TigerByte Interpreter Update: Version and About Commands

🚀 FastAPI-Maker 0.2 is here! Been dealing with some health stuff that slowed me down, but I'm finally getting back into coding rhythm. Here's what's new in this release: What's better in 0.2: • Full documentation - proper docstrings everywhere • English-only codebase - variables, methods, classes • Complete CRUD operations with proper error handling • Separate DTOs for in, out and update • Better Swagger docs with examples and validations • Proper dependency injection setup • Type hints and Pydantic v2 support What I'm thinking about next: • Making migrations automatic (tired of typing alembic commands manually) • Adding custom fields to entities more easily • Simplifying relationships between entities #FastAPI #Python #DeveloperTools #OpenSource #BackendDevelopment #API #CLI #Programming #CodeGenerator

Did you know you can submit build and test results to CDash even if you’re not using CMake to build your software? Using a simple Python example (py_square.py), this workflow demonstrates how to: 🧪 Define tests with CTest without compiling a project. 🐍 Integrate frameworks like PyTest. 📊 Submit results directly to a CDash dashboard. ⚙️ Automate with CTest drivers and scripts. This approach shows how CTest and CDash can be applied beyond traditional CMake projects, making it easier to track test results, integrate existing frameworks, and gain insight into project health without requiring a full build system. 📖 Read the full walkthrough here: https://ow.ly/ZaO550X4up9 #CDash #CTest #PythonTesting #SoftwareTesting

🚀 Day 33 of #100DaysOfCode — LeetCode + HackerRank Edition! Today’s challenge was a classic twist on binary search — searching in a rotated sorted array. 🔗 Problem: search(self, nums: List[int], target: int) (LeetCode) 📌 Challenge: Given a rotated sorted array, find the index of a target value in O(log n) time. 🔍 Approach: → Used binary search with a twist: at each step, determined which half of the array is sorted → If the left half is sorted, checked if the target lies within it → If not, searched the right half — and vice versa → Carefully updated low, high, and recalculated mid inside the loop → Avoided brute-force by leveraging the sorted structure of one half at every step 💡 What made it click: → Realized that one half is always sorted, even after rotation → Visualized the array and dry-ran examples like [4,5,6,7,0,1,2] to understand the logic → Fixed a key bug: I was calculating mid only once — moving it inside the loop made everything work! → Debugging this helped me appreciate how small details (like mid placement) can make or break binary search 📚 What I learned: ✅ Binary search can be adapted to rotated arrays with clever logic ✅ Always recalculate mid after updating low and high ✅ Dry runs are powerful for catching logic bugs Have you tackled rotated binary search before? Did you use recursion, iteration, or something else? Let’s swap strategies 💬 #Day33 #LeetCode #BinarySearch #RotatedArray #ProblemSolving #Python #DebuggingJourney #CleanCode #LearnToCode #CodeNewbie #SoftwareEngineering #TechJourney #100DaysOfCode

The library you learn first isn't the one you scale with. httpx picks up where Requests stops-async, HTTP/2, the works. Requests works great for getting started, but httpx is where serious Python projects are heading. The difference becomes obvious once you hit real-world scaling challenges. Here's what makes httpx worth the switch: • Dual mode support: Same API for both sync and async requests. No more juggling different libraries when you need concurrent HTTP calls. • HTTP/2 protocol: Built-in support means faster, more efficient connections without extra setup. • Better connection pooling: Advanced timeout controls and resource management that actually matter under load. • Drop-in compatibility: The API feels familiar if you know Requests. Migration is straightforward. Requests handles simple scripts just fine. But when you're dealing with hundreds of concurrent requests or integrating multiple third-party APIs efficiently, httpx's async support becomes the difference between a system that works and one that performs. The performance gains in high-concurrency scenarios are substantial. Plus, you're future-proofing your HTTP client layer instead of painting yourself into a corner. For new projects that need to scale, httpx is the modern choice. For quick scripts, Requests still gets the job done. #Python #BackendDevelopment #AsyncProgramming

JSON vs TOML — Which One Should You Choose? Both are popular configuration and data serialization formats, but they serve slightly different purposes JSON (JavaScript Object Notation): 1. Best for APIs and data exchange between systems. 2. Simple, lightweight, and supported almost everywhere. 3. Lacks comments (which can make configs harder to explain). TOML (Tom’s Obvious Minimal Language) 1. Built for configuration files — clean, human-readable, and organized. 2. Supports comments, arrays, and tables natively. 3. Increasingly used in tools like Python’s pyproject.toml and Rust’s Cargo. Rule of Thumb: Use JSON for data interchange. Use TOML for configuration management. #Python #Developers #JSON #TOML #Programming #DataEngineering #SoftwareDevelopment #OpenSource

💡 Ever build a small tool just to make everyday tasks a little smoother? I recently put together a lightweight Python-based Alarm Clock using Pygame, and it turned out to be a fun way to play with real-time updates, loops, and audio triggers — all through the command line. The attached demo show it in action. The big takeaway for me was how even simple scripts can teach you a lot about timing logic, testing, and using external libraries effectively. It’s the kind of project that reminds you how rewarding small wins can be. ⏰✨ If you’re curious, find the project link in the comment section below: Have you built any small utility scripts lately that ended up being surprisingly useful? Would love to hear about them! #Python #Automation #ProgrammingProjects #LearningInPublic #CLI #Git #Github #script #code #Pygame

Serious traders often move beyond out-of-the-box solutions to gain direct control and optimize performance. While platforms like Interactive Brokers have their place, the data feeds may be aggregated and less pure compared to high-frequency trading platforms like Sierra Chart. Some third-party services even note limitations with Interactive Brokers' API. Exploring robust market data and flexible trading endpoints through APIs like Rhythmic can offer a competitive edge, especially with programming knowledge. #Trading #API #MarketData #Python #Rhythmic

The more I build with FastAPI, the more I realize why companies really like it ✨ Auto-generated Swagger Docs ⚡ Insane Speed thanks to async support 🧩 Pydantic models make validation clean and predictable. 🌟 When you understand HTTP exceptions, error handling becomes intuitive. 💫 Folder structure matters, and moving main.py to /app taught me how imports & module paths actually work. Debugging has taught me more than any theory could : 🎯 Wrong JSON casing → 422 error 🎯 Missing comma → JSON decode error 🎯 Shadowing status function → unexpected AttributeError 🎯 In-memory lists resetting after reload → why databases matter Missing package imports → server loading forever All of these issues became "aha!" moments. #learningjourney #python #FastAPI #VisualisingTech

🚀 DSA Progress – Day 94 ✅ Problem #338: Counting Bits 🧠 Difficulty: Easy | Topics: Bit Manipulation, Dynamic Programming 🔍 Approach: Implemented a Dynamic Programming approach to efficiently count the number of 1 bits in every number from 0 to n without converting numbers to binary strings. Step 1 (Initialization): Create an array result of size n + 1, initialized to zeros. Base cases: result[0] = 0 → Binary of 0 is 0, so 0 ones. result[1] = 1 → Binary of 1 is 1, so 1 one. Step 2 (Even–Odd Relation): For every number i from 2 to n: If i is even, the number of 1s = result[i // 2] (same as half of i, because last bit is 0). If i is odd, the number of 1s = result[i // 2] + 1 (same as half, plus 1 for the last bit). Step 3 (Iterative Build): Use the above relation to fill the result array for all numbers up to n. 🕒 Time Complexity: O(n) Each number is processed once. 💾 Space Complexity: O(n) We store bit counts for all numbers from 0 to n. 📁 File: https://lnkd.in/ghuw8Vea 📚 Repo: https://lnkd.in/g8Cn-EwH 💡 Learned: This problem deepened my understanding of bitwise patterns and how small observations (like even–odd relationships) can lead to elegant DP-based optimizations. It showed how dynamic programming can simplify repetitive bit-counting logic and achieve linear time solutions. ✅ Day 94 complete — counted every single bit of progress, one 1️⃣ at a time! 💡⚙️💻✨ #LeetCode #DSA #Python #DynamicProgramming #BitManipulation #CountBits #100DaysOfCode #DailyCoding #InterviewPrep #GitHubJourney