Shubham Saini’s Post

After learning Flask, I’m now diving into FastAPI — and the difference is exciting. 🚀 FastAPI gives me built-in data validation, automatic API docs, async performance, and cleaner, more scalable architecture out of the box. Even better, by using Jinja2 templates with FastAPI, I can still build full web applications — not just APIs — combining modern backend performance with traditional server-rendered pages. Best of both worlds. The animated gif shows a blog web application I am building as I follow the excellent Python FastAPI Tutorial  series on YouTube created by Corey Schafer. Source code: https://lnkd.in/eaNkVYHb #FastAPI #Flask #Python #WebDevelopment #APIDevelopment #BackendEngineering #SoftwareEngineering #LearningJourney

🧠 LEETCODE CONSISTENCY SERIES 🚀 Day 1️⃣1️⃣ of 365 Days 🔁 📘 Topic: Strings 🧩 Problem: Longest Common Prefix (LeetCode #14) ⏱ Time Taken: ~15 minutes 🔹 Approach: Sorting + Character Comparison 🧠 After sorting the array of strings, only the first and last strings need to be compared. Any common prefix shared by all strings must also be common between these two. 🧩 Step-by-Step Algorithm 🔍 🧱 Step 1: Initialization / Setup Sort the list of strings lexicographically. This groups similar prefixes together. 🧵 Step 2: Starting State / Base Case Store the first and last strings from the sorted list. Initialize an empty string prefix to store the result. 🔁 Step 3: Main Loop / Traversal Iterate character-by-character up to the minimum length of the first and last strings. ⚖️ Step 4: Key Logic / Condition Check If characters at the same index match, add them to prefix. Stop the loop as soon as a mismatch is found. 🎯 Step 5: Termination Condition Loop ends on the first mismatch or when one string ends. 🏁 Step 6: Return Result Return the accumulated prefix. 🧠 Why This Works 💡 Sorting ensures the maximum possible difference appears between first and last strings. If these two share a prefix, all middle strings must share it too. Avoids unnecessary comparisons with every string. ⏱ Time Complexity ⌛ Best Case: O(n log n) Average Case: O(n log n) Worst Case: O(n log n) (Sorting dominates the complexity) 💾 Space Complexity 📦 O(1) (Ignoring input sorting space) Only a few variables are used for comparison. 🚀 What I Learned Today 📚 Sorting can simplify string comparison problems. Comparing just two extreme elements can be enough. Clean logic often beats brute force. 📌 Next Goal 🎯 Explore vertical scanning and Trie-based solutions for this problem. Consistency > Motivation 💪 🔗 GitHub: https://lnkd.in/dRGB_B8Z #DSA #LeetCode #Python #ProblemSolving #365DaysOfCode #CodingJourney 🚀

The year is young and we’re already shipping new features to enhance our app observability product Honeybadger Insights. 🔍 Insights enables faster, more powerful querying with far less data ingest so you can learn more from your errors and logs. We’re pleased to announce: 1. Auto-refresh features for Insights dashboards. 2. The ability to configure, edit, and share Insights dashboards in YAML. 3. Clickable URLs in Insights query results. Explore these updates on the changelog → https://lnkd.in/gk63p87X #Ruby #Python #ElixirLang

Async isn’t a nice-to-have when requests start piling up. I’ve been looking at FastAPI for backend work because it makes asynchronous endpoints feel approachable without hiding what’s happening. The combination of Python type hints, automatic request validation, and clear routing keeps the codebase readable, while async/await support helps when the app is doing a lot of I/O (database calls, external APIs). What stands out is how quickly you can go from a small REST endpoint to something that’s structured and testable, without fighting the framework. If you’ve built with FastAPI, where did async genuinely improve performance for you, and where did it add complexity? #Python #FastAPI

Kimi K2.5 is probably going all in on being the next Claude. First of all, it is open sourced. It is 1 trillion parameter count which is pretty insane for a open sourced model. Here are some notable features: 1. It can actually do things, not just talk about them: If you need it to search the web, pull financial data, run calculations in Python, or find images, it can do that right there in the conversation. It doesn't just tell you how to do it—it actually does it. 2. It can tackle complex projects from start to finish: Whether it's researching a topic thoroughly, building a website, creating a presentation, or generating images, it can plan out the steps, use the right tools, and get the job done without you having to guide it through every little detail. 3. There's this feature called Agent Swarm, but I haven't used that yet so no insights on that yet. So, it is going to be meet both of your requirements Claude and could be Lovable as well since there website maker is far greater than decent, I think it might even be better than Lovable.

🚀 Day 8/30 | LeetCode Problem: Sort Colors (75) Problem: Given an array nums containing only 0s, 1s, and 2s, sort the array in-place so that colors are ordered as: 🔴 0 → ⚪ 1 → 🔵 2 Without using the built-in sort function. 🧠 Approach: Dutch National Flag Algorithm We use three pointers: a → position for 0 (left) b → position for 2 (right) c → current index Logic: If nums[c] == 0 → swap with a, move both forward If nums[c] == 2 → swap with b, move b backward If nums[c] == 1 → just move c This sorts the array in one pass. ⏱️ Complexity Time: O(n) Space: O(1) (in-place) 🧾 Python Code class Solution: def sortColors(self, nums): a = 0 # left pointer (0s) b = len(nums) - 1 # right pointer (2s) c = 0 # current index while c <= b: if nums[c] == 0: nums[a], nums[c] = nums[c], nums[a] a += 1 c += 1 elif nums[c] == 2: nums[b], nums[c] = nums[c], nums[b] b -= 1 else: c += 1 ✅ Result Accepted ✅ Runtime: 0 ms (Beats 100%) In-place & optimal solution 🎯 Takeaway Understanding pointer-based algorithms helps solve array problems efficiently without extra space . 🔖 Hashtags #LeetCode #30DaysOfLeetCode #Day8 #Python #Arrays #TwoPointers #DSA #ProblemSolving #CodingJourney #SoftwareEngineering

Why FastAPI is Becoming the Go-To Framework for Modern Python APIs If you’re still building APIs the old way, you might be missing out on one of Python’s fastest-growing tools: FastAPI. After diving deep into FastAPI, here’s why developers and tech teams are switching fast: Blazing Fast Performance – Built on Starlette & Pydantic, delivering speed comparable to Node.js & Go Type Hints = Fewer Bugs – Cleaner code, better validation, and smarter IDE support Auto-Generated API Docs – Swagger UI & ReDoc out of the box (no extra work!) Built-in Validation – Say goodbye to manual request checking Production-Ready – Async support, dependency injection, middleware, and more FastAPI isn’t just another framework — it’s a productivity multiplier for backend engineers and API developers. I’ve attached a detailed FastAPI guide (PDF) that covers everything from basics to advanced topics like: ✅ CRUD operations ✅ Pydantic models ✅ Authentication & dependencies ✅ File uploads, templates, and more Are you already using FastAPI, or planning to try it? Drop your thoughts below!

Flask is powerful. Starting a Flask project is not. Every time, it’s the same ritual: • folders • routes • controllers • “why isn’t my template loading?” So I built flask-commands — a Laravel-style scaffolding CLI for Flask. I just shipped 18 new tutorial videos inside the docs, so you can: 📖 read 🎥 watch ⚡ ship faster 🎬 Full tutorial playlist on YouTube: https://lnkd.in/eTFUKTqp 👉 Docs + videos: https://lnkd.in/ev23PN5R If you’ve ever thought “Flask deserves better tooling”… You’re not wrong. Would love feedback from fellow Python devs 👇 #flask #python #opensource #developerexperience #cli #webdevelopment

𝗫(𝗧𝘄𝗶𝘁𝘁𝗲𝗿) 𝗷𝘂𝘀𝘁 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝘁𝗵𝗲 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗙𝗼𝗿 𝗬𝗼𝘂 𝗳𝗲𝗲𝗱. few things that really really stood out: • It is built around 𝘅𝗔𝗜’𝘀 𝗚𝗿𝗼𝗸 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿, moving away from hand crafted ranking rules • The system was refactored from 6 languages down to just Rust and Python, a huge win for simplicity and long-term maintainability • 𝗛𝗼𝗺𝗲𝗠𝗶𝘅𝗲𝗿 acts as the brain, orchestrating and scoring candidate posts • 𝗧𝗵𝘂𝗻𝗱𝗲𝗿 focuses on fast retrieval of in-network (people you follow) content • 𝗣𝗵𝗼𝗲𝗻𝗶𝘅 job is to find and rank posts the user doesn’t already follow and predict which ones the user is most likely to engage with. It is rare to see a production scale recommendation system opened up like this. For those who want to dig in: 🔗 https://lnkd.in/d8UU2vyx