#Api Response Codes

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#Api Response Codes Reel by @mrgenz25 - . Professional API Calls with Async/Await & Try-Catch #JavaScript #API #WebDevelopment #AsyncAwait #ErrorHandling #Frontend #Coding #Programming #Tech
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@mrgenz25
. Professional API Calls with Async/Await & Try-Catch #JavaScript #API #WebDevelopment #AsyncAwait #ErrorHandling #Frontend #Coding #Programming #TechShorts #LearnToCode #ProfessionalCoding #WebDev Master API Data Fetching with Async/Await & Try-Catch! In this short, I demonstrate professional API integration — fetching backend data, handling errors gracefully, and displaying results cleanly using modern JavaScript patterns! ✅ 🛠️ What You'll See: ✅Async/Await for clean asynchronous code ✅Try-Catch for robust error handling ✅ Fetch API for making HTTP requests ✅State management for loading & data states ✅UI updates based on API response
#Api Response Codes Reel by @codewith_sushant - Actually, I wanted to make it 0.2 Sec ,but by mistake,it became 2 Sec.🙆 Read Caption 🧑‍💻👇 💬 "How did you improve an API response time from 15 s
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@codewith_sushant
Actually, I wanted to make it 0.2 Sec ,but by mistake,it became 2 Sec.🙆 Read Caption 🧑‍💻👇 💬 "How did you improve an API response time from 15 seconds to 2 seconds?" ✅ Answer (Practical & Impact-Driven): “We had an API in our application that initially took around 15 seconds to respond, which was unacceptable for user experience. Here’s how I brought it down to under 2 seconds, using a combination of profiling, architecture changes, and smart engineering decisions.” 🔍 1. Identified the Root Cause (Profiling) Used Spring Boot Actuator, New Relic, and Zipkin to profile the API. Found that the major delay came from heavy DB queries and sequential calls to multiple services. ⚙️ 2. Optimized Database Queries Rewrote complex joins and replaced them with indexed views. Used DTO-based projection to fetch only required fields instead of full entities. Applied pagination to reduce result set size. ✅ Result: Query time dropped from 9s → 1.5s 🚀 3. Introduced Asynchronous and Parallel Processing Converted sequential service calls into parallel execution using CompletableFuture. For independent remote calls, used @Async to reduce wait time. ✅ Result: Reduced total processing time by 60–70% 💾 4. Implemented Redis Caching Cached static and repetitive data like config values and product details using Redis. This avoided unnecessary DB hits on every API call. ✅ Result: Bypassed DB completely for 30% of requests 📦 5. Reduced Payload and Serialization Overhead Limited the API response to only required fields using lightweight DTOs. Enabled GZIP compression for large responses. Removed unnecessary JSON nesting and circular dependencies. ⚠️ 6. External Service Hardening Set timeouts and circuit breakers using Resilience4j for third-party APIs. Added fallback logic to return cached or default data if needed. 📈 Final Result: "With these changes, we reduced the API time from 15s to 1.8s on average, improved user satisfaction, and enabled the system to handle 3x more traffic without scaling infrastructure." Follow @codewith_sushant for more tech tips. #code #api #hiring #interview #corporate #itindustry #coder #spring #codereview #tips #improveapi
#Api Response Codes Reel by @cheat.x.code - Free me API keys chahiye toh yaha aajao.....aur again am saying this for Educational purpose only. And Perfect #cheatxcode #ai #techtrends #cheatcod
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@cheat.x.code
Free me API keys chahiye toh yaha aajao.....aur again am saying this for Educational purpose only. And Perfect #cheatxcode #ai #techtrends #cheatcode #chatgpt #aigenerated #gemini #learning #learncoding #api #apikey #postman #claude #claudeai #chatgpt4 #chatgptapi #freehack
#Api Response Codes Reel by @techdoodless - Comment api for the webiste #hackathon #apikeys #tredingreels #tech
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@techdoodless
Comment api for the webiste #hackathon #apikeys #tredingreels #tech
#Api Response Codes Reel by @softwarewithnick (verified account) - Access to free APIs 😎 This website sorts a bunch of free public APIs by category. There's pretty much a category for everything, so whatever you're
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@softwarewithnick
Access to free APIs 😎 This website sorts a bunch of free public APIs by category. There’s pretty much a category for everything, so whatever you’re interested in, odds are you can find an API for it here! APIs are how you can access data quickly using code, without the need for storing massive amounts of data locally. There’s also quite a bit of other things you can accomplish using them. This is the perfect site for people looking to practice API usage! Follow for more free coding resources ✅ #code #coding #tech #api #learntocode
#Api Response Codes Reel by @this.girl.tech - Comparing how different API approaches behave conceptually, not raw protocol benchmarks. #coding #engineering #api #computerscience #development
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@this.girl.tech
Comparing how different API approaches behave conceptually, not raw protocol benchmarks. #coding #engineering #api #computerscience #development
#Api Response Codes Reel by @drcintas (verified account) - Comment "API" to get these free API key sites. You don't need to spend hundreds of dollars on API keys anymore. Here are 3 platforms that give you
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@drcintas
Comment “API” to get these free API key sites. You don’t need to spend hundreds of dollars on API keys anymore. Here are 3 platforms that give you free access to top models like Gemini, DeepSeek, and more. First, Google AI Studio — it lets you grab a free key for all of Google’s latest models, including Gemini 2.5. Next, OpenRouter — fast, easy to use, and gives you 50+ free APIs like DeepSeek R1 and Qwen 3. Finally, Groq — super fast and supports models like LLaMA, Mistral, DeepSeek, and even OpenAI Whisper. These three alone can save you hundreds on AI tools and they’re completely free.
#Api Response Codes Reel by @sujan.codes - 1 million requests per minute ≈ 16,600 requests per second. If all requests hit: one server one database one CPU 💥 Everything crashes. So the g
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@sujan.codes
1 million requests per minute ≈ 16,600 requests per second. If all requests hit: one server one database one CPU 💥 Everything crashes. So the goal is simple: 👉 Don’t let everything hit one place. ⭐ Step 1: Add a Load Balancer Instead of one server, you use multiple servers. A load balancer sits in front and says: “You go to Server 1” “You go to Server 2” “You go to Server 3” Now traffic is distributed, not concentrated. ⭐ Step 2: Scale Horizontally Instead of making one server bigger, you add more servers. This is called horizontal scaling. More traffic → add more servers → system survives. ⭐ Step 3: Cache Everything Possible Most requests ask for the same data. So instead of hitting the database every time: Store responses in cache (Redis) Now: First request → DB Next 10,000 requests → cache ⚡ Response becomes super fast. ⭐ Step 4: Rate Limiting Some users or bots may spam your API. So you set rules like: Max 100 requests per user per minute If someone exceeds it → block or slow them down. This protects your system from abuse. ⭐ Step 5: Use Queues for Heavy Work Some tasks don’t need instant response: Emails Notifications Reports So instead of processing them immediately: Put them in a queue Background workers handle them slowly Your API stays fast.
#Api Response Codes Reel by @sanskriti_malik11 - 🚨 Your API gets flooded with 1M fake requests. What now? That's a DDoS attack - and here's how real systems survive 👇 1️⃣ Rate Limit Requests Limi
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@sanskriti_malik11
🚨 Your API gets flooded with 1M fake requests. What now? That’s a DDoS attack — and here’s how real systems survive 👇 1️⃣ Rate Limit Requests Limit how many requests one client can send. 🧠 Like allowing only 5 people per minute at an ATM so one person can’t block everyone. 2️⃣ Block Bad Traffic at the Edge Drop malicious IPs before they hit your servers. 🧠 Stop unwanted visitors at the main gate, not inside your house. 3️⃣ Let CDN Absorb the Blast Cloudflare / Akamai handle massive traffic spikes. 🧠 1 million users don’t rush one shop — they spread across 1,000 branches. 4️⃣ Web Application Firewall (WAF) Blocks bots, weird headers, SQL injection attempts. 🧠 A security guard rejecting fake IDs. 5️⃣ Challenge Suspicious Users CAPTCHA / JS challenge to separate bots from humans. 🧠 No OTP? No entry. 6️⃣ Auto-Scale or Go Down Spin up more servers when traffic explodes. 🧠 Open extra billing counters when the crowd shows up. 💡 DDoS protection isn’t one trick — it’s layered defense. #tech #interview #career #backend #softwareengineer (DDOS protection, API security, rate limiting, WAF, cloud architecture, scalable backend, high traffic systems, system design interview, backend engineering, distributed systems, load balancing, microservices security)
#Api Response Codes Reel by @chris.raroque - by far the biggest misconception though… Is that people assume their app code is private 😬 You basically have to assume that anybody can take your
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@chris.raroque
by far the biggest misconception though… Is that people assume their app code is private 😬 You basically have to assume that anybody can take your app, decompile it, and view the code Same goes with web apps. If you store any environment variables or credentials, it's pretty easy for someone to find them If you make any calls directly from the front end, you also have to assume someone can find those and deconstruct them The number one mistake I'm seeing is people making API calls from the front end and passing in sensitive environment variables in there. Like API keys for AI providers, for example 🙅‍♂️ This is also a big reason why people have a problem with providers like Supabase and Firebase. They make extremely sensitive calls from the front end directly to the database and the only protection layer is real level security or Firebase security rules One slight misconfiguration could leave you completely exposed and anybody who figures out your endpoints can start manipulating and viewing sensitive data The best thing you can do is to move all sensitive calls and ideally even all database reads to a backend service that they can't snoop around #softwaredeveloper #coding #appdesign

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