2026-01-10 05:35:14
Processing video is one of the heaviest tasks a web application can handle. If you are still running shell_exec(‘ffmpeg …’) inside a Controller, you are likely blocking your PHP-FPM threads, frustrating your users, and risking timeouts.
\ In 2026, we don’t do that. We treat video processing as an asynchronous, distributed pipeline.
\ With the release of Symfony 7.4, we have new tools that make this robust and native. We now have a dedicated #[Video] validation constraint, native support for shared directories, and the mature Messenger component to orchestrate parallel pipelines.
\ In this article, we will build a production-grade video processing architecture that:
Before writing code, we must solve the physical storage problem. When a user uploads a video to your Web Container, your Worker Container (which might be on a different server) needs to access it.
\ In Symfony 7.4, we lean into the “Shared Directory” pattern — a standardized location for stateful data shared across nodes (like NFS mounts or Docker Volumes).
\ Infrastructure setup (Conceptual):
\ We will configure this path in services.yaml to ensure our code is agnostic of the physical location.
# config/services.yaml
parameters:
# The absolute path to the shared storage (mapped volume)
app.storage_dir: '%kernel.project_dir%/var/storage'
services:
_defaults:
autowire: true
autoconfigure: true
bind:
$storageDir: '%app.storage_dir%'
In previous versions, we had to rely on the generic File constraint and guess MIME types, or write complex custom validators to check duration and codecs.
\ Symfony 7.4 introduces the native #[Video] constraint. It uses FFmpeg internals (via ffprobe) to validate metadata before you even accept the business logic.
\ Let’s create a DTO for our upload.
// src/Dto/VideoUploadDto.php
namespace App\Dto;
use Symfony\Component\HttpFoundation\File\UploadedFile;
use Symfony\Component\Validator\Constraints as Assert;
final readonly class VideoUploadDto
{
public function __construct(
#[Assert\NotBlank]
#[Assert\Video(
maxSize: '500M',
mimeTypes: ['video/mp4', 'video/quicktime', 'video/webm'],
minWidth: 1280,
maxWidth: 3840, // 4K limit
minDuration: 5, // Seconds
maxDuration: 3600,
allowPortrait: false, // Enforce landscape
suggestedExtensions: ['mp4', 'mov']
)]
public UploadedFile $file,
#[Assert\NotBlank]
#[Assert\Length(min: 5, max: 255)]
public string $title
) {}
}
This constraint requires the ffprobe binary to be executable by the web user.
To process video efficiently, we shouldn’t just run one giant script. We should split the work into Pipelines. When a video is uploaded, we will dispatch a “Manager Message,” which then dispatches sub-tasks to be run in parallel.
We use readonly classes for immutable message objects.
// src/Message/ProcessVideoUpload.php
namespace App\Message;
/**
* The Trigger: Dispatched immediately after upload.
*/
final readonly class ProcessVideoUpload
{
public function __construct(
public string $videoId,
public string $filename
) {}
}
// src/Message/TranscodeVideo.php
namespace App\Message;
/**
* Sub-Task: Heavy encoding work.
*/
final readonly class TranscodeVideo
{
public function __construct(
public string $videoId,
public string $filename,
public string $targetFormat // e.g., 'hls', 'mp4-720p'
) {}
}
// src/Message/GenerateThumbnail.php
namespace App\Message;
/**
* Sub-Task: Image extraction.
*/
final readonly class GenerateThumbnail
{
public function __construct(
public string $videoId,
public string $filename,
public int $timestamp
) {}
}
We need at least two transports:
# config/packages/messenger.yaml
framework:
messenger:
failure_transport: failed
transports:
# Fast lane
async_priority:
dsn: '%env(MESSENGER_TRANSPORT_DSN)%'
options:
queue_name: priority_queue
# Slow lane (Heavy video processing)
async_heavy:
dsn: '%env(MESSENGER_TRANSPORT_DSN)%'
options:
queue_name: video_encoding_queue
# Increase timeout for workers on this queue
auto_setup: true
failed: 'doctrine://default?queue_name=failed'
routing:
'App\Message\ProcessVideoUpload': async_priority
'App\Message\GenerateThumbnail': async_priority
'App\Message\TranscodeVideo': async_heavy
We will use the php-ffmpeg library. First, install it:
composer require php-ffmpeg/php-ffmpeg
This handler receives the initial upload event and “fans out” the work. This is the Parallel Pipeline pattern.
// src/MessageHandler/ProcessVideoUploadHandler.php
namespace App\MessageHandler;
use App\Message\GenerateThumbnail;
use App\Message\ProcessVideoUpload;
use App\Message\TranscodeVideo;
use Psr\Log\LoggerInterface;
use Symfony\Component\Messenger\Attribute\AsMessageHandler;
use Symfony\Component\Messenger\MessageBusInterface;
#[AsMessageHandler]
final readonly class ProcessVideoUploadHandler
{
public function __construct(
private MessageBusInterface $bus,
private LoggerInterface $logger
) {}
public function __invoke(ProcessVideoUpload $message): void
{
$this->logger->info("Starting pipeline for video: {$message->videoId}");
// 1. Dispatch Thumbnail Generation (Fast)
// We generate 3 thumbnails in parallel by dispatching 3 messages
$this->bus->dispatch(new GenerateThumbnail($message->videoId, $message->filename, 5));
$this->bus->dispatch(new GenerateThumbnail($message->videoId, $message->filename, 30));
$this->bus->dispatch(new GenerateThumbnail($message->videoId, $message->filename, 60));
// 2. Dispatch Transcoding (Slow/Heavy)
// These will go to the 'async_heavy' transport
$this->bus->dispatch(new TranscodeVideo($message->videoId, $message->filename, 'mp4-720p'));
$this->bus->dispatch(new TranscodeVideo($message->videoId, $message->filename, 'webm-720p'));
$this->logger->info("Pipeline dispatched successfully.");
}
}
Now, we implement the actual logic using FFmpeg.
// src/MessageHandler/TranscodeVideoHandler.php
namespace App\MessageHandler;
use App\Message\TranscodeVideo;
use FFMpeg\FFMpeg;
use FFMpeg\Format\Video\X264;
use Psr\Log\LoggerInterface;
use Symfony\Component\Filesystem\Filesystem;
use Symfony\Component\Messenger\Attribute\AsMessageHandler;
#[AsMessageHandler]
final readonly class TranscodeVideoHandler
{
public function __construct(
private string $storageDir,
private LoggerInterface $logger,
) {}
public function __invoke(TranscodeVideo $message): void
{
$inputFile = $this->storageDir . '/' . $message->filename;
$outputFile = $this->storageDir . '/processed/' . $message->videoId . '-' . $message->targetFormat . '.mp4';
// 1. Verify existence (Robustness)
if (!file_exists($inputFile)) {
// In a shared dir setup, there might be a slight sync delay or error.
// Throwing exception triggers Messenger retry policy.
throw new \RuntimeException("File not found: $inputFile");
}
$this->logger->info("Transcoding {$message->videoId} to {$message->targetFormat}...");
// 2. Initialize FFMpeg
$ffmpeg = FFMpeg::create();
$video = $ffmpeg->open($inputFile);
// 3. Configure Format (x264 codec, AAC audio)
$format = new X264();
$format->setKiloBitrate(1000)
->setAudioChannels(2)
->setAudioKiloBitrate(128);
// 4. Save
// This is a blocking process that can take minutes.
// Because it's in a worker, the user is not waiting.
$filesystem = new Filesystem();
$filesystem->mkdir(dirname($outputFile));
$video->save($format, $outputFile);
$this->logger->info("Transcoding complete: $outputFile");
}
}
The controller’s job is now incredibly simple:
Validate -> Move to Storage -> Dispatch -> Return 202 Accepted.
// src/Controller/VideoController.php
namespace App\Controller;
use App\Dto\VideoUploadDto;
use App\Message\ProcessVideoUpload;
use Symfony\Bundle\FrameworkBundle\Controller\AbstractController;
use Symfony\Component\HttpFoundation\JsonResponse;
use Symfony\Component\HttpFoundation\Request;
use Symfony\Component\HttpKernel\Attribute\MapUploadedFile;
use Symfony\Component\Messenger\MessageBusInterface;
use Symfony\Component\Routing\Attribute\Route;
use Symfony\Component\Uid\Uuid;
#[Route('/api/videos')]
class VideoController extends AbstractController
{
public function __construct(
private string $storageDir,
private MessageBusInterface $bus
) {}
#[Route('/upload', methods: ['POST'])]
public function upload(
// Validates automatically using our DTO constraints
#[MapUploadedFile] VideoUploadDto $uploadDto
): JsonResponse {
$file = $uploadDto->file;
$videoId = Uuid::v7()->toRfc4122();
// 1. Move file to Shared Directory
// We use the ID as the filename to avoid collisions
$filename = $videoId . '.' . $file->guessExtension();
$file->move($this->storageDir, $filename);
// 2. Dispatch the Manager Message
$this->bus->dispatch(new ProcessVideoUpload($videoId, $filename));
// 3. Immediate Response
return $this->json([
'status' => 'processing',
'id' => $videoId,
'message' => 'Video accepted. Processing pipelines initiated.'
], 202);
}
}
To see this parallelism in action, you need to run your workers. In a production environment (like Kubernetes or Docker Swarm), you would scale these deployments independently.
\ Terminal 1 (The Priority Worker): Handles the dispatching and thumbnails.
php bin/console messenger:consume async_priority -vv
\ Terminal 2 (The Heavy Worker): Handles the actual video encoding. You might run 4 or 5 of these containers.
php bin/console messenger:consume async_heavy -vv
By leveraging Symfony 7.4, we have transformed a complex problem into a clean, manageable architecture.
\ This architecture is “production-ready” but allows for growth. As you scale, you might replace the local Shared Directory with an Object Storage abstraction (using league/flysystem-aws-s3-v3), but the Messenger pipeline concepts remain exactly the same.
\ Video processing doesn’t have to be scary. With the right constraints and queue architecture, it becomes predictable and observable.
\ Have questions about scaling Symfony pipelines? Let’s connect. I share daily tips on modern PHP architecture.
\ LinkedIn: Connect with me [https://www.linkedin.com/in/matthew-mochalkin/]
\
2026-01-10 03:04:25
You probably type go build or go run dozens of times every week without thinking much about what happens under the hood. On the surface, these commands feel almost magical: you press Enter, and suddenly your code is compiled, linked, and - sometimes - executed. But beneath that simplicity lies a carefully orchestrated system, optimized to make your life as a developer easier while also being fast and predictable for machines.
Understanding how Go handles building, running, and caching code isn't just an academic exercise. It explains why incremental builds are so fast, why CI pipelines behave consistently, and why sometimes a seemingly trivial change can trigger a full recompilation. This article walks through the modern Go toolchain as it exists today, presenting a mental model you can trust.
By the end, you'll have a clear picture of:
go build or go run.If you've ever been curious about why Go builds "just work" or why your temporary go run binaries seem almost instantaneous, this is the deep dive that connects the dots - for humans and machines alike.
At first glance, go build, go run, and go test look like separate commands, each with its own behavior. In reality, they are just frontends for the same underlying pipeline. Every Go command goes through a predictable sequence: it loads modules, resolves package dependencies, compiles packages, optionally links them into an executable, and sometimes executes the result. The differences between commands mostly come down to what happens to the final artifact, not the mechanics of building it.
A key concept to internalize is that Go builds packages, not individual files. Every .go file in a package is treated collectively, and the package itself is the unit that the compiler and build cache track. This has several consequences:
The pipeline is conceptually simple, but highly optimized: Go knows exactly what needs recompilation and what can be reused, which is why incremental builds feel almost instantaneous. You can think of the toolchain as a smart coordinator: it orchestrates compiling, linking, caching, and execution so you rarely have to worry about the details. Once you internalize this mental model, the behavior of go build and go run stops feeling like magic and starts making predictable sense.
go.mod to a Build PlanBefore Go ever touches your source files, it needs to figure out what to build and in what order. This begins with the module system, centered around your go.mod and go.sum files. These files define the module graph, which is the full dependency tree of your project, along with precise versions for every module. By reading these files, the Go toolchain knows exactly which packages are part of your build and which external code to fetch, verify, and incorporate.
Once the module graph is loaded, Go evaluates each package to determine its source set. This includes every .go file that belongs to the package, filtered by build tags, operating system, architecture, and any constraints you've specified. Only after this evaluation does the compiler know what code it actually needs to process. This ensures that your builds are deterministic: the same go build command run on different machines produces identical results, assuming the same module versions.
An important aspect of modern Go is the role of the go directive in go.mod. This directive declares the minimum Go version your module is designed for. It influences several characteristics of the build: language semantics, compiler behavior, and even static analysis. Depending on the go directive, language semantics, compiler behavior, and checks can differ - the toolchain enforces these during compilation. This is part of Go's focus on reproducibility, ensuring that your code behaves consistently across environments.
By the end of this stage, the toolchain has a complete, ordered build plan: it knows which packages to compile, in what sequence, and which files belong to each package. With this information in hand, it moves on to the next step: compiling packages and linking them into binaries, confident that nothing will be missed or miscompiled.
Once Go has the build plan from the module system, it begins turning your code into something the machine can execute. This happens in two distinct stages: compilation and linking. Understanding these stages is key to appreciating why Go builds are fast, deterministic, and scalable.
Go compiles one package at a time. Each package - whether it's part of your project or an external dependency - is treated as an independent unit. The compiler produces intermediate artifacts for every package, which are stored in the build cache. This means that if a package hasn't changed since the last build, Go can skip recompiling it entirely, even if other packages that depend on it are being rebuilt.
Parallelism is another advantage of this per-package approach: since the compiler knows the dependency graph, it can compile multiple independent packages concurrently, fully leveraging multi-core CPUs. This is why large Go projects often feel surprisingly fast to build: a lot of work is done in parallel, and nothing is recompiled unnecessarily.
Linking is the process of combining compiled packages into a single executable. Go only links main packages into binaries. Library packages never get linked on their own, they exist purely as reusable artifacts for other packages. This distinction is important: when you run go build ./... on a project, Go may compile dozens of packages but produce zero binaries if none of the packages are main!
Linking is often the most expensive step in a build because it involves combining all dependencies into a single executable, resolving symbols, and embedding metadata. By keeping linking selective, and relying on cached package compilation, builds remain efficient.
The final binary is more than just your compiled code. It includes:
This combination is why Go binaries are self-contained and reproducible: they include everything needed to run without relying on external libraries or runtime environments. From a human perspective, this makes deployment straightforward. From a machine perspective, the build system can verify and cache everything efficiently, ensuring that repeated builds are fast and deterministic.
At the heart of Go's speed and predictability is its build cache. Every compiled package, every intermediate artifact, and even some tool outputs are stored in a content-addressed cache, which allows Go to reuse work across builds, commands, and even go run invocations. Understanding how the cache works is essential to grasping why Go builds feel almost instantaneous, even for large projects.
The build cache is more than just compiled binaries. It contains:
The cache lives on disk (by default in $GOCACHE) and is fully deterministic, meaning the same package compiled with the same inputs will always produce the same cache entry. This ensures that repeated builds, or builds across different machines, produce identical results.
Unlike traditional build systems that rely on file timestamps, Go uses content-based hashing to determine cache keys. Each cache key is a function of:
GOOS/GOARCH)This design guarantees that builds are reproducible and avoids false cache misses due to innocuous changes like timestamps or file order.
Even with a robust cache, Go will sometimes recompile packages. Common causes include:
Go's caching system is smart: it only rebuilds what actually needs rebuilding. Even small, non-semantic changes can trigger recompilation if they affect the package’s build hash, but otherwise, the cache is trusted implicitly.
The build cache is designed to be transparent and reliable:
go run, go test, and go build all leverage it consistentlyThis is why Go's incremental builds are so fast: the compiler never does more work than necessary. From a developer perspective, it feels magical. From a systems perspective, it's simply an optimized pipeline that treats package artifacts as first-class citizens.
go build: Producing ArtifactsThe go build command is the workhorse of the Go toolchain. Its job is simple to describe but sophisticated in execution: compile packages, link them if necessary, and produce a binary that is correct and reproducible. Understanding what go build actually does helps you predict its behavior and avoid common surprises.
When you run go build on a module or package, the tool first examines the dependency graph derived from your go.mod. Every package in the graph is checked against the build cache: if the cache contains a valid compiled artifact for a package, Go reuses it instead of recompiling. Only packages that have changed - or whose dependencies changed - are rebuilt.
Because Go operates at the package level, touching a single file inside a package can trigger a rebuild of the entire package. Conversely, if a dependency hasn't changed, it's never rebuilt, even if other packages rely on it. This per-package granularity is one of the reasons Go's incremental builds scale so well, even for large projects.
As we mentioned earlier, go build only produces an executable for main packages. Library packages are compiled into intermediate artifacts but never linked on their own. When linking a main package, Go combines all compiled packages into a single binary. This process also embeds metadata into the executable, including:
By default, inclusion of version control details is governed by the -buildvcs flag, which defaults to "auto" and stamps VCS information when the repository context allows (use -buildvcs=false to omit or -buildvcs=true to require it). More details can be found in the documentation here.
This makes Go binaries self-contained and highly reproducible, allowing you to deploy them confidently without worrying about missing dependencies.
By default, go build writes the binary in the current directory, named after the package. If the package is a library, go build doesn't produce a binary at all, it only ensures that the package and its dependencies are compiled. You can control output locations with the -o flag or use ./... to build multiple packages in one go.
On Windows, executables have a .exe suffix. When building multiple main packages at once (for example, ./cmd/...) without -o, Go writes one binary per main package into the current directory.
The combination of per-package compilation, caching, and selective linking ensures that go build is predictable. You can trust that:
In short, go build is not just compiling code, it's orchestrating a deterministic pipeline that balances human convenience with machine efficiency.
go run: Convenience Without Special PrivilegesIf go build is the workhorse that produces artifacts you can deploy, go run is the fast lane for experimenting and executing code immediately. Many developers think of it as "compiling and running in one step", but it's not: under the hood, it leverages the same build system as go build, it's just optimized for convenience rather than artifact persistence.
go run Actually DoesWhen you type go run main.go (or a list of files), Go first evaluates the package and its dependencies just as it would for go build. Any cached compiled packages are reused, so the compiler does minimal work for unchanged code. Then, Go links the main package into a temporary binary, executes it, and deletes the binary once the program finishes.
From a caching perspective, go run is not a special path, it fully participates in the build cache. This explains why repeated invocations of the same program often feel instantaneous: the heavy lifting has already been done, and only linking or changed packages may trigger compilation.
go run Feels DifferentDespite sharing the same underlying pipeline, go run can feel slower in certain scenarios. Because it produces a temporary binary every time, linking is repeated, even if all dependencies are cached. For small programs, this overhead is negligible, but for projects with large dependency graphs, it can be noticeable.
Another difference is that go run does not leave a persistent artifact. This is exactly the point: it trades binary reuse for ease of execution. You don't need to think about where to place the binary or what to call it, the tool handles it automatically.
go run Is the Right Tool - and When It Isn'tgo run is ideal for:
It's less suitable for:
For these cases, the recommended pattern is go build && ./binary, which gives you the benefits of caching, reproducibility, and a persistent artifact without sacrificing performance.
go test and Cached CorrectnessThe go test command builds on the same principles as go build and go run, but adds a layer of test-specific caching and execution logic. Understanding how tests interact with the build system helps explain why some tests run instantly while others trigger a rebuild, and why Go's approach feels both fast and predictable.
When you run go test, Go first determines the dependency graph for the test package, including any imported packages. Packages that haven't changed are reused from the build cache, just as with go build or go run. This means that large test suites can often start executing almost immediately, because most of the compilation work has already been done.
Even when multiple packages are involved, Go only rebuilds the packages that actually changed. The combination of per-package compilation and caching ensures that incremental test runs are fast, even in large projects.
In addition to caching compiled packages, Go also caches test results. If a test passes and none of its dependencies or relevant flags have changed, Go can skip re-running the test entirely.
Test result caching applies only in package list mode (e.g., go test . or go test ./...). In local directory mode (go test with no package args), caching is disabled.
This behavior is controlled by the -count flag. For example, go test -count=1 forces execution regardless of cached results. (-count repeats tests/benchmarks. -count=1 is the idiomatic way to bypass cached results. See the documentation for further details.)
Caching test results improves developer productivity and CI efficiency, especially for large projects with extensive test coverage. It also reinforces Go's philosophy: the system should avoid unnecessary work while preserving correctness.
A test may be re-run automatically if:
Otherwise, Go trusts the cached result, knowing it is deterministic and reproducible. This approach reduces "flaky" builds caused by unnecessary rebuilds and emphasizes predictability over blind convenience.
Here are some useful go test invocations that leverage caching behavior:
go test -count=1 ./... - as we saw earlier, this disables test result caching.go test -run '^TestFoo$' -count=100 ./pkg - runs TestFoo 100 times to check for flakiness.go test -bench . -count=3 - runs all benchmarks 3 times to get stable measurements.From a developer's perspective, the combination of build caching and test result caching creates a workflow that feels instantaneous and reliable:
By treating both packages and test results as first-class cacheable artifacts, Go makes testing fast and predictable, reinforcing the same "human + machine" optimization that underlies go build and go run.
Most of the time, Go's build system does exactly what you expect, quietly and efficiently. When something feels off, though, the toolchain gives you direct, low-level visibility into what it's doing. The key is knowing which switches to flip and how to interpret what you see.
Go provides a small set of flags that expose the build pipeline without changing its behavior:
-x prints the actual commands executed during the build. This includes compiler invocations, linker steps, and tool executions. It’s the fastest way to answer the question: "What is Go actually doing right now?"-n shows what would be executed, without running the commands. This is useful when you want to understand the build plan without triggering a rebuild.-work preserves the temporary build directory instead of deleting it. This lets you inspect intermediate files, generated code, and temporary artifacts produced during compilation or linking.These flags turn the Go toolchain from a black box into a transparent pipeline. Importantly, they don't disable caching, they simply make cache hits and misses visible.
One of the most common sources of confusion is a package rebuilding "for no apparent reason". With the right mental model, this becomes easier to diagnose:
Using -x, you can often see whether Go reused a cached artifact or recompiled a package, and infer why from the context. This removes the temptation to reach for blunt tools like go clean -cache as a first response.
Sometimes you really do want to bypass the cache. For example, when validating a clean build or debugging toolchain issues. Go supports this explicitly:
-a forces rebuilding of packages, ignoring cached compiled artifactsgo clean -cache clears the entire build cacheThese options are intentionally explicit and slightly inconvenient. Go is designed to make correct reuse the default, and manual cache invalidation the exception. If you find yourself clearing the cache regularly, it's often a sign that something else in the build setup needs attention.
Because Go's build system is deterministic, guessing rarely helps. Flags like -x, -n, and -work give you concrete evidence of what's happening, which is almost always enough to explain surprising behavior.
Once you trust that:
debugging build behavior becomes a matter of observation rather than trial and error.
The design choices behind Go's build system aren't accidental. They show up most clearly once you move beyond small examples and start working on real codebases: continuous integration pipelines, large repositories, and editor-driven workflows. The same principles that make go build feel fast locally are what make Go scale so well in production environments.
Go's emphasis on deterministic, content-addressed builds makes it particularly well-suited for CI. Because build outputs are derived entirely from source content, module versions, and explicit configuration, CI builds behave consistently across machines and environments. There's no reliance on filesystem timestamps, hidden state, or global configuration.
This predictability also makes Go builds highly cache-friendly. Whether you're using a shared build cache, container layers, or remote caching infrastructure, Go's package-level compilation model fits naturally. When a build is slow in CI, it's usually because something actually changed, not because the system decided to do extra work.
In large repositories, the build cache becomes a performance boundary. Because Go caches compiled packages independently, small, well-defined packages can be reused across many builds with minimal overhead. This encourages a code structure where dependencies are explicit and packages remain focused.
The flip side is that overly large or tightly coupled packages can become bottlenecks. A small change in a heavily used package can invalidate a large portion of the cache, increasing build times across the entire repository. Go doesn't hide this cost though, it makes package boundaries visible and meaningful, rewarding good structure and exposing poor separation early.
The same build model powers Go's tooling ecosystem. Code editors, language servers, linters, and code generators all rely on the same package-level understanding of your code. Because the toolchain exposes a clear, deterministic build pipeline, tools can integrate deeply without guessing or reimplementing build logic.
This is one reason Go tooling feels unusually consistent: editors and CI systems see your code the same way the compiler does. From autocomplete to refactoring to automated testing, everything builds on the same assumptions about packages, dependencies, and caching.
Go's build system succeeds because it makes a clear trade-off: it optimizes for predictability over cleverness, and for explicit structure over implicit behavior. At the surface, this looks like simplicity. Underneath, it's a carefully engineered pipeline that treats packages as the unit of work, content as the source of truth, and caching as a correctness feature rather than a performance hack.
Once you internalize this model, many everyday behaviors start to make sense. Builds are fast not because Go is doing less work, but because it avoids doing unnecessary work. go run feels convenient because it reuses the same machinery as go build, not because it shortcuts correctness. Test execution is reliable because test results are cached using the same deterministic rules as compiled packages.
For humans, this means fewer surprises, faster feedback loops, and tooling that behaves consistently across code editors, machines, and CI systems. For machines, it means reproducible builds, cache-friendly artifacts, and a system that scales naturally as codebases grow. The same design choices serve both audiences.
If there's one takeaway, it's this: Go's build system isn't something to fight or work around. It's an API in its own right - one that rewards understanding. Once you trust the model, the toolchain stops feeling magical and starts feeling dependable, which is exactly what you want from the infrastructure that builds your code.
\
2026-01-10 03:00:09
Writing with AI assistance is not the same as writing by AI.
When you write directly with AI, you’re prompting it to generate content from scratch. When you write with AI assistance, you remain the author—AI simply helps refine your work rather than doing most of it for you.
Yes, you can create a story from scratch using AI prompts, but the result will likely be short and won’t capture exactly what you wanted to say.
There are workarounds for the length problem. The most common approach involves asking AI to first create a summary of a story, then expand it by dividing it into chapter summaries—for example, 12 chapters. Each chapter summary then extends into story beats, perhaps 12 beats per chapter. Finally, you can ask AI to expand each story beat to roughly 500 words. This method can yield approximately 6,000 words per chapter and 72,000 words for an entire manuscript.
Quantity-wise, you’ll have a solid manuscript, and it might even have a coherent story structure. But it won’t be your story if AI generates everything itself.
Depending on your goals, you could set up automation and generate one or even multiple manuscripts per day. If your aim is to mass-produce low-effort content and earn money through sheer volume, that’s technically possible. But at that point, you’re not really an author—you’re a salesperson.
Another significant issue with fully AI-generated manuscripts is the near-total loss of creative control.
Think of your story as a path on a map. If you simply give AI a starting direction, the path will wander in unpredictable ways. You can try to guide it by creating detailed story beats yourself, essentially placing pins on the map to mark where you want the path to go. But AI will find its own route between those points, and you’ll have limited influence over what happens in between.
Those gaps are largely out of your hands. AI might introduce a subplot that derails your intended theme, or shift a character’s motivation in ways that contradict what comes later. By the time you notice, you’ve built subsequent chapters on a foundation you didn’t choose. Fixing it means backtracking—or accepting a story that drifted from your original vision.
You might write faster by only creating story beats, but the end result still won’t be the story you want to tell. It might be close enough—but “close enough” isn’t always good enough.
The alternative is writing with AI assistance. With this approach, you’re in the driver’s seat while AI handles the supporting work.
Imagine going on a road trip and using GPS for navigation. You’re in full control of the vehicle, choosing where to stop, which route to take, and how fast to drive. But when you need help finding your way, the GPS is there to guide you. Sometimes it suggests a faster route, and you take it. Other times, you ignore its recommendation because you know a scenic detour worth making. The GPS doesn’t argue—it recalculates and continues supporting your journey. That’s exactly what AI-assisted writing offers: you maintain creative control while AI provides helpful support when needed.
Simply put, when you write with AI assistance, you compose the manuscript yourself, and AI helps with polishing, style consistency, voice consistency, and overall line editing. AI is almost always better at rephrasing existing content than creating something entirely new.
With fiction, you also face fewer issues with hallucinations—the tendency for AI to generate incorrect information. Since you’re not creating a factual document, any invented details might actually work within your story. And for the factual elements you do include, they’re easy to verify yourself.
A common concern among writers considering AI assistance is authenticity: “If AI helped me, is it still my writing?”
Consider the tools you already use. Spell-check catches your typos. A thesaurus helps you find better word choices. Grammar software flags awkward constructions. Have these tools ever made you feel like less of a writer? Probably not—because the ideas, the story, and the creative decisions remain entirely yours.
AI assistance works the same way, just with greater capability. It’s a more sophisticated tool in your toolkit, not a ghostwriter. The plot you crafted, the characters you created, the themes you’re exploring—none of that changes because AI helped you smooth out a paragraph or maintain consistent dialogue patterns. You’re still the author. AI is simply helping you execute your vision more effectively.
One of the significant benefits of AI-assisted writing is the ability to create multiple characters with unique speech patterns. In traditional writing, you’d need to carefully track each character’s voice and ensure consistency throughout. With AI assistance, you can write dialogue naturally and then instruct AI to convert specific characters’ lines into their distinctive styles.
For example, consider this neutral line: “How was your trip? I was so worried about you. I hope you’re all right now.”
When asked to rephrase it for different character types, AI produces distinctly different results:
20-year-old valley girl:
“Oh my God, so like, how was your trip? I was literally SO worried about you—like, I couldn’t even. Are you okay now? Please tell me you’re okay!”
75-year-old grumpy man:
“Well? How’d it go? I wasn’t gonna say anything, but I didn’t sleep a wink worryin’ about you. You doin’ all right or what?”
35-year-old mother:
“Hey, sweetheart. How was the trip? I have to be honest—I was worried sick the whole time. Are you feeling okay now? Do you need anything?”
45-year-old Victorian gentleman:
“Pray tell, how did you find your journey? I confess I was most anxious during your absence. I do hope you have returned to us in good health and spirits.”
30-year-old tech professional:
“Dude, how was the trip? Not gonna lie, I was low-key stressing about you. You good now? Everything optimized?”
15-year-old teenage boy:
“Yo, how was your trip or whatever? I mean, I wasn’t like worried worried, but… yeah. You good now?”
As you can see, the core intent remains identical, but each line takes on a completely different personality. If you can already write fluently in multiple voices, this feature might not be necessary. But for most writers, it’s a valuable tool for filling gaps in their range.
AI-assisted writing also excels at direct line editing: checking spelling, punctuation, passive voice, and other mechanical issues.
Consider what AI can catch that you might miss after staring at your manuscript for hours. It can identify passive voice constructions and suggest active alternatives—transforming “The door was opened by Sarah” into “Sarah opened the door.” It can flag repetitive word usage, alerting you when you’ve used “suddenly” four times in two pages. It can analyze sentence rhythm, noting when you’ve strung together five short sentences in a row or written a paragraph of nothing but complex constructions.
AI can also help with consistency issues that are notoriously difficult to track manually. Are your dialogue tags consistent throughout? Have you accidentally switched from past to present tense mid-chapter? Does your character’s name spelling stay the same? These mechanical details matter, and AI catches them without fatigue or frustration.
Of course, a human editor with years of experience will perform better—and charge accordingly. If you can afford professional editing, it’s worth the investment. But AI can do a competent job, likely better than most writers can manage on their own.
If you’re ready to try AI-assisted writing, here’s a straightforward workflow to begin.
First, write your draft yourself. Get your ideas down without worrying about perfection. This is your creative foundation—the story, the voice, the vision. Don’t involve AI at this stage; you want the raw material to be authentically yours.
Second, identify specific tasks for AI. Rather than asking AI to “make this better,” be precise. Ask it to check for passive voice. Request that it rephrase a particular character’s dialogue in a gruff, working-class style. Have it identify repetitive words in a chapter. Specific prompts yield better results than vague ones.
Third, review everything. Accept the suggestions that improve your work. Reject the ones that don’t fit. Modify others to better match your voice. You’re the final authority—AI proposes, but you decide.
Finally, iterate. AI-assisted editing isn’t a one-pass process. You might run dialogue through voice refinement, then check the whole chapter for consistency, then do a final polish for flow. Each pass serves a different purpose.
AI may occasionally add extra details beyond what you’ve written. Sometimes these additions are welcome surprises that enhance your manuscript. Other times, they’re not.
The golden rule: always read everything AI generates. Never blindly trust AI output. Review every suggestion, every edit, every addition. You can always accept what works and reject what doesn’t—but only if you’re paying attention.
Be especially cautious about over-reliance. If you find yourself accepting every AI suggestion without thought, you risk losing your distinctive voice. The goal is collaboration, not abdication. AI should enhance your writing, not replace your judgment.
AI-assisted writing represents a middle path between doing everything yourself and letting AI do everything for you. It keeps you in creative control while leveraging AI’s strengths: consistency, polish, and the ability to transform content in ways that might be tedious or difficult to do manually.
Think of AI as a skilled assistant rather than a replacement author. Use it to enhance your writing, maintain consistency across characters and scenes, catch errors you might miss, and refine your prose. But remember—the story, the voice, and the vision should always be yours.
When used thoughtfully, AI assistance doesn’t diminish your role as an author. It amplifies it.
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2026-01-10 00:02:49
How are you, hacker?
🪐 What’s happening in tech today, January 9, 2026?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Steve Jobs announced the iPhone at the Macworld convention in 2007, Connecticut became the fifth state to join the United States of America in 1788, The government of Tunisia fell following a month of protests and demonstrations in 2011, and we present you with these top quality stories. From How a PM Can Transform Art Production: A Case Study in AAA Gaming to Everything You Need to Know About HackerNoon’s Proof of Usefulness Hackathon, let’s dive right in.
By @hackernoon-courses [ 7 Min read ] Interview with Saaniya Chugh, Senior Technical Consultant and tech author, on ITSM, AI, and the power of authentic storytelling in the digital age. Read More.
By @tolstykhzhe [ 6 Min read ] Project Manager of a AAA game reveals how he optimized art production by a factor of 3.3. Read More.
By @romanaxelrod [ 7 Min read ] AI-powered XR won’t be won by smart glasses alone. Why Big Tech is stuck optimizing and how deep tech, AI-driven RD, and new materials are reshaping computing Read More.
By @dmytrospilka [ 4 Min read ] The prospect of OpenAI becoming Wall Street’s largest-ever debut isn’t beyond the realms of possibility, but does it represent value to investors? Read More.
By @proofofusefulness [ 7 Min read ] Proof of Usefulness Hackathon FAQ: Learn how to submit projects, get scores, access sponsor credits, and compete for cash and software prizes. Read More.
🧑💻 What happened in your world this week?
It's been said that writing can help consolidate technical knowledge, establish credibility, and contribute to emerging community standards. Feeling stuck? We got you covered ⬇️⬇️⬇️
ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME
We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.See you on Planet Internet! With love, The HackerNoon Team ✌️
2026-01-10 00:00:05
\
I’m Saaniya Chugh, a Senior Technical Consultant at ServiceNow with a background in IT service management, digital transformation, and AI-driven automation. Over the last decade, I’ve worked across consulting, strategy, and leadership roles, helping organizations harness technology in meaningful ways.
Alongside my professional journey, I’m passionate about writing and storytelling especially making complex ideas in ITSM and AI accessible to everyone. I’ve published thought pieces, contributed to HackerNoon, and recently authored a book exploring how enterprises can integrate AI into their IT ecosystems.
At the heart of everything I do is a simple goal: to empower people, whether they’re readers, learners, or fellow professionals - to see technology not just as a tool, but as a collaborator in creativity and growth.
\
For me, writing started as a way of making sense of things. I’ve always been surrounded by complex ideas in technology and business, and I realized early on that if I couldn’t explain something simply, I probably didn’t understand it deeply enough myself. Writing became my way of breaking things down - first for me, and then for others.
On a more personal note, I’ve always loved stories. Growing up, I would hear my grandmother tell the same bedtime story over and over again, and yet every time, it felt new because of the way she told it. That taught me something powerful: stories aren’t just about information, they’re about connection.
So when I started publishing articles, and later my book, it wasn’t just about documenting knowledge. It was about creating a bridge, between technical and non-technical, between machines and people, between ideas and emotions. That’s what keeps me hooked: the ability of words to connect us in ways no algorithm ever fully can.
\
In a world full of algorithms, your authentic voice is still the greatest differentiator.
\
I’m most passionate about the space where IT Service Management, AI, and consulting intersect. ITSM has been the foundation of my career - it’s the discipline that taught me how structure, governance, and processes can enable organizations to scale responsibly. Over the years, I’ve seen ITSM evolve from being perceived as “just the plumbing of tech” into a strategic enabler of digital transformation.
What really excites me now is how AI is reshaping this space. Intelligent automation, predictive insights, and generative models are no longer buzzwords, they’re becoming part of everyday IT operations. For me, writing about this isn’t just about documenting technology, it’s about helping people understand how these tools can make their work more meaningful, reduce repetitive toil, and open up creative possibilities.
As a consultant, I get to work directly with enterprises that are figuring out how to bring these technologies into their environments. Writing about ITSM and AI lets me share those lessons more widely, taking what I’ve learned in boardrooms, client workshops, and transformation projects, and making it accessible to readers everywhere. It’s a way of bridging worlds: between technical and business, between curiosity and clarity, and between today’s challenges and tomorrow’s opportunities.
\
I’ve had two defining moments that shaped my career in very different but connected ways.
The first came during a major ITSM transformation project early in my consulting career. I was focused on the “how” - how to configure systems, how to automate workflows. But a client once asked me, “Saaniya, we know you can make the technology work. But what we really need is your perspective: how should we change as an organization?” That shifted everything for me. It made me realize that technology is only half the story - the real value comes when you can guide people, build trust, and align systems with culture and business outcomes. That question pushed me to step into strategic thinking, which later led me into AI-driven ITSM, where the conversations aren’t just about tools but about reshaping how work itself is done.
The second moment came when I published my first article on LinkedIn. Until then, writing felt personal, notes to myself, occasional blogs. But when that article went live, people across the world started engaging with it. That was the moment I realized writing wasn’t just about sharing knowledge; it was about joining a global conversation, simplifying complex ideas, and giving people the confidence to approach technology differently. That led me to keep writing, eventually publishing my book, ServiceNow’s Intelligent IT Service Management.
Together, these two moments taught me that my role isn’t just about implementing systems or writing words, it’s about creating connection. Whether it’s guiding an enterprise through change or helping a reader understand AI through a story, the lesson is the same: expertise matters, but empathy and perspective matter more.
\
I think the biggest shift is that content is no longer just about information - it’s about connection and context. In the past, storytelling was mostly one-way: you wrote, and readers consumed. Today, with AI, social platforms, and interactive formats, content has become a dialogue. Readers don’t just want facts, they want narratives they can relate to, voices they can trust, and stories that reflect both technology and human experience.
We’re also entering an age where discoverability is changing. With AI engines summarizing and surfacing content, it’s not enough to simply optimize for search engines anymore. Writers need to think about clarity, authority, and voice, because that’s what ensures your work gets cited, shared, and remembered.
Looking ahead, I believe storytelling will become even more important as technology advances. AI can generate words, but it can’t generate meaning. The role of the writer will be to weave together facts, insights, and emotions in a way that machines can’t replicate. For me, that’s the exciting part: the digital age doesn’t diminish the value of human storytelling, it magnifies it. Writers who embrace tools like AI as collaborators, while holding on to their authentic voice, will shape the narratives of the future.
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I chose to support the HackerNoon Writing Course because I truly believe in the power of community-driven learning. HackerNoon has always been a place where diverse voices come together to share ideas, challenge norms, and simplify complex technology for everyone. Being invited as a guest speaker felt like a natural extension of what I already love doing, making AI and IT concepts accessible, and inspiring others to find their own voice in the process.
I genuinely love HackerNoon both as a writer and as a reader. As a writer, it has given me the opportunity to share my perspective with a global audience; as a reader, it has exposed me to bold, curious voices that keep me learning every day. It has provided opportunities to grow in both spaces, and I’m deeply grateful for that.
For me, writing has been more than a skill - it’s been a way to connect across cultures, industries, and generations. I know how intimidating it can feel to put your words out into the world for the first time, and I want to help new writers see AI not as a threat, but as a collaborator that can amplify their reach and creativity.
My hope is to add value by sharing both the technical insights I’ve gained from consulting and the storytelling lessons I’ve learned from my own journey and if even one aspiring writer walks away with the confidence to hit “publish,” I’ll consider my role a success.
\
My first piece of advice is: just start writing. Don’t wait for the “perfect” topic, the “perfect” draft, or the “perfect” timing, because they never come. The act of putting words on the page is where the real clarity emerges.
Second, write for connection, not perfection. Whether you’re explaining a technical concept or sharing a personal story, your job as a writer isn’t to sound flawless, it’s to make someone feel understood. That’s where your voice matters more than polished grammar or trendy buzzwords.
Third, don’t be afraid to use AI as a collaborator. It can help you brainstorm, outline, or simplify your drafts, but remember: it’s scaffolding, not the house. The real power comes from your perspective, your experiences, your stories.
Finally, be consistent. Writing is like building a muscle; the more you practice, the stronger your voice becomes. Celebrate the small wins: publishing that first blog, getting your first comment, hitting “send” on a newsletter. Each step builds confidence.
Above all, remember this: your words have the power to create connection. Somewhere out there, someone needs to hear your story told in your way.
\ If you’d like to continue the conversation or share your own journey, I’d be happy to connect. You can find my work here:
📖 HackerNoon : https://hackernoon.com/u/saaniyachugh
💼 LinkedIn: https://www.linkedin.com/in/saaniyachugh/
🌐 Know more about my 1st book: https://link.springer.com/book/10.1007/979-8-8688-1706-9
I’m always open to hearing new ideas, stories, or perspectives. So if something I’ve said resonates with you, or if you’re just starting out and want a little encouragement, please feel free to reach out anytime. I’d love to connect, listen, and learn alongside you.
\
\
==Good Luck and wishing you an amazing writing experience!==
\
2026-01-09 20:30:07
Slump in the market divides good projects and bad projects. Whereas the vast majority of cryptocurrencies are struggling, there are some memecoins that exhibit odd persistence and accretion trends that are historically followed by significant movements. There are three tokens now which have different bullish indicators. PEPE at $0.000006619 holds on to $2.78B experiencing a strong increase in holders. BONK is trading with accumulation at the price of $0.00001177 with the market capital of $1.04B, despite the other wider market weak performance.
Then there's Pepeto ($PEPETO) at $0.000000176, showing presale momentum that seems completely disconnected from market conditions. This discussion deconstructs the bullish indicators that each of them presents, and which provide the most decisive road to 100x returns.
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PEPE is trading at the moment at $0.000006098 and a market cap of $2.78B (CoinMarketCap). Most memecoins have fallen in the recent market volatility, but PEPE has been relatively stable in its trading ranges. The number of holders is also increasing slowly. Exchange listing is still effective on all key platforms. The trading volume is maintained even in the market lows.
The optimistic indicator of this case is strength. PEPE has already survived several market cycles. Social groups that retain each other in tough times tend to have greater rebounds when the markets go up. The token is relevant in the social platforms in terms of memes. Recognition of the brand is also high. The above factors indicate that PEPE may engage in the coming upswing of the cycle.
Nevertheless, the math of 100x is problematic. Out of a total of $2.78B, a 100x gives an approximate of $278B market cap. That would make PEPE larger than Ethereum's current $377.2B. More realistic assumptions indicate 2x to 5x in bull markets that are strong. PEPE has demonstrated staying power, not geometrical returns on existing levels.
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BONK is traded at 0.00001177/$1.04B indicating interesting on-chain trends. The balances of exchanges have been dwindling despite the fact that the holders of tokens are migrating to self-custody. That is generally a positive indication of conviction as opposed to trading motive. Solana is resilient despite the wider market issues. BONK continues to be the top Solana memecoin.
The recent trends are elaborated by the increased DeFi additions as well as gaming collaborations. The combustion process proceeds to lower supply. Social participation remains alive. These indicators indicate that BONK might perform better on the following up leg, especially when Solana keeps attracting more developers and institutions.
The 100x question is difficult however. The 100x value is at $1.04B, which means that the market cap will need $104B to attain this level. It is ambitious even in the most optimistic cases. Under extraordinary circumstances, BONK would be able to perform 5x to 10x. But 100x returns are life-changing only mathematically bounded at the present values.
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Pepeto shows the most remarkable bullish signal. The presale has generated $7.14M despite weakness in the market on a market-wide basis. Participation of community is over 100K+. The number of projects applications in the exchange platform is over 850. These metrics grow regardless of Bitcoin or Ethereum price action. That is fundamental driven demand and not speculatively following the market trends.
The infrastructure roadmap gives numerous catalysts that do not rely on wider market feeling. PepetoSwap launch doesn't depend on bull market conditions. The cross-chain bridge is a solution that solves the issues irrespective of aggregate crypto prices. The free market does not charge any fee to exchange memecoin projects when markets rise or fall. The 216% staking brings the yields that are absolutely independent of the price volatility.
\ Operating on Ethereum positions Pepeto to benefit when DeFi activity increases, but the platform utility creates value even during quiet periods. This freedom of the market situation is a different risk profile than pure speculation tokens.
Most importantly, the 100x mathematics really work. Since $0.000000176 to 100x will take time, it will take around $300M to $400M market cap. That is less than 15% of the PEPE valuation and less than 4% of BONK. The calculation of the probability becomes easy. Lesser market caps require capital in the percentages by exponential means.
\
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The bullish indicator of PEPE is resilience. It was the token that made it through the death of others. That's valuable but passive. Expansion requires an extension of market recovery and re-interest in retail. Active catalysts do not have independent momentum.
Accumulation is a bullish indicator of BONK. Con conviction is demonstrated by self-custody moves by holders. The development of the ecosystem goes on. That is more robust than passive resilience and still reliant on the performance of Solana and the health of the memecoin sector in general.
Pepeto's bullish signal is active growth. $7.14M raised during market weakness. 100K+ participants joining regardless of market conditions. 850+ projects applying for platform access. These metrics improve independent of Bitcoin price. That is the best indicator since it is an indication of actual demand and not a mere positioning.
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Bitcoin trades at $91,243.14 with $1.82T. Ethereum sits at $3,131.71 with $377.2B. Both are stable and yet volatile in the recent past. Recessions in the market are strategic positioning opportunities. Constructions that take place in quiet times tend to do much better when markets are in a dramatic recovery.
Historical trends indicate that early-cycle projects where the development and the community is in full blast during tough times will give the best returns in the later bull markets. PEPE and BONK established their bases in the past quiet periods. Pepeto appears to be following that playbook now.
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PEPE and BONK are traded at primary exchanges. Regular methods of buying cryptocurrencies are used. Pepeto requires visiting Pepeto.io during presale. Connect an Ethereum wallet, use ETH, USDT, BNB, or payment cards, then optionally stake to earn 216% yields while awaiting exchange listings.
https://www.youtube.com/watch?v=wR3oOlNJj64&embedable=true
There are three memecoins that indicate a bullish signal in the times of market weakness but have a different connotation. PEPE at $0.000006619 is resilient by 2 to 5 times. The accumulation patterns in BONK at $0.00001177 indicate 5x to 10x. Pepeto at $0.000000176 displays active growth independent of market conditions, creating conditions for 100x probability through lower entry price and utility infrastructure.
With $7.14M raised, 100K+ participants, and 850+ platform applications during market downturn, Pepeto shows the strongest bullish conviction. Building projects that occur during quiet time in history work better during recoveries. For investors seeking 100x opportunities rather than moderate gains, Pepeto's combination of earlier-stage entry, comprehensive infrastructure, and market-independent momentum presents the clearest path.
\ Buy Pepeto Now Through The official Website: https://pepeto.io
\
\ To stay ahead of key updates, listings, and announcements, follow Pepeto on its official channels only:
Website: https://pepeto.io \n X (Twitter): https://x.com/Pepetocoin \n Telegram: https://t.me/pepeto_channel \n Instagram: https://www.instagram.com/pepetocoin/
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:::tip This story was published as a press release by Tokenwire under our Business Blogging Program.
:::
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