Sending Emails With Python Tutorial

🐍📰 Python eval(): Evaluate Expressions Dynamically In this tutorial, you'll learn how eval() works and how to use it effectively in your programs. Additionally, you'll learn how to minimize the security risks associated to the use of eval() #python

✍️ New post introducing profiling-explorer, a tool for exploring Python profiling data (pstats files). Use it with the classic cProfile (now called profiling.tracing) or Python 3.15’s new sampling profiler, Tachyon (profiling.sampling). https://lnkd.in/eZ6D8ZMD #Python

🐍📰 Dictionaries in Python Learn how dictionaries in Python work: create and modify key-value pairs using dict literals, the dict() constructor, built-in methods, and operators https://lnkd.in/dWNJjc4a

🐍📰 Variables in Python: Usage and Best Practices Explore Python variables from creation to best practices, covering naming conventions, dynamic typing, variable scope, and type hints with examples https://lnkd.in/dUFf5QGE

Python for Absolute Beginners: Your First 1,000 Lines — updated for Python 3.14 (April 2026). This tutorial teaches Python from scratch with security built into the foundation. The capstone mini project is a working password strength checker, not a "Hello, World" throwaway — so the same hour that introduces variables, conditionals, loops, functions, lists, and dictionaries also introduces the habit of thinking about security while writing code. What the tutorial covers: - Variables, data types, operators, and f-strings - Conditionals, for/while loops, and functions - Lists and dictionaries with real use cases - A complete password-strength checker mini project that pulls every concept together - The 10 errors every beginner hits (SyntaxError, IndentationError, NameError, TypeError, and friends) with worked fixes - An interactive code-builder and spot-the-bug challenge - A 1,000 Lines Roadmap of four follow-on projects: habit tracker, number-guessing game, expense splitter, flashcard quiz generator Reading code is not the same as writing it. The tutorial is structured so that anyone who types every example, completes every exercise, and finishes the four roadmap projects will have written over 1,000 lines of real, working Python. Pass the 12-question final exam at 80% or higher to earn a downloadable certificate of completion. https://lnkd.in/gk5udmim #Python #LearnToCode #Programming #PythonProgramming #Cybersecurity

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines.

Day 3.5 of 10: Setup — setup our laptop to write code I missed set up the required tools and started showing the code, let me fix that before we go further.  The old-school parallel: Project setup isn't new. .NET SDK + NuGet for C#. Same pattern every time: install the runtime, manage dependencies, separate config from code Four steps. Step 1 — Install Python (3.10+) and VS Code. Python.org for the runtime. VS Code for the editor. Install the Python extension inside VS Code IDE. Step 2 — Create a project with a virtual environment. Multiple command in the same line to reduce the words bash mkdir triage-agent && cd triage-agent python -m venv venv source venv/bin/activate  # Windows: venv\Scripts\activate A virtual environment is an isolated folder of Python packages — like node_modules, but for Python. One per project. Step 3 — Install what we need. bash pip install langgraph langchain-anthropic python-dotenv pydantic We're using Anthropic's Claude for this series. Why Claude and not OpenAI? Honestly, team at gNxt is using Claude — it follows structured-output schemas more reliably, and it handles multi-step reasoning well. LangChain lets you swap providers in one line later, so this isn't a lock-in. Step 4 — Handle API keys safely. Create a .env file in your project root: ANTHROPIC_API_KEY=sk-ant-... Add .env to .gitignore. Never commit secrets in repo. Project structure I use: triage-agent/ ├── venv/ ├── .env                 # API keys (gitignored) ├── .gitignore ├── requirements.txt ├── state.py             # TicketState from Day 3 ├── agents/ │  ├── classifier.py    # Day 4 │  ├── router.py        # Day 5 │  └── ... └── main.py              # wires the graph together The cost question: Anthropic console → API keys. Add $5 to start. Claude Haiku costs roughly $1 per million input tokens. Your first test: python from dotenv import load_dotenv from langchain_anthropic import ChatAnthropic load_dotenv() llm = ChatAnthropic(model="claude-haiku-4-5") print(llm.invoke("Say hello").content) If that prints something, you're ready. Set your .env, and you're set up in 2 minutes.   Tomorrow — Day 4 (for real this time): The Classifier. If you get stuck on any step, drop it in the comments. I'll help. 👇 #AI #ArtificialIntelligence #GenAI #GenerativeAI #AIAgents #MultiAgent #LangGraph #LangChain #LLM #RAG #LLMOps #AIEngineering #SoftwareArchitecture #EnterpriseAI #DigitalTransformation #CustomerSupport #CustomerExperience #BuildInPublic #LearningInPublic #TechLeadership #CEOLife #Startups #Founders #IndianStartups #gNxtSystems #Noida #ITServices #CloudComputing #DevOps #MachineLearning

🐍📰 Python mmap: Improved File I/O With Memory Mapping In this tutorial, you'll get a quick overview of the different types of memory before diving into how and why memory mapping with mmap can make your file I/O operations faster. #python

One small Python change that will be genuinely useful in production: timestamps in tracebacks. PEP 830 proposes adding time information directly to exceptions and tracebacks in Python 3.15. At first glance, this may look minor. In practice, it is not. When you work with async flows, retries, parallel tasks, or ExceptionGroup, timing matters. Not just which error happened, but when exactly it happened relative to the others. That makes debugging grouped failures much easier: - you can see the sequence of exceptions more clearly - sorting inside ExceptionGroup becomes more practical - incident analysis gets better without relying only on external tooling What also stands out here is that the performance impact looks negligible, except for control-flow cases that are expected to be excluded from this logic. A lot of teams already get similar value from external error trackers. The interesting part is that Python itself is moving in this direction natively. How do you handle this today? Do you rely mostly on Sentry or other observability tools for exception timing, or do you see value in having this directly in Python tracebacks?