Augmented Reality Developer For Hire

**Spec-driven development vs. vibe-coding — why your AI agents need a plan.** We’ve all seen the demos. You type a vague prompt, the AI spits out code, and it *mostly* works. That’s vibe-coding. It feels fast, creative, and magical. But when you’re building for production — with multiple services, edge cases, security constraints, and an actual team — vibe-coding breaks fast. The agent has no context of your architecture, no spec to validate against, and no way to know if the output actually satisfies the business logic. Enter spec-driven development. Give your AI agent a structured plan: clear tasks, explicit acceptance criteria, and a defined data model. Suddenly, the agent isn’t guessing. It’s executing. It can reason about trade-offs, check its work against the spec, and produce consistent, maintainable code — every time. The difference isn’t just quality. It’s *predictability*. With vibe-coding, you debug by trial and error. With spec-driven agents, you trace the task → spec → code chain. **Why spec-driven wins for production:** - ✅ Traceable reasoning: you know *why* the agent made each decision - ✅ Repeatable results: no more “it worked yesterday, now it doesn’t” - ✅ Testable outputs: each spec maps directly to unit/integration tests - ✅ Team alignment: specs become shared contracts, not black-box prompts Vibe-coding is great for prototypes and one-off scripts. But if you want an AI agent that acts like a senior engineer — not a brilliant intern — give it a spec. What’s your experience? Are you vibing or specifying? #AIEngineering #SpecDrivenDevelopment #VibeCoding #AIAgents #SoftwareDevelopment #PromptEngineering #ProductionAI #MobileDev #AppDevelopment #iOS #Android

The AI agent space is maturing fast. The latest wave isn't about chat demos — it’s about **durable, replayable workflows** that actually deliver value in production. Three open-source projects are converging on exactly this vision: **JustHireMe** – A framework for building agent teams with built-in persistence, checkpointing, and rollback. Treats agent execution like a state machine you can pause, debug, and replay. No more “it worked in the notebook” surprises. **Spec Kit** – Turns ambiguous agent instructions into executable specs. By separating intent (the “what”) from implementation (the “how”), it makes workflows deterministic and audit-friendly. Replay becomes a first-class debugging tool. **Cowork pattern** – A multi-agent architecture where each agent operates as a colleague with shared context, versioned memory, and forced checkpoints. Enables safe resumption after failures and transparent collaboration logs #MobileDev #AppDevelopment #iOS #Android

🔥 Okay, this is wild in the best way. GitHub just blew up with **UditAkhourii/adhd** – a skill for coding agents that flips “ADHD” from a limitation into a superpower for creative and interdisciplinary work. The approach? 🧠 Tree-of-thought reasoning + pruning ⚡ Parallel divergent thinking under different cognitive frames ✂️ Score, prune traps, deepen the survivors Built on the Claude Agent SDK. As someone building for Augmented Reality, I see this immediately clicking for spatial ideation. AR isn’t linear – it’s a web of simultaneous possibilities (UX, lighting, physics, user intent). This kind of branching-then-pruning reasoning is exactly what agents need to navigate messy, high-dimensional design spaces. Imagine a coding agent that doesn’t just follow one thread, but fans out 20 creative paths, scores them, kills dead-ends, and doubles down on the survivors. That’s not a bug – it’s the architecture of innovation. 524 upvotes and 0 comments yet. Let’s fix that. 👇 What’s your take – is this the future of agentic coding, or just a clever hack? #ADHD #CodingAgents #TreeOfThought #AugmentedReality #ClaudeSDK #CreativeCode #GenAI #ARDev #Innovation Drop "ADHD" in the comments and I'll DM you the link.

Here's a LinkedIn post reacting to the trending story, written from the perspective of your **Augmented Reality Developer** page. --- **Excited to see this gem trending on GitHub!** 🚀 As someone who spends most days wrangling AR frameworks, SLAM pipelines, and real-time rendering, I've learned the hard way that clean code ≠ great system design. You can write the cleanest Unity C# or RealityKit Swift, but if the backing architecture for your cloud services, inference serving, or multi-agent orchestration isn't solid, you'll hit scalability walls fast. [@study8677/awesome-architecture](https://lnkd.in/gegpAZ-7) does something I *wish* existed when I started: **21 architecture maps** covering AI gateways, RAG pipelines, vector DB setups, inference serving, and even agent architectures — all with real open-source prototypes linked. What's especially relevant for AR devs? The **RAG + vector DB** patterns are directly applicable to our persistent spatial anchors and real-time knowledge retrieval for context-aware experiences. And the inference serving maps are gold for on-device ML model orchestration. No comments yet on the repo — so if you're into system design (or just want to level up from "coder" to "architect"), go drop a 🌟 and start a discussion. **Repositories over resumes. Architecture over syntax.** #SystemDesign #SoftwareArchitecture #GitHub #AugmentedReality #MachineLearning #RAG #Developers Drop "AWESOMEARCHI" in the comments and I'll DM you the link.

**Stop "vibe-coding" your way to chaos. Start shipping with specs.** 🚀 **GitHub Spec Kit** A spec-driven development lifecycle that *actually* works with AI agents. The problem: ↳ Agents "vibe" their way through ambiguities → spaghetti code ↳ No shared contract between human & AI → constant rewrites ↳ Missing context → hallucinations, broken features The fix: Three markdown files that become your team’s (human + AI) single source of truth. 📄 **plan.prompt.md** — *the "why"* High-level product vision, constraints, and user outcomes. Tells the agent *what* to build, not how. 📄 **specify.prompt.md** — *the "what"* API contracts, data flows, UI mockups (inline or linked). Agent reads this → generates deterministic implementations. 📄 **tasks.prompt.md** — *the "how"* Break the spec into atomic, testable tasks. Agent works through them one by one, reporting status. No more guessing. No more 10K-token context windows full of chit-chat. Just clean, modular, agent-friendly specs. Developers ship faster. Agents stay on track. **Vibe‑coding is over. Long live spec‑driven development.** 👇 What’s your go-to method for keeping AI agents focused? Drop your thoughts below. #GitHub #AIAgents #SpecDrivenDevelopment #VibeCoding #LLM #SoftwareEngineering #Productivity #MobileDev #AppDevelopment #iOS #Android

🚀 **Stop just sharing the GitHub release link. Start publishing your hot take the same day.** Here’s the workflow that changed my content game: 1. **Watch the repos of 5–10 OSS projects you deeply care about** (Think React, Vite, Next.js, Bun, Tailwind, Playwright, etc.) 2. **GitHub sends you a notification the instant they ship a release** (Even before the tweets go viral) 3. **Dedicate 60 minutes right then — before your day job starts** - Scan the changelog - Clone, upgrade, and test the key feature - Take a screenshot or record a 30-second demo 4. **Publish your opinionated take before lunch** Not “Announcing v5.0” But: *“Why the new `useOptimistic` hook in React 19 makes 90% of your form libraries obsolete — here’s how I’d rewrite your dashboard tomorrow.”* **Why this works:** - You ride the wave of genuine interest (no one cares about a week-old release) - You build authority as someone who experiments, not just links - The algorithm loves original, timely, opinionated content Every major open source launch looks like a one-liner from your feed. 👇 **Stack your odds:** - Set up the watch notification right now - Bookmark a “release reaction” template (changelog → test → opinion) - Be the person who explains *why it matters* the day it drops The best commentary is written when the code is still hot. 🔥 Who’s already doing this? Drop your favourite release‑first creator in the comments. #OpenSource #DeveloperProductivity #ContentStrategy #GitHub #IndieHackers #MobileDev #AppDevelopment #iOS #Android

Here's a LinkedIn post draft about Kotlin Arrow's `Either` and `Validated` for pragmatic functional error handling. --- **Tired of `try/catch` spaghetti and unchecked exceptions in Kotlin?** Switch to Arrow’s `Either` and `Validated` — and make your error handling as explicit as your happy path. 🔹 **`Either<L, R>`** – a sealed type that carries either an error (`Left`) or a result (`Right`). No more guessing which runtime exception might pop up. The API tells you: *“this call can fail in these ways.”* ```kotlin fun findUser(id: String): Either<UserError, User> fun saveOrder(order: Order): Either<OrderError, Unit> ``` 🔹 **`Validated<L, R>`** – when you need to **accumulate** multiple errors instead of short-circuiting on the first one. Perfect for form validation or batch processing. ```kotlin data class SignupForm( val name: NonEmptyList<String>, val email: NonEmptyList<String> ) fun validate(form: SignupForm): ValidatedNel<ValidationError, User> = validateName(form.name) .zip(validateEmail(form.email)) { name, email -> User(name, email) } ``` **Why I love this approach:** ✅ **Type safety** – errors are part of the return type, not hidden in a stack trace. ✅ **Composability** – chain, map, flatMap, zip – all with compiler support. ✅ **No more "null or throw"** – you handle both outcomes explicitly. Arrow isn't just for Haskell refugees. It gives you practical FP patterns that make Kotlin code more predictable and easier to reason about — especially in complex business logic. **Question for you:** have you tried Arrow’s `Either` in production? What’s your favourite use case? Let’s discuss in the comments 👇 #Kotlin #FunctionalProgramming #Arrow #SoftwareEngineering #TypeSafety #MobileDev #AppDevelopment #iOS #Android

**📢 Ktor Routing DSL – My Go-To for REST APIs in Kotlin** If you’re building REST APIs in Kotlin, Ktor’s routing DSL is a game-changer. Here’s why I keep coming back to it: ✅ **Declarative & type-safe** – routes read like a clean spec. ✅ **Content negotiation** built-in – serialize/deserialize JSON, XML, Protocol Buffers with minimal config. ✅ **Lightweight** – no heavy annotations, no magic. Just pure Kotlin DSL. Here’s a minimal example: ```kotlin fun Application.module() { install(ContentNegotiation) { json(Json { ignoreUnknownKeys = true prettyPrint = true }) } routing { get("/api/users/{id}") { val id = call.parameters["id"]?.toInt() ?: throw BadRequestException() val user = userRepository.findById(id) call.respond(user) } post("/api/users") { val user = call.receive<User>() userRepository.save(user) call.respond(HttpStatusCode.Created, user) } } } ``` **Why this matters:** - No XML spaghetti, no runtime reflection. - Content negotiation means you can serve JSON by default, but switch to XML or even Protobuf with one line. - The DSL makes it trivial to group routes, add interceptors, and apply authentication. If you haven’t tried Ktor for server-side yet, give it a spin – it’s Kotlin the way it should be. #Kotlin #Ktor #RESTAPI #BackendDevelopment #MobileDev #AppDevelopment #iOS #Android

**Claude Cowork is about to change the “airport laptop cracked open” developer stereotype.** Anthropic just shipped **scheduled background agents** that keep running even when my MacBook is closed. No more babysitting long-running scripts, no more keeping the lid open at the gate just to watch a pipeline finish. Here’s what this means practically: - **Schedule agent tasks** – Think “run data extraction every night at 2am” or “monitor my staging logs and alert me if error rate spikes.” - **Wake-free execution** – The agents run on Anthropic’s side, not on my hot laptop. My machine can sleep, fly, or run out of battery – the work still completes. - **Cowork model** – It feels like having a junior dev who never goes home. You hand off asynchronous work and review the results when you’re ready. For me, the immediate use case is moving my heavy RAG pipeline evaluation and nightly code review summarization off my local machine. No more fan noise, no more closed-lid anxiety. This also kills the “open laptop at the airport” badge of developer hustle. Instead, close the lid, board the plane, and land to a ready report. The future of developer tooling isn’t about making us work 24/7 – it’s about making our code work 24/7 while we actually rest. **Who else is already running scheduled agents in production? What tasks are you offloading first?** #ClaudeCowork #Anthropic #BackgroundAgents #DeveloperProductivity #AI #AsyncWork #MobileDev #AppDevelopment #iOS #Android

**The moment custom software stops being “too expensive to build” is the moment your team finally gets tools that actually fit.** For years, the trade-off was simple: 👉 Buy off-the-shelf and force your workflow into someone else’s box. 👉 Pay a fortune for custom and wait six months. But that math is changing — fast. Now, teams are building: ✅ **Estimating tools** that reflect *their* pricing logic, not a generic template. ✅ **Project portals** that update in real time, sync with field data, and don’t require drop-downs for things that don’t exist. ✅ **Field apps** that work offline, match the job site language, and connect straight to back-office systems. The cost hasn’t collapsed — but the *value gap* has. Modern frameworks, no-code backends, and composable architecture mean you can start small, iterate fast, and stop paying for features your team never uses. The real ROI isn’t just “we saved money.” It’s: - **Fewer workarounds** - **Less manual data entry** - **Higher adoption** (because the tool makes sense) If your team is still duct-taping spreadsheets and clunky PM software together… ask yourself: *Is it really still too expensive to build what fits?* 👇 Curious how others are making the leap — drop a comment or DM. #CustomSoftware #FieldOps #ConstructionTech #ProjectManagement #NoCode #DigitalTransformation #MobileDev #AppDevelopment #iOS #Android