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Stop writing manual validation logic In traditional frameworks, you spend a lot of time writing code like: if not data.get("email"): raise ValueError... With FastAPI, you stop writing "checks" and start defining Schemas. By using Pydantic models, FastAPI does the heavy lifting for you: ✅ Automatic Parsing: Converts incoming JSON directly into Python objects. ✅ Data Validation: If a user sends a string where an integer should be, FastAPI catches it instantly. ✅ Clear Errors: It sends a detailed 400 error back to the client automatically—your function logic doesn't even have to run. The result? Cleaner code, fewer bugs, and a backend that "just works." Check out the snippet below to see how 5 lines of code can replace dozens of if/else statements. #Python #FastAPI #Pydantic #WebDevelopment #Backend #CleanCode

Most “slow APIs” in Python aren’t CPU-bound. They’re blocking the event loop without realizing it. Classic FastAPI mistake: @app.get("/users") async def get_users(): users = db.fetch_all() # blocking call return users Looks async. Isn’t. Result: * event loop stalls * requests queue up * latency spikes under load Fix → respect async boundaries @app.get("/users") async def get_users(): users = await db.fetch_all() return users Or offload properly: from asyncio import to_thread users = await to_thread(sync_db_call) Advanced production pattern: * separate sync + async layers clearly * use connection pools (asyncpg, aiomysql) * never mix blocking ORM calls inside async routes Hidden issue: One blocking call can freeze thousands of concurrent requests. Build-in-public lesson: Async isn’t about syntax. It’s about protecting the event loop at all costs. AI can convert code to async— but only experience catches where it’s still secretly blocking. #Python #BackendEngineering #FastAPI #Scalability #SystemDesign

☕ Why Choose JSONata? 📺 JSONata is a lightweight, open-source query and transformation language designed specifically to navigate, manipulate, and restructure JSON data. It uses a compact, declarative syntax to extract nested values, filter data, and restructure payloads into new formats, often acting as a powerful alternative to JavaScript or Python for data processing. 🗒️ Summary, JSONata is the best powerful query and transform the JSON structure. #jsonata #KPI #dashboard #design

Copying projects with "node_modules" feels like it takes eternity. Now imagine having multiple subfolders, each with Node.js and Python projects. The problem: Huge, unnecessary folders ("node_modules", "__pycache__") slow everything down. What I used to do: Manually go into each subfolder → delete "node_modules" and cache → then copy (Not scalable. Just repetitive work.) The smarter way: Automate it with Robocopy: robocopy "C:\source" "D:\dest" /E /MT:16 /XD node_modules __pycache__ ● Works across all subdirectories ● Skips unnecessary files ● Cuts transfer time drastically

Just recently solving a DSA pattern based question "2 pointer" method Question : Move Zeros The question is simple but there is a core python understanding that clear a small difference that usually gets ignore : Look the Brute force code : a = [0,1,0,3,12] new = [] zero_count = 0 # First for loop for num in a:   if num != 0:     new.append(num)   else:     zero_count += 1 # second for loop # add zeros at end for _ in range(zero_count):   new.append(0) print(new) There is a small difference in both the for loops : 1 for loop : for num in a 2 for loop : for _ in range(zero_count) The first for loop " for num in a " gives the actual element not indices ( num = 0,1,0,3,12) The second for loop "for _ in range(zero_count)" -> range just repeat something or iterate over the indices not values N times . means if "zero_count" is 2 it repeat 2 times . This is the basic knowledge that often ignore while solving any problem

How fast is your "fast" model when pushed to the limit? It is not just about whether an LLM can find the information, but how quickly it can start delivering it. NEO built Context Cost Map : A Python tool that maps accuracy, cost, and latency. By precisely tracking the "time to first token" across varying context sizes, the Context Cost Map tool exposes the real-world speed of models under pressure 5 models tested across 9 context sizes (1K-64K) with 3 trials each (135 API calls total). How is this measured? Context Cost Map runs a rigorous "Needle-in-Haystack" evaluation. The tool dynamically generates filler text to reach target sizes from 1K up to 128K tokens, hides a secret target fact "DELTA-7", and forces the LLM to retrieve it. The Context Cost Map orchestrates API calls via OpenRouter. It automatically tracks binary accuracy, latency, and USD cost, instantly generating interactive HTML subplots to visualize performance inflection points. Context Cost Map tool is fully open-source and ready for your own custom model evaluations Map the precise intersection of cost, latency, and accuracy for your production stack today.

Don't pay a 77× cost premium for zero accuracy benefit. This tool maps the precise intersection of cost-efficiency, latency, and 100% retrieval accuracy for any OpenRouter model, ensuring you deploy the most cost-effective model for your production needs.

🚀 FastAPI 𝗷𝘂𝘀𝘁 𝘂𝗻𝗹𝗼𝗰𝗸𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗯𝗶𝗴. With FastAPI 0.136.0 officially supporting free-threaded Python (No-GIL), I wanted to move beyond the hype and measure what actually changes in real-world APIs. So I ran controlled benchmarks comparing: • Python 3.12 (GIL) • Python 3.13.0t (No-GIL) Same code. Same FastAPI app. Zero changes to the source. 🔬 How I benchmarked it: I isolated CPU-bound workloads — the kind that the GIL historically serializes — and hit the endpoints with concurrent requests using a fixed thread pool. Both environments ran on identical hardware with warm-up rounds to eliminate JIT noise. No async tricks, no multiprocessing — pure threading, the way most real backends actually work. 💥 Result: ~8× improvement in CPU-bound throughput under concurrency. This isn't just a micro-benchmark win. It directly impacts: • ML inference APIs serving parallel requests • Data processing and transformation workloads • CPU-heavy backend systems under real load I've broken down the full experiment, setup, and results here: 👉 Medium Post : https://lnkd.in/guUZEyiV Curious — are you already running experiments with free-threaded Python, or waiting for broader ecosystem support? 👇 #FastAPI #Python #Performance #Backend #Concurrency #AI

🚀 𝐃𝐚𝐲 𝟐: 𝐌𝐚𝐬𝐭𝐞𝐫𝐞𝐝 𝐑𝐨𝐮𝐭𝐢𝐧𝐠 & 𝐏𝐚𝐭𝐡 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬 𝐢𝐧 𝐅𝐚𝐬𝐭𝐀𝐏𝐈! ⚡ The journey into FastAPI continues! Today was all about how we handle data directly within the URL. Coming from a Django background, I’m loving how clean and intuitive the routing feels here. 𝙃𝙚𝙧𝙚’𝙨 𝙬𝙝𝙖𝙩 𝙄 𝙩𝙖𝙘𝙠𝙡𝙚𝙙 𝙩𝙤𝙙𝙖𝙮 : 📍 𝙋𝙖𝙩𝙝 𝙋𝙖𝙧𝙖𝙢𝙚𝙩𝙚𝙧𝙨 & 𝙃𝙏𝙏𝙋 𝙈𝙚𝙩𝙝𝙤𝙙𝙨 : I explored how to capture dynamic values from the URL using {curly_brackets} and how they interact with standard HTTP methods like GET and POST. 🔢 𝙋𝙖𝙩𝙝 𝙋𝙖𝙧𝙖𝙢𝙚𝙩𝙚𝙧𝙨 𝙬𝙞𝙩𝙝 𝙏𝙮𝙥𝙚𝙨 : This is a game-changer! By using Python type hints (like : int or : str), FastAPI automatically handles: 𝗗𝗮𝘁𝗮 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻:It returns a clear error if the wrong type is sent. 𝐃𝐚𝐭𝐚 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧: It automatically converts the URL string into the correct Python type. 🔄 𝘿𝙤𝙚𝙨 𝙊𝙧𝙙𝙚𝙧 𝙈𝙖𝙩𝙩𝙚𝙧? (𝙋𝙖𝙩𝙝 𝙋𝙖𝙧𝙖𝙢𝙚𝙩𝙚𝙧 𝙊𝙧𝙙𝙚𝙧𝙨) : I learned that in FastAPI, the order of your route functions matters. If you have a static path like /users/me and a dynamic path like /users/{user_id}, the static one must come first to avoid being "caught" by the dynamic parameter! 📋 𝙋𝙧𝙚𝙙𝙚𝙛𝙞𝙣𝙚𝙙 𝙑𝙖𝙡𝙪𝙚𝙨 : Using Python’s Enum, I learned how to restrict a path parameter to a specific set of valid options. This makes APIs incredibly robust and self-documenting. 🛠️ 𝙋𝙖𝙩𝙝 𝘾𝙤𝙣𝙫𝙚𝙧𝙩𝙚𝙧𝙨 : I dived into using :𝗽𝗮𝘁𝗵 to capture entire file paths (like files/images/photo.jpg) within a single parameter. 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐒𝐭𝐚𝐭𝐮𝐬:Feeling more confident with every line of code. The way FastAPI handles documentation and validation simultaneously is a massive productivity boost! 🛠️💻 #FastAPI #Python #BackendDevelopment #WebAPI #LearningJourney #Coding #SoftwareEngineering #PythonDeveloper #Day2

LeetCode Problem 380 Insert Delete GetRandom O(1): "Implement the RandomizedSet class: RandomizedSet() Initializes the RandomizedSet object. bool insert(int val) Inserts an item val into the set if not present. Returns true if the item was not present, false otherwise. bool remove(int val) Removes an item val from the set if present. Returns true if the item was present, false otherwise. int getRandom() Returns a random element from the current set of elements (it's guaranteed that at least one element exists when this method is called). Each element must have the same probability of being returned. You must implement the functions of the class such that each function works in average O(1) time complexity." Approach: Maintain two data structures one hash_table to get elements in constant time and other a list so that getRandom() function can be implemented by using random.choice(list). Time Complexity: O(1) Space Complexity: O(n) #Python #LeetCode #DSA #Algorithms #HashTable #Lists #Arrays #OptimalSolution #DataStructures