Sravani A’s Post

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/e9Y3xGNF. #python #softwaredesign #designpatterns #statemachine #cleancode

Free Threading — Python's way to "goroutines", sort of. I’ve been experimenting with the new python3.14t builds. By combining Free-threading with AsyncIO, you can now: 1. Spawn worker threads (Parallelism). 2. Run an AsyncIO loop inside each (Concurrency). 3. Use queue.Queue as your "Channels" for thread-safe communication. The Result? True parallelism without the memory overhead of multiple processes. I’ve broken down the benchmarks and the "serialization tax" of subinterpreters in my latest write-up. If you're building high-scale backends, this is required reading. Read more: https://lnkd.in/gp3KBR_G #Python #DistributedSystems #Concurrency #Scalability #Performance

Ran a 1 billion nested loop iteration test using the Fibonacci sequence across C, Rust, Go and Python. Expected results, but still interesting to see the actual numbers. C and Rust came in at 0.32s. Go at 0.84s. Python timed out after 10 minutes so we just won't talk about that. The C and Rust parity makes sense, both compile down to highly optimized machine code. Go has a runtime and a garbage collector, so that gap is expected. Python is a different category of language entirely. It's not built for this and that's fine, it's just not what you reach for when raw compute speed matters.

Stateful UDFs just changed how Python scales. With @daft.cls, you can turn any Python class into a distributed operator that initialises once per worker and reuses state across every row. That means models, API clients, and database connections no longer get rebuilt on every call. The mental model stays simple: write normal Python classes, add a decorator, and Daft handles execution, scheduling, and parallelism. Find out more: https://lnkd.in/e79SePbN #PythonScaling #DaftCls #DistributedComputing #PythonClasses

Most implementations of the State pattern in Python look very “clean”. Lots of small classes. A base interface. One class per state. But if you’ve ever worked with one in a real project, you know the downside: transitions are scattered, behaviour is hard to see in one place, and adding new states often means touching multiple files. In today’s video, I rebuild the State pattern in a very different way. Instead of relying on inheritance, I make the state machine explicit as data and use decorators to define transitions. The result is a small, reusable engine where the entire flow becomes visible at a glance. If you’re interested in writing Python that’s easier to reason about and extend, this is a pattern worth understanding. 👉 Watch here: https://lnkd.in/eg22yEHR. #python #softwaredesign #designpatterns #statemachine #cleancode

Simple way to understand vector search in RAG I made a small Python example using SentenceTransformers + FAISS to understand how retrieval works in RAG. What happens here: A few documents are converted into embeddings Those embeddings are stored in FAISS A user question is also converted into an embedding FAISS finds the most similar document chunks This is the basic idea behind RAG: store meaning as vectors, then retrieve the most relevant context before generation Very small code, but it explains a very important concept. Text → Embedding → Similarity Search → Relevant Chunks That is why vector databases are so important in RAG systems. #RAG #FAISS #Embeddings #AIEngineering #Python #LLM Code source: https://lnkd.in/g-cm4BB2

Python Dunder Methods: Making code more "Pythonic" 🐍 I’ve been playing around with operator overloading today! Instead of writing a bulky function like add_packages(pkg1, pkg2), Python allows us to use the __add__ magic method. By defining __add__, I can simply use the + operator to combine two package objects. Cleaner syntax? Check. More readable? Absolutely. I also added __str__ to ensure that when I print the result, I get a clear, formatted summary of the dimensions and weight rather than a messy memory address. #Python #CodingTips #SoftwareDevelopment #ObjectOrientedProgramming #CleanCode

🚀 Day 5 — DSA with Python Solved the classic “Product of Array Except Self” problem today. This one introduced me to an important concept: 👉 Precomputation (Prefix & Suffix Pattern) Instead of recalculating products again and again, I learned how to: • Store prefix products (left side) • Store suffix products (right side) • Combine them to get the result efficiently 💡 Key Learning: Optimizing brute-force solutions using precomputation can significantly reduce time complexity. ⚡ What challenged me: Understanding how to manage two passes (left → right and right → left) without using extra space initially felt confusing — but breaking it step by step helped. 📈 Growth Insight: DSA is less about memorizing solutions and more about recognizing patterns like this one. On to Day 6 🔥 #DSA #Python #CodingJourney #ProblemSolving #100DaysOfCode

🚀 Day 2 of #100DaysOfCode Today I learned how to check whether a number is a Palindrome using Python 🐍 🔍 Problem: A number is called a palindrome if it reads the same forward and backward (like 121, 1331). 💡 Approach: Reverse the number using a loop Compare it with the original number 🐍 Code: num = int(input("Enter a number: ")) original = num reverse = 0 while num > 0: digit = num % 10 reverse = reverse * 10 + digit num = num // 10 if original == reverse: print("Palindrome Number") else: print("Not a Palindrome Number") 📌 Key Learning: Learned how loops and basic logic can solve interesting problems. 💬 Next: Armstrong Number 🔥 #Python #Coding #100DaysOfCode #Learning #CSE

Day 64 of the #three90challenge 📊 Today I learned about Functions in Python — a key concept for writing clean and reusable code. Instead of repeating the same logic multiple times, functions allow us to define it once and reuse it whenever needed. What I practiced today: • Creating functions using def • Passing inputs (parameters) • Returning outputs using return • Writing reusable and organized code Example thinking: Instead of writing the same code again and again, functions help turn it into a single reusable block. Example: def calculate_total(a, b): return a + b print(calculate_total(5, 10)) This makes code more efficient, readable, and scalable. From writing code → to structuring it better 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #DataAnalytics #LearningInPublic #Consistency #Upskilling #PythonBasics