- Step 1: Download and install StarUML Version 6 from main website https://staruml.io
- Step 2: Download
app.asarfile from https://drive.google.com/drive/folders/1gbhjOEYH1NPZNB_uMDDNr34sMsgGw8kf?usp=sharing - Step 3: Copy
app.asarfile download in step 2 (Overrideapp.asarfile)- Window:
C:\Program Files\StarUML\resources - MacOS:
/Applications/StarUML.app/Contents/Resources/ - Linux:
/opt/StartUML/resources
- Window:
- Step 4: Open StarUML app to use
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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.
| public class BleLssService { | |
| public static final UUID AUTHENTICATION = UUID.fromString("00002000-3DD4-4255-8D62-6DC7B9BD5561"); | |
| public static final UUID BATTERY_LEVEL = UUID.fromString("00002A19-3DD4-4255-8D62-6DC7B9BD5561"); | |
| public static final UUID CLIENT_DEVICE_NAME = UUID.fromString("00002002-3DD4-4255-8D62-6DC7B9BD5561"); | |
| public static final UUID CONNECTION_CONFIGURATION = UUID.fromString("00002004-3DD4-4255-8D62-6DC7B9BD5561"); | |
| public static final UUID CONNECTION_ESTABLISHMENT = UUID.fromString("00002005-3DD4-4255-8D62-6DC7B9BD5561"); | |
| public static final UUID CURRENT_TIME = UUID.fromString("00002006-3DD4-4255-8D62-6DC7B9BD5561"); | |
| public static final UUID DESCRIPTOR = UUID.fromString("00002902-0000-1000-8000-00805F9B34FB"); | |
| public static final UUID LOCATION_INFORMATION = UUID.fromString("00002007-3DD4-4255-8D62-6DC7B9BD5561"); | |
| public static final UUID LSS_CABLE_ATTACHMENT = UUID.fromString("0000200A-3DD4-4255-8D62-6DC7B9BD5561"); |
オレは高校生シェル芸人 sudo 新一。幼馴染で同級生の more 利蘭と遊園地に遊びに行って、黒ずくめの男の怪しげな rm -rf / 現場を目撃した。端末をみるのに夢中になっていた俺は、背後から近づいてきたもう1人の --no-preserve-root オプションに気づかなかった。
俺はその男に毒薬を飲まされ、目が覚めたら・・・ OS のプリインストールから除かれてしまっていた!
『 sudo がまだ $PATH に残っていると奴らにバレたら、また命を狙われ、他のコマンドにも危害が及ぶ』
上田博士の助言で正体を隠すことにした俺は、 which に名前を聞かれて、とっさに『gnuplot』と名乗り、奴らの情報をつかむために、父親がシェル芸人をやっている蘭の $HOME に転がり込んだ。ところが、このおっちゃん・・・とんだヘボシェル芸人で、見かねた俺はおっちゃんになりかわり、持ち前の権限昇格能力で、次々と難タスクを解決してきた。おかげで、おっちゃんは今や世間に名を知られた名エンジニア、俺はといえばシェル芸 bot のおもちゃに逆戻り。クラスメートの convert や ojichatや textimg にお絵かきコマンドと誤解され少年ワンライナーお絵かき団を結成させられる始末。
ではここで、博士が作ってくれたメカを紹介しよう。最初は時計型麻酔 kill 。ふたについた照準器にあわせてエンターを押せば、麻酔シグナルが飛び出し、プロセスを瞬時に sleep させることができる。
次に、蝶ネクタイ型 banner 。裏についているダイヤルを調整すれば、ありとあらゆる大きさのメッセージを標準出力できる。必殺のアイテムなら fork 力増強シューズ。電気と磁力で足を刺激し、 :(){ :|:& };: でプロセステーブ
iOS, The Future Of macOS, Freedom, Security And Privacy In An Increasingly Hostile Global Environment
This post by a security researcher who prefers to remain anonymous will elucidate concerns about certain problematic decisions Apple has made and caution about future decisions made in the name of “security” while potentially hiding questionable motives. The content of this article represents only the opinion of the researcher. The researcher apologises if any content is seen to be inaccurate, and is open to comments or questions through PGP-encrypted mail.
TL;DR
These standards describe how to design and write TypeScript code in this codebase. They are especially intended for agents: before adding patterns, libraries, adapters, or abstractions, read the existing code and prefer the local convention unless it conflicts with the safety/correctness principles below.
When rules pull in different directions, use this order:
- Preserve correctness, safety, and debuggability.
- Follow established project architecture and conventions.
This report summarizes the local Dreamcast rendering/tooling fork work since the recorded clone baselines. It is written for maintainers of the Dreamcast raylib/GLdc ecosystem who need to understand what changed, why it changed, and which parts are broadly reusable versus downstream integration glue.
The short version: the forks move the Dreamcast path away from many tiny immediate-mode helper submissions and toward fewer, larger, state-coherent array submissions that GLdc can process through its fastest paths. A small offline mesh format (.dcmesh) was added to pre-stripify static model geometry. GLdc was instrumented and tuned around the same fast path assumptions. SH4ZAM support exists, but the TODO plan correctly treats it as incomplete and in need of a staged follow-up.
The Dreamcast rendering stack used here is:
Information, resources, and products.