I’ve spent a lot of time in the Next.js, Python, and SQL Server ecosystem, building systems like my Plate Making System for flexography. But over the last few months, my workflow has undergone a massive shift thanks to AI. Using tools like coding assistants hasn't just made me faster; it’s changed how I solve problems. Instead of manual data entry scripts, I'm now building AI agents to handle the heavy lifting. My biggest takeaway? AI doesn't replace the need for strong foundational skills. You still need to know how to structure a database and manage a GitHub repo but AI allows you to spend more time on the architecture and less time on the syntax. #FullStack #NextJS #AIinEngineering #WorkflowOptimization #BuildInPublic

💻 Just built something I’m genuinely proud of I created a Real-Time AI System Monitor that not only tracks system performance but also predicts future CPU usage and detects anomalies. What started as a “let me try this” idea turned into a full system with: • Real-time monitoring (CPU, memory, disk, network) • AI-based predictions using a Python ML service • Anomaly detection with explainable insights • Interactive dashboard with live charts At one point, I noticed I had multiple things running at the same time like coding, experimenting with AI tools, and a bunch of random tabs running in the background and it became difficult to understand how much load I was actually putting on my system. That moment made this project feel even more relevant. One of my favorite parts was watching the system respond in real time while I was working and it made everything feel much more tangible. Tech stack: React • Node.js • MongoDB • Python (Flask + NumPy) Built, deployed, and tested end-to-end: 🔗 Live demo: https://lnkd.in/gX_nGWAX 🔗 GitHub: https://lnkd.in/gHbzdVs4 If you found this interesting, feel free to check out the repo, feedback and ⭐ are always appreciated :) #FullStackDevelopment #MachineLearning #WebDevelopment #ReactJS #NodeJS #Python #BuildInPublic

Stop using Pandas for everything. I just published a full breakdown of 7 Python libraries that are redefining how developers build in 2026 with install commands + real code examples for each. Here's the quick cheat sheet: ⚡ Polars → 10x faster than Pandas for big data 📄 MarkItDown → Converts PDFs/Word docs into AI-ready Markdown 🤖 Smolagents → Build your first AI agent in 10 lines 🧑✈️ GPT Pilot → An AI that writes entire features, not just autocomplete 📱 Flet → Build web + mobile + desktop apps in pure Python 🛡️ Pyrefly → Catch bugs BEFORE you run your code (Meta-built) 🌐 Morphik-Core → AI that understands images and videos, not just text You don't need to learn all 7 today. Pick the one that solves YOUR problem right now. Working with data? → Polars Building an app? → Flet Curious about agents? → Smolagents The full blog (with code examples for every library) is linked in the comments 👇 Which of these are you already using? Drop it below 🔽 #Python #AI #MachineLearning #Programming #Developer #TechIn2026 #AITools #OpenSource #PythonDeveloper #CodingTips

In 2026, "should I add AI to my Django app?" is the wrong question. The right question is: how fast can you ship it? I just published a complete production guide on building AI-powered REST APIs with Django & Python — covering the exact stack modern teams are using right now. Here's what's inside: → pgvector + PostgreSQL for semantic search (no separate vector DB needed) → Async Django views for real-time LLM streaming → RAG architecture for Q&A on your own data → Celery + Redis for non-blocking embedding generation → Clean, copy-paste-ready Python code throughout Django is more capable than ever for AI workloads. This guide proves it. If you're building backends in 2026, this one's worth bookmarking. 🔗 Full article: https://lnkd.in/g4GZu6ib — Tahamidur Taief | tahamidurtaief.com #Django #Python #AI #MachineLearning #LLM #pgvector #RAG #BackendDevelopment #SoftwareEngineering #AIEngineering

 **𝗪𝗵𝘆 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝘀 𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗶𝗻 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁** When it comes to backend development… Python is always in the conversation 👇 𝗕𝘂𝘁 𝘄𝗵𝘆 𝗶𝘀 𝗶𝘁 𝘀𝗼 𝗽𝗼𝗽𝘂𝗹𝗮𝗿? 💡 👉 Because Python focuses on simplicity *without losing power.* 💻 Here’s what makes Python stand out: ✔ Clean & readable syntax 👉 Easy to learn, easy to maintain ✔ Rapid development 👉 Build APIs and systems faster ✔ Powerful frameworks 👉 Django, Flask, FastAPI ✔ Huge ecosystem 👉 Libraries for almost everything ✔ Scalability 👉 Used by startups & big tech companies 🔥 The real advantage? 👉 You spend less time fighting syntax… 👉 And more time solving real problems 📌 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝘆 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝘀 𝘂𝘀𝗲𝗱 𝗳𝗼𝗿: ➡ Web backend (APIs & services) ➡ AI & Machine Learning ➡ Data processing ➡ Automation scripts 💡 Whether you're building a startup or scaling a system — Python gives you speed + flexibility. Because in modern development — #Python #BackendDevelopment #WebDevelopment #Django #Flask #FastAPI #FullStackDeveloper #SoftwareEngineering #CodingTips #DeveloperLife #TechStack #LearnToCode

I’ve been working on an open-source Python library for building AI agents. It’s called Dendrux. The idea is that agent runtimes should handle more than just calling an LLM and tools. In production, you usually need persistence, crash recovery, human approvals, budgets, and guardrails. Dendrux brings it into the runtime.  It handles:  1. Tool deny policies and human approval with pause/resume  2. PII redaction at the LLM boundary, so the model sees placeholders while tools receive real values  3. Advisory token budgets with threshold warnings  4. Crash recovery with stale-run sweeping  5. Client-tool bridging for browsers and spreadsheets It’s still early, currently v0.1.0a5, but the foundation is in place. Feedback, issues, and design critiques are welcome. GitHub: https://lnkd.in/gYbhpcdM

Python is too slow for the backend. 🥱 This was a valid take in 2023. In 2026? It’s a misunderstanding of how the Agentic Economy actually works. Despite the rise of high-performance languages, Python remains the undisputed king of the backend for AI-native systems. If you want to know why the world’s most advanced Sovereign AI architectures are still built on Python, here are the three non-negotiable reasons: 🚀 1. The "No-GIL" Revolution With the final removal of the Global Interpreter Lock (GIL), Python finally unlocked true multi-core concurrency. We can now run complex Agentic Orchestration and heavy data processing in a single process without the "performance tax" we used to pay. It’s no longer just a "scripting language"; it’s a high-velocity engine. 🧠 2. The "Gravity" of the Ecosystem Every breakthrough from Llama 4 to the latest MCP (Model Context Protocol) servers drops in Python first. When you’re building in a field that moves this fast, "Developer Velocity" is more important than raw execution speed. In the time it takes to write a memory-safe wrapper in another language, a Python dev has already shipped a self-correcting agent to production. 🔗 3. The Ultimate "Glue" for Hybrid Systems Modern backends aren't monolithic. We use Rust for the heavy math and C++ for the kernel, but Python is the connective tissue. It’s the language of LangGraph, PyTorch, and FastAPI. It allows us to orchestrate a "Polyglot Architecture" where we get 100% of the performance with 0% of the boilerplate. The 2026 Reality: We don't use Python because it’s the fastest. We use it because it’s the smartest. It allows us to spend less time fighting the compiler and more time architecting the intelligence. Are you still optimizing for nanoseconds, or are you optimizing for orchestration? Let’s talk about the 2026 stack below. 👇 #Python #BackendEngineering #AgenticAI #SoftwareArchitecture #2026TechTrends #MLOps #SystemDesign #DeveloperVelocity

🚀 Why uv is replacing pip in modern Python workflows For years, pip has been the default tool for installing Python packages. It works—but it was never designed to handle today’s complexity around environments, reproducibility, and speed. That’s where uv comes in. --- 🔹 1. Speed that actually matters uv is written in Rust and is insanely fast—often 10–100x faster than pip. 👉 Example: Installing a heavy stack like pandas + numpy + scikit-learn - pip → noticeable wait time - uv → installs in seconds For data scientists and ML engineers, this alone is a game changer. --- 🔹 2. One tool instead of many With pip, you usually combine: - venv (for environments) - pip (for install) - pip-tools/poetry (for dependency management) 👉 uv replaces all of these in a single unified tool No more juggling multiple commands and tools. --- 🔹 3. Better dependency resolution pip can sometimes: - install conflicting versions - behave inconsistently across machines uv provides more reliable and deterministic installs, reducing “works on my machine” issues. --- 🔹 4. Built-in lockfiles (Reproducibility) uv generates lockfiles to ensure: - same versions - same environment - same results This is critical in: - ML experiments - production pipelines - team collaboration --- 🔹 5. Easy migration (Drop-in replacement) You don’t need to relearn everything. 👉 Same workflow: uv pip install numpy uv pip install -r requirements.txt So you get better performance without changing habits much. --- 🔹 6. Real-world workflow comparison 👉 Using pip: python -m venv env source env/bin/activate pip install -r requirements.txt 👉 Using uv: uv venv uv pip install -r requirements.txt Cleaner. Faster. Simpler. --- 💡 Final Thoughts pip isn’t “bad”—it’s just outdated for modern workflows. If you’re working in: - Data Science - AI/ML - Backend Python Switching to uv can save time, reduce friction, and improve reliability. --- ⚡ Bottom line: uv is not just an alternative—it’s an upgrade. #Python #DataScience #AI #MLOps #SoftwareEngineering #Developers #Productivity

Python is more than just a language in 2026—it’s the entry point to AI, Data Science, and Automation. 🚀 I’ve been mapping out the most efficient way to go from "Hello World" to building real-world projects. Here is my 4-Phase Python Roadmap for anyone starting this month: 📍 Phase 1: The Essentials (Weeks 1-2) Syntax: Variables, Data Types (Strings, Integers, Floats). Logic: If/Else statements and Loops (For/While). Functions: Learning to write reusable code. 📍 Phase 2: Data Handling (Weeks 3-4) Data Structures: Lists, Dictionaries, Tuples, and Sets. File I/O: Reading and writing CSV/JSON files. APIs: Using the requests library to get data from the web. 📍 Phase 3: The "Pro" Shift (Weeks 5-6) OOP: Classes, Objects, and Inheritance (crucial for big projects!). Error Handling: Using try/except to build crash-proof apps. Virtual Environments: Keeping your projects organized with venv. 📍 Phase 4: Specialized Paths (Week 7+) AI/Data: NumPy, Pandas, Matplotlib. Web Dev: FastAPI or Django. Automation: Selenium or Beautiful Soup. The secret? Don’t just watch tutorials. Build one small script every single day. What are you currently building with Python? Let’s connect and share progress! 🤝 #Python #Roadmap2026 #SoftwareEngineering #ICTStudent #CodingCommunity #PythonLearning