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What Are the 36 Stratagems? If you've heard of The Art of War, think of the Thirty-Six...

Many Python beginners can write basic programs but get stuck when they try to run a real project on their own laptop. The issue is not always coding. Sometimes the real problem is setup . You may know loops, functions, and lists, but still face problems like: ModuleNotFoundError Python is not recognized Package installed but not working in VS Code Wrong interpreter selected These are common beginner setup issues. Why online compilers are not enough Online compilers are good for quick practice. But real Python projects need: project folders multiple files external packages virtual environments dependency files terminal commands debugging tools Git basics So, when the goal is to build real projects, it is better to move to a local Python setup early. Basic Python project setup flow A simple Python project setup flow looks like this: Install Python Install VS Code Create project folder Create virtual environment Activate virtual environment Install packages Save requirements.txt This setup may look basic, but it prevents many beginner-level errors later. Example folder structure A beginner-friendly Python project folder can look like this: python-project/ │ ├── main.py ├── requirements.txt ├── README.md └── venv/ Here is what each file or folder means: main.py is the main Python file. requirements.txt stores project dependencies. README.md explains the project. venv/ contains the virtual environment. Create a virtual environment Create a virtual environment using: python -m venv venv Activate it on Windows: venv \S cripts \a ctivate Activate it on Mac/Linux: source venv/bin/activate A virtual environment keeps each project’s packages separate. This helps avoid package conflicts when working on multiple Python projects. Install a package After activating the virtual environment, install packages using pip . Example: pip install requests Now create a Python file: import requests response = requests . get ( " https://api.github.com " ) print ( response . status_code ) If

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There's a version of dogfooding that's a slogan, and a version where your own employees can't ship code until the bug is fixed. We run the second kind. The Authagonal staff console, the one we use to manage every tenant, authenticates through Authagonal itself: SAML single sign-on from our Entra directory, with SCIM deciding who gets in and what they can do. There is no separate admin password table. We deleted it. If our own SAML is broken, we are locked out of our own product. That's uncomfortable in exactly the useful way. It turns "SSO is an enterprise feature we support" into "SSO is the only way the people who built this get to work today." Here's what being our own customer caught. A trimmed build that broke signature verification We publish the auth server trimmed, to keep the image small. Trimming aggressively deletes code it can't prove is used, and reflection hides usage from it. .NET resolves its XML-signing crypto algorithms by name, reflectively, through CryptoConfig . The trimmer couldn't see those types were needed, removed them, and SignedXml quietly came back unable to build the algorithm. SAML signature verification, the step that proves the login is real, threw a null reference at runtime. The unit tests passed, because they ran against the untrimmed build where the types still existed. Only the trimmed production artifact failed, and it failed at the exact moment a human tried to sign in. We ship the auth server untrimmed now, with a healthy distrust of trimming anything near reflection-based crypto. If we only supported SAML rather than living on it, this is a customer's incident report instead of ours. Provisioning is the actual login Authenticating a user is the easy half of SSO. The hard half is deciding what they're allowed to do and keeping it in sync as people join and leave. We drive that with SCIM: Entra group membership maps to roles, resolved at the moment a token is issued, not copied once at account creation. Add someone to the righ

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