Fixing Azure Local Updates Crash with Enum Resolution Maps

How We Slashed ~40ms Latency with One Line of Django Config We recently noticed something odd — our API response times were spiky. Even for simple GET requests, there was a consistent 30–50ms overhead that didn’t add up. After digging into DB logs, we found : connection handshake exhaustion. By default, Django sets: CONN_MAX_AGE = 0 Which means for every request, your app: 1.Opens a new TCP connection 2.Performs SSL/TLS handshake 3.Authenticates with the database 4.Closes the connection immediately We were spending more time opening the door than actually walking through it. ✅ The Fix: Persistent Connections DATABASES = { 'default': { 'CONN_MAX_AGE': 300, # Reuse connections for 5 minutes 'CONN_HEALTH_CHECKS': True, # Ensures stale connections are handled }} The Impact 📈 ~35ms drop in average latency 📉 ~15% reduction in DB CPU usage ⚡ Eliminated handshake timeout errors during traffic spikes Pros: ✔️ Skips expensive handshakes ✔️ Reduces DB overhead ✔️ Improves consistency under load Watchouts: ⚠️ Each worker holds a connection → can hit DB connection limits ⚠️ Needs proper pooling/limits in high-scale systems #Django #BackendEngineering #PerformanceOptimization #WebDevelopment #DevOps #SoftwareEngineering #Python

Production-ready multi-agent orchestration is finally here. With Agent Framework v1.0 introducing some exciting new features. My personal favourites: 1. Agent workflows 2. Middleware hooks 3. Integration with managed agents in Foundry Agent Service 4. Multi-agent orchestration with HITL 5. Skills 6. Foundry Tools, Memory, Observability and Evaluations and many more. Check out the amazing blog post from Shawn Henry detailing the various features with concrete examples in Python and .NET https://lnkd.in/dKBRDujY

🐳 I just containerized a Python Flask API using Docker and the results surprised me. Starting point: a naive Dockerfile that produced a 1.15 GB image running as root with zero health monitoring. After applying production best practices: ✅ Image size: 1.15 GB → 136 MB (88% reduction) ✅ Runtime user: root → appuser (non-root) ✅ Build context: 24.18 MB → 1.22 KB ✅ Health checks configured for orchestration compatibility ✅ Docker Compose with Flask + Redis fully orchestrated ✅ Automated score: 100/100 ✅ GitHub Actions CI: Passing I then went a step further; pushed the image to Docker Hub, deleted it locally to simulate a fresh machine, pulled it back down, and ran it. Every security configuration survived the round trip intact. That is what genuine portability looks like. Key things I learned: → The base image you choose is the single biggest factor in image size → Instruction ORDER in a Dockerfile is a performance decision, not just syntax → Running as root inside a container is never acceptable in production → Docker Compose makes multi-container apps manageable with one command I wrote up the full breakdown, every decision, every optimization, and every result in a detailed Medium article. 📖 Read the full article: https://lnkd.in/dzUkHybc 💻 GitHub: https://lnkd.in/dhckiN3d 🐳 Docker Hub: https://lnkd.in/d4hP6Hdf #Docker #DevOps #Python #Flask #Containerization #CloudEngineering #SoftwareEngineering #OpenToWork

We just open sourced OpenTrace. Apache 2.0. The core is free. It will stay free. When we build paid tiers for MCP and team collaboration, the graph explorer you're using today isn't going anywhere. → oss.opentrace.ai What you're looking at in this screenshot is the OpenTelemetry repo - 2000 nodes, 2524 edges, Go, Python, TypeScript, C#, Java, Rust - fully rendered in a browser in seconds. No install. No account. No infrastructure. And those 14 open PRs on the right? OpenTrace pulls those in too. You can see exactly what each one touches before anyone merges it. Here's just one little use case that one of our dev's shared with me earlier: Load two repos that solve the same problem. Ask OpenTrace to compare the architectures. Which scales better? Where are the hidden dependencies? What's the better approach for our use case? It answers - grounded in the actual graphs of the two repo's, not a guess. Bring your own Anthropic or OpenAI API key to power the AI queries. Your code stays in your browser. Your queries never touch our infrastructure. That's a deliberate choice - your codebase is yours. Go load a repo. The one you know best, or one you've always wanted to understand. Then tell us in the comments what you found. oss.opentrace.ai #OpenSource #Apache2 #DeveloperTools #AIEngineering #CodeIntelligence #BuildInPublic

We've spent 20 years helping engineering teams see what's happening inside their systems. In that time, the single most consistent thing we've heard from customers is this: visibility isn't enough. You need context. That's exactly the problem our sister company OpenTrace just shipped something to fix. OpenTrace OSS is live today. Free. Apache 2.0. No account. Runs in your browser. → oss.opentrace.ai Here's why we think this matters for every engineering team using AI coding tools right now. Cursor, Claude Code, and Copilot are fast (almost magical), but they're blind to runtime behavior, service dependencies and blast radius. They generate code without understanding how your system actually behaves in production. If you've ever merged an AI-generated change and watched something unexpected break - this is why. OpenTrace builds a live knowledge graph of your entire codebase and connects it directly to your AI tools via MCP. Your AI stops guessing. You stop cleaning up after it. Drop in any repo at oss.opentrace.ai and see what your codebase actually looks like when it's properly understood. We think you'll feel the difference immediately.

Today, we’re thrilled to announce that Microsoft Agent Framework has reached version 1.0 for both .NET and Python. This is the production-ready release: stable APIs, and a commitment to long-term support. Whether you’re building a single assistant or orchestrating a fleet of specialized agents, Agent Framework 1.0 gives you enterprise-grade multi-agent orchestration, multi-provider model support, and cross-runtime interoperability via A2A and MCP.

System Architecture Design: Participated in an insightful session focused on mapping out full-stack architecture, specifically looking at the flow between Frontend (F), Logical Backend Layers (B), and Database Storage. Web Framework Integration: Explored the implementation of modern Python frameworks like FastAPI, Flask, and Django to build scalable and efficient backend services. Technical Deep Dive: Discussed complex logic flows, including function mapping ( ) and data retrieval patterns, to better understand how components communicate in a production-ready environment.

System Architecture Design: Participated in an insightful session focused on mapping out full-stack architecture, specifically looking at the flow between Frontend (F), Logical Backend Layers (B), and Database Storage. Web Framework Integration: Explored the implementation of modern Python frameworks like FastAPI, Flask, and Django to build scalable and efficient backend services. Technical Deep Dive: Discussed complex logic flows, including function mapping ( ) and data retrieval patterns, to better understand how components communicate in a production-ready environment.