GitHub Copilot SDK for Node.js Developers

We just added Provenance Support to our Alerting and App Platform APIs. Why it’s a win for SREs: It essentially creates a "source of truth" for your configurations. When you provision alerts or app resources via Terraform or API, you can now "lock" them—preventing accidental manual overrides in the UI. ✅ True GitOps: Your code remains the master. ✅ Less Noise: No more "who changed this?" during an incident. ✅ Scalability: Manage notification policies and templates with total confidence. Check out the full update here: https://lnkd.in/e_7-bntZ #Grafana #GitOps #SRE #Observability #DevOps

AI app builders like Vibecode and Base44 charge $40-50/month just to access your own source code. Here's why code ownership should be free, and how Appifex does it differently.

REST vs GraphQL — stop guessing, start choosing. 🧵 Most devs pick one based on hype. Here's the actual rule: Use REST when: → Simple CRUD app → Public API for third parties → Team is already familiar with it Use GraphQL when: → Multiple clients (mobile + web) needing different data → Over-fetching / under-fetching is a real problem → Complex, nested data relationships The truth? Most startups don't need GraphQL. Most large products eventually do. Start simple. Scale smart. ♻️ Repost if this saved someone a debate. 💬 Which one are you using and why? #FullStackDeveloper #GraphQL #REST #APIDevelopment #WebDev #SoftwareEngineering

Go Team Unveils Zero-Allocation JSON Parser for High-Performance Apps 📌 Go’s zero-allocation JSON parser delivers 10x speed gains by parsing directly from byte slices-no structs, no allocations-ideal for high-throughput systems. Built for unpredictable data, it lets devs extract values via path strings without schema constraints, slashing memory pressure and latency. Perfect for apps where garbage collection pauses matter more than convenience. 🔗 Read more: https://lnkd.in/dkwjXpyn #Jsonparser #Golang #Zeroallocation #Bytelookup

Just finished building my own multi-agent AI software developer from scratch using LangGraph and LangChain. The goal was to spin up a crew of autonomous agents (Planner -> Architect -> Coder -> Reviewer) that could iteratively design, code, and QA-test a web app until it passed inspection. I finally got the loop working, but building this exposed so many hidden pitfalls about LLM architecture that standard tutorials ignore. Here are the biggest technical walls I hit and how I bypassed them: 1. Tool-Calling vs. Structured Outputs My Reviewer agent needed to read the source code the Coder just generated, and return a strict JSON payload (Approved or Needs Changes). But if you give an agent file-reading tools, it becomes conversational and loses the ability to force a strict Pydantic output.  The Fix: I actually stripped tools away from the Reviewer completely. Instead, I used standard Python to loop through the generated project folder and dynamically inject the raw code as a massive string directly into the Reviewer’s System Prompt before the API call. It resulted in a faster, cheaper, and 100% structured response. 2. Deep JSON Hallucinations When asking smaller/OSS models to write hundreds of lines of JavaScript and pass it as a content argument into a "write_file" tool payload, their attention mechanism completely decayed. The model kept randomly hallucinating hanging punctuation (like "]) into the middle of the JSON string, immediately crashing the API parser. No amount of aggressive system prompting fixed it; some models are just inherently broken for massive tool payloads. 3. OpenAI's "Strict Schema" Firewall I ultimately switched the engine to gpt-4o-mini, which completely fixed the JSON hallucinations. But it introduced a new bug: OpenAI's new "Structured Outputs" violently rejects unbounded Pydantic types (like Dict[str, Any]), which my Planner agent relied on for flexible ideation.  The Fix: I had to explicitly configure LangChain's adapter to fallback to method="function_calling", bypassing the strict schema firewall while keeping the structured Pydantic maps. Working with autonomous agent loops is incredible, but the hardest part isn't the "AI" at all—it’s babysitting the structured data boundaries between the nodes. I pushed the code to GitHub if you want to poke around the LangGraph routing: 🔗 https://lnkd.in/ddsyeXtv #LangGraph #AI #Python #SoftwareEngineering #LLMs

Your app breaks at 3 a.m., and PagerDuty wakes you up. Now you’re jumping between pods, logs, and guesswork, trying to figure out what’s wrong. There’s a better way. Dejan Lukić shows how to quickly understand what’s happening in Kubernetes with AppSignal: https://lnkd.in/ejk4z4Zz

With the GitHub Copilot SDK, you can add the same AI that powers Copilot Chat to your own applications. To test this out, Andrea Griffiths integrated the Copilot SDK into a React Native app to generate AI-powered issue summaries, with production patterns for graceful degradation and caching. Here's how she did it. 👇 https://lnkd.in/dxaBnzWh

🚀 I’ve just published a new deep-dive on: We Thought Firebase Was Just a Backend… Until It Solved Our Production Problems At one point in our Flutter journey, Firebase felt simple 👇 ✔ Authentication ✔ Database ✔ Storage And honestly? 👉 We thought it was just another backend tool. Everything looked fine. ✔ No major crashes ✔ No obvious bugs ✔ App working smoothly So we assumed: 👉 “We’re production-ready.” But production tells a different story. 💥 What started happening: 📉 Users dropping after login 📉 Features behaving inconsistently 📉 No clear idea what was breaking 👉 The worst part? We had zero visibility. We weren’t debugging… 👉 We were guessing. 🔥 Where things changed: Firebase stopped being “just a backend” 👉 And became our production safety net Not because of one feature… 👉 But because everything worked together 💡 What actually made the difference: ✔ Crash reporting gave us real issues (no more guesswork) ✔ Authentication became reliable and predictable ✔ Real-time data kept UI always in sync ✔ Analytics showed what users actually do ✔ Notifications helped bring users back 🎯 The biggest realization? It’s not about adding features… 👉 It’s about understanding production 👉 It’s about visibility 👉 It’s about control 📈 The shift in our mindset: Before: • “Let’s just build the feature” • Assumption-driven decisions • Reactive debugging Now: • “How will this behave in production?” • Data-driven decisions • Proactive monitoring 🚀 The result: ✔ Fewer unknown issues ✔ Better user experience ✔ More confidence in releases 👉 And most importantly: Our app became production-ready in the real sense. ⚠️ The truth? Firebase doesn’t magically fix your app. 👉 But it gives you what actually matters: • Visibility • Reliability • Confidence 📖 Read the full blog: 👉 Medium https://lnkd.in/g_syKMEp 👉 Substack https://lnkd.in/g244zBhj 💬 Curious: What’s one production issue you couldn’t debug at first? 👇 Let’s learn from each other #Flutter #Firebase #MobileDevelopment #SoftwareEngineering #Debugging #AppDevelopment 🚀

I built a sample app to showcase how I use Riverpod and synthetic_api_cli for mock backends I put together a bookmarks manager app in Flutter to demonstrate two things I've been working on. synthetic_api_cli (https://lnkd.in/dXt9HG2H) — a CLI tool I wrote that generates a real, HTTP-served mock backend from a JSON config file, complete with a Dockerfile for deployment anywhere. No hardcoded data - your app talks to an actual server over the network. The backend for this demo is deployed on Render. The second piece is my approach to structuring Flutter apps with Riverpod. The project uses experimental Mutation<void> for discrete async actions, freezed state notifiers for multi-step flows, keepAlive notifiers with optimistic update helpers, paginated AsyncNotifier for infinite scroll, and stream providers for push notification orchestration. It's a combination of strategies that have worked well for me across different use cases over the years. Source code: https://lnkd.in/djWrFZJ3 Would love to hear your feedback or how you're approaching similar patterns in your own projects.

🚀 Day 16/50 — ConfigMaps Hardcoding values in your app? Kubernetes has a better way. 👉 What is a ConfigMap? A way to store configuration data separately from your application. 💡 Why it matters? • Keeps config separate from code • Easy to update without changing image • Improves flexibility ⚙️ Example (Manifest): apiVersion: v1 kind: ConfigMap metadata: name: app-config data: APP_ENV: production DB_HOST: mysql-service 🧠 Simple Understanding: ConfigMap = External configuration for your app 🔥 Key Takeaway: “Separate config from code for better scalability.” 📌 Follow my journey as I learn Kubernetes step by step. #Kubernetes #DevOps #LearningInPublic