Switch to UV for Faster AI Development

“Good Python outcomes don’t happen by accident. They’re designed.” Over the last 14 days, we’ve shared insights on frameworks, speed, security, team models, AI readiness, and delivery systems. The conclusion is simple: Predictability beats raw speed - every time. If your Python roadmap is growing, delivery discipline matters more than ever. Read the final wrap-up here 👇 🔗 https://lnkd.in/gR-BRzrB DM us or comment “Python” — we’ll reach out personally. #PythonDevelopment #Backend #StartupGrowth #ARIS #SoftwareDelivery

Day 02: From static code to interactive logic. 🤖 Today’s focus for #30DaysOfPython was Variables and Built-in Functions. To stay aligned with my goal of building AI models, I moved beyond the basics and built a small interactive CLI script. It uses Python’s input() and type conversion to "communicate" with the user. Key takeaways: 🔹 Understanding how Python handles data types (Strings vs Integers). 🔹 The importance of naming variables for readability (crucial for complex ML models). 🔹 Getting comfortable with the Git workflow. One step closer to the AI goal. 🚀 📂 View today's code: https://lnkd.in/gNEUAqPS #AI #MachineLearning #Python #BuildInPublic #CareerTransition #30DaysPythonChallenge

One character change. 10,000x faster. When you check if something exists in a list: username in allowed_users_list Python checks every element. One by one. That's O(n). When you check if something exists in a set: username in allowed_users_set Python computes a hash and jumps directly to the answer. That's O(1). See the code 👇 With 10,000 users, the set version is roughly 10,000x faster for each lookup. The mental model: → List membership = Reading every name on a guest list → Set membership = Looking up a name in a phone book index In AI applications, this shows up everywhere: → Checking if a document is already in your vector store → Filtering duplicate embeddings → Validating allowed API keys The structure you choose isn't just about "what works." It's about what works at scale. This is adapted from my upcoming book, Zero to AI Engineer: Python Foundations. I share excerpts like this on Substack → https://lnkd.in/eFVTjauz #Python #Programming #DataStructures #AI #Performance

🐍 Why Python Is Essential for Building AI Agents If you're serious about building with AI, one skill stands out: Python. Most leading agent frameworks — like LangChain, AutoGPT, and CrewAI — are built in Python. Why? Because it’s: ✅ Simple to learn – Clean, readable syntax ✅ Packed with AI libraries – TensorFlow, PyTorch, scikit-learn, transformers ✅ Flexible – Great for scripting tools, managing APIs, and building workflows ✅ Widely supported – Huge community, tons of tutorials, and integrations with every major platform 💡 Whether you’re customizing prompts, calling APIs, chaining tools, or adding memory and logic, Python is the core language behind it all. No need to be a coding expert — just start with the basics. Your future agents will thank you. #Python #AIagents #LangChain #AutoGPT #AIdev #LearnToCode #PythonForAI #AgentFrameworks #AItools #CodingSkills #FutureOfWork

Day 10 of #30DaysOfPython: Powering the Training Process with Loops 🔄 Today’s focus was on Loops — the driving force behind automation in Machine Learning. From iterating through datasets to running multiple training cycles, loops are what allow models to learn and improve over time. I built a script to simulate a model training cycle, where I explored: 🔁 Tracking accuracy changes across epochs using for loops 📉 Continuing training with while loops until a target loss threshold is achieved ⚡ Automating repetitive processing tasks that would be unrealistic to handle manually Shifting from one-time decisions to continuous automated iterations really shows the true power of Python in AI workflows. 📂 Check out the training simulation here: https://lnkd.in/g_Q25442 #Python #MachineLearning #AI #SoftwareEngineering #Automation #30DaysOfPython #BuildInPublic

Mojo in the Age of AI Agents Wes McKinney's recent post on "agent ergonomics" hit a nerve—especially his take on why Python struggles when AI agents become primary code authors. His thesis: agents need fast compile-test cycles, standalone binaries, and predictable performance. Python's dynamic nature (slow feedback, dependency hell, runtime surprises) works against reliable agent iteration. Go and Rust shine here, but come with a learning curve for Python-heavy teams. Enter Mojo from Modular (Chris Lattner and Tim Davis) —the missing piece I've been exploring in my GPU programming work. Mojo is a Python superset designed specifically for AI/systems programming. It keeps Python's syntax that agents already know, but adds: ✅ Static typing + borrow checking (compile-time safety)  ✅ Sub-second compiles with no GIL or JIT unpredictability  ✅ Direct GPU/TPU control (10x+ Python speedups)  ✅ Seamless NumPy/Python interop (no ecosystem lock-in) The result? Agents can prototype in Python, then "mojofy" for production performance without retraining on Go's channels or Rust's ownership. I've written up how Mojo fits into Wes's landscape, with a side-by-side comparison to Go/Rust and a practical agent workflow example: 👉 https://lnkd.in/gKzzB8Va If you're building agent-driven data pipelines or AI workflows, Mojo might be the bridge to scalable code without a full rewrite. Curious what others think—are we really shifting from "human ergonomics" to "agent ergonomics" in language design? #Mojo #AIAgents #Python #PerformanceEngineering #DataScience

Day 6 of #30DaysOfPython: When Data Should Stay Fixed 🔒 Today’s topic was Tuples. Unlike lists, tuples are immutable — once created, they cannot be changed. What seems like a limitation at first actually plays a crucial role in writing safe and reliable code. From a Machine Learning perspective, immutability helps protect critical data from accidental edits. I practiced using tuples to store fixed model specifications, such as: ⚙️ Hyperparameters that must remain constant during training 🖼️ Data dimensions to ensure consistency across the pipeline (e.g., 224 × 224) 🏷️ Metadata like labels and categories that shouldn’t be altered Understanding when to use a list for flexibility and a tuple for stability feels like an important step toward cleaner, more professional code. 📂 Explore today’s work here: https://lnkd.in/g_Q25442 #Python #MachineLearning #AI #BuildInPublic #LearningToCode #30DaysOfPython

📘 Day 2 of My Machine Learning Journey 🚀 Today was all about building strong Python fundamentals, because no matter how advanced ML gets, everything starts here. 🔍 What I worked on today: ✅ Anaconda installation & environment setup ✅ Different ways to create virtual environments (and why they matter) ✅ Python basic syntax ✅ Variables & data types in Python ✅ Operators and how they actually work under the hood 💡 Key takeaway: Machine Learning isn’t just about models — it’s about writing clean, reliable, and understandable Python code. Strong basics today = fewer problems tomorrow. I’ll continue sharing my daily learnings, notes, and practical insights as I move forward. 👉 If you’re also learning Python, ML, or AI — or planning to start — feel free to follow along or share your experience in the comments. Day 2 done. On to Day 3 🔥 #MachineLearningJourney #LearningInPublic #Python #DataScience #AI #Upskilling #Consistency

Day 14 of #30DaysOfPython: Functional Programming in AI Workflows 🧪 Today’s focus was on Higher Order Functions. As AI systems grow more complex, writing code that is both efficient and predictable becomes essential. Functional tools like map, filter, and reduce help process data in a cleaner and more structured way compared to traditional loops. I worked on a small AI prediction pipeline, where I practiced: 🎯 Using filter to isolate high-confidence model outputs 📐 Applying map and lambda functions to transform raw data into user-friendly formats 🔄 Understanding how functional programming minimizes side effects, leading to fewer bugs and more reliable code These concepts feel like an important step toward writing production-level Machine Learning software rather than just basic scripts. 📂 Explore the functional implementation here: https://lnkd.in/g_Q25442 #Python #FunctionalProgramming #DataScience #MachineLearning #AI #BuildInPublic #30DaysOfPython #SoftwareEngineering

Programming languages still matter in the age of AI 💻 I just watched a great interview with Armin Ronacher, the creator of Flask and one of the first engineers at Sentry. He shared some very practical advice on how to choose the right tools for your work. Here are 3 big takeaways: Choose the right language for the job: * Go is great for building web services because it is simple and works well with AI. Rust is amazing for high-performance tasks like building databases or working with binary data. Python is still the king for AI, machine learning, and infrastructure. [17:35] AI is a "force multiplier": Armin used to be skeptical of AI, but now he uses it to build custom tools in minutes instead of weeks. He calls his AI tools "interns" that handle the boring parts of coding so he can focus on the big picture. Pragmatism wins: When you are starting a company, the product matters more than having "perfect" code. Use the tools that help you move fast and solve problems for your users. Whether you are a fan of Python, Go, or Rust, the goal is the same: build something that works! Check out the full talk here: https://lnkd.in/dBYQYYNr #SoftwareEngineering #Programming #AI #Python #Golang #Rust #Startups