Saturday, 22 August 2026
利用openrung翻墙
下载客户端程序：
https://github.com/openrung/openrung/releases/download/v0.1.3/OpenRung-windows-amd64.zip
from https://openrung.org/#download
（工作原理： https://openrung.org/#how）
项目地址： https://github.com/openrung/openrung
-------------
使用说明：
下载客户端程序-
https://github.com/openrung/openrung/releases/download/v0.1.3/OpenRung-windows-amd64.zip
解压，运行openrung.exe ,程序窗口里会显示一幅世界地图，点击某个节点城市名称，然后
点击程序窗口左下角的connect按钮，等待连接成功。连接成功后，程序就自动设好你的系统代理，你就直接可用chrome/edge浏览器翻墙了。至于火狐浏览器，还要设置一下：
在火狐浏览器的地址栏里，访问 about:preferences#connectionSecurity
把页面往下拉，拉到proxy settings那里，点击configure proxy .在弹出的窗口中，选择use system proxy settings, 然后点击ok, 火狐浏览器即可翻墙。
开源的自托管的云平台程序patr
Patr is an incredibly simplified cloud platform that can deploy static sites, web apps, databases, containers and more in seconds. In fact, You can build, deploy and host ANYTHING with a Dockerfile. You also get instant support from our dev team.
--------------
Patr is a self-hosted, production-grade DevOps automation platform built for fast-moving teams.
It lets developers deploy and manage services without needing to understand or operate traditional DevOps infrastructure. No Kubernetes expertise required. No bespoke CI/CD glue. No hand-rolled observability stacks.
Patr is designed to feel closer to a control plane than a hosting provider.
⚠️ Early Beta
Patr is still in the early stages of development. APIs and resource models may change.
Not recommended for mission-critical production workloads yet. We would LOVE to have contributors onboard, but please be aware that the project is not yet ready for production use.
To manage Patr resources from your terminal, install the CLI:
curl -fsSL https://raw.githubusercontent.com/patr-cloud/patr/develop/assets/cli/install.sh | shSee cli/README.md for channels, upgrade, and uninstall instructions.
Nobody reads large text anyway (other than LLMs), so here's a skimmed down version:
- Rust:
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh - Node.js (>=22) and pnpm (the frontend is a SolidJS app)
- Postgres
- Redis
Copy ./config/api.sample.json to ./config/api.json and fill in the required fields. You'll primarily need to fill in database and redis fields.
cargo apiYou can now access the project at http://localhost:3001.
Patr is built with Rust, Axum, and Postgres. It is designed to be fast, secure, and scalable. Your code can be deployed to any environment from a single dashboard. For example, you can manage your deployments that are running on a VM (say, EC2 instance) as well as your deployments that are running on a Kubernetes cluster, all from a simple, easy-to-use, unified dashboard. Where your code runs is up to you, Patr just helps you manage it. This means that you can also use Patr to manage your deployments on your local machine, if you so desire. A common use-case of having a home-box on a Raspberry Pi, for example, and managing your deployments on it with Patr is supported.
from https://github.com/patr-cloud/patr
---------
相关帖子：
https://briteming.blogspot.com/2022/07/coolify-heroku-netlify.html
https://briteming.blogspot.com/2025/11/vercel-netlify-dokploy.html
LLMinator
An easy-to-use tool made with Gradio, LangChain, and Torch.
- Context-aware Streaming Chatbot.
- Inbuilt code syntax highlighting.
- Load any LLM repo directly from HuggingFace.
- Supports both CPU & CUDA modes.
- Enable LLM inference with llama.cpp using llama-cpp-python
- Convert models(Safetensors, pt to gguf etc)
- Customize LLM inference parameters(n_gpu_layers, temperature, max_tokens etc)
- Real-time text generation via websockets, enabling seamless integration with different frontend frameworks.
To use LLMinator, follow these simple steps:
```
git clone https://github.com/Aesthisia/LLMinator.git
cd LLMinator
pip install -r requirements.txt
```
Build LLMinator with llama.cpp:
-
Using
make:-
On Linux or MacOS:
make
-
On Windows:
- Download the latest fortran version of w64devkit.
- Extract
w64devkiton your pc. - Run
w64devkit.exe. - Use the
cdcommand to reach theLLMinatorfolder. - From here you can run:
make
-
-
Using
CMake:mkdir build cd build cmake ..
- Run the LLMinator tool using the command
python webui.py. - Access the web interface by opening the http://127.0.0.1:7860 in your browser.
- Start interacting with the chatbot and experimenting with LLMs!
Checkout this youtube video to follow installation steps
| Argument Command | Default | Description |
|---|---|---|
| --host | 127.0.0.1 | Host or IP address on which the server will listen for incoming connections |
| --port | 7860 | Launch gradio with given server port |
| --share | False | This generates a public shareable link that you can send to anybody |
Connect to ws://localhost:7861/ for real-time text generation. Submit prompts and receive responses through the websocket connection.
Integration with Frontends:
The provided example/index.html demonstrates
basic usage of text generation through websocket connection. You can
integrate it with any frontend framework like React.js
- Compatible Versions: This project is compatible
with Python versions 3.8+ to 3.11. Ensure you have one of these versions
installed on your system. You can check your Python version by running
python --versionorpython3 --versionin your terminal.
- Cmake Dependency: If you plan to build the project using Cmake, make sure you have Cmake installed.
- C Compiler: Additionally, you'll need a C compiler
such as GCC. These are typically included with most Linux distributions.
You can check this by running
gcc --versionin your terminal. Installation instructions for your specific operating system can be found online.
- Visual Studio Installer: If you're using Visual Studio Code for development, you'll need the C++ development workload installed. You can achieve this through the Visual Studio Installer
- CUDA Installation: To leverage GPU acceleration, you'll need CUDA installed on your system. Download instructions are available on the NVIDIA website.
- Torch Compatibility: After installing CUDA, confirm CUDA availability with
torch.cuda.is_available(). When using a GPU, ensure you follow the project's specificllama-cpp-pythoninstallation configuration for CUDA support.
from https://github.com/Aesthisia/LLMinator
Friday, 21 August 2026
PI-Desktop
A local-first desktop app for AI coding agents.
Bring your own models. Keep your code, your keys, and your conversations on your machine.
Download · Getting started · Highlights · Screenshots · How it works · Development · 简体中文
PI-Desktop puts an AI coding agent in a native desktop app. Point it at a project, describe what you want — explore and understand code, build a feature, review changes, fix a failing test — and watch it work, with every file edit and shell command surfaced for your approval.
There is no account, no subscription, and no cloud in the middle: you connect the model provider you already use, and everything else — sessions, settings, API keys — stays local.
- Any model, your keys. Anthropic, OpenAI, or anything that speaks an OpenAI-compatible API — hosted relays as well as local gateways like Ollama or LM Studio. Model IDs are free-form (no hardcoded allowlist), with per-model context window, output limit, temperature, and thinking-mode controls.
- Agent, Plan, and Goal modes. Agent mode reads, edits, and runs commands to get things done. Plan has the same agent inspect the project and submit an immutable implementation checkpoint for approval. Goal lets the agent agree on an outcome and acceptance criteria, then continue autonomously after approval.
- You approve every change. File writes and shell commands ask first, with session-scoped grants and a configurable default policy. Unanswered prompts deny by default.
- A real workbench. Review the agent's edits as message-scoped diffs with guarded rollback, inspect command output in the transcript, preview a local app in a browser, and browse project files — all in a side panel, without leaving the conversation.
- Projects and sessions. Sessions are grouped by project in a multi-project sidebar, with pinning, archiving, sorting, branching, notifications, and throwaway scratch sessions.
- Local-first and private. Transcripts live on disk as plain JSONL with a SQLite index — easy to back up, grep, or delete. API keys go into the OS keychain. Logs stay local; there is no telemetry.
- Agent capabilities beyond plugins. Manage
standalone MCP servers, Skills, and global Subagents from Settings →
Agent, with project-scoped Skills and MCP overrides. The Extensions page
is reserved for installed plugins and the marketplace. Plugins can add
commands, panels, agent tools, skills, themes, MCP servers, resident
services, and a message bus; the local/official marketplace and
.piplugpackage workflow are available today. - A fast daily workflow. Use slash commands and
@file references, paste files into session scratch, stage the next turn while an answer streams, search plugins globally with Option/Alt+Space, and create manual or recurring task prompts. - Comfortable to live in. English and 简体中文, light/dark/system and plugin themes, command palette, onboarding checklist, local notifications, context checkpoints, and update notifications for packaged builds.
Plugin APIs and panels are permission-gated and run out-of-process. Plugin code is still user-trusted code rather than a complete OS sandbox, so review permissions and only install plugins you trust.
 |
 |
| Every turn in one transcript, with a minimap rail for long conversations | Install from the official catalog, a mirror, or a custom URL you configure |
 |
 |
| Switch the model per session — any provider you have configured, keys in the OS keychain | Language, theme, and appearance, including themes plugins contribute |
Grab the latest build from the Releases page.
| Platform | Package | Status |
|---|---|---|
| macOS (Apple Silicon) | .dmg / .zip |
✅ Published with each release |
| Windows (x64) | NSIS installer | ✅ Published with each release; in-app auto-update |
| Linux (x64) | .AppImage / .deb |
✅ Published with each release; AppImage auto-updates in-app |
macOS note: builds are not yet code-signed or notarized. If macOS refuses to open the app, right-click it and choose Open, or clear the quarantine flag:
xattr -cr /Applications/PI-Desktop.app
Packaged builds check GitHub Releases for new versions and show an in-app update banner.
- Add a model provider. Open Settings → Model configuration → Add provider: pick the API style, paste the base URL and your API key, then choose or type a model ID. The key is stored in your OS keychain and never shown again.
- Open a project. Add a project folder from the sidebar — sessions, tools, and permissions are scoped to it.
- Describe the task. Start in Agent mode to make changes, switch to Plan for an approval checkpoint, or use Goal when you want to approve an outcome rather than prescribe the steps. Review work in the Review diff panel before you commit anything.
- Extend the workspace when needed. Open Settings → Agent to manage Skills, MCP servers, and global Subagents; open Extensions for installed plugins and the marketplace. Use Settings → Import to bring in local sessions from Claude Code, OpenCode, Codex, or Pi.
PI-Desktop keeps renderer privileges narrow and separates the agent loop from the desktop UI:
- Electron shell — a sandboxed React renderer plus the main/preload bridge for desktop-only services such as panels, browser preview, updates, and supervision.
- Rust host core — owns SQLite, transcript persistence, secrets, permissions, and workspace access over stdio JSON-RPC.
- pi agent sidecar — a Node process running the pi agent engine (
pi-ai+pi-agent-core) for the actual agent loop.
The full picture lives in the architecture spec.
PI-Desktop is an early preview under active development. The current 0.5.x line ships the app shell, streaming agent runtime, Agent/Plan/Goal contracts, workspace tools with permissions, the workbench, projects and sessions, imports, agent capability management (MCP/Skills/Subagents), extensions (plugins), context checkpoints, notifications, and cross-platform packaging with update checks.
Still in progress: signed and notarized macOS builds, native Windows/Linux qualification, a stronger plugin sandbox and publisher-signature path, and full UI-driven E2E coverage. See the milestones and the project board.
Prerequisites: Node.js >=22.19, pnpm >=10 (the repository uses pnpm 11), and a stable Rust toolchain.
# build the Rust host core
cargo build -p host-core
# install JS dependencies and build packages + app
pnpm install
pnpm build:js
# run in dev mode
pnpm dev
# protocol e2e smoke test
PI_DESKTOP_TEST_API_KEY=... pnpm test:e2e
# Plan host acceptance (includes the real 60-second default timeout)
PI_DESKTOP_E2E_LONG_TIMEOUT=1 pnpm test:e2e:plan
# rendered English / Simplified Chinese Plan acceptance through Electron CDP
pnpm test:e2e:plan-ui
# focused desktop probes
pnpm test:e2e:boot
pnpm test:e2e:supervision
pnpm test:e2e:subagentsCI runs JS build / typecheck / lint / unit tests plus cargo test for
code-related pull requests and pushes to main; documentation-only changes are
skipped. Releases are cut by tag:
node scripts/release.mjs 0.2.0 --tag # bump versions + commit + tag v0.2.0
git push origin main v0.2.0 # Release workflow builds & publishes- Plugin development: zero to one
- Screens — every surface, captured from the running app
- Spec index — start here
- Product scope
- Baseline decisions
- Architecture
- UI information architecture
- E2E test plan
- Plugin system
- ADRs · Milestones · Agent guide
PI-Desktop is built with and informed by these open-source projects:
- Agent runtime: pi-mono, whose
pi-aiandpi-agent-corepackages provide the agent loop and provider abstractions. - Desktop and UI foundation: Electron, React, Vite, Tailwind CSS, Lucide, Shiki, Mermaid, KaTeX, TypeBox, and i18next.
- Behavioral and visual references: OpenAI Codex informs parts of the shell and context-management UX. OpenCode DCP was studied as a behavioral reference for context compaction; it is not a PI-Desktop dependency, and no code is copied from it.
- Bundled fonts: Geist,
Inter,
Noto Sans SC (Source Han Sans lineage),
and LXGW WenKai, which includes work
from Klee. Their SIL Open Font
License texts are included under
apps/desktop/src/assets/fonts/licenses/.
from https://github.com/vastsa/PI-Desktop
初秋时节的三首经典诗词
初秋，说不出有多美好。仅是默念“初秋”两个字，便能感受到一丝丝清凉与心灵的宁静。
林语堂先生曾坦言对初秋的喜爱：“大概我所爱的不是晚秋，是初秋，那是喧气初消，月正圆，蟹正肥，桂花皎洁。”
初秋的美，美在清凉宁静，美在丰盈饱满。当我们踏入初秋，仿佛走进了一幅诗意的画卷。
01
宋·徐玑《 秋行》
戛戛秋蝉响似筝，听蝉闲傍柳边行。
小溪清水平如镜，一叶飞来浪细生。
秋蝉的“戛戛”鸣叫声，就像古筝悠扬的旋律，诗人便在这样的音符中，悠闲地在柳树林边漫步。
小溪的水面，平静得仿佛一面未经触摸的镜子，映照着四周的景致。忽然间刮起了一阵风，一片落叶轻轻飘落在水中，泛起了无数细小的涟漪。
徐玑这首《 秋行》，宛如一曲清绝的短笛，吹奏出秋天的韵味。
诗中意境轻快闲适，短短几句，便将秋日风物呈现得栩栩如生，有一种别致的情趣。
02
清·陈文述 《夏日杂诗》
水窗低傍画栏开，枕簟萧疏玉漏催。
一夜雨声凉到梦，万荷叶上送秋来。
秋风轻拂，酷暑退场，清爽宜人的初秋如约而来。
诗人打开窗户，窗户临水敞开。他躺在卧室的竹席上，便能感受到秋天的凉意。荷叶上的雨声送来了秋意，一夜雨声把清凉带入了梦境。梦境犹凉，凉得温柔细腻，凉得体贴入微，凉得惬意舒适。陈文述以其独到的视角，生动地描绘了秋凉的到来，营造了一个清丽而奇趣的意境，令人回味无穷。
元代盍西村在《莲塘雨声》中写道：“忽闻疏雨打新荷，有梦都惊破。”写的是荷上雨声，惊破好梦；而陈文述写的是荷上雨声，为梦送凉，一动一静，各具韵味，堪称妙绝。
03
杨万里《秋凉晚步》
秋气堪悲未必然，轻寒正是可人天。
绿池落尽红蕖却，荷叶犹开最小钱。
自古以来，文人墨客多爱悲叹秋天萧条、凄冷与空旷。
杨万里却认为，秋气轻寒，天高云淡，正是四季之中最宜人的时候。
潋滟池塘里的荷花虽然都落尽了，但无穷碧的荷叶却仍生机盎然，新生的小荷叶只有铜钱大小，异常可爱喜人。
秋，固然带着几分寥落与清冷。但秋也是丰满的，成熟的，金黄的。秋思之不同，实际上是心境之不同。
一如杨万里，在一片秋日的悲凉中，他看到的却不是凄冷，而是新生，是碧绿，是生命的另一种绽放与妩媚。
人生之路，即使挑战重重，境遇多舛，却仍要满怀希望，蓬勃向上。由是，才能迎来自己的柳暗花明，"荷叶新开。”
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