FastAPI Library Book Management System Built with CRUD Operations and Validation

🚀 Top FastAPI Packages You Should Know in 2026 (Part 2) FastAPI already gives you speed. The real difference comes from the tools you add on top. These are a few packages I’ve been exploring recently while building APIs 👇 . . . . . . 💡 Small observation: Most performance gains don’t come from FastAPI itself. They come from how you structure, cache, and protect your APIs. If you’re interested in MCP + FastAPI use cases, I recently explored it here: https://lnkd.in/dbMFET_A Which one are you using in your projects right now? #FastAPI #Python #BackendDevelopment #APIs #WebDevelopment #OpenSource #DeveloperTools

Shipping a discussion guide is just the beginning. Running the sessions can sometimes reveal thin findings, often due to overlooked details. Introducing qual-sniffer, a Python CLI designed to review your discussion guides before you run them. This tool: 1. Flags leading questions, embedded assumptions, and double-barreled questions. 2. Catches yes/no questions that may cut participant responses short. 3. Checks for a warm-up, adequate follow-up probes, and ensures no sensitive topics are present in the first three questions. 4. Returns a score from 0 to 100, along with a letter grade and fix suggestions for any identified issues. Qual-sniffer works without requiring an API key and offers an optional LLM summary for a written critique. This is tool #3 of 40 in my CPR Orbital series, providing at least one open-source research tool every week throughout 2026. #UXResearch #ComputationalProductResearch #OpenSource #QualitativeResearch #AIForResearch (GitHub link in first comment)

🚀 Day 25 of Consistency | #75DaysLeetCodeChallenge 🧠 Today’s Problem: Search a 2D Matrix (#74) 💡 Key Learning: This problem teaches how to apply binary search on a 2D matrix by treating it as a flattened sorted array. It’s all about visualizing the structure differently. ⚡ Approach: First apply binary search on rows to find the potential row Check range using first and last element of each row Once row is identified → apply binary search on that row Return true if target found, else false 🧠 Why this works: Matrix follows sorted properties row-wise and across rows Binary search efficiently reduces search space Achieves optimal time complexity → O(log(m × n)) 🔥 Result: ✔️ Runtime: 0 ms (Beats 100%) 📈 This is a classic problem combining matrix + binary search, frequently asked in interviews. A big thanks to Shivam Singh, Nikhil Yadav & Akshat Tiwari for this amazing challenge 🙌 Consistency is compounding. Keep going. 💪 #Day25 #LeetCode #DSA #CodingJourney #100DaysOfCode #Python #BinarySearch #Consistency

#PathSim goes #Embedded 🚀 The C-code generator for the pathsim ecosystem is live at code.pathsim.org, free for academic and personal use. Just copy your pathsim script into the editor or export directly from #PathView and generate single file #MISRA compliant flat C-code and compile it for your embedded target. The platform also provides a validator that runs trajectory tests of the generated code against the pathsim model directly in the UI and then provides a report for qualification and documentation. See the full pathsim to hardware workflow in our upcoming webinar: https://lnkd.in/dn3_emMZ The pathsim ecosystem at pathsim.org For professional inquiries visit milanrother.com #C #Hardware #HiL #Python

Milan has the productivity of a small corporation! I haven't dug into this yet, but it looks like a really important step forward for developing complex code for embedded systems. For academic users of existing proprietary block diagram modelling tools this can potentially reduce cost and make it easier to create labs where control algorithms are tested in simulation and practice. Check it out.

🚀 Another LeetCode Problem Solved: Palindrome Number! 🔗 Check out my solution: https://lnkd.in/dwDMqXXn 💡 Problem Overview Given an integer x, determine whether it is a palindrome — meaning it reads the same forward and backward. (LeetCode) Examples: ✔ 121 → Palindrome ❌ -121 → Not a palindrome ❌ 10 → Not a palindrome 🧠 My Approach (Digit Reversal) Instead of converting the number to a string, I used a mathematical approach: Extract digits using % 10 Reverse the number step by step Compare reversed number with original ⚙️ Key Learnings ✔ Strong understanding of number manipulation ✔ Importance of handling edge cases (negative numbers, trailing zeros) (leet-solution.com) ✔ Practicing clean and efficient logic ⏱️ Complexity • Time Complexity: O(log n) • Space Complexity: O(1) 🔥 Why this problem matters Even though it’s an “easy” problem, it builds: Logical thinking Problem-solving fundamentals Confidence for bigger challenges #LeetCode #DSA #Python #CodingJourney #ProblemSolving #100DaysOfCode

🚀 Day 2 of Learning FastAPI & Pydantic Today I explored Pydantic, which is used for data validation in Python (especially with FastAPI). Here’s what I learned: 🔹 How to define data models using BaseModel 🔹 Field validation (min/max length, ranges, etc.) 🔹 Type enforcement (int, float, str) 🔹 How Pydantic ensures clean and structured API data One thing I found interesting is how simple it becomes to validate user input without writing complex logic. Looking forward to integrating this with FastAPI for building more robust APIs ⚡ #Python #FastAPI #Pydantic #BackendDevelopment #LearningJourney

Excited to share that my R package discoCVI is now submitted to CRAN! discoCVI implements the DISCO metric a Cluster Validity Index for evaluating density-based clustering results without ground truth labels. Key features: - Handles arbitrary-shaped clusters (rings, spirals, crescents) - Explicitly evaluates noise point quality - Returns interpretable scores in [-1, 1] The package is a faithful R translation of the Python reference implementation by Beer et al. (2025) arXiv:2503.00127, verified across 8 benchmark datasets with differences about < 10^-14. Install from GitHub: devtools::install_github("aminentezari/discoCVI") Special thanks to Prof. Davide Chicco for supervision and guidance. #RStats #DataScience #MachineLearning #Clustering #OpenSource #CRAN

Turbovec is now available on PyPi 🐍 and Crates.io 📦 Turbovec is a vector index built on Google's TurboQuant algorithm, written in Rust with Python bindings. Turbovec has identical or better speed, compression and recall compared to Faiss while also being data-oblivious. Because adding new vectors doesn't require re-indexing, Turbovec is dramatically simpler to operate in production.             → pip install turbovec → cargo add turbovec Check out the open-source repo: https://lnkd.in/e5M4dVRk #RAG #LLM #OpenSource #Gemma4

Day 22/100 – DSA Journey Problem: Find Mode in Binary Search Tree (BST) Today’s problem focused on understanding how Binary Search Trees (BST) behave and how we can efficiently extract useful insights from them. Understanding the BST A Binary Search Tree follows a structured property: Left subtree → values ≤ root Right subtree → values ≥ root Because of this, when we perform an Inorder Traversal (Left → Root → Right), the values are visited in sorted order. Why Inorder Traversal? Since duplicates appear consecutively in a sorted sequence, inorder traversal allows us to: Track frequency without using extra space Compare current value with previous value Efficiently determine the most frequent element (mode) Approach Used Traverse the BST using inorder traversal Maintain: Previous value Current count Maximum frequency Update result list whenever a new maximum frequency is found Key Learning This problem highlights how leveraging tree properties can help optimize solutions. Instead of using extra space (like hashmaps), we used traversal behavior to achieve an efficient solution. Conclusion Understanding the underlying structure of data (like BST properties) is often more powerful than brute-force approaches. Smart traversal choices can significantly reduce space complexity and improve performance. #Day22 #100DaysOfCode #DSA #BinarySearchTree #Python #CodingJourney #LeetCode #ProblemSolving