Unlock FastAPI's Hidden Gems for Scalable Backends

Your FastAPI backend is fast to build. But is it fast to run? Most developers find out the answer at the worst possible moment when real users hit it at the same time. Endpoints slow down. Requests pile up. Users drop off. Not because the code is wrong. Because it is blocking. Here is what blocking actually looks like in production: Your user hits an endpoint. FastAPI calls the database. That query takes 200ms. During those 200ms your server is frozen. Not slow. Frozen. Every other request sits in a queue waiting for that one query to finish. 100 users hit your API at the same time. User 1 gets served. Users 2 to 100 wait in line. That is sync. That is blocking I/O. FastAPI was built to never work that way. With async/await while your database query runs in the background, your server is already picking up the next request. And the next. And the next. 200ms of database wait becomes invisible to every other user. In real backend terms. SYNC — blocks: def get_orders(user_id: int): return db.query(user_id) ASYNC — non blocking: async def get_orders(user_id: int): return await db.query(user_id) Same logic. Same database. Same server. But now 100 users get served in the time it used to take to serve 1. This matters even more when your endpoints call external services. 1. Payment gateway 300ms wait. 2. AI model response 2 to 3 seconds wait. 3. Email service 500ms wait. Sync every user feels every millisecond of every one of those waits. with Async none of them do. FastAPI gives you non-blocking I/O natively. No extra setup. No plugins. No workarounds. Just write async. Add await. Let FastAPI handle the rest. Your backend was already fast to build. Now make it fast to run. Are you using async endpoints in your FastAPI projects? 👇 #FastAPI #Python #BackendDevelopment #AsyncProgramming #SoftwareEngineering #APIDesign #PythonDeveloper #WebDevelopment #TechIn2026 #BuildInPublic

🚀 Building High-Performance Backend Systems with FastAPI Recently, I’ve been deep into optimizing backend systems, and one thing stood out: 👉 Performance issues are often hidden in small decisions. While working on a transaction-heavy system, I noticed how minor inefficiencies were compounding into real bottlenecks. So I focused on fixing the fundamentals: ⚡ Eliminating redundant operations at the logic level ⚡ Designing async workflows in FastAPI to handle high concurrency ⚡ Optimizing database queries for pagination and scalability ⚡ Using dependency injection for clean, reusable architecture ⚡ Structuring APIs for both performance and maintainability Key takeaways: 💡 Small inefficiencies scale faster than expected 💡 Async design is essential for modern backend systems 💡 Clean architecture directly impacts performance 💡 Optimization is not a one-time task — it’s continuous Still iterating, still improving — but the learning curve has been worth it. If you’re working on: • FastAPI • Scalable backend systems • High-performance APIs Let’s connect and exchange ideas 🤝 #FastAPI #BackendDevelopment #Python #PostgreSQL #SystemDesign #AsyncProgramming #SoftwareEngineering

Claude Code's entire source code just leaked. 512,000 lines of TypeScript. 1,900 files. Everything exposed. Anthropic shipped v2.1.88 of Claude Code to npm this morning with a 59.8 MB source map file inside. Source maps map minified code back to original source. They're supposed to stay internal. Always. Someone forgot to exclude it from the build. By 4:23 AM ET, a security researcher posted the download link on X. Within hours, the codebase was mirrored across GitHub. 🔍 Here's what devs found inside: 🐣 A virtual pet system called "Buddy" with rarity tiers, shiny variants, and procedurally generated stats. 🕵️ An "Undercover Mode" that scrubs AI traces from commit messages. The prompt literally says: "Do not blow your cover." ⚡ An autonomous daemon called "KAIROS" that works in the background while you're idle, consolidating memory and sharpening context. 🧠 "ULTRAPLAN" that offloads complex tasks to a cloud container running Opus 4.6 for up to 30 min of deep thinking. 🚩 44 feature flags. 20 for features fully built but not shipped yet. The irony? The codebase included a system designed to prevent internal info from leaking. It leaked anyway. Anthropic confirmed it was a packaging error. They've pulled the package. But the internet doesn't forget. And this is the second time a .map file caused this. 🛠️ Takeaway for every engineer: Your .npmignore is not optional. Your CI/CD pipeline needs automated checks for source maps in production. Doesn't matter how good your code is if your build config ships your secrets. #claude

Application security usually lives outside your codebase. AIWAF flips that model. Instead of relying on static rules at the edge, AIWAF sits at the middleware layer in frameworks like Django, Flask, and FastAPI — analyzing request behavior in real time. It combines feature extraction, adaptive learning, and anomaly detection to decide what gets through and what doesn't. There's even a Rust-based accelerator behind the scenes to keep performance tight while validating requests at scale. Tomorrow, April 16th, the PySTL meetup breaks down how the AIWAF ecosystem works and what it looks like to build applications that can defend themselves dynamically. RSVP for Aayush Gauba's talk here: https://hubs.la/Q04c1cms0 If you want to put some of these ideas into practice in a Django context, Django in Action by Christopher L. Trudeau is a solid place to start: https://hubs.la/Q04c13dh0

🚀 Why I’m Exploring MessagePack Over JSON for APIs I recently came across MessagePack (https://lnkd.in/gRAXW3G4) while exploring ways to optimize data transfer in distributed systems—and it genuinely changed how I think about API payloads. (Shoutout to @piyushgarg195 for the insightful YouTube video that sparked this deep dive 🙌) At a glance, MessagePack feels like “JSON, but smarter.” But once you look closer, it’s a powerful upgrade for performance-critical systems. Here’s what stood out to me 👇 🔹 𝗦𝗺𝗮𝗹𝗹𝗲𝗿 𝗣𝗮𝘆𝗹𝗼𝗮𝗱𝘀 = 𝗙𝗮𝘀𝘁𝗲𝗿 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 MessagePack is a binary format that can reduce payload size by ~30–50% compared to JSON. Less data → faster transfer → lower latency. 🔹 𝗕𝗲𝘁𝘁𝗲𝗿 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗨𝗻𝗱𝗲𝗿 𝗟𝗼𝗮𝗱 No heavy string parsing like JSON. Serialization and deserialization are significantly faster, especially in high-throughput systems. 🔹 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗬𝗲𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 It keeps the schema-less flexibility we love in JSON, but with efficient type handling under the hood. 💡 𝗪𝗵𝗲𝗿𝗲 𝗶𝘁 𝗿𝗲𝗮𝗹𝗹𝘆 𝘀𝗵𝗶𝗻𝗲𝘀 → 𝗟𝗼𝘄-𝗹𝗮𝘁𝗲𝗻𝗰𝘆 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 Think about: • Real-time analytics pipelines • High-frequency systems • Multiplayer game backends • Microservices talking at scale In these scenarios, even small gains in payload size and processing time can have a huge impact. ⚖️ 𝗧𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀 𝘁𝗼 𝗸𝗲𝗲𝗽 𝗶𝗻 𝗺𝗶𝗻𝗱 • Not human-readable (debugging takes extra effort) 📌 My takeaway • If you're building internal services or performance-sensitive systems, MessagePack is a strong alternative to JSON • It has solid support across most well-known programming languages like Python, Go, Javascript, Rust etc, making adoption easier than expected • For public APIs or debugging-heavy workflows, JSON still wins in simplicity #APIs #DistributedSystems #BackendEngineering #Performance #Microservices

In Multi Agent world and many Junior Dev's committing code in bulk - Most AI code reviewers read the diff. They don't know your codebase. I built Mnemos to fix that. Mnemos is an open-source GitHub App that maintains a persistent memory graph of your repo - every commit, PR, review, and ADR stitched into Postgres + pgvector. When you open a PR, three agents run against that graph in parallel: → Conflict Detector finds breaking changes the diff hides - a  renamed function whose callers nobody updated, a change that  contradicts an accepted ADR, drift from a convention used  everywhere else. → Context Packager gives the reviewer a 30-second briefing  before they read the diff: related past PRs, applicable ADRs,  recent commits on each touched file, linked issues. → Reviewer Router ranks humans for the review using authorship,  past review patterns, call-graph overlap, and current load.  No LLM. The senior who's already drowning in 12 open PRs gets  demoted automatically. Self-hosted. Apache 2.0. Three agents, one comment, ~60 seconds per PR. Runs on `docker compose up`. This is an early alpha (v0.1.0-alpha.0). 400+ tests behind it, but it hasn't lived on your codebase yet — that's why I'm posting. I'm looking for five engineering teams willing to install Mnemos on one real repo for two weeks and tell me what's broken. In return: a 10-min install pairing call, direct access to me on issues, and a real seat at the v0.2 priority list. If your team would be a fit, comment or DM me. Repo + architecture doc + install guide: #opensource #devtools #codereview #github

A very productive day wrapping up both frontend polish and backend infrastructure for CRag. We made some major leaps in how the application handles data ingestion and user experience. Here is what I tackled today:  • 𝗦𝗰𝗼𝗽𝗲𝗱 𝗔𝗜 𝗖𝗵𝗮𝘁: Built the logic to let users chat with a specific document instead of querying the entire organization's knowledge base. I updated the 𝗠𝗼𝗻𝗴𝗼𝗗𝗕 𝘃𝗲𝗰𝘁𝗼𝗿 𝗮𝗻𝗱 𝗸𝗲𝘆𝘄𝗼𝗿𝗱 𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘁𝗼 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗶𝗹𝘁𝗲𝗿 𝗯𝘆 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗜𝗗.  • 𝗦𝘁𝗮𝗹𝗲 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝘂𝗽: Fixed a bug where re-uploading a document would mix 𝗼𝗹𝗱 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 𝘄𝗶𝘁𝗵 𝗻𝗲𝘄 𝗼𝗻𝗲𝘀. The processing worker now properly wipes stale chunks before generating and storing fresh AI embeddings.  • 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Resolved a 𝘁𝗿𝗶𝗰𝗸𝘆 𝗦𝟯 𝘂𝗽𝗹𝗼𝗮𝗱 𝗶𝘀𝘀𝘂𝗲 𝗰𝗮𝘂𝘀𝗶𝗻𝗴 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗿𝗲𝗳𝘂𝘀𝗮𝗹𝘀 𝗯𝘆 𝘀𝘄𝗶𝘁𝗰𝗵𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗳𝗶𝗹𝗲 𝘀𝘁𝗿𝗲𝗮𝗺𝘀 𝘁𝗼 𝗯𝘂𝗳𝗳𝗲𝗿𝘀, making file storing much more stable.  • 𝗕𝗲𝘁𝘁𝗲𝗿 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻: Upgraded the document library tables to include items-per-page selection and a jump-to-page dropdown, fixing some underlying TypeScript metadata bugs along the way.  • 𝗨𝗜 𝗮𝗻𝗱 𝗨𝗫 𝗣𝗼𝗹𝗶𝘀𝗵: Designed a new custom delete confirmation modal with a 𝗯𝗹𝘂𝗿𝗿𝗲𝗱 𝗯𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 𝗼𝘃𝗲𝗿𝗹𝗮𝘆 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝘁𝗵𝗲 𝗺𝗮𝗶𝗻 𝗹𝗮𝘆𝗼𝘂𝘁 𝘀𝗼 𝘁𝗵𝗲 𝘀𝗶𝗱𝗲𝗯𝗮𝗿 𝗮𝘂𝘁𝗼-𝗰𝗼𝗹𝗹𝗮𝗽𝘀𝗲𝘀 when clicking outside of it. Getting the vector search to accurately scope down to a single file took some debugging, but the results are incredibly accurate now. #buildinpublic #softwareengineering #webdevelopment #ai #rag #reactjs #nodejs #mongodb #learninpublic

14 AI applications now use mcp-memory-service for persistent memory. The latest didn't come from me. The memory awareness hooks I built for Claude Code inject relevant memories at session start, compress them during context compaction apparently worked well enough as a pattern. @irizzant took that architecture and ported it to OpenCode. Same concept, different host, built entirely against the REST API. The plugin searches semantically by project name, deduplicates across queries, handles timeouts gracefully. Read-only by design. No Python imports, no protocol coupling. That's the real test for whether your abstractions are right: can someone who's never seen your codebase replicate the pattern for a different platform? Turns out the answer is yes because the HTTP API carries the same capabilities as the MCP tools. 😊 https://lnkd.in/ePYekaAF v10.36.0 #SemanticMemory #OpenSource

Building a CRUD API with FastAPI One of the first practical projects backend developers build is a CRUD API, which allows applications to Create, Read, Update, and Delete data. Using FastAPI, developers can build these APIs quickly while maintaining strong performance and clean code architecture. FastAPI uses Python type hints and modern asynchronous features to simplify both request validation and response handling. In a typical CRUD API, developers define models representing resources such as users, posts, or products. These models describe the structure of the data and help ensure that requests contain valid information. FastAPI integrates with libraries like Pydantic to automatically validate incoming data, reducing the risk of incorrect or malformed requests reaching the database. Beyond simplicity, FastAPI provides automatic API documentation using OpenAPI and Swagger UI. This allows developers to test endpoints directly in the browser without needing external tools. As a result, FastAPI not only speeds up development but also improves collaboration between backend developers, frontend developers, and API consumers. #FullStackDeveloper #WebEngineering #TechCommunity #BuildInPublic #LearnToCode

I published a write-up about a design decision I care about when adding AI capabilities to backend systems: How to use LangChain4j in a Spring Boot app without letting it take over the architecture. What changed in this project was not just "adding AI support". The bigger improvement was architectural: - the code is now organized by context - use cases stay in the application layer - LangChain4j sits behind clear ports and adapters - PostgreSQL + pgvector still own retrieval - tests were reorganized to match the architecture instead of generic technical layers The project now shows a more realistic RAG-style flow with: - document ingestion through REST - chunking and embedding generation - vector storage in PostgreSQL - hybrid retrieval with vector similarity, full-text search, and metadata filters - prompt building and answer generation through LangChain4j adapters What I like most is that the code did not become framework-shaped. The application core still owns the use cases. The infrastructure stays at the edges. Replacing providers is much closer to a wiring change than a rewrite. That is the lesson I think matters in real projects: Use frameworks as adapters. Do not let them become your architecture. Article: https://lnkd.in/dqf2mcRj Repository: https://lnkd.in/dCC5WPNB #java #springboot #postgresql #pgvector #langchain4j #softwarearchitecture #hexagonalarchitecture #cleanarchitecture #rag #backend