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If you've been watching the open-source LLM space, you've probably noticed it's been a great couple of years. Llama, Mistral, Phi, Qwen — a whole zoo of models you can download and run on your own machine. Google's entry into that zoo is Gemma, and the fourth generation, Gemma 4 (released April 2, 2026), is the biggest leap yet: built from Gemini 3 research, multimodal (text + image + video + audio), 256K context, native function calling, configurable "thinking mode," and — finally — a clean Apache 2.0 license. In this post, we're going to: Understand what Gemma 4 actually is, with an architecture diagramGet it running on your laptop with Ollama in about 5 minutesChat with it from the terminalSend it an image and ask questions about itTurn on thinking mode for harder problemsCall it from a Python script like a real APIBuild a small project that glues it all together No GPU rental, no API keys, no telemetry. Let's go. Heads up: This guide assumes zero ML background. If you can install software and run a terminal command, you can do this. What Is Gemma 4? Gemma is Google DeepMind's family of open-weight language models. "Open-weight" means the actual neural network weights — the giant matrices of numbers that make the model work — are freely downloadable. You can run them, modify them, fine-tune them, and ship them in your product. Gemma 4 brings several big changes over Gemma 3: Apache 2.0 license. Earlier Gemma releases used a custom license with a Prohibited Use Policy that made some enterprise legal teams nervous. Gemma 4 is plain Apache 2.0 — unlimited commercial use, no MAU caps, no special permissions. This alone is a big deal for production deployments.Mixture-of-Experts. A new 26B MoE variant activates only ~4B parameters per token, giving you 13B-class quality at 4B-class cost.Thinking mode. A configurable reasoning mode where the model thinks step-by-step before answering. Toggle it on for hard problems, off for fast chat.Native function calling. Built-in support for structured tool use — write an agent without needing prompt engineering hacks.More modalities. Image, video frames, and (on the smaller E2B/E4B models) native audio input. Native system prompt support, too.Bigger context. 128K on the small models, 256K on the larger ones. Model Sizes at a Glance ModelDisk (Ollama)Active paramsTotal paramsMultimodalContextBest forE2B~7.2 GB~2B~2.3Btext + image + audio128KPhones, edge devices, browserE4B~9.6 GB~4B~4.5Btext + image + audio128KMost laptops — the sweet spot26B A4B (MoE)~18 GB~4B26Btext + image256KConsumer GPUs, agentic workloads31B Dense~20 GB31B31Btext + image256KWorkstations, highest-quality answers Two naming notes worth understanding: E2B / E4B. The "E" stands for Effective parameters. These are dense edge-first models that use a trick called Per-Layer Embeddings (PLE — more on this below) to do more with fewer active parameters.26B A4B. This is the Mixture-of-Experts model. 26B parameters total, but only ~4B "activate" per forward pass. Latency and cost behave like a 4B model; quality is closer to a 13B dense model. Caveat: you still need to load all 26B into memory. For most readers on a laptop, E4B is the right starting point. It runs comfortably on a 16 GB Mac or any modern dev machine. Gemma 4 vs. the Rest of the Open-Model Zoo (May 2026) ModelSizesMultimodalContextLicenseGemma 4E2B / E4B / 26B MoE / 31Btext + image + video + audio (small)128K / 256KApache 2.0Llama 4varioustext + image128K+Llama community licenseQwen 3.5varioustext + image128K+Apache 2.0DeepSeek V4 FlashMoEtext128KMIT Gemma 4's pitch: the only family that spans phones to servers under Apache 2.0, with multimodal and audio in the same release. The Architecture (in Plain English) You don't need this section to use Gemma 4 — feel free to skip to the install steps. But if you've ever wondered what's actually happening when a multimodal model "sees" and "hears," here it is. A few pieces worth understanding: Three input paths. Text goes through a SentencePiece tokenizer (shared with Gemini). Images go through a vision encoder that handles variable aspect ratios and resolutions natively (no more square-only inputs like Gemma 3). On the E2B and E4B models, audio goes through a USM-style conformer encoder borrowed from Gemma 3n. All three paths produce tokens that get interleaved in a single stream — so you can freely mix text, images, and audio in any order in one prompt.Alternating local/global attention. Most layers only look at a sliding window of recent tokens (cheap). A subset of layers attends to the full context (expensive but rare). This is the standard trick for keeping the KV cache from blowing up at 256K context.Per-Layer Embeddings (PLE) — the small-model secret. In a normal transformer, each token gets one embedding vector at input, and that's all the residual stream has to work with. PLE adds a parallel pathway: for each token, every layer gets its own small conditioning vector from a lookup table. The embedding tables are large (lots of memory), but the "active" parameters per token stay small — that's why a 4-billion-active-parameter E4B can punch above its weight.Mixture-of-Experts (26B A4B). The MoE layer has multiple "expert" feed-forward networks. A small router picks 2 of 8 (or similar) for each token. Total params = 26B (all loaded), active params per token = ~4B (only those fire). Pareto-optimal for quality-per-FLOP.Thinking mode. When you include the special <|think|> token at the start of the system prompt, the model emits internal reasoning between <|channel>thought\n...<channel|> markers before the final answer. Disable it for fast chat; enable it for math, code, and multi-step reasoning. That's most of what's worth knowing. Now let's actually run it. Step 1: Install Ollama There are a few ways to run Gemma 4 locally, but the easiest by a mile is Ollama. Think of it as "Docker for LLMs" — it handles downloading the model, managing memory, GPU acceleration, and exposing a local API. You don't have to think about CUDA versions or PyTorch. Install it: macOS / Windows: Download the installer at ollama.com/download and run it.Linux: Shell curl -fsSL https://ollama.com/install.sh | sh Verify: Shell ollama --version You should see a version number. Gemma 4 requires Ollama v0.20.0 or later — if you're on an older version, update first. Step 2: Pull a Gemma 4 Model Download the default (E4B, ~9.6 GB): Shell ollama pull gemma4 This downloads about 9.6 GB. Grab a coffee. Other sizes, if you want them: Shell ollama pull gemma4:e2b # ~7.2 GB — smallest, for low-RAM machines ollama pull gemma4:e4b # ~9.6 GB — the default; same as `gemma4` ollama pull gemma4:26b # ~18 GB — the MoE; 256K context ollama pull gemma4:31b # ~20 GB — biggest dense model Hardware reality check: On Apple Silicon, 16 GB unified memory handles E4B comfortably. NVIDIA users need the model to fit entirely in VRAM for GPU-accelerated inference. The 26B model fits on 24 GB but leaves very little headroom — treat it as the ceiling, not the target. List what you've got: Shell ollama list Step 3: Chat With It in the Terminal Easiest possible test: Shell ollama run gemma4 You'll get an interactive prompt: Plain Text >>> Explain what a hash map is, like I'm a junior dev. Hit enter and watch it stream a response. To exit, type /bye. That's it. You're running a state-of-the-art LLM locally with zero cloud dependency. Try: "Write a Python function that finds duplicates in a list, with three different approaches and their tradeoffs.""What's the difference between TCP and UDP? Use an analogy.""Translate 'Where is the nearest train station?' into Japanese, Spanish, and Hindi." Step 4: Send It an Image Gemma 4 can see. Drop any image file in your current directory, then: Shell ollama run gemma4 >>> Describe what's in this image: ./screenshot.png Ollama loads the image, sends it through the vision encoder, and the model answers. Unlike Gemma 3 (which resized everything to 896×896), Gemma 4 handles variable aspect ratios and resolutions natively — so tall screenshots, wide diagrams, and high-res photos all work without manual cropping. Try: "What error is shown in this screenshot?" (paste a stack trace)"What's the bounding box for the 'submit' button in this UI?" (Gemma 4 will answer in JSON — natively!)"Read the handwriting in this note and transcribe it." Step 5: Turn on Thinking Mode For harder problems — multi-step math, complex code, logic puzzles — turn on thinking mode. Include the <|think|> token at the very start of your system prompt: Shell ollama run gemma4 >>> /set system "<|think|>You are a careful, methodical assistant." >>> Three friends split a $73.42 dinner bill. Alice had a $12 appetizer, Bob had a $9 drink. The rest is shared. What does everyone pay? The model will emit its reasoning in a <|channel>thought\n...<channel|> block before the final answer. For fast chat, leave the token out, and the model answers directly. When to use it: Code generation, math, multi-hop reasoning, agentic planning — yes. Single-turn factual questions, summarization, translation — no, it just adds latency. Step 6: Call Gemma 4 From Python A chat prompt is nice, but you're a developer — you want to call this thing from code. When Ollama is running, it exposes a local REST API on http://localhost:11434. There's also an official Python client. Install it: Shell pip install ollama Basic Chat Shell import ollama response = ollama.chat( model="gemma4", messages=[ {"role": "system", "content": "You are a senior code reviewer. Be concise and direct."}, {"role": "user", "content": "Review this code:\n\ndef add(a, b):\n return a+b"}, ], ) print(response["message"]["content"]) Streaming Responses (ChatGPT-Style) Shell import ollama stream = ollama.chat( model="gemma4", messages=[{"role": "user", "content": "Write a haiku about debugging."}], stream=True, ) for chunk in stream: print(chunk["message"]["content"], end="", flush=True) Sending an Image Shell import ollama response = ollama.chat( model="gemma4", messages=[{ "role": "user", "content": "What's in this image?", "images": ["./my_photo.jpg"], }], ) print(response["message"]["content"]) Thinking Mode + Function Calling (the Agentic Combo) This is where Gemma 4 actually starts feeling like a "real" agent. You declare your tools as JSON schemas, the model decides when to call them, and you execute the call and pass results back. No prompt engineering hacks needed. Shell import ollama tools = [{ "type": "function", "function": { "name": "get_weather", "description": "Get the current weather for a city.", "parameters": { "type": "object", "properties": { "city": {"type": "string", "description": "City name, e.g. 'Tokyo'"}, }, "required": ["city"], }, }, }] def get_weather(city: str) -> str: # Pretend this hits a real API. return f"{city}: 22°C, partly cloudy" response = ollama.chat( model="gemma4", messages=[ {"role": "system", "content": "<|think|>You are a helpful weather assistant."}, {"role": "user", "content": "Should I bring an umbrella in Tokyo today?"}, ], tools=tools, ) # If the model wants to call a tool, execute it and feed the result back: for tool_call in response["message"].get("tool_calls", []): name = tool_call["function"]["name"] args = tool_call["function"]["arguments"] if name == "get_weather": result = get_weather(**args) # Send result back for the model to finalize its answer followup = ollama.chat( model="gemma4", messages=[ {"role": "user", "content": "Should I bring an umbrella in Tokyo today?"}, response["message"], {"role": "tool", "content": result, "name": name}, ], ) print(followup["message"]["content"]) Raw HTTP (No Python Client Needed) For any other language: Shell curl http://localhost:11434/api/chat -d '{ "model": "gemma4", "messages": [{"role": "user", "content": "Hello!"}], "stream": false }' Same JSON shape works from Node, Go, Rust, your shell — anything that can make an HTTP request. A Small Project: Folder-Watching Image Describer Here's a useful ~30-line script. It watches a folder, and any new image dropped in gets automatically described by Gemma 4. Great for accessibility tools, content moderation prototypes, or just learning. Python import os, time import ollama WATCH_DIR = "./inbox" os.makedirs(WATCH_DIR, exist_ok=True) SEEN = set(os.listdir(WATCH_DIR)) print(f"Watching {WATCH_DIR}/ — drop an image in to describe it.") print(" (Ctrl+C to stop)\n") IMAGE_EXTS = (".png", ".jpg", ".jpeg", ".webp", ".gif") try: while True: current = set(os.listdir(WATCH_DIR)) new_files = sorted(current - SEEN) for filename in new_files: if not filename.lower().endswith(IMAGE_EXTS): continue path = os.path.join(WATCH_DIR, filename) print(f" New image: {filename}") response = ollama.chat( model="gemma4", messages=[{ "role": "user", "content": ( "Describe this image in 2-3 sentences. " "Mention any visible text. Be specific." ), "images": [path], }], ) print(f" → {response['message']['content']}\n") SEEN = current time.sleep(2) except KeyboardInterrupt: print("\n Stopped.") Run it, drag images into the inbox/ folder, and watch descriptions appear. That's a real, useful, completely local AI tool — written in 30 lines. Things to Know Before Shipping Anything Serious A few honest caveats: CaveatWhy it mattersHallucinationLocal models still confidently make things up. Don't trust factual claims without verification. Thinking mode reduces this for reasoning tasks, but doesn't eliminate it.CPU latencyExpect 1–3 tokens/sec on a CPU-only laptop with E4B. A GPU gives 3–10× speedup.Context costs RAM256K context is real, but actually filling it eats memory. Most use cases need <16K tokens.MoE memoryThe 26B MoE runs fast (only 4B active per token), but you still need to load all 26B into RAM. Don't confuse active params with memory footprint.Audio is small-model onlyE2B/E4B have native audio input. The 26B and 31B models do not.Apache 2.0 ≠ no responsibilitiesThe license is permissive, but you're still on the hook for safety, bias, and compliance in whatever you ship. References and Further Reading Gemma 4 announcement — Google blog – The launch post (April 2, 2026).Gemma 4 model overview — Google AI for Developers – Official docs: sizes, capabilities, hardware requirements.Welcome Gemma 4 — Hugging Face blog – Best technical write-up: covers PLE, MoE, USM audio encoder, benchmarks, and code samples.Gemma 4 model card on Hugging Face – E4B instruct model weights and configuration.Gemma 4 Complete Guide 2026 — dev.to – Community guide with architecture details and competitor comparisons.SigLIP (Zhai et al., 2023) – The vision encoder family Gemma's image path builds on.Mixture-of-Experts (Shazeer et al., 2017) – The original sparsely-gated MoE paper. The 26B A4B is a direct descendant.Switch Transformer (Fedus et al., 2021) – Modern MoE techniques.Llama 4 – Meta's competing open-weight family.

By Jubin Abhishek Soni CORE

In this article, we will dive deep into actors, nonisolated methods, @MainActor and @GlobalActors, and the concept of actor reentrancy. We will also explore what happens behind the scenes in the Swift concurrency runtime, including jobs, executors, workers, and schedulers, so you can understand not just how to use these tools, but why they work the way they do. Whether you’re already using Swift’s async/await features or just starting to explore concurrency, this guide will give you a solid understanding of the mechanisms that keep your concurrent code safe and efficient. Actors and Isolation in Swift Concurrency If you’ve spent years working with Grand Central Dispatch (GCD), you already know the core problem: shared mutable state. When multiple threads can read and write the same data at the same time, you risk data races: inconsistent reads, lost updates, or crashes that only appear under heavy load. With GCD, we relied on discipline using serial queues or locks. But discipline fails. One forgotten .sync call and your correctness vanishes. Swift concurrency introduces Actors to make data-race freedom a language-level guarantee. Class vs. Struct vs. Actor Type Semantics Thread Safety Mutation Model Struct Value By-copy safe Explicit mutating Class Reference Unsafe by default Shared mutable state Actor Reference Data-race safe Serialized access Actors sit exactly where classes used to be, but with correctness guarantees. Actor Basics An actor is a reference type that protects its mutable state through isolation. Unlike a class, you cannot accidentally touch an actor’s internal state from multiple threads. Swift actor BankStore { private var balance: Int = 0 func deposit(_ amount: Int) { balance += amount } func withdraw(_ amount: Int) -> Bool { guard balance >= amount else { return false } balance -= amount return true } Key properties of actors: Reference semanticsOnly one task at a time can access actor-isolated stateExternal access requires await nonisolated: Opting Out of Isolation Sometimes you need functionality that doesn’t touch the actor’s state or needs to be callable synchronously. Use the nonisolated keyword for these “pure” utilities. Swift actor ImageCache { nonisolated static let maxItems = 100 nonisolated func cacheKey(for url: URL) -> String { url.absoluteString } } Rule of thumb: if it reads or writes actor state - it should not be nonisolated. The Actor Model: The Mailbox Mental Model Think of an actor as having a mailbox: Each actor has a queue of pending work.Messages (calls) are enqueued as tasks.The actor processes these one at a time. When you write await store.deposit(50), you aren’t calling a function in the traditional sense. You are sending a message to the actor and suspending your current thread until the actor finishes processing that message. This is why await is mandatory: the actor might be busy with someone else’s request. Working With @MainActor and Other @GlobalActors When building scalable iOS applications, managing shared state across isolated domains like UI components, network layers, and local caches becomes a complex puzzle. Swift simplifies this with @GlobalActor. A global actor is essentially a singleton actor. It allows you to isolate state and operations globally without needing to pass an actor reference around your entire dependency graph. The most famous of these is, of course, the @MainActor. The @MainActor is uniquely tied to the main thread. Anything marked with this attribute is guaranteed to execute on the main thread, making it the bedrock for all UI updates. Swift @MainActor final class FlashcardViewModel: ObservableObject { @Published var currentCard: Card? func loadNextCard() async { // Safe to update UI state directly; we are isolated to the MainActor. self.currentCard = await fetchCard() } } However, the power of global actors isn’t limited to the main thread. You can define your own global actors to serialize access to highly contested shared resources, such as a centralized local database or an aggressive retry policy manager. Swift @globalActor public actor SyncActor { public static let shared = SyncActor() } @SyncActor final class OfflineSyncManager { var pendingMutations: [Mutation] = [] func queue(mutation: Mutation) { pendingMutations.append(mutation) } } By annotating OfflineSyncManager with @SyncActor, you guarantee that all accesses to pendingMutations are serialized on that specific actor’s executor, completely eliminating data races from different parts of your app trying to queue offline changes simultaneously. Actor Reentrancy Explained If you’re coming from the world of Grand Central Dispatch (GCD) and DispatchQueue, actors require a fundamental mental shift. A serial dispatch queue executes tasks strictly one after another. If a task is running, nothing else can run on that queue until it finishes. Swift actors are different: they are reentrant. Reentrancy means that while an actor guarantees mutual exclusion for synchronous code execution (only one thread can be inside the actor at a time), it explicitly allows other tasks to interleave at suspension points. When an actor encounters an await, it suspends the current task. Crucially, it also gives up its lock on the executor. During this suspension, the actor is completely free to pick up and execute other pending tasks. Once the awaited operation finishes, the original task is scheduled to resume on the actor when it’s free again. This design prevents deadlocks. If actors weren’t reentrant, two actors awaiting each other would instantly freeze your application. However, reentrancy introduces its own subtle class of concurrency bugs. The Hidden Risks of Suspending Inside Actor Methods Because the actor unblocks during an await, the state of your actor before the await might not match the state after the await. This is the single biggest trap engineers fall into when adopting Swift concurrency. Imagine implementing a session manager that fetches a fresh authentication token. If multiple requests fail and trigger a token refresh simultaneously, you might accidentally fire off multiple network requests if you don’t account for reentrancy. Swift actor SessionManager { private var cachedToken: String? func getValidToken() async throws -> String { // 1. Check local state if let token = cachedToken { return token } // 2. Suspend! The actor is now free to process other calls to `getValidToken()` let freshToken = try await performNetworkRefresh() // 3. State mutation. // DANGER: If another task interleaved during step 2, we might overwrite a valid token, // or we just unnecessarily performed multiple network requests. self.cachedToken = freshToken return freshToken } } To protect against this, you must rethink how you handle in-flight asynchronous operations. Instead of caching just the result, you often need to cache the Task itself. Swift actor SessionManager { private var cachedToken: String? private var refreshTask: Task<String, Error>? func getValidToken() async throws -> String { if let token = cachedToken { return token } // Return the in-flight task if one exists if let existingTask = refreshTask { return try await existingTask.value } // Otherwise, create a new task and cache IT immediately let task = Task { let freshToken = try await performNetworkRefresh() self.cachedToken = freshToken self.refreshTask = nil // Clean up return freshToken } self.refreshTask = task return try await task.value } } Always remember: across an await, your actor’s state is completely unguarded. Inside the Swift Concurrency Runtime To truly master structured concurrency, we need to step out of the syntax and into the engine room. Swift’s concurrency model isn’t just syntactic sugar over GCD; it is a completely bespoke, highly optimized runtime built around a cooperative thread pool. Understanding Jobs In the Swift runtime, a Job is the fundamental unit of schedulable work. When you write an async function, the compiler breaks your function down into partial tasks or “continuations” split at every await keyword. Each of these segments is wrapped into a Job. When a task suspends, the current Job finishes. When the awaited result is ready, a new Job is enqueued to resume the remainder of the function. Jobs are lightweight, heavily optimized, and managed entirely by the Swift runtime. How Executors Work If Jobs are the work, Executors are the environments where the work is allowed to happen. An executor defines the execution semantics for a set of Jobs. Every actor has a serial executor. This executor acts as a funnel, ensuring that only one Job associated with that actor runs at any given microsecond. When you call an actor method, you are submitting a Job to that actor’s executor. Custom Serial Executors (Actor Level) In the first example, we create a MainQueueExecutor conforming to SerialExecutor. This is particularly useful when you have a legacy codebase heavily dependent on a specific DispatchQueue and you want to wrap that logic into a modern Actor. Swift final class MainQueueExecutor: SerialExecutor { func enqueue(_ job: consuming ExecutorJob) { let unownedJob = UnownedJob(job) let unownedExecutor = asUnownedSerialExecutor() DispatchQueue.main.async { unownedJob.runSynchronously(on: unownedExecutor) } } func asUnownedSerialExecutor() -> UnownedSerialExecutor { UnownedSerialExecutor(ordinary: self) } } @globalActor actor CustomGlobalActor: GlobalActor { static let sharedUnownedExecutor = MainQueueExecutor() static let shared = CustomGlobalActor() nonisolated var unownedExecutor: UnownedSerialExecutor { Self.sharedUnownedExecutor.asUnownedSerialExecutor() } } Task Executors (Task Level) While a SerialExecutor protects an actor’s state, a TaskExecutor influences the “ambient” environment where a task and its children run. It doesn’t provide serial isolation; it provides a preferred execution location. Swift final class MainQueueExecutor: TaskExecutor { func enqueue(_ job: consuming ExecutorJob) { let unownedJob = UnownedJob(job) self.enqueue(unownedJob) } func enqueue(_ job: UnownedJob) { let unownedExecutor = asUnownedTaskExecutor() DispatchQueue.main.async { job.runSynchronously(on: unownedExecutor) } } func asUnownedTaskExecutor() -> UnownedTaskExecutor { UnownedTaskExecutor(ordinary: self) } } let executor = MainQueueExecutor() Task.detached(executorPreference: executor) { // TODO: Perform an async operation } What Workers Do Executors don’t magically run code; they need CPU threads. This is where Workers come in. In Swift concurrency, there is a global, cooperative thread pool. The threads inside this pool are the “workers.” Unlike GCD, which can spawn hundreds of threads, leading to thread explosion and massive memory overhead, the Swift thread pool is strictly limited, generally to the number of active CPU cores. However, this isn’t a hard-and-fast rule; there are specific cases where the pool may spawn more threads. We took a deep dive into this behavior in the article Swift Concurrency: Part 1. Workers ask executors for Jobs. When a worker thread picks up a Job from an executor, it executes it until completion or suspension. Because the number of workers is limited, Swift enforces a strict rule: you must never use blocking APIs (like semaphores or synchronous network calls) inside an async context. If you block a worker thread, you are permanently stealing a core from the concurrency runtime. The Role of Schedulers The Scheduler is the invisible conductor orchestrating this entire process. It decides which Jobs sit on which Executors, and which Workers get assigned to process them. The scheduler is highly priority-aware. When you spawn a Task(priority: .userInitiated), the scheduler ensures the resulting job jumps ahead of background jobs in the queue. It handles the complex logic of priority inversion avoidance, waking up worker threads, and balancing the load across the CPU. Types of Executors and How They’re Chosen Swift utilizes different types of executors depending on the context of your code: The global concurrent executor: If your code is not isolated to any actor (e.g., a detached task or a standalone async function), it runs on the default global concurrent executor. This executor distributes Jobs freely across all available workers in the cooperative thread pool.The main actor executor: This is a specialized serial executor permanently bound to the application’s main thread. The scheduler ensures that any Job submitted here is handed off to the main runloop.Default serial executors: Every standard actor you create gets its own default serial executor. The runtime dynamically maps this executor to any available worker thread in the pool as needed.Custom executors (Swift 5.9+): Advanced use cases might require overriding how an actor executes its jobs. By implementing the SerialExecutor protocol, you can create custom executors, for instance, to force an actor to run its jobs on a specific, legacy DispatchQueue to interoperate with older C++ or Objective-C codebases seamlessly. How the Runtime Chooses an Executor Understanding that executors exist is one thing; predicting exactly where your code will run is another. When a Job is ready to execute, the Swift runtime evaluates a precise decision tree to route that workload. Here is the exact algorithm the runtime uses to select an executor: Is the method isolated? (i.e., is it bound to a specific actor?) No (Non-isolated): Is there a preferred Task executor? Yes: The task executes on the Preferred Task Executor.No: The task executes on the standard Global Concurrent Executor.Yes (Actor-isolated): Does the actor provide its own custom executor? Yes: The task executes strictly on the Actor’s Custom Executor.No: Does the current Task have a preferred executor? Yes: The task executes on the Preferred Task Executor (while still strictly upholding the actor’s serial isolation).No: The task executes on the Default Actor Executor. This cascading logic ensures that actors maintain their state safety while allowing developers to influence the underlying execution environment when necessary. Inspecting Your Context: The #isolation Macro When dealing with deep call stacks and complex async boundaries, you might lose track of your current execution context. Swift 5.10 introduced a brilliant diagnostic tool to solve this: the #isolation macro. This macro evaluates at compile time to capture the actor isolation of the current context. It returns an any Actor? representing the actor you are currently isolated to, or nil if you are executing concurrently. Swift func debugCurrentContext() { // Prints the instance of the actor (like MainActor), or "no isolation" print(#isolation ?? "no isolation") } Sprinkling this into your logging infrastructure is invaluable when debugging data races or verifying that a heavy computation isn’t accidentally blocking the @MainActor. Task Executors vs. Actor Executors With recent advancements in Swift Evolution (specifically SE-0417 and SE-0392), developers now have the unprecedented ability to provide custom executors. However, to wield this power safely, you must deeply understand the difference between the two primary executor protocols: TaskExecutor and ActorExecutor (via SerialExecutor). What is a Task Executor? A Task Executor governs the execution environment for a specific Task hierarchy. Crucially, a Task Executor is inherently concurrent. It represents a thread pool or a concurrent queue where multiple jobs can be processed simultaneously. When you assign a preferred Task Executor, you are telling the runtime, “Unless an actor says otherwise, run the asynchronous work for this task pool over here.” What is an Actor Executor? An Actor Executor (which conforms to the SerialExecutor protocol) governs the execution environment for a specific actor instance. Unlike a Task Executor, an Actor Executor is strictly serial. It processes one job at a time, enforcing the mutual exclusion that makes actors safe from data races. The Danger of Custom Implementations Understanding the concurrent nature of Task Executors and the serial nature of Actor Executors is not just trivia, it is a strict runtime invariant. If you decide to write a custom executor (for example, wrapping an old C++ thread pool or a specific Grand Central Dispatch queue), you carry the burden of upholding these invariants: If you implement a SerialExecutor for an actor, but your underlying implementation accidentally allows concurrent execution, you will break the actor’s state isolation and introduce impossible-to-reproduce data races.Conversely, if you implement a TaskExecutor but back it with a serial queue, you risk starving the cooperative thread pool and introducing unexpected deadlocks across your async task hierarchies. The compiler trusts you to maintain these semantic guarantees. If you break them, the concurrency model shatters. Conclusion Swift concurrency is more than syntactic sugar for asynchronous code. It is a carefully designed execution model that formalizes how work is scheduled, isolated, and resumed. Actors provide safety guarantees, but understanding reentrancy and executor behavior is what allows engineers to reason about concurrency with confidence. By understanding these low-level mechanics when an actor temporarily releases isolation and how the runtime schedules jobs across worker threads, you can build iOS applications that are not only performant but also resilient to the subtle concurrency bugs that once plagued asynchronous systems.

Testing is an essential step in the API development process to ensure that APIs are working correctly. There are multiple HTTP methods in RESTful APIs, including POST, GET, PUT, PATCH, and DELETE. In our earlier articles, we learned how to perform automated testing of POST, PUT, and GET APIs using Rest-Assured Java. In this tutorial article, we will discuss and cover the following points: What is a PATCH API request?How to test PATCH API requests using REST-Assured Java? What Is a PATCH API Request? A PATCH request is used to update a resource partially. While it is similar to a PUT request, the key difference is that PUT requires the entire request body to be sent, whereas PATCH allows you to send only the specific fields that need to be updated. Let’s take an example of the following PATCH API that we’ll be using in this tutorial for demonstration purposes: PATCH (/partialUpdateOrder/{id}) This API endpoint partially updates the existing order in the system as per the provided Order ID. To update an existing order, this API requires the order ID as a path parameter so it knows which record to modify. The updated details should be provided in JSON format in the request body. Since this is a PATCH request, there’s no need to send the entire payload. Only the required field that needs to be updated should be included in the request body. Difference Between PATCH and PUT APIs The following table shows the difference between the PATCH and PUT APIs: criteriapatchputPurposePartially updates a resourcePartially updates a resourceRequest BodyOnly includes fields that need to be updatedRequires the full resource representationData SentOnly changed fieldsEntire data payloadIdempotencyNot always idempotentAlways idempotentUse CaseUpdating specific fieldsReplacing an entire recordRisk of Data LossLow, as the unchanged fields remain intactHigh, if some fields are omitted, they may be overwritten or removed How to Test PATCH APIs Using REST-Assured Java Let’s use the PATCH API /partialUpdateOrder/{id} and update an existing order partially in the system. Test scenario: Markdown ## Test Scenario Title: Partially update an existing orders in the system. ## Pre-condition: Valid orders are available in the system ## Test 1. Update the product_name and product_id for the order ID - 2 2. Verify that the Status Code 200 is returned in the response. 3. Assert that the order details have been updated correctly. Test Implementation The PATCH API is protected with authentication, so we would need the authentication token to access it successfully. To implement this test scenario, we’ll have to: Write a test to hit the Authorization API, generate and extract the token.Use the token generated in the first step and hit the PATCH API to update the order partially. Step 1: Write a test to hit the Authorization API, generate and extract the token. The POST /auth API endpoint should be hit with the following valid credentials to generate the token. JSON { "username": "admin", "password":"secretPass123" } It returns the following response: JSON { "message": "Authentication Successful!", "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6ImFkbWluIiwiaWF0IjoxNzc1NjMzMDU5LCJleHAiOjE3NzU2MzY2NTl9.jHQwCyts9IejhwKGAZEm4Uyo9dgu5Kpe4OjTiYw1dm8" } The following test method is created to execute the POST /auth API request and extract the token from the response. Java @Test public void testTokenGeneration () { String requestBody = """ { "username": "admin", "password": "secretPass123" }"""; token = given ().contentType (ContentType.JSON) .when () .body (requestBody) .post ("http://localhost:3004/auth") .then () .statusCode (201) .and () .body ("token", notNullValue ()) .extract () .p The testTokenGeneration() test sends a POST request with login credentials to generate an authentication token using REST Assured. It verifies that the response returns a 201 status code and checks that a token is included in the response. Once the token is received, it’s extracted and stored in a global variable called token, so it can be reused across other test cases. Step 2: Partially updating the record with a PATCH API request. In this step, let’s add a new test method, testPartialUpdateOrder(), that sends a partial update request using the PATCH endpoint. The request body needs to be constructed with the required fields,i.e., product_id and product_name. We’ll use the Google Gson and Datafaker library to generate the request body. Java public class TestPatchRequestExamples { private String token; @Test public void testPartialUpdateOrder () { Faker faker = new Faker (); String productId = String.valueOf (faker.number () .numberBetween (1, 2000)); String productName = faker.commerce () .productName (); JsonObject orderDetail = new JsonObject (); orderDetail.addProperty ("product_id", productId); orderDetail.addProperty ("product_name", productName); //.. } This piece of code uses the Faker class from the Datafaker library and generates a random value for the product_id and product_name fields. The JSON object required for the request body is generated using the JsonObject class of the Google Gson library. The following request body is generated using this code: JSON {"product_id":"702","product_name":"Sleek Silk Plate"} Next, let’s write the automated test to update the record using the PATCH API endpoint. Java @Test public void testPartialUpdateOrder () { int orderId = 2; //.. given ().contentType (ContentType.JSON) .header ("Authorization", token) .when () .log () .all () .body (orderDetail.toString ()) .patch ("http://localhost:3004/partialUpdateOrder/" + orderId) .then () .log () .all () .statusCode (200) .and () .assertThat () .body ("message", equalTo ("Order updated successfully!"), "order.product_id", equalTo (productId), This test sends a PATCH API request for the order ID 2. The request body we created earlier is included in the request, containing only the fields that need to be updated. given().contentType(ContentType.JSON): It specifies that the request body will be in JSON format..header(“Authorization”, token): It adds the authentication token to the request header, which is required to authorize the API call..when().log().all(): This statement starts the request execution and logs all request details(headers, body, etc.)..body(orderDetail.toString()): It sets the request payload. The orderDetails (created earlier) JSON contains only the fields that need to be updated..patch(“http://localhost:3004/partialUpdateOrder/”+ orderId): It sends the PATCH request to update the order partially with the specified order ID..then().log().all(): It logs the full response for better visibility of the test execution..statusCode(200): It verifies that the API request was sent and the API responded with a 200 OK status..and().assertThat().body(…): It performs multiple assertions on the response body as shown below: The value of the “message” field should be “Order updated successfully!”The value of the “product_id” and “product_name” fields in the order object should be the same as supplied in the request. Using a dynamic approach to generate the request body with DataFaker helps eliminate repetitive code and promotes better reusability across test cases. Check out this tutorial for more information related to response verification Test Execution As we discussed in the earlier tutorial on testing PUT API requests with REST Assured, we need to follow the same approach to generate the token first, then use it to hit the PATCH API request. Let’s create the following testng.xml file for executing the tests sequentially: XML <?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE suite SYSTEM "http://testng.org/testng-1.0.dtd"> <suite name="Restful ECommerce Test Suite"> <test name="Restful ECommerce End to End tests"> <classes> <class name="restfulecommerce.tutorial.TestPatchRequestExamples"> <methods> <include name="testTokenGeneration"/> <include name="testPartialUpdateOrder"/> </methods> </class> </classes> </test> </suite> The following screenshot of test execution shows that the tests were executed successfully and the order was partially updated. The following log was printed in the console after test execution, showing the request and the response details: Plain Text Request method: PATCH Request URI: http://localhost:3004/partialUpdateOrder/2 Proxy: <none> Request params: <none> Query params: <none> Form params: <none> Path params: <none> Headers: Authorization=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VybmFtZSI6ImFkbWluIiwiaWF0IjoxNzc1NjMyMDEzLCJleHAiOjE3NzU2MzU2MTN9.A10-amp24LKDrDKrRJ6BW1KKtkVLQ-QK71U_Jl1ctDs Accept=*/* Content-Type=application/json Cookies: <none> Multiparts: <none> Body: { "product_id": "702", "product_name": "Sleek Silk Plate" } HTTP/1.1 200 OK X-Powered-By: Express Content-Type: application/json; charset=utf-8 Content-Length: 188 ETag: W/"bc-NGDglqodj+ZJKoZsbosa9746aT0" Date: Wed, 08 Apr 2026 07:06:54 GMT Connection: keep-alive Keep-Alive: timeout=5 { "message": "Order updated successfully!", "order": { "id": 2, "user_id": "1", "product_id": "702", "product_name": "Sleek Silk Plate", "product_amount": 750, "qty": 1, "tax_amt": 7.99, "total_amt": 757.99 } } It can be seen that the two fields product_id and product_name have randomly generated values and are sent in the request along with the order ID 2. In the response, a 200 OK status code is returned along with the full response body showing the same product_id and product_name. These details, as well as the assertions used in the tests, confirm that the order was successfully updated. Summary Effectively testing PATCH APIs in automation involves validating partial updates by sending only the required fields and verifying that unchanged data remains intact. Using a dynamic approach such as DataFaker, Google Gson, and constructing a request body with POJOs, Builder Pattern, JSON files, or Java Map helps generate fresh test data, reducing duplication issues and easing maintenance. Following best practices such as proper authentication handling, validating response body and status code, and keeping tests reusable and maintainable ensures robust and scalable API test automation. Happy testing!

By Faisal Khatri CORE

TL;DR: Understand the Claude Desktop Architecture and Save Time You configured Claude in Claude Desktop, wrote instructions, uploaded reference files, and set your preferences. Then you clicked the Cowork tab. Unfortunately, Claude had no memory of what you just did. Your instructions were gone, as were your files and preferences. You assumed this was a bug, but it is a feature: You switched applications. The Claude Desktop App Hosts 3 Separate Applications The tabs at the top of Claude Desktop (Chat, Cowork, Code) appear to be views of the same product. They are not. For example, Anthropic’s own documentation describes Cowork as using “the same agentic architecture that powers Claude Code”. However, in practice, each tab runs on a different execution layer with its own sandbox, memory system, and instruction hierarchy. The architectural split matters: Cowork and Code share an engine. Chat is a separate system entirely. A useful functional shorthand is as follows: Chat is for thinking: It runs in the cloud on Anthropic’s servers. It cannot access files on your machine; you have to provide those. In Chat, you converse, you reason, you get answers.Cowork is for doing: It runs inside a sandboxed Linux virtual machine, or VM in short, on your local computer. It reads and writes files in folders you mount, works autonomously in the background, and wipes the VM after every session. (Which is also, as you may imagine, the reason that Cowork does not remember previous sessions: The previously used VM is gone.)Code is for building: It runs natively in your terminal with full system access and no sandbox. It is made for engineers. So, there is an architectural reason why the instructions you just spent 20 minutes writing do not follow you when you move between tabs. Let’s see what crosses the tab boundary and what does not: The Word “Project” Means 3 Different Things This is the collision that wastes the most time. Which of these three did you configure last week? The Cowork Projects documentation confirms that Cowork projects live locally on your desktop, separate from Chat Projects. Your Chat Project knowledge base is invisible to Cowork and Code. When Cowork says “choose a project,” it offers three options: start from scratch (a new folder), import from a Chat Project (a one-way snapshot, not a live link, not future synchronization between the two either), or use an existing folder on your hard drive. The word “Project” appears three times on that screen, referring to different things. Memory, Artifacts, and Instructions Collide, too Given the current architectural state of three different Claude apps, posing as one, this pattern repeats across every shared term. Memory: Chat auto-summarizes your conversations in the cloud. Cowork has project-scoped memory only (Note: that refers to “projects” listed in the sidebar.) Standalone Cowork sessions without a project remember nothing, because the VM that ran the session is wiped when it ends. Code uses CLAUDE.md files, plus an auto-memory system.Artifacts: In Chat, an artifact is a rendered preview in a side panel (HTML, React, SVG). In Cowork, the same word means a real file on your disk (.docx, .xlsx, .pdf) or a Live Artifact (a persistent interactive dashboard that survives session restarts).Instructions: Chat has two instruction locations (Profile Preferences and Project Instructions) plus a Styles selector for writing tone. Cowork has three different locations (Global, Folder, Project). Code has a five-tier hierarchy: managed policies, CLI flags, .claude/settings.local.json, .claude/settings.json, and ~/.claude/settings.json, plus CLAUDE.md files at user, project, and local levels. None of the instructions syncs across tabs. Count the instruction locations you have configured. Now count the ones you assumed were active in a different tab. That is the gap. Watch Out When Working With Claude Desktop: Back Up Your Folder Before Your First Cowork Session Cowork’s sandbox prevents access to files outside your mounted folder. Inside that folder, Cowork has full read and write access. It does not archive. It does not move files to a trash folder. When it deletes, the files are gone. On the day Cowork launched in January 2026, a user recorded their first session on video. They asked Cowork to “clean up” a folder. Cowork ran an rm -rf command inside the autonomous Linux VM and permanently deleted 11 GB of files. The video went viral on Hacker News. Anthropic has since added a deletion confirmation prompt that requires explicit permission before Cowork permanently deletes any files. The underlying access model has not changed: inside your mounted folder, Cowork can do anything. As of May 2026, these actions leave no audit trail. Anthropic states this directly: “Do not use Cowork for regulated workloads.” If you work in a regulated industry, that sentence applies to you. If it is gone, it is gone. Back up every folder you mount to Cowork, that’s non-negotiable. Obviously, Anthropic Knows About the Tab Isolation Dispatch, available as a research preview for Pro and Max plans, lets you send tasks from your phone to a Cowork session running on your desktop. It is a mobile-to-desktop bridge. The isolation between Chat, Cowork, and Code remains. Dispatch signals where the product is heading. 2 Documents So You Do Not Have to Discover This The Hard Way I put together three companion documents for the introductory module of my upcoming Claude Cowork Online Course. They cover the architecture, the terminology collisions, and the practical setup steps. I am sharing two of them here because the confusion they address is real and widespread, and nobody should have to discover these things by losing work: The Quick Reference Card maps Chat, Cowork, and Code across nine dimensions: environment, file access, sandbox, execution model, project type, memory, output type, extensions, and instruction locations. Pin it to your wall or keep it open during your first week with Cowork: Working with Cowork: Quick Reference Card.The Terminology Collision Glossary maps eight terms (Project, Memory, Artifacts, Instructions, Workspace, Session, Tool, Agent) across four surfaces (Chat, Cowork, Code, API). The “Project” row alone will save you thirty minutes of confusion: Working with Cowork: Terminology Glossary. Conclusion: Before You Start With Claude Desktop and Cowork, Take 4 Steps in 5 Minutes If you are about to use Cowork for the first time, do these four things: Create a dedicated folder for Cowork. Not your Documents folder. Not your Desktop. A purpose-built folder with a clear name within your existing local file system.Set up backups for that folder before you mount it. Time Machine on macOS. File History on Windows. Git if you prefer. Do this before you give Cowork access.Open Cowork, create a project by choosing Project from the sidebar and clicking “New Project”, and point it at that folder. Write one sentence of instructions describing what you use this workspace for. (You can iterate on the instructions later.)Switch between all three tabs. Verify for yourself that your Project, your instructions, and your memory do not follow you. Invest five minutes of your time, and these four steps prevent the mistakes that cost people hours. Once you stop fighting Claude Desktop’s architecture and start working with it, Cowork becomes a different tool entirely. That is what the rest of the course is about.

By Stefan Wolpers CORE

A Practical Guide In the first part, I covered the two initial signals to diagnose that something is wrong: LatencyTraffic Those two alone explain a surprising number of production incidents. But they don’t explain everything. Rising latency tells you a problem is developing. Traffic tells you what the system is dealing with. I mentioned two more signals: ErrorsSaturation These two tell you something more important - whether the system is approaching failure. And this is where monitoring becomes truly operational. I will cover those two signals in this blog. Let us start with Errors. Errors - The Most Misunderstood Signal Many teams think error monitoring is simple. It is about counting failures. Raise an alert when they increase. In practice, error metrics are rarely that straightforward. The first mistake teams make is treating all errors as equal. They are not. Some errors are expected and some errors are harmless. Others indicate an outage in progress. Monitoring must differ between them. Otherwise alerts become noise. And noisy alerts get ignored, which defeats the entire purpose. I have seen production systems where engineers simply muted error alerts because they fired every few hours. Error Rate Is More Important Than Error Count Raw error counts are misleading. What do you think - ten errors per minute might be catastrophic or irrelevant? It depends on traffic. If you process: 100 requests per minute → 10 errors = disaster100,000 requests per minute → 10 errors = background noise Error rate is what matters. A simple production alert looks like this: It means alert when:Error rate > 2% This works far better than static thresholds because it scales automatically with traffic. 4xx vs 5xx - Critical Distinction One of the most common monitoring mistakes is combining 4xx and 5xx errors. They represent completely different problems. Let me talk through them. 5xx errors These indicate system failures: ExceptionsTimeoutsDependency failuresResource exhaustion 5xx errors should almost always trigger alerts. They mean the system is failing users. 4xx errors These usually indicate client behaviour: Invalid inputAuthentication failuresMissing resources Most of the time, 4xx errors should not page engineers. But they should still be monitored. Their spikes often reveal integration problems. Partner systems misbehavingClients sending unexpected requestsSometimes bots discovering your APIs I once saw a system where 40% of traffic suddenly became 401 responses. Nothing was broken in my service. A client service had deployed a change with an incorrect token configuration. The service was healthy. The integration was not. Without separate 4xx monitoring we would never have noticed. Error Budget Thinking Once services mature, error monitoring becomes less about incidents and more about error budgets. Instead of asking “Did we have errors?” You ask “Did we exceed acceptable failure levels?” Example SLO: 99.9% success rate That allows: 0.1% failure Error budgets prevent overreaction to minor fluctuations. Without them, teams end up firefighting dashboards instead of protecting user experience. In most post-mortems, latency and errors are symptoms. Saturation is usually the cause. Let us move to the next indicator – saturation. Saturation — Where Failures Actually Begin If latency is the early warning signal, saturation is the root cause signal. Most production outages start with a resource limit somewhere. I am not necessarily talking about CPU or memory. I am talking about less obvious resources like thread pools, connection pools, queue consumers, file descriptors, and rate limits. These limits quietly fill up until requests start waiting and then timing out. Then they start failing. By the time error rates increase, saturation has usually been happening for a while. CPU and Memory - Necessary but Not Enough Infrastructure metrics still matter. They just don’t tell the whole story. Monitor: CPU utilizationMemory usageDisk I/ONetwork throughput Example: rate(container_cpu_usage_seconds_total[1m]) and: container_memory_usage_bytes The Metrics That Break Systems Most Often As I mentioned in my previous blog, you need effective metrics. In this section I will list a few metrics that can prove useful. Connection Pool Usage Monitor connection pool usage. When a connection pool fills up - requests queue internally, latency increases, timeouts appear, and errors follow. In this scenario CPU can still be 30%. Memory can still be healthy. The service still looks “green.” Except users are waiting seconds for responses. Example — Monitoring a connection pool Micrometer automatically exposes Hikari metrics: hikaricp_connections_activehikaricp_connections_idlehikaricp_connections_pending The critical one is:hikaricp_connections_pending If pending connections increase steadily, saturation is approaching and action is needed. Kubernetes Saturation Signals Container platforms introduce new saturation points. An important metric to monitor is:kube_pod_container_status_restarts_total Restarts indicate instability. And:container_cpu_cfs_throttled_seconds_total CPU throttling causes latency spikes even when CPU usage looks normal. That one surprises a lot of teams. Dependency Metrics — The Missing Visibility Layer Most services are only as reliable as their dependencies – databases, caches, APIs, queues, and third-party integrations. When dependencies slow down, your service slows down. But if you only monitor your service, you won’t see the cause. You only see the symptoms. Dependency metrics close that gap. Without them, incident investigations turn into guesswork. Downstream Latency Metrics Every external call should have a latency metric. Even if the dependency is “reliable.” Especially then. Simple example: Java Timer.Sample sample = Timer.start(registry); Response response = paymentClient.process(request); sample.stop( registry.timer("payment.api.latency") ); During incidents, this metric often points directly at the problem. Dependency Error Metrics Track dependency failures separately. Example:payment_api_errors_total This helps answer:Are we failing… or is the dependency failing? That distinction saves time during incidents. Database Metrics — Where Many Incidents Begin Databases rarely fail suddenly. They slowly degrade. I have seen these follow a pattern. First queries take slightly longer. Then pools begin filling. Then request latency increases. Then timeouts appear. The progression is almost always the same. Which means the signals are predictable. Query Latency Slow queries often trigger cascading failures. Track:db_query_duration_seconds Watch percentiles and not averages. The same rule applies as service latency. Connection Pool Usage Database pools deserve dedicated dashboards. Track:db_connections_activedb_connections_idle Pool exhaustion is a classic outage pattern. Lock Contention Lock waits produce unpredictable latency spikes, especially under load. Important metrics include: Lock wait timeDeadlocksBlocked queries These metrics explain incidents that otherwise look random. Queue Metrics — The Early Warning Event-driven systems fail differently and have a different pattern. Instead of request latency increasing, queues begin filling. Messages accumulate silently. Until delays become visible. Queue metrics often detect issues earlier than service metrics. Queue Depth Example metric:messages_available If depth increases steadily, it means something is wrong. Either: Producers too fastConsumers too slowDependencies degraded Queue depth is one of the most reliable early warning signals in distributed systems. Consumer Lag For streaming systems, lag is critical. Example:kafka_consumer_lag Lag increasing means consumers cannot keep up. Eventually processing delays impact users. Pattern Worth Recognizing After enough incidents you start recognizing patterns. One of the most common looks like this: Dependency latency increasesConnection pools fillRequest latency increasesQueues growErrors appear When you see that progression on dashboards, you already know the story before investigation begins. Good monitoring turns incidents into recognizable shapes. And recognizable shapes reduce stress during outages. Experienced engineers eventually learn that most outages are not mysterious. They follow patterns. Because uncertainty is what makes incidents difficult. Not complexity. I hope you find these useful, I will continue the discussion in the final blog of this series.

By Gaurav Gaur CORE

The agentic IDE space has gotten crowded fast. Cursor, Claude Code, Copilot, Windsurf — they all share the same core model: you type a prompt, the AI writes some code, you iterate. It works well for prototyping. It breaks down when you're building production systems on a large codebase with a team of more than one. AWS Kiro takes a different bet. Instead of chat-first, it's spec-first. The unit of work isn't a prompt — it's a structured specification that the agent uses to plan, implement, verify, and document your feature end to end. That's a meaningful philosophical difference, and in practice it changes what the tool is useful for. Here's what Kiro actually is, how its core concepts fit together, and an honest take on when it makes sense over the alternatives. What Kiro Is Kiro launched from AWS in mid-2025 and is built on top of Amazon Bedrock, routing between Claude Sonnet for reasoning-heavy work and Amazon Nova for high-throughput code generation. It ships in three forms: Kiro IDE – a VS Code-compatible editor (built on Code OSS, so you can import your existing themes, keybindings, and Open VSX plugins)Kiro CLI – the same agent in your terminal, useful for SSH sessions or scripted workflowsKiro Autonomous Agent – a background agent that picks up tasks, implements them, and opens PRs without you sitting in the loop You don't need an AWS account to get started — you can sign in with GitHub or Google. The IDE feels immediately familiar if you've used VS Code, which removes one of the usual adoption barriers for new tooling. In January 2026, AWS also announced the end of Amazon Q Developer for new signups (effective May 15, 2026), explicitly directing users to Kiro as its successor for IDE-based AI assistance. That's a significant signal about where AWS is placing its bets. The Three Concepts That Make Kiro Different 1. Specs When you start a new feature in Kiro, you don't jump straight to code. You describe what you want to build, and Kiro generates three structured files: requirements.md — user stories and acceptance criteriadesign.md — system design, component breakdown, data flowtasks.md — a numbered implementation checklist the agent works through These become the source of truth. Code is a build artifact of the spec. When you come back to the feature a month later, or hand it to a new team member, the reasoning behind every decision is documented — not in a Confluence page nobody reads, but in the repo next to the code it describes. This is the thing chat-first tools can't replicate. Cursor or Claude Code can generate excellent code from a good prompt. What they can't do is maintain a structured paper trail of why the code looks the way it does. 2. Hooks Hooks are event-driven automations that fire when things happen in your workspace — file save, new file created, commit opened. You define what Kiro should do in response, and it runs those actions in the background without you having to think about them. Common hooks teams set up: Run the linter and auto-fix on every file saveRegenerate unit tests when implementation files changeUpdate the relevant section of design.md when a module is modifiedRun a security scan before any commit The practical effect is that a junior developer's output passes the same automated quality bar as a senior's, because the standards are enforced by the environment rather than by code review heroics. 3. Steering Files Steering files are Markdown files that give Kiro persistent context about your project — your conventions, the libraries you've standardized on, your architecture decisions, your security requirements. You create them once, and Kiro reads them on every interaction without you having to re-explain your stack in every prompt. They live in two places: ~/.kiro/steering/ – global rules that apply across all your projects.kiro/steering/ – project-specific overrides checked into the repo A typical global steering file might say things like "always use TypeScript strict mode," "prefer AWS CDK over raw CloudFormation," or "all Lambda functions must have structured logging with a correlation ID." Project steering files add things like "this service is a multi-tenant SaaS, tenant ID is always passed in the request context." The result is that Kiro's context isn't reset between sessions and doesn't depend on whoever wrote the last prompt being thorough. The Hooks + Specs Flywheel The real power emerges when hooks and specs work together. Here's what that looks like in practice: You describe a new feature. Kiro generates requirements.md, design.md, and tasks.md.You review and refine the spec. Add an edge case to the requirements, adjust the component breakdown in design.Kiro implements the task list, following your steering files for conventions.On each file save, hooks run: linter, tests, security scan. Issues surface immediately.When you're done, a hook generates the commit message from the spec diff.The PR description writes itself from requirements.md. The spec doesn't go stale because hooks keep it in sync with the code. The code doesn't drift from the design because the design was written before the code. This is what "engineering rigor" means in the context of agentic development — not slower, but structured. AWS-Native Advantages (and the Honest Tradeoff) Kiro has deep integration with the AWS ecosystem: CodeCatalyst for repositories and CI/CD, Bedrock for model access, IAM Identity Center for enterprise auth, and "Kiro Powers" — pre-packaged MCP servers for AWS-specific domains like CDK, CloudFormation, pricing, and (recently) HealthOmics workflows. If your team is already AWS-first, this is a genuine multiplier. Your Kiro agent can query your actual AWS account context, reference live Bedrock documentation, and generate CDK constructs that match your organization's guardrails. The honest tradeoff: if your team isn't AWS-first, some of this integration feels like overhead rather than lift. Kiro works perfectly well as a general-purpose agentic IDE — the spec/hooks/steering system has value regardless of your cloud provider — but the ecosystem integrations are clearly designed for AWS shops. Most teams running mixed infrastructure (some AWS, some not) find it practical to use Kiro for the AWS-native services and keep their existing editor for everything else. The two coexist fine. How It Compares to the Alternatives KiroCursorClaude CodePrimary paradigmSpec-drivenChat-drivenTask-driven (CLI)Persistent contextSteering filesRules / .cursorrulesAGENTS.mdAutomationHooks (event-driven)ManualManualAWS integrationNativeNoneNoneIDEStandalone (VS Code-compatible)Fork of VS CodeTerminal onlyBackground agentYes (autonomous agent)LimitedYesBest forProduction features, team consistencyFast prototyping, explorationComplex refactors, agentic tasks Kiro and Claude Code aren't direct competitors in practice — Kiro is an IDE product, and Claude Code is a terminal agent. Many teams run both, using Kiro for structured feature work and Claude Code for open-ended refactors or one-off tasks. Getting Started Download the IDE from kiro.dev — no AWS account required. Sign in with GitHub or Google, point it at an existing repo, and run through the onboarding to import your VS Code settings. A good first experiment: take a feature you're planning to build anyway, describe it to Kiro, and look at the spec it generates before writing any code. The value of the approach becomes obvious when you see your vague "add user preferences" idea turn into a concrete requirements doc with six acceptance criteria and a data model. From there: Create one global steering file in ~/.kiro/steering/ with your language and framework defaultsSet up one hook that runs your linter on file saveBuild the feature using the task list Kiro generated That's the feedback loop that makes the tool click. The full power of the hooks and autonomous agent comes later, but even the basic spec workflow is a meaningful improvement over prompt-and-iterate for anything that takes more than a day to build. Worth Watching A few things that make Kiro worth keeping an eye on, even if you're not ready to switch: The spec-as-artifact model is genuinely novel. When agents get better, spec-driven codebases will be better positioned to benefit — the structured requirements and design docs give future agents a much richer context than a commit history and some comments. Kiro Powers (the MCP server marketplace) is growing fast. The HealthOmics extension in February 2026 showed that domain-specific agent packs are a real product direction, not just a demo. And with Amazon Q Developer sunsetting for new users, AWS is clearly consolidating its developer AI bet onto Kiro. Whatever the roadmap looks like from here, it's going to get resources. Kiro isn't the right tool for every workflow. If you're prototyping solo or doing exploratory work, the spec-first overhead is friction you don't need. But for teams shipping production features that need to be documented, tested, and maintained — the bet that specs should be the unit of work is a compelling one. Kiro vs. the Alternatives FeatureKiroCursorClaude CodeGitHub CopilotPrimary paradigmSpec-drivenChat-drivenTask-driven (CLI)Inline completionPersistent contextSteering files.cursorrulesAGENTS.mdNoneEvent automationHooks (file save, commit)NoneNoneNoneStructured specs✅ Native❌❌❌Background agent✅ Autonomous agentLimited✅❌AWS-native integration✅ Deep❌❌❌Dynamic MCP loading✅ PowersManualManual❌IDE baseCode OSS (VS Code compat.)VS Code forkTerminal onlyPluginFree tier✅✅✅✅ How Spec-Driven Development Works Plain Text ┌─────────────────────────────────────────────────────────┐ │ YOU: describe a feature │ └─────────────────────────┬───────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────┐ │ KIRO GENERATES SPECS │ │ │ │ .kiro/specs/my-feature/ │ │ ├── requirements.md ← user stories + EARS notation │ │ ├── design.md ← architecture, data flow, APIs │ │ └── tasks.md ← ordered implementation plan │ └─────────────────────────┬───────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────┐ │ YOU: review + refine specs │ │ add edge cases, adjust design, approve task list │ └─────────────────────────┬───────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────┐ │ KIRO IMPLEMENTS task by task │ │ guided by steering files + spec context │ └─────────────────────────┬───────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────┐ │ HOOKS FIRE AUTOMATICALLY │ │ on every file save: │ │ → linter + autofix │ │ → test generation / update │ │ → security scan │ │ → design.md sync │ └─────────────────────────┬───────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────┐ │ PR OPENS — description from requirements.md │ │ commit message generated from spec diff │ └─────────────────────────────────────────────────────────┘ Steering File Layout Markdown ~/.kiro/steering/ ← global, applies to every project ├── typescript.md "always use strict mode, no any" ├── aws.md "prefer CDK over raw CloudFormation" ├── security.md "IAM roles must follow least privilege" ├── git.md "use conventional commits" └── testing.md "80% coverage minimum, jest + RTL" your-repo/ └── .kiro/ └── steering/ ← project-specific overrides (checked in) ├── architecture.md "multi-tenant SaaS, one DB schema per tenant" ├── api.md "all endpoints versioned under /v1" └── data-model.md "tenant ID always in request context, never inferred" Hook Definition Example YAML # .kiro/hooks/test-sync.yaml name: Sync Tests on Component Save trigger: event: onSave pattern: "src/**/*.tsx" instructions: | When a React component file is saved: 1. Check if a corresponding test file exists in __tests__/ 2. If not, create one with basic render and snapshot tests 3. If it exists, update it to cover any new props or exported functions 4. Run the test file and report failures inline YAML # .kiro/hooks/security-scan.yaml name: Pre-commit Security Scan trigger: event: onCommit instructions: | Before every commit: 1. Scan staged files for hardcoded secrets, API keys, and credentials 2. Check for any 0.0.0.0/0 ingress rules in IaC files 3. Flag any new IAM policies that use wildcard actions (*) 4. Block the commit and explain any findings — do not auto-fix How Powers Solve Context Rot Without Powers, connecting multiple MCP servers front-loads your entire context window before you write a single line: Plain Text Without Powers ────────────────────────────────────────────────── Context window (200K tokens) [Figma MCP tools] ~12K tokens ████ [Postman MCP tools] ~18K tokens ██████ [Stripe MCP tools] ~10K tokens ███ [Supabase MCP tools] ~15K tokens █████ [Datadog MCP tools] ~9K tokens ███ ────────────────── Total overhead ~64K tokens (32% gone before first prompt) With Powers (dynamic loading) ────────────────────────────────────────────────── You mention "payment" → Stripe power activates You mention "database" → Supabase activates, Stripe deactivates Workspace Architecture for AWS Teams Plain Text AWS Organization └── Management Account ├── Client A Account │ ├── Kiro workspace (.kiro/ scoped here) │ ├── CodeCatalyst repo │ ├── Bedrock access (us-east-1) │ └── Secrets Manager (client A secrets only) │ ├── Client B Account │ ├── Kiro workspace (.kiro/ scoped here) │ ├── CodeCatalyst repo │ ├── Bedrock access (us-east-1) │ └── Secrets Manager (client B secrets only) │ └── Shared Services Account ├── IAM Identity Center (SSO for all Kiro logins) This pattern keeps client IP, secrets, and Bedrock spend isolated by account boundary — IAM does the enforcement, not convention. Resources kiro.dev – download is free, no AWS account requiredIntroducing Kiro – the original launch post, good context on the design philosophy behind specs and hooksIntroducing Powers – explains why dynamic MCP loading matters and how Powers solve context rotTeaching Kiro new tricks with steering and MCP – practical deep dive on using steering + MCP to handle custom libraries and DSLsSpecs documentation – full reference, including the Design-First and Bugfix spec workflowsKiro Powers marketplace – browse Figma, Stripe, Supabase, Datadog, Terraform, and moreIDE Changelog – how fast the product is movingAmazon Q Developer end-of-support announcement – official AWS post confirming Kiro as Q Developer's successorgithub.com/kirodotdev/Kiro – issue tracker and feedback repo

By Jubin Abhishek Soni CORE

We had hundreds of microservices. Thousands of enterprise customers. And alerts firing constantly — CPU at 80%, memory at 75%, disk at 60%. Engineers were drowning in noise, and still, every few weeks, a customer would open a ticket before we knew anything was wrong. The problem wasn't a lack of monitoring. It was a lack of structure. After years of running large-scale cloud platforms, I built a top-down, five-layer monitoring framework that changed how my team operated. This article walks through how it works, why it works, and how you can start adopting it without a big-bang overhaul. The Core Problem With Most Observability Setups Here's the typical pattern I see: teams instrument what's easy — CPU, memory, disk, request count — and then wonder why they're constantly chasing false alarms while real customer issues go undetected. The root cause is that there's no hierarchy. Your infrastructure metrics don't know about your business SLOs. Your service health dashboards don't connect to your capacity model. Everything is siloed, and when something breaks, engineers manually trace across six dashboards to find the actual problem. What's missing is explicit traceability — a clear chain from customer pain all the way down to infrastructure, so any engineer at any layer can navigate up and down without guesswork. The Five-Layer Framework The framework organizes monitoring into five explicit layers, each with a defined scope and clear connections to the layers above and below it. Plain Text Layer 1: Business Transactions ← What customers actually experience Layer 2: Service Health ← How your services are performing Layer 3: Pod Behavior ← How individual containers are behaving Layer 4: Data Service Performance ← How your databases and caches are doing Layer 5: Capacity Planning ← Are you running out of headroom? The key design principle: alerts fire at Layer 1. Investigation flows downward. You start from customer pain, not from infrastructure noise. Layer 1: Business Transactions — The Source of Truth This is the most important layer, and the most commonly missing one. Layer 1 metrics answer one question: Are customers being affected right now? Examples: Transaction error rate by workflow typeSession availability percentageP99 latency for top customer-facing operationsBusiness-critical operation success rate Why alert here and not on CPU? A CPU alert at 80% fires constantly in a healthy system under normal load. A transaction error rate alert at 1% fires only when customers are actually affected. One of these matters, the other creates on-call fatigue. SQL # Error rate by workflow label — fires when customers are hurting sum(rate(http_requests_total{status=~"5..", workflow!=""}[5m])) by (workflow) / sum(rate(http_requests_total{workflow!=""}[5m])) by (workflow) The workflow label here is critical. It groups requests by business function — not by service, not by pod, but by what the customer is actually trying to do. This is what makes cross-service error aggregation possible. Layer 2: Service Health — Where Investigation Starts When a Layer 1 alert fires, the first question is: which service is responsible? Layer 2 gives you the answer. This layer tracks the health of each individual service using the RED method (Rate, Errors, Duration): Request rate: Is traffic normal?Error rate: Is this service returning errors?Duration: Is this service slow? SQL # Service-level error rate sum(rate(http_server_requests_seconds_count{status=~"5.."}[5m])) by (service) / sum(rate(http_server_requests_seconds_count[5m])) by (service) Layer 2 is also the best place to start adoption (more on this below). It gives you immediate operational value — you know which services are unhealthy — without requiring full instrumentation of all five layers. Layer 3: Pod Behavior — When the Service Looks Fine but Isn't Sometimes a service reports healthy aggregate metrics, but individual pods are struggling. This layer catches those cases. Layer 3 applies the USE method (Utilization, Saturation, Errors) at the pod level: Utilization: Is this pod close to its resource limits?Saturation: Is it queuing work it can't handle?Errors: Are individual pod errors being masked by healthy pods? Saturation metrics are often better early-warning signals than utilization: SQL # Thread pool queue depth — saturation indicator tomcat_threads_busy_threads / tomcat_threads_config_max_threads A pod at 75% CPU utilization might be fine. A pod with its thread pool queue at 90% capacity is about to drop requests, and you want to know that before it happens. Layer 4: Data Service Performance — The Hidden Bottleneck In most distributed systems, database and cache performance is where latency problems actually live. This layer monitors your databases, caches, and message queues using the same USE methodology. Key signals: Connection pool exhaustion (saturation)Query latency by operation typeCache hit rateGC pause time (often the most undermonitored metric) SQL # Connection pool saturation hikaricp_connections_active / hikaricp_connections_max GC pause time deserves special attention. Long GC pauses cause latency spikes that look like application slowness but are actually JVM behavior. Without Layer 4, you'll spend hours debugging your application code when the fix is a heap size adjustment. Layer 5: Capacity Planning — Getting Ahead of Problems This layer is about the future. While Layers 1–4 tell you what's happening now, Layer 5 tells you what's coming. The key insight: business metrics drive capacity needs. If your Layer 1 metrics show that customer transaction volume is growing 15% month-over-month, you can project when your current infrastructure will saturate — before it does. Layer 5 connects business growth metrics to infrastructure headroom: SQL # Days until connection pool exhaustion at current growth rate (hikaricp_connections_max - hikaricp_connections_active) / deriv(hikaricp_connections_active[7d]) / 86400 This kind of metric transforms capacity planning from a reactive scramble into a scheduled, predictable activity. The High-Cardinality Problem You Need to Avoid One of the most common Prometheus mistakes I've seen: putting user IDs, session tokens, or dynamic URL paths into metric labels. SQL # DO NOT DO THIS http_requests_total{user_id="usr_12345", url="/api/v1/query/abc123"} High-cardinality labels cause Prometheus performance to degrade severely — each unique label combination creates a separate time series. With millions of users or dynamic URLs, you'll bring your Prometheus instance to its knees. The rule: high-cardinality analysis belongs in your logging layer, not your metrics layer. Keep metric labels to a small, bounded set of values — service name, workflow type, environment, status code. If you need to debug a specific user session, go to your logs. How to Adopt This Without Starting Over You don't need to instrument all five layers at once. Here's the sequence that delivers value at each step: Step 1: Start With Layer 2 (Service Health) This gives you immediate value — you know which services are healthy and which aren't. Most teams already have some of this instrumentation; structure what you have into a consistent RED dashboard. Step 2: Add Layer 1 (Business Transactions) Define your customer-facing workflows and instrument them. Move your primary alerts here. This is when on-call noise drops dramatically. Step 3: Build Downward (Layers 3–5) Add pod behavior monitoring, then data service monitoring, then capacity planning. Each layer makes the one above it easier to debug. The framework delivers operational value at each step — you're not waiting for a big-bang implementation before anything is useful. What This Looks Like in Practice Here's a real incident pattern this framework resolved: Symptom: Layer 1: alert fires — transaction error rate for the report-generation workflow exceeds 2%.Layer 2: report-service shows elevated error rate. Other services healthy.Layer 3: Two of five report-service pods show thread pool saturation above 90%. The other three look fine.Layer 4: Database connection pool for the reporting DB is at 95% capacity. Root cause: A new query introduced in last week's release had a higher connection hold time than expected. Under normal load, the connection pool held. Under peak load, two pods exhausted their connections, causing errors that surfaced as customer-visible failures. Fix: Increase connection pool size, optimize query connection hold time, add saturation alert at Layer 4. Without the framework, this would have been a multi-hour investigation across disconnected dashboards. With it, the trace from customer pain to root cause took under 20 minutes. Key Takeaways Structure your monitoring into explicit layers — business transactions, service health, pod behavior, data service performance, and capacity planning. Each layer has a defined scope and connects to the layers above and below it.Alert on Layer 1 customer pain metrics, not infrastructure thresholds. CPU at 80% is noise. Transaction error rate at 1% is signal.Apply the USE method consistently across Layers 3 and 4 — utilization, saturation, and errors give you a shared vocabulary that makes cross-team debugging faster.Keep metric labels low-cardinality. High-cardinality labels like user IDs and dynamic URLs belong in logs, not metrics.Start with Layer 2, then Layer 1, then build downward. You don't need all five layers on day one — each step delivers value on its own.

Software engineering prioritizes optimization, focusing on distributed systems, caching, cloud elasticity, observability, and AI-assisted development to boost productivity and speed. However, one of the most costly and overlooked inefficiencies is meeting culture. Research from Harvard Business Review, Atlassian, and Microsoft Work Trend Index consistently shows that professionals spend much of their week in meetings, many of which fail to produce decisions, clarity, or measurable outcomes. In software development, this issue is amplified, as meetings disrupt deep focus, a critical asset for engineers. A poorly structured one-hour meeting with ten engineers not only wastes an hour but also disrupts concentrated work, delays delivery, and increases organizational latency. This challenge has historical roots. The word meeting comes from the Old English mētan, meaning “to encounter” or “to come together.” Today, organizations often use meetings as a default response to uncertainty, rather than intentionally designing communication systems. As a result, companies experience frequent calls, unfocused discussions, and repeated meetings that end without reaching a decision. The problem is not meetings themselves, but poorly designed ones. Leading engineering organizations recognize that communication, like software architecture, requires intentional design focused on outcomes, scalability, and efficiency. The 7 Pillars of Meeting Design offer a practical framework to turn meetings into valuable decision-making assets, reducing wasted time and increasing clarity, ownership, and execution. Why Meetings Fail — and How to Fix Them Meetings are often criticized in modern software development because organizations sometimes mistake activity for progress. A packed calendar can create the illusion of collaboration while reducing actual delivery capacity. Engineers lose focus, architects spend more time explaining decisions than designing systems, and managers respond to uncertainty by increasing meeting frequency. This leads to excessive communication overhead, which can consume more resources than business execution itself. As a result, terms like “meeting fatigue” and “Zoom exhaustion” have become common in the post-remote-work era. The core issue is not communication, as software engineering relies on collaboration and alignment across teams. Instead, many organizations have not learned to design meetings with the same intentionality used to build scalable software systems. Well-designed meetings can be a powerful driver of progress in engineering organizations. Effective technical discussions can resolve weeks of uncertainty in minutes. Architectural reviews help reduce long-term technical debt, while incident response meetings minimize downtime and coordinate recovery. Strategic alignment conversations prevent teams from building the wrong solutions. Many major engineering achievements have relied on structured collaboration and coordinated decision-making. Productive meetings create clarity, reduce ambiguity, share knowledge, strengthen team cohesion, and accelerate execution. Meetings should function as decision engines, not just routine conversations. The challenge is not to eliminate meetings, but to redesign them with a focus on outcomes, efficiency, and scalability. Just as top software teams use architecture principles to manage complexity, leading organizations apply communication principles to reduce organizational complexity. Meetings should have a clear scope, constraints, ownership, measurable outcomes, and documentation. They should minimize delays rather than create them. This transformation is achievable through a straightforward and effective framework: the 7 Pillars of Meeting Design. Each pillar addresses decision-making in software organizations, including unclear objectives, wasted synchronization, conversational drift, insufficient preparation, and missing accountability. Collectively, these principles ensure meetings are outcome-driven, scalable, and efficient, safeguarding focused cognitive work in engineering teams. Pillar 1: Scope and Objective Every effective system begins with a clear contract. APIs have specifications, databases have schemas, and software requirements define expected behavior. Meetings should follow this principle. Meetings often fail when participants lack a shared understanding of the purpose, expected outcome, or success criteria. This leads to drifting discussions, repeated explanations, and differing interpretations. Titles like “Weekly Sync” or “Architecture Discussion” provide little clarity about intent, ownership, or desired decisions. Defining scope and objective makes meetings goal-oriented rather than routine. A clear invitation should state the meeting’s purpose, the problem to solve, and what success looks like. This aligns participants before the meeting begins, similar to defining acceptance criteria before implementation. Without this clarity, participants pursue different goals, increasing organizational entropy. A clear scope also helps attendees decide if their participation is necessary, reducing unnecessary meetings and protecting productivity. Pillar 2: Parkinson’s Law In 1955, historian Cyril Northcote Parkinson noted that “work expands so as to fill the time available for its completion.” This principle, known as Parkinson’s Law, is evident in modern meeting culture. Organizations often default to one-hour meetings due to calendar norms, not actual need. As a result, discussions expand to fill the allotted time, even when decisions could be made more quickly. Shorter meetings create productive pressure, increasing focus and prioritization. Meetings of thirty to forty minutes encourage participants to avoid unnecessary context and low-value discussions. Time constraints, like resource constraints in system design, drive optimization. Many leading organizations find that shorter meetings yield better outcomes by promoting clarity and decisiveness. The goal is not to rush important topics, but to prevent unnecessary discussion from draining cognitive energy. Pillar 3: Active Facilitation A common misconception is that productive meetings happen naturally. In reality, group discussions often lose focus without active coordination. Social dynamics, hierarchy, personal interests, and cognitive bias can distract from the original objective. In software engineering, this is known as “bikeshedding,” where groups spend excessive time on trivial topics because they are easier to discuss than complex issues. Active facilitation serves as the meeting’s control layer. The facilitator does more than schedule; they maintain focus, manage participation, redirect off-topic discussions, and protect the meeting’s objective. This role is similar to a scheduler in an operating system, prioritizing critical topics and preventing low-value discussions from dominating. Effective facilitation fosters psychological safety and enforces discipline. Without it, meetings are often dominated by the loudest voices instead of the most relevant topics. Pillar 4: No Surprises Many meetings fail before they even begin because participants encounter critical information for the first time during the call itself. Teams: Many meetings fail because participants encounter key information for the first time during the call. Teams then spend valuable time reading documents together, repeating context, or reacting to unexpected proposals. This increases latency and reduces decision quality, as participants lack time for critical analysis. In engineering, this is like deploying changes to production without proper review; it should be shared at least 24 hours before the meeting, whenever possible. This enables participants to arrive informed, prepared, and ready to make decisions rather than passively consume information. Mature engineering cultures understand that synchronous communication is expensive and should be reserved primarily for clarification, negotiation, prioritization, and final decisions. Meetings should convey understanding, not initiate it from zero. Pillar 5: Scale via Registration A major inefficiency in organizations is the repeated recreation of knowledge. Teams revisit decisions, repeat context, and rely too much on tribal memory. Writing historically enabled knowledge to persist beyond immediate interaction. Engineering organizations face a similar challenge. If key decisions remain only in conversations, the organization depends on constant synchronization to stay aligned. Documentation enables asynchronous communication. Recording decisions, rationales, action items, and trade-offs reduces latency and allows others to understand outcomes without another meeting. This is similar to persistence in distributed systems: without durable storage, state is lost. Meeting registration turns conversations into reusable knowledge assets. Well-documented decisions also reduce ambiguity by clarifying both what was decided and why. Pillar 6: Asynchronous First Modern software systems scale by minimizing unnecessary synchronization. Distributed systems avoid excessive blocking communication because synchronous dependencies increase latency and reduce resilience. Organizations face similar issues. Too many meetings create bottlenecks, making progress dependent on everyone being present. This is especially challenging for global teams across time zones and schedules. An asynchronous-first approach redefines meetings. Rather than starting discussions, meetings become convergence points after asynchronous preparation. Pull requests, documents, ADRs, prototypes, and comments should be developed before the meeting. This improves meeting quality, as participants arrive prepared with insights and analysis. Asynchronous preparation also fosters inclusivity, allowing quieter team members to contribute more effectively through written communication. Pillar 7: Decisive Outcome A meeting without a decision often results in structured ambiguity. Teams frequently leave meetings unclear about next steps, ownership, priorities, or deadlines. This leads to repeated discussions because no actionable outcome was reached. In systems thinking, this is like generating logs without triggering state changes. Every meeting should conclude with clear outcomes: what was decided, who is responsible, deadlines, and next steps. If no final decision is possible, define the next action to unblock progress. This ensures accountability and operational clarity. Decisive outcomes should be documented to support organizational knowledge. Leading engineering organizations measure meetings by execution progress, not by the amount of discussion.

By Otavio Santana CORE

In 2023, a New York lawyer was sanctioned after submitting a brief containing fabricated case citations generated by ChatGPT. The model invented plausible-sounding but nonexistent precedents. Legal RAG tools from LexisNexis and Thomson Reuters still hallucinate between 17 and 33% of the time, even with retrieval grounding, according to a 2025 Stanford empirical study. A 2025 Scientific Reports analysis of 3 million mobile app reviews found that roughly 1.75% of user complaints explicitly described hallucination-like errors in everyday AI features. Hallucination is not a fixable bug. Learning theory research published at arXiv shows it is a provably inevitable property of any general-purpose LLM used outside the scope of its training distribution. The Fluency Trap Hallucinated text reads exactly like the correct text. The model's writing quality gives no signal that a fact is fabricated. Fluency and truthfulness are entirely orthogonal properties in LLMs. 3 Ways an LLM Hallucinates 1. Intrinsic Hallucination The model generates output that directly contradicts facts in its own training data or in the user-provided context. It knows the answer but produces the wrong one anyway, often because the conflicting fact was underrepresented during pre-training. Example: a model states that a historical event happened in 1945 when it occurred in 1953. 2. Extrinsic Hallucination The model fabricates content that cannot be verified or contradicted by any source it was given. Invented citations, nonexistent API endpoints, and fictional statistics fall here. The model has no facts to contradict because the facts never existed to begin with. 3. Factuality Hallucination The model generates statements that are syntactically perfect and contextually plausible but factually wrong against the external ground truth. These are the most dangerous in production because they pass basic coherence checks. A confident wrong answer to a medical or legal question is a factual hallucination. The Incentive Problem Baked Into Every Benchmark Next token prediction does not encode factual truth. LLMs predict the statistically likely next token, not the factually correct one. A token that sounds right, given the sentence pattern, scores well even when it is wrong.Accuracy-only benchmarks penalize admitting uncertainty. On standard leaderboards, guessing has a 1-in-365 chance of being right. Saying "I do not know" scores zero. Over thousands of questions, the guesser ranks higher than the honest model.RLHF alignment can amplify fluency over truth. Human raters reward responses that sound confident. A hedged but accurate answer often scores lower than a confident but wrong one, pushing models toward plausible-sounding fabrication. OpenAI's September 2025 paper shows this is systemic: leaderboards that measure accuracy but not calibration actively incentivize hallucination. Fixing evals is as important as fixing models. The Gaps in Parametric Memory What the model does not know still gets answered. Rare and niche facts are underrepresented in training data. Pre-training corpora reflect the web. Obscure events and specialized domains appear far less often, leaving the model with a weak signal and high fabrication risk on niche queries.Knowledge has a hard cutoff date. Any event released after training does not exist in parametric memory. Querying post-cutoff facts forces the model to extrapolate from outdated patterns, producing confident but stale answers.Training data noise propagates directly into model beliefs. Web-scraped corpora contain errors, and AI-generated text. A model trained on inaccurate claims absorbs those as valid patterns, making some hallucinations a direct replay of the corrupted training signal. The model cannot distinguish between what it knows and what it has inferred from patterns. Ask it about a person who became famous after its cutoff, and it will construct a plausible but fabricated biography. Attention Has Blind Spots Transformer architecture contributes to fabrication. Self-attention processes context in parallel, but with documented failure modes that directly produce hallucinations at inference time. Positional Bias Attention heads weigh tokens at the start and end of contexts more heavily. Facts in the middle are deprioritized, causing the model to answer from memory rather than the provided text. Overconfidence in Generation The model conditions next token prediction on its own partially generated output. As a response grows, it locks onto prior text, amplifying small errors into large fabrications. The Lost in the Middle Effect: Research shows retrieval accuracy drops sharply for facts placed in the middle of long contexts. Keep critical grounding evidence near the start or end of your prompt, not buried in the middle. My Practical Hallucination Mitigation Pipeline Mitigation Methods Compared MethodEffortLatencyReductionRAG GroundingMediumLow overhead35 to 60% errorsChain-of-ThoughtLowModerate increasePrompt sensitiveFine tuning on factsHighNone after trainDomain specificTemperature 0.1-0.4Very lowNoneReduces varianceGuardrails + validationMediumUnder 200msUp to 97% detectSelf-consistencyLow code3-5x slowerStrong for math No single method eliminates hallucination. RAG plus guardrails plus low temperature is a standard production stack. Add self-consistency sampling only for high-stakes outputs where latency permits. Prompts That Fight Fabrication Chain-of-Thought cuts prompt sensitive errors by forcing intermediate claims to surface. Adding "if uncertain, say so explicitly" to your system prompt makes hedging acceptable. Keep the temperature between 0.1 and 0.4 for factual tasks. Restating the key constraint at both the beginning and end of a prompt reduces mid-generation drift. Train on Better Data, Get Fewer Lies Curated fine-tuning anchors the model to your domain facts. Remove AI-generated content from RAG knowledge bases. Audit training data before fine-tuning. Errors in fine-tuning data propagate directly into model behavior. Guardrails: Catch It Before It Ships Build a verification layer around every LLM call. Hybrid RAG plus validation reaches 97 percent detection. Self-consistency sampling catches logical hallucinations. Multi-agent systems where one model critiques another's output can reduce critical errors significantly. Pick Your Mitigation Stack in 4 Steps Classify task risk. High-stakes tasks require guardrails plus human review.Decide on grounding. If the task requires facts beyond training or post-cutoff, RAG is mandatory.Set the temperature first. Drop to 0.1 to 0.2 for factual queries.Add validation last. Wire guardrails before going live. Mistakes Engineers Keep Making Treating RAG as a complete solution. Running factual tasks at high temperatures. Burying the grounding context in the middle of long prompts. Skipping output validation before launch. Confusing fluency with accuracy. A Production Reality Check An LLM that cannot say it does not know is not a reasoning system. It is an autocomplete engine with a confidence problem. Build for calibrated uncertainty, not for the appearance of certainty. Key Takeaways Hallucination is structural. Guardrails are mandatory. RAG grounds outputs. Prompts and temperature matter. References Magesh, V. et al. (2025). Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools. Journal of Empirical Legal Studies.Massenon, R. et al. (2025). User-reported LLM hallucinations in AI mobile apps reviews. Scientific Reports.OpenAI (2025). Research on hallucination and calibration in large language models (September 2025).

By Ram Ghadiyaram CORE

It was a normal Tuesday until someone dropped a real-time dashboard link into a big team group. A few people opened it, and then a few hundred did. Within minutes, a slack pattern appeared: queries timing out, dashboards spinning, and the inevitable 'Is the data broken?'. The confusing part here is that the CPU wasn't paged, the warehouse didn't look obviously maxed out, and nothing was 'red.' Yet the platform was unusable. That's what concurrency incidents look like in data: not a clean failure but a slow collapse into queues and retries. This article is a practical playbook to make spikes boring. When demand explodes, the system should degrade intentionally, keeping the most important BI experiences alive. Why Warehouses Melt Down Under Concurrency When concurrency explodes, four things usually happen at once: Queues form everywhere: Even if you have enough compute, shared bottlenecks start to dominate: contention on resources, compilation, storage and network IO, and metadata calls.Mixed workloads: Executive dashboards compete with scheduled jobs, notebooks, and bulk reports in the same pool.Retry storms: timeouts cause automatic retries, which create a second wave of load.One click becomes many queries: A dashboard isn't one query. It's often 10 to 40 queries multiplied by 300 viewers, and you are suddenly pushing thousands of queries. Without traffic rules, the warehouse becomes first-come, first-served, which under stress becomes the noisiest workload wins. The 'Super Bowl Standard' for BI Platforms In distributed systems, there's a concept I love: when you expect a huge surge, you don't rely only on reactive scaling. You decide what must work, what can degrade, and what should pause. You make the behavior predictable. For data platforms, the 'must work' path is usually Incident dashboardsTier-0 operational/executive dashboardsCritical refresh jobs (only the ones that keep Tier-0 accurate) Exploration can slow down. Background jobs can pause. Exports can wait. The goal isn't for everything to work perfectly, but the goal is to keep the right things working. The Concurrency Playbook The playbook consists of four parts: Classify queries Control admissionPrioritize fairlyShed load gracefully Step 1: Classify Queries Most warehouses don't die because of one bad query. They die because the system treats all queries as equal. So, the first step is labelling: every query should fall into many classes. Here is the practical set you can use: The Signature Table: Query Class -> Limits -> Fallbacks Class A: Tier-0 Dashboards (Must Stay Up) Some examples are to check orders/minute, today's revenue, and incident health Limits: Reserved concurrency, retries off, short timeouts, highest priorityFallbacks: Precomputed rollups/materialized views, cached results with 'as-of timestamp' Class B: Standard Dashboards (Should Mostly Work) Some examples are team reports and weekly org KPI dashboards Limits: Limited retries, concurrency cap, small queue allowed, medium priorityFallbacks: Reduced dimensions, cached results for recent windows, top-N outputs Class C: Ad Hoc Exploration (Allowed to Slow) Some examples are adhoc cohort slicing and analyst notebooks Limits: Strict concurrency cap, fail fast, low priority during spikes, short queueFallbacks: Forces filters, sampling, async execution Class D: Background Jobs (Can Pause) Some examples are transforms, non-critical exports, and scheduled refreshes Limits: Shifted off-peak, throttled by default, separate pool if possibleFallbacks: Run later, backfill, skip non-critical Step 2: Admission Control This steps answers: should this query start now? The minimum controls that work are: Queue limitsConcurrency caps by tenant/teamStart-time budget (if it can't start soon, fail fast or degrade)Concurrency caps by class A simple policy here is: Class A: admit immediately unless system is in hard outageClass B: admit if queue less than threshold and warehouse health is goodClass C: admit only if spare capacity exists, otherwise queue briefly then fail fast with guidanceClass D/E: only admit in off-peak windows or if explicitly authorizedClass F: sandbox only This is how you prevent the worst failure mode which is the warehouse becoming slow for everyone. Step 3: Prioritization Once you have queues, ordering matters. Two rules are: Priority across classes: A>B>C>D>E>FFairness within a class: don't let one dashboard or one team consume the whole lane. Fairness is what prevents a single popular dashboard from starving everything else. Step 4: Load Shedding Load shedding is not denying everything. It is a controlled degradation strategy. Good load shedding options for BI are: Sampling for exploration queriesPre-aggregated rollups (swap to a smaller table or to a materialized view)Async execution Reduced fidelity (fewer dimensions, top N only, coarser time buckets)Fail fast with guidance (tell the user what to change)Cached results with an explicit as-of timestamp Note: Load shedding should never violate guidance. If a user is not allowed to see raw data, do not degrade into exposing it. The fallback must be policy aware. Sample Policy Config YAML # concurrency_policy.yaml (example) classes: A_tier0: priority: 100 max_running: 60 max_queue: 200 start_deadline_ms: 2000 timeout_ms: 8000 retries: 0 fallback: cache_or_rollup B_standard: priority: 70 max_running: 250 max_queue: 800 start_deadline_ms: 8000 timeout_ms: 20000 retries: 1 fallback: cache_recent_or_reduce_dims C_explore: priority: 30 max_running: 40 max_queue: 100 start_deadline_ms: 1500 timeout_ms: 12000 retries: 0 fallback: sample_or_async D_background: priority: 20 max_running: 25 max_queue: 100 start_deadline_ms: 30000 timeout_ms: 60000 retries: 1 fallback: defer_to_window E_bulk_extract: priority: 10 max_running: 5 max_queue: 20 start_deadline_ms: 0 timeout_ms: 90000 retries: 0 fallback: require_approval_or_offpeak tenants: default: max_running_per_tenant: 40 exec_dashboards: max_running_per_tenant: 80 global: hard_reject_when_unhealthy: true unhealthy_signals: - queue_depth_p99_gt: 5000 - compilation_latency_p95_gt_ms: 5000 - retry_rate_gt: 0.05 Sample Admission And Fallback Logic Python def handle_request(req): cls = classify(req) # A/B/C/D/E/F tenant = req.tenant_id if unhealthy() and policy.global.hard_reject_when_unhealthy: if cls == "A_tier0": # Tier-0 still gets a shot, but we try the safest path first return serve_fallback(req, cls, reason="unhealthy_fast_path") return reject(req, reason="warehouse_unhealthy") if running_count(cls) >= policy[cls].max_running: if queue_count(cls) >= policy[cls].max_queue: return serve_fallback(req, cls, reason="queue_full") enqueue(req, cls) if not started_within(req, policy[cls].start_deadline_ms): dequeue(req) return serve_fallback(req, cls, reason="start_deadline_exceeded") # Admitted result = execute(req, timeout=policy[cls].timeout_ms, retries=policy[cls].retries) if result.timed_out or result.over_budget: return serve_fallback(req, cls, reason="timeout_or_budget") return result What 'Good' Looks Like During A Spike When 300 people open the same dashboard, here is how it works: It is class A or B, so it runs in a protected laneRollups/caching absorb repeated refreshesExploration (class C) slows, samples, or becomes asyncBackground jobs (class D) pause temporarilyBulk exports (class E) move off-peakUnknown clients (class F) are sandboxed The result is Tier-0 stays usable, the platform stays alive, and on-call isn't fighting retry storms. What to Measure Queue depth over timeTop dashboards by fanoutP95/p99 latency by class Bytes scanned/cost by classRetry rateAdmitted vs. rejected vs. shed counts Common Failure Modes Retry storms: Cause: timeouts trigger auto retries; load doublesFix: retries for Class A; fail fast for Class C; capped retries elsewhereUnknown clients/backdoor load Cause: misconfigured tools or bots hammer the warehouseFix: default Class F sandbox, registration, and quotasDashboard bombs: Cause: one dashboard triggers many queries and hundreds of viewers amplify itFix: caching or rollups, class A/B priority lanes, per dashboard capsBackground jobs complete with humans: Cause: Scheduled refreshes saturate shared resources during peakFix: Class D throttling, off-peak windows, and keep the lights on subset Conclusion Concurrent surges are not rare. Successful platforms attract them. The question is whether your warehouse behaves like a panicked crowd or a managed stadium. With query classes, admission control, prioritization, and load shedding, you can keep tier-0 alive under extreme concurrency and turn the 'Super Bowl Moment' from an outage into an operating mode.

By Anusha Kovi CORE