Integrate Copilot AI into React Native Apps

This article provides insights on integrating the Copilot SDK into React Native apps to create AI-generated issue summaries. I found it interesting that such tools can significantly enhance productivity and streamline issue management for developers. How do you see AI tools transforming the way we handle software development challenges in the future?

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

This article explores how to integrate the Copilot SDK into a React Native app for generating AI-powered issue summaries. I found it interesting that the use of AI can significantly streamline the issue triage process, allowing developers to focus on more complex tasks. What are your thoughts on the potential of AI tools like Copilot in enhancing productivity in software development?

Most AI apps quietly become a billing problem. Every user, every retry, all routed through a single shared API key your server owns. What started clean quickly becomes a cost and access-control headache. Here's what most developers miss: if your users already have GitHub Copilot, your Node.js app doesn't need to own the AI layer. It can borrow it. The 𝐆𝐢𝐭𝐇𝐮𝐛 𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐒𝐃𝐊 is minimal to set up and gets you a working request in five lines. But the default setup isn't enough for multi-user apps, and that's where it gets interesting. I wrote a two-part series breaking down exactly how to do this right: 𝐏𝐚𝐫𝐭 𝟏 - How the SDK works, how auth resolves, and why a shared server token is the wrong model from day one. 𝐋𝐢𝐧𝐤 𝐭𝐨 𝐏𝐚𝐫𝐭-𝟏 𝐀𝐫𝐭𝐢𝐜𝐥𝐞 𝐢𝐧 𝐟𝐢𝐫𝐬𝐭 𝐜𝐨𝐦𝐦𝐞𝐧𝐭 𝐏𝐚𝐫𝐭 𝟐 - 𝐆𝐢𝐭𝐇𝐮𝐛 𝐎𝐀𝐮𝐭𝐡 𝐢𝐧 𝐄𝐱𝐩𝐫𝐞𝐬𝐬, each user's token locked in an HttpOnly cookie, auth middleware per request, and every Copilot call running under that user's own GitHub identity. 𝐋𝐢𝐧𝐤 𝐭𝐨 𝐏𝐚𝐫𝐭-𝟐 𝐀𝐫𝐭𝐢𝐜𝐥𝐞 𝐢𝐧 𝐭𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐜𝐨𝐦𝐦𝐞𝐧𝐭 Access control that scales. An architecture you won't regret six months later. #GitHub #Copilot #NodeJS #Express #OAuth #AI #TypeScript

My client challenged me to rebuild our entire app MCP-first. (it's embarrassingly simple) I feel silly telling you this, because it's nothing revolutionary... → Separate your front end and back end. That's it. is your mind blown? 😉 WHY VIBE CODING SUCKS FOR THIS Most platforms (Lovable included) hook you straight into the Supabase JS library, and then you have to vibe-code a hacky API/MCP later. This sets you up for head aches - Your business logic/permissions live in two places - Edge functions are doing jobs they were never meant to do - It's a mystery what's going on / who has access to what MY NEW TECH STACK 👇 🦫 BHVR → Bun + Hono + Vite + React 📦 Turbo repo → one codebase, two deploys (so AI has full context) 🔌 Backend API documented with Scalar 🎨 Front end that talks to the API (not the database) 🤖 MCP layer has roles and permissions build in by default The front end has no concept that Supabase even exists! It just knows there's an API. And because the API exists from day one... 1. MCP is EASY. 2. Every feature can be built with chat interactions in mind. 3. If it's a SaaS, you're ready to integrate with anyone's existing stack --- I'm kicking myself for not building my last few projects this way. But I'm excited where this one's headed. Slackbot here I come Come join the BHVR train with me!

Code review feedback shouldn't require leaving your workflow to find it. That's the problem I'm solving with PRism — an AI-powered code reviewer I've been building: https://lnkd.in/eM8Bm3jY It can already analyze pull requests and generate structured, actionable feedback. The next step: turning it into a native GitHub App, so reviews appear directly inside PRs — immediate, contextual, and useful during development. New territory for me (authentication, webhooks, reliable production systems) — but that's the point. Building this in public. If you're working on developer tooling or know someone who is, I'd love to connect. #BuildInPublic #DevTools #SoftwareEngineering #OpenSource

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.

I have decided to make my codebase for Spectyra open source. I created this app over 4 months and it has over 40+ underlying algorithms that work under the hood. The current focus has been to optimize LLM and agent workflows, specifically for OpenClaw. I created a simple OpenClaw skill that makes it very easy to launch and save on the LLM api calls and entire workflow. I am actively now working on a separate SDK that can be integrated into major apps that will save 40% - 60% on app api LLM costs. This will save companies thousands a month if not more depending on AI spend. If anyone wants to contribute, feel free in the open sourced repo: https://lnkd.in/eii7HhFV The algorithms are already in place to reduce cost and wasted work without changing outputs which will be integrated into the SDK. Most cost isn’t just “tokens”, it’s inefficiency across the entire flow. What it actually does (technical level) Each request goes through a pipeline locally and not passed to outside servers. What really halpens: 1. Context analysis • Breaks prompt + history into structured units • Identifies repeated, stable, and low-entropy segments 2. Transform layer A set of independent transforms (can be composed or skipped): • RefPack → replaces repeated historical blocks with references ([[R1]]) • PhraseBook Encoding → compresses recurring phrases into short tokens • CodeMap → converts large codebases into structured signatures (imports, exports, dependencies, selective snippets) These are not simple truncation, they preserve meaning while reducing redundant processing. 3. Spectral / structural scoring • Evaluates which parts of context are stable vs. compressible • Uses graph-based relationships between segments (not just token frequency) • Helps decide what should persist, compress, or be removed 4. Flow optimization • Detects unnecessary retries, loops, and redundant steps • Reduces total number of LLM calls in multi-step agents • Reuses prior computation where possible 5. Output + analytics • Returns optimized request (same expected output) • Tracks: • input/output tokens • estimated cost • per-flow savings • before/after traces Most existing libs/tools: • trim prompts • or switch models Spectyra gets smarter over time and is an AI optimization layer, not a simple library. Even if you removed “compression”, a large part of the savings would still exist from: • fewer steps • less recomputation • better flow structure It is also designed with these in mind: • No proxying LLM calls • No storing raw prompts • Works with any provider (OpenAI, Claude, etc.) • Doesn’t degrade output quality • Can run as SDK or local companion There’s a lot more that can be done which is why I open sourced it. My next post will explain algorithms in more detail. If you’re building with: • agentic systems • coding agents • large-context apps Feel free to give feedback and contribute: https://lnkd.in/eii7HhFV

💡Live Captions are an excellent accessibility feature that enhances your app's user experience. By making video calls more enjoyable and inclusive, they help ensure your app is both consumer-friendly and impactful. 👉Our latest blog post walks you through integrating the #Vonage Live Captions #API with Node.js. Whether you're building educational platforms, virtual meetings, or any video application, live captions are essential for accessibility and user engagement. The guide covers everything from server setup to implementation, try it out for yourself -> https://gag.gl/tODCnl 👀 Oh, and did I mention that we support 50+ languages including English, Spanish, French, Chinese, and many more? #mycompany

The last mile is no joke in vibe coding! Actually "building" the app is pretty fun. You sort of feel like Harry Potter receiving your Hogwarts letter when you had no idea you were a wizard. But, deploying it to production is a slogggg. Today I spent two hours getting a React app into Azure Static Web Apps, and the issues had nothing to do with code. It was: -A ghost resource Azure couldn't find or delete -A deployment token that didn't match anything -A GitHub org that had never authorized Azure -An output folder named one thing when the workflow expected another Every time I thought was finally going to see a green checkmark, a red X came back to keep my ego in check. So if it helps: if you're building things with AI and hitting a wall the moment you try to ship, you're not doing it wrong. It's just a process. I'll also say: the last mile is just genuinely hard, but very much worth it when you see it "in lights!" #vibecoding #marketing