Common mistakes in API development: Versioning, error handling, and security

REST APIs Made Simple! Whether you're building a backend or integrating third-party services, understanding REST API methods is a must for every developer. Here's a quick refresher GET - Fetch data POST - Create data PUT - Update entire data PATCH - Update partial data DELETE Remove data And don't forget those status codes! 200 OK 201 - Created 400 - Bad Request 404 - Not Found 500-Internal Server Error A solid grasp of these basics can make API integration smoother, faster, and error-free. What was your biggest learning moment while working with REST APIs? #API #RESTAPI #Backend Development #Software Engineering #Developers #Programming #Coding #WebDevelopment #LearningREST API

🚀 Mastering REST APIs Made Simple! Whether you're building a backend or integrating third-party services, understanding REST API methods is a must for every developer. Here’s a quick refresher 👇 🔹 GET – Fetch data 🔹 POST – Create data 🔹 PUT – Update entire data 🔹 PATCH – Update partial data 🔹 DELETE – Remove data And don’t forget those status codes! ✅ 200 – OK ✨ 201 – Created ⚠️ 400 – Bad Request ❌ 404 – Not Found 💥 500 – Internal Server Error A solid grasp of these basics can make API integration smoother, faster, and error-free. 💬 What was your biggest learning moment while working with REST APIs? #API #RESTAPI #BackendDevelopment #SoftwareEngineering #Developers #Programming #Coding #WebDevelopment

🚀 Mastering REST APIs Made Simple! Whether you're building a backend or integrating third-party services, understanding REST API methods is a must for every developer. Here’s a quick refresher 👇 🔹 GET – Fetch data 🔹 POST – Create data 🔹 PUT – Update entire data 🔹 PATCH – Update partial data 🔹 DELETE – Remove data And don’t forget those status codes! ✅ 200 – OK ✨ 201 – Created ⚠️ 400 – Bad Request ❌ 404 – Not Found 💥 500 – Internal Server Error A solid grasp of these basics can make API integration smoother, faster, and error-free. 💬 What was your biggest learning moment while working with REST APIs? #API #RESTAPI #BackendDevelopment #SoftwareEngineering #Developers #Programming #Coding #WebDevelopment

2025 is almost over, and with 2026 around the corner, now’s a great time to check out some tools that could seriously level up your developer environment and workflows next year. Whether you're aiming to simplify local dev or make your pipelines truly programmable, these are worth having on your radar before you head into planning season. 1. Dagger – Pipelines as Code, Finally Done Right Dagger is an open-source runtime for composable CI/CD workflows — not a CI server, but a code-first way to build/test/deploy anything, anywhere. ✅ Write pipelines in Go, Python, TypeScript, or Java — not YAML ✅ Compose steps using containers with real IDE support ✅ Skip Dockerfiles and build images in code, publish to registries ✅ Cross-language workflows with type-safe SDKs ✅ Utilise 3rd party packages of Go, Python, TypeScript and Java ✅ Debug in real-time with built-in caching, logs, and a powerful TUI If you’ve ever duct-taped Bash scripts or wrestled with brittle CI configs, Dagger makes pipelines feel like a proper software project. 2. Aspire – Your Local Dev Stack, Streamlined Aspire is a code first alternative to docker compose, but with way more firepower. It lets you spin up your full distributed app locally: frontend, APIs, DBs, background workers - all wired together and observable by default. ✅ Define everything in code (no YAML!) with a fluent API ✅ Mix containerized services with host-based ones ✅ Out-of-the-box OpenTelemetry tracing, logs, metrics ✅ Easy to move from local dev to deployment manifest If you’re tired of managing a mess of shell scripts and compose files to simulate your app locally, Aspire makes the whole loop faster and cleaner. Bottom line: both tools improve the developer experience for real-world systems from local orchestration to CI/CD pipelines. They’ve made my projects cleaner, more repeatable, and easier to debug. Worth checking out both!!!!! Portable AI Agents, Workflows, Pipelines, Code first, Container native 👉 https://dagger.io Polyglot dev-time orchestration toolchain that lets you build, run, debug, and even deploy distributed applications from one source of truth 👉 https://aspire.dev

🚨 I learned about scalability the expensive way. Last month, our API completely died at 2 AM. We had maybe 50 concurrent users — not even that many — but the whole thing just... stopped responding. I woke up to 23 Slack messages and a very unhappy client. Turns out, I’d hardcoded a few database queries that worked fine during testing with 10 users — but under real load, each request was hitting the database 40+ times. 😬 That’s when it hit me: 👉 Writing code that works is one thing. 👉 Writing code that scales is a completely different game. When you’re starting out, everything feels fine — localhost runs smooth, tests pass, deploy works. But then real users show up. Traffic grows. Data piles up. And suddenly your “clean code” starts breaking. The first things to go: ⚙️ API timeouts 🐢 Slow queries 💥 Crashed servers I’ve been there. And fixing it after the fact? Way harder (and more stressful) than building it right from the start. Now I think about scalability before I write a single line — not because I’m some architecture guru, but because I’ve debugged enough 2 AM crashes to know better. If you’re building anything that might grow — even a side project — ask yourself: 💭 What happens when 100 people use this at once? 💭 What about 1,000? You don’t need to over-engineer everything. But caching, indexing, and async tasks can save you from those 2 AM panic moments. Trust me on this one. #Python #BackendDevelopment #FastAPI #Django #ScalableArchitecture #Developers #TechCommunity

Building a Representational State Transfer (REST) Application Programming Interface (API) with Flask can be fast and reliable if you keep to a simple playbook. 1) Start contract first with OpenAPI 3.1. Define your paths and models, then generate docs and tests. OpenAPI 3.1 aligns with JSON Schema, which keeps validation simple and tooling consistent. 2) Validate inputs early. Use Pydantic or Marshmallow to check and clean request data before it reaches your business code. Thin route handlers and a clear service layer make changes safer. 3) Ship with the right foundation. Use Flask Blueprints for modular design, SQLAlchemy for data access, environment based config, and run behind Gunicorn in production. Offload long work to Celery or RQ instead of blocking requests. 4) Performance and security first. Add pagination and caching with Redis to cut load. Apply rate limits with Flask Limiter, Cross Origin Resource Sharing (CORS) rules, and short lived JSON Web Token (JWT) tokens. Do not log secrets or return stack traces in production. 5) Observe and test. Use OpenTelemetry for traces and metrics, structured JSON logs, pytest for unit and contract tests, and Continuous Integration and Continuous Delivery (CI/CD). Python 3.13 performance gains help with startup and throughput, which is great for APIs. What would you add to this checklist? #Python #Flask #APIs

Google's Agent Development Kit now supports Go, joining Python and Java as language options for building AI agents with code-first control. The Go implementation ships with Agent2Agent protocol support for multi-agent orchestration and includes MCP Toolbox integration for 30+ databases out of the box https://lnkd.in/dQEZ92bF

Code Execution with MCP — Anthropic new take on agent efficiency MCP (Model Context Protocol) setup has a few big pain points: ⚙️ Current problems 🧱 Every tool definition (like “getCustomer”, “updateRecord”) gets loaded into the model’s context — wasting tokens. 📊 Huge intermediate results (spreadsheets, transcripts, etc.) get passed through the model unnecessarily. 🐢 This leads to slower responses and higher compute costs. Anthropic’s proposal ✍️ Let the agent write and execute real code (e.g., TypeScript). 🧩 That code imports only the tools it needs — no extra context bloat. 🧠 Heavy data processing happens in a sandbox, and the model only sees the final results. Limitations 🔒 Requires careful sandboxing and security. 🧑💻 Adds more setup for developers (code environment, libraries, governance). 🧩 Still early — not fully open-sourced or production-ready. Here is the link to the original blog: https://lnkd.in/dhR7jvpa Don’t want to read the full blog? Watch this short video instead 👉 by Arseny Shatokhin https://lnkd.in/d7ecHXfP 🙏 Big thanks to Gino Marín León for sharing this with me earlier!

APIs are the backbone of modern applications — but when things go wrong, those little error codes can make or break your debugging session. 🚧⁣ ⁣ Follow PrivoLabs For More Interesting Posts Here are the most common types of API errors every developer should understand:⁣ ⁣ 1️⃣ 𝟒𝟎𝟎 – Bad Request⁣ The client sent something invalid (like missing or malformed parameters).⁣ ⁣ 2️⃣ 𝟒𝟎𝟏 – Unauthorized⁣ Authentication failed — usually due to an invalid or missing API key or token.⁣ ⁣ 3️⃣ 𝟒𝟎𝟑 – Forbidden⁣ You’re authenticated but don’t have permission to access the resource.⁣ ⁣ 4️⃣ 𝟒𝟎𝟒 – Not Found⁣ The requested endpoint or resource doesn’t exist.⁣ ⁣ 5️⃣ 𝟒𝟎𝟓 – Method Not Allowed⁣ You used the wrong HTTP method (e.g., POST instead of GET).⁣ ⁣ 6️⃣ 𝟒𝟎𝟖 – Request Timeout⁣ The server took too long to respond.⁣ ⁣ 7️⃣ 𝟒𝟐𝟗 – Too Many Requests⁣ Rate limit exceeded — the client made too many API calls in a short time.⁣ ⁣ 8️⃣ 𝟓𝟎𝟎 – Internal Server Error⁣ Something went wrong on the server side (often unhandled exceptions).⁣ ⁣ 9️⃣ 𝟓𝟎𝟐 – Bad Gateway⁣ The server received an invalid response from an upstream server.⁣ ⁣ 1️⃣0️⃣ 𝟓𝟎𝟑 – Service Unavailable⁣ The server is overloaded or under maintenance — try again later.⁣ ⁣ 💡 𝐏𝐫𝐨 𝐓𝐢𝐩: Always log both the error code and message to quickly trace and debug API failures.⁣ ⁣ Which API error do you encounter most often? 😅⁣ ⁣ By The Way Follow Shivani Sharma For More Interesting Posts #api #backenddevelopment #softwareengineering #webdevelopment #coding #programming #developers #debugging #restapi #http #techlearning #softwaredeveloper #devlife #apierrors #webapi #frontend #backend #computerscience #developercommunity #techinsights

🚀 Just shipped: Multi-language dependency monitoring for create-quality-automation After v3.0.0's free beta launch, I'm excited to announce PREMIUM-002 is live - extending our framework-aware dependency monitoring beyond JavaScript to Python, Rust, and Ruby ecosystems. What's new: 🐍 Python/Pip Support • Django, Flask, FastAPI detection • Data science stack grouping (numpy, pandas, scikit-learn) • requirements.txt & pyproject.toml parsing 🦀 Rust/Cargo Support • Actix Web, Rocket framework detection • Async runtime grouping (Tokio, async-std) • Serde ecosystem intelligence • Cargo.toml parsing with inline table support 💎 Ruby/Bundler Support • Rails, Sinatra detection • Testing framework grouping (RSpec, Minitest) • Gemfile parsing with version constraints 🔄 Polyglot Repository Support Single Dependabot config managing npm + pip + cargo + bundler simultaneously with framework-aware batching across all languages. Why this matters: Modern teams work across multiple languages. Your monorepo might have a React frontend, FastAPI backend, and Rust microservices. Traditional Dependabot creates 50+ individual PRs. Our framework-aware grouping reduces this to 5-10 meaningful PRs: • "Django Core Updates" • "Data Science Stack Updates" • "Rust Async Runtime Updates" Technical approach: ✅ Zero external dependencies - simple regex-based parsing ✅ 15 comprehensive tests covering all languages ✅ Backward compatible architecture extension ✅ Production-ready error handling This is part of our freemium strategy - all premium features unlocked during free beta, then tiered pricing in Q1 2026. Try it: npm create quality-automation@latest Open source, MIT licensed, built with community feedback. What language ecosystem should we add next? Go? PHP? Let me know in the comments. #OpenSource #DevTools #DependencyManagement #Python #Rust #Ruby #JavaScript #DevOps