I don't use Codex, hm yes you need to let it make an mcp server a .toml file for configuration. It will get much easier with the next v2.8 update, I'm trying to make the setup as easy as possible while preserving flexibility. Here is a Codex install guide: **✅ Full AEP dynAEP Codex Install Guide (Without npm where possible)** This is a complete, unified guide for setting up **both**: - **AEP** (main repo) - deterministic build-time governance - **dynAEP** (side repo) - runtime/dynamic event governance …specifically for **OpenAI Codex** (CLI IDE extension), which uses **`.codex/config.toml`** (TOML format). We prioritize **Python paths** and avoid `npm`/`npx` installs as much as possible. --- ### 1. Prerequisites - Python 3.10 - Git - Codex installed (CLI or IDE extension) - (Optional but recommended) Node.js - only if you want the official MCP servers (otherwise we use pure Python) --- ### 2. Clone Both Repos ```bash # Main AEP (build-time governance) git clone github.com/thePM001/AEP-agen… # dynAEP side repo (runtime dynamic governance) git clone github.com/thePM001/dynAEP-d… ``` --- ### 3. Setup Main AEP (Python minimal Node) Main AEP is mostly Node-based, but we can use its Python-compatible parts and CLI via direct execution. ```bash cd AEP-agent-element-protocol npm install # Only needed once for building the CLI (minimal use) npm run build ``` **Python usage** (no further npm needed after build): - Add to `PYTHONPATH` when using Codex-generated Python code. **Main AEP CLI commands** (after build): ```bash node dist/cli.js assist setup node dist/cli.js assist preset strict node dist/cli.js assist status ``` --- ### 4. Setup dynAEP (Pure Python - Recommended for no-npm) This is the **side repo** you asked for. ```bash cd dynAEP-dynamic-agent-element-protocol ``` No `npm install` required for Python usage. **Set up Python access** (add this to your shell or Codex environment): ```bash export PYTHONPATH="$PYTHONPATH:/full/path/to/dynAEP-dynamic-agent-element-protocol/sdk" ``` Test it: ```bash python -c "import dynaep; print('dynAEP Python SDK ready')" ``` **Key files you’ll use**: - `dynaep-config.yaml` - `registries/aep-lattice.yaml` (example lattice) - Python code in `sdk/` --- ### 5. Configure for Codex (`.codex/config.toml`) Create the config file in your project: ```bash mkdir -p .codex touch .codex/config.toml ``` #### Recommended: Pure Python MCP servers (no npm) Because you want to avoid npm, we use **Python-based MCP servers** that wrap the dynAEP (and AEP) Python logic. **Add this to `.codex/config.toml`**: ```toml # dynAEP (runtime governance - recommended) [mcp_servers.dynaep] command = "python" args = ["dynaep_mcp_server.py"] # Create this script (see below) env = { PYTHONPATH = "/full/path/to/dynAEP-dynamic-agent-element-protocol/sdk" } startup_timeout_ms = 30000 # Main AEP (build-time governance) [mcp_servers.aep] command = "python" args = ["aep_mcp_server.py"] # Create this script (see below) env = { PYTHONPATH = "/full/path/to/AEP-agent-element-protocol" } startup_timeout_ms = 30000 ``` > **Note**: You need to create the two small Python MCP server scripts (`dynaep_mcp_server.py` and `aep_mcp_server.py`). > Ask **Grok Build** or **Codex** with this prompt: > “Create a minimal stdio MCP server script called `dynaep_mcp_server.py` that exposes dynAEP tools (lattice_query, lattice_validate, event validation) using the Python SDK from the cloned dynAEP repo. Do the same for `aep_mcp_server.py` using the main AEP repo.” --- ### 6. Quick Start - Using AEP dynAEP in Codex 1. Open your project in Codex (or run `codex` in terminal). 2. Codex should now detect the two MCP servers (`dynaep` and `aep`). 3. Use natural language prompts like: - “Set up strict AEP governance for this frontend project using the AEP MCP server.” - “Validate this UI event using dynAEP lattice from the dynaep MCP server.” - “Build a new component following AEP 3-layer structure (Structure / Behaviour / Skin) and dynAEP runtime rules.” - “Create a lattice config for multi-agent UI updates using dynAEP.” 4. For direct Python usage (no MCP): ```python import sys sys.path.append("/path/to/dynAEP-dynamic-agent-element-protocol/sdk") from dynaep import LatticeEngine # example import (adjust based on actual modules) ``` --- ### 7. Optional: Use Official Node MCP Servers (if you allow minimal Node) If you later decide to use the official servers: ```toml [mcp_servers.dynaep] command = "npx" args = ["dynaep", "mcp-serve"] [mcp_servers.aep] command = "node" args = ["/full/path/to/AEP-agent-element-protocol/dist/cli.js", "serve"] ``` This gives you the full official tools but uses Node. --- ### 8. Summary - Recommended No-npm Flow | Component | Setup Method | Config in Codex | Recommendation | |---------------|---------------------------|--------------------------|----------------| | **dynAEP** | Clone Python SDK | Python MCP or direct | Preferred | | **Main AEP** | Clone build once | Python MCP or CLI | Use for build-time | | **Codex** | `.codex/config.toml` | TOML `[mcp_servers.xxx]` | Full support | **Best practice**: - Use **dynAEP** for runtime dynamic UI/events - Use **main AEP** for initial structure/scene graph creation - Let Codex call both via the MCP server

取れないこともあるな。　なんだろこれ pythonPath通すには　やはり　環境変数指定できるわ

🔴 Linux Privilege Escalation: Python Library Hijacking 🔥 Telegram: t.me/hackinarticles ✴ Twitter: x.com/hackinarticles One insecure Python import = instant root shell ⚠️ ⚡ Attack Highlights 🔍 Enumerate sudo Python scripts (sudo -l) 📦 Identify imported libraries/modules 🛠 Abuse writable Python modules 🧪 Hijack PYTHONPATH execution order 🎯 Load malicious library before legit one 🚀 Execute code as root 💡 Python loads modules based on search order (sys.path) — attackers abuse writable paths, unsafe imports, or environment variables to inject malicious code () ⚠️ Misconfigured Python environments can lead directly to privilege escalation 📖 Article: hackingarticles.in/linux-pri… #cybersecurity #linux #python #privilegeescalation #redteam #pentesting #infosec

做短剧最重要的是什么？当然是剧本，今天给大家介绍一款写小说神器。 而且可以解决用AI写小说的都遇到过同一个问题，写着写着角色性格变了，设定前后矛盾，第三章的伏笔到第七章就忘了。 Bragi解决的就是这件事，它是一个开源的架空世界与长篇故事引擎，给一句设定就能生成完整的世界观、历史脉络、地理结构和角色关系网络，后续每次续写都会自动记住之前的设定。 跟普通AI写作工具不同的地方在于，Bragi把世界拆成了时间树和空间树。每个历史阶段、每个地点都用数字路径标注，AI每次写作时只加载当前节点需要的上下文，不会把所有信息一次性塞进有限的token窗口里。写完一段故事后，角色状态、事件记录、人物关系都会自动更新保存，下次续写直接接着用。 一句话生成世界、按需扩展历史，空间节点、自动管理角色网络、支持互动式故事续写、提供CLI和Web可视化界面。 安装三步： 1. 克隆仓库 git clone github.com/wailers9/bragi.gi… cd bragi 2. 创建虚拟环境 macOS/Linux： python3 -m venv .venv && source .venv/bin/activate Windows： python -m venv .venv && .\.venv\Scripts\Activate.ps1 3. 安装 python -m pip install -e . 然后直接跑本地Demo，不需要任何API key： PYTHONPATH=src python -m story_world.cli init-demo \ --prompt "一个漂浮群岛上的魔法工业时代" \ --time-id 2,1,3 \ --space-id 1,2,2 \ --protagonist "年轻的飞艇机械师" Demo跑通后接上DeepSeek就能正经写故事。推荐用deepseek-chat，成本和速度更适合长篇续写。一条命令开始互动写作： PYTHONPATH=src python -m story_world.cli play \ --agent deepseek \ --model deepseek-chat \ --prompt "一个被月亮潮汐撕裂的海上王国" \ --protagonist "年轻的灯塔学徒" 每生成一段后可以直接输入方向，比如"调查昨夜消失的船队"，故事会接着推进。不想互动就加--once，生成一段即停。 Web可视化界面： PYTHONPATH=src python3 -m story_world.web --host 127.0.0.1 --port 8765 浏览器打开127.0.0.1:8765，在网页上创建世界、生成故事、查看角色变化。 仓库： github.com/wailers9/bragi

🔴 Linux Privilege Escalation: Python Library Hijacking 🔥 Telegram: t.me/hackinarticles ✴ Twitter: x.com/hackinarticles One insecure Python import = instant root shell ⚠️ ⚡ Attack Highlights 🔍 Enumerate sudo Python scripts (sudo -l) 📦 Identify imported libraries/modules 🛠 Abuse writable Python modules 🧪 Hijack PYTHONPATH execution order 🎯 Load malicious library before legit one 🚀 Execute code as root 💡 Python loads modules based on search order (sys.path) — attackers abuse writable paths, unsafe imports, or environment variables to inject malicious code () ⚠️ Misconfigured Python environments can lead directly to privilege escalation 📖 Article: hackingarticles.in/linux-pri… #cybersecurity #linux #python #privilegeescalation #redteam #pentesting #infosec

Core setup: • HF_HOME → central Hugging Face cache • PYTHONPATH → lets LIBERO OpenVLA work together • MUJOCO_GL=egl → enables headless GPU rendering • TOKENIZERS_PARALLELISM=false → avoids noisy threading behavior Then: source ~/.bashrc conda activate openvla

What to configure: • Set HuggingFace cache paths (HF_HOME, TRANSFORMERS_CACHE) • Update PYTHONPATH for LIBERO openvla-oft • Enable headless MuJoCo with EGL • Disable tokenizer parallelism to avoid conflicts If this is clean, everything downstream runs reliably.

Try it yourself: git clone github.com/AbdelStark/nostra… cd nostrain python3 -m venv .venv && source .venv/bin/activate pip install -e ".[torch]" # Headless 4-worker GPT demo (everything in-process) PYTHONPATH=. python demo/gpt/train.py --rounds 5 # Or with tmux for the visual version bash demo/gpt/run.sh Runs on a MacBook or any machine with CPU. No GPU needed.

If you're trying out FA4, you're likely to run into not being able to load cutlass.cute - the issue is that nvidia-cutlass-dsl wheel installs its python files into - wait what - site-packages/nvidia_cutlass_dsl/python_packages/cutlass so of course python can't find these packages - so to overcome I had to use export PYTHONPATH=$CONDA_PREFIX/lib/python3.12/site-packages/nvidia_cutlass_dsl/python_packages/ now I can load FA4.

Ran into a small library resolution issue while playing around with Hermes-Agent on @solana . The sandbox was being a bit picky about PYTHONPATH and that specific HErmes module casing. Decided to put together a quick "Sandbox Fixer" to get things running smoothly. Just submitted a PR to @NousResearch to help the agent handle its own analysis library (HErmes.analysis) a bit better. 🛠️ A nice little Sunday morning exercise and a small contribution back to the open-source community. Check out the PR here: github.com/NousResearch/herm…

何ヶ月かけて、少し余裕ができて、今日少し頑張って、pluginのやり方として、lerobotのMeta-WorldでFTしてた。5K step初回の保存がこういう感じ FastVLM → FastVLA plugin？つまり、export PYTHONPATH=/home/<you>/VLA-from-FastVLM/src:${PYTHONPATH} そこからLerobot trainのコマンドで回してる

The Kernels library is basically trying to fix the kernel DX problem: building kernels, publishing them, and then loading them reliably. We really care about three properties: 1) you can load them from outside PYTHONPATH 2) you can load multiple versions in one process without collisions, and 3 ) they stay compatible across Python PyTorch build matrices and many different backends. We ship: - kernel-builder: builds packages the variant matrix for you (Torch/CUDA/C ABI/etc.) - kernels: the runtime loader that selects the right variant - kernels-community: shared, common kernels implementations you can reuse

PYTHONPATH: The GPS for Your Missing Modules

just set up my own AI agent i can control from telegram 🤖 runs 24/7 on a $6/month server. here's exactly how: the projectGru by @zscole - spawn Claude-powered AI agents from your phone. they run bash, write code, work autonomously. basically a coding assistant you can text from anywhere. github.com/zscole/gru what you need first telegram bot token (free from @BotFather) your telegram user ID (message @userinfobot) anthropic API key (console.anthropic.com) setup (15 mins) go to digitalocean.com, create droplet: Ubuntu 24.04, $6/month plan, use password auth click Console button on your droplet (opens terminal in browser) run: apt update && apt upgrade -y apt install python3 python3-pip python3-venv git screen -y git clone github.com/zscole/gru.git cd gru nano .env paste this (swap in your values): GRU_TELEGRAM_TOKEN=your_bot_token GRU_ADMIN_IDS=your_user_id ANTHROPIC_API_KEY=your_api_key GRU_MASTER_PASSWORD=any_password GRU_DATA_DIR=/root/.gru GRU_WORKDIR=/root/gru-workspace GRU_DEFAULT_MODEL=claude-sonnet-4-20250514 GRU_MAX_TOKENS=8192 GRU_DEFAULT_TIMEOUT=300 GRU_MAX_AGENTS=10 save: Ctrl X → Y → Enter run: mkdir -p /root/gru-workspace /root/.gru python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt pip install slack_bolt screen -S gru PYTHONPATH=src python -m gru.main press Ctrl A then D to detach. close browser. bot runs 24/7. message your bot: /gru status that's it. AI agent in your pocket for $6/month ☕

if you are curious how much back and forth needed to get to this point: Initial Working Setup with peacock-trame -> Qt5 Peacock Attempt (peacock-trame seems to not have all features / GUI not as compact) -> Peacock-trame broke, cannot build examples, had to checkout older moose and rebuild 1. Install miniforge3 and create moose conda environment with Python 3.10 2. Install moose-dev from INL conda channel 3. Install peacock-trame via pip 4. Clone MOOSE repository (latest main branch, --depth 1) 5. Set PYTHONPATH to include ~/peacock-work/moose/python 6. Set build environment variables (LIBMESH_DIR, WASP_DIR, PETSC_DIR) pointing to conda libs 7. Add MOOSE_NO_CODESIGN=1 for macOS 8. Build ex08-opt executable successfully 9. Create run_peacock.sh with hit.explode() monkey patch 10. Peacock-trame mostly working but viewer not working 11. Install moose-peacock conda package in moose environment 12. Encounter Qt5/Qt6 conflict (segfault) 13. Create separate peacock-qt5 environment with VTK 9.2.6 (Qt5 only) 14. Fix duplicate RPATH in libopenblas, libgfortran, libquadmath using install_name_tool 15. Qt5 Peacock launches but Input File tab empty (executable won't run) 16. MOOSE repository deleted/corrupted during Qt5 troubleshooting 17. Attempt to rebuild with latest MOOSE main branch 18. Build fails: missing DynamicArrayWrapper and SemiDynamicSparseNumberArrayGeneric classes 19. Version mismatch: MOOSE main (Dec 2025) incompatible with conda libs (May 2025) 20. Peacock-trame broken: no executable available 21. Remove incompatible MOOSE repository 22. Clone MOOSE at 2025-09-05-release tag (May 9, 2025) matching conda package dates 23. Build ex08-opt successfully with same environment variables 24. Peacock-trame now runs with 3D mesh viewer functional

I auto-activate virtualenvs with direnv, never noticed a problem. Just launched claude in a project and asked which python it's using and it correctly picks up my project's venv. Every project has a .envrc of: export PYTHONPATH="$PWD" export VIRTUAL_ENV=.venv layout python