaglio viene poi caricato come layer nel progetto. |
