![](\"https://cdn-images-1.medium.com/max/1370/1*hzzDUeHxfmK853xWQFTxxA.png\")
Advanced TypeScript Patterns Every Senior Developer Uses in 2026
Most point-cloud workflows still start the same way.
\n\nInstall a desktop tool.
\nImport the scan.
\nWait.
\nInspect.
\nMeasure.
\nExport.
That workflow makes sense when you are doing serious GIS, photogrammetry, survey processing, classification, or production work.
\n\nBut there is another moment that is much simpler:
\n\nYou just received a scan and want to know what is inside it.
\n\nDoes it open?
\nIs it clean?
\nDoes it have color, intensity, or classification?
\nCan I measure something quickly?
\nCan I show it to someone without making them install software?
That is the moment I wanted to improve with OpenLiDARViewer.
\n\nIt is an open-source, browser-based LiDAR and point-cloud viewer built around one simple idea:
\n\nDrop a scan into the browser and inspect it locally.
\n\nNo upload.
\nNo account.
\nNo desktop install.
\nNo conversion step for supported formats.
Live demo: https://lidar.aurtech.mx/
\nGitHub: https://github.com/Aurtechmx/openlidarviewer/
There are already great tools for point-cloud work.
\n\nCloudCompare is powerful.
\nQGIS is powerful.
\nPotree is excellent for publishing point clouds on the web.
\nProfessional LiDAR software exists for a reason.
OpenLiDARViewer is not trying to replace those tools.
\n\nIt is focused on a smaller, earlier step:
\n\nthe first 60 seconds after you get a scan.
\n\nBefore you process it.
\nBefore you classify it.
\nBefore you prepare a report.
\nBefore you decide what workflow it belongs to.
Sometimes you just need a fast first look.
\n\nThat sounds simple, but LiDAR data makes “just open it” surprisingly complicated.
\n\n\n\n\nOne thing I wanted from the start was simple:
\n\nThe scan should not have to leave the user’s machine just to be inspected.
\n\nFor LiDAR and 3D scan data, that matters.
\n\nA scan can represent a private site, a client project, a building, a terrain model, an industrial asset, or a research dataset. Uploading it to a random cloud viewer just to take a quick look is not always acceptable.
\n\nSo OpenLiDARViewer runs client-side.
\n\nThe browser loads the app, but the scan itself is read, parsed, analyzed, and rendered locally.
\n\nThere is no backend parser.
\nNo server-side preprocessing.
\nNo cloud ingestion pipeline.
\nNo “upload your file and wait.”
Just the browser, the GPU, and the file on your machine.
\n\n\n\n\nThe viewer now supports common point-cloud and scan formats such as:
\n\nThat mix is intentional.
\n\nI wanted the same viewer to handle both:
\n\nThose worlds often feel separate, but in practice they are starting to overlap. People capture data from drones, phones, scanners, photogrammetry tools, and AR apps. A lightweight viewer should not care where the scan came from as long as the data can be parsed and rendered.
\n\nE57 support was especially important because it is a serious professional exchange format. It also brings more complexity: metadata, scan structure, transforms, optional attributes, and vendor differences.
\n\nGetting that kind of file working in the browser is not just a checkbox. It is an interoperability milestone.
\n\n\n\n\nThe first measurement tool was simple: pick two points and get a distance.
\n\nUseful, but limited.
\n\nThe newer measurement workflow is more practical. It includes:
\n\nIt does make it more useful for quick inspection.
\n\n\n\n\nA point cloud viewer should do more than show dots.
\n\nWhen I open a scan, I want quick answers:
\n\nThat is why OpenLiDARViewer includes scan intelligence and validation modules.
\n\nThe goal is not to replace professional QA/QC.
\n\nThe goal is to give a useful first read before committing to deeper processing.
\n\nSeeing the scan matters.
\n\nUnderstanding whether the scan looks intact matters too.
\n\n\n\n\nA lot of spatial tools inherit GIS interaction patterns.
\n\nThat is fine for GIS users, but it can be intimidating for people who just want to move through a 3D scan.
\n\nOpenLiDARViewer uses a more direct navigation model:
\n\nThe goal is to make the scan feel like a space you can enter, not just a dataset you loaded.
\n\nOrbit works well for objects and small sites.
\nWalk makes more sense for interiors or street-level scans.
\nFly feels better for terrain, drone LiDAR, and wide-area data.
This is one of the parts I care about most because good interaction design can make technical data much easier to understand.
\n\n\n\n\nAt first, this kind of tool feels desktop-first.
\n\nThen reality shows up.
\n\nPeople want to open scans:
\n\nMobile support creates its own set of problems:
\n\nA viewer that technically opens on mobile but feels terrible is not really mobile-friendly.
\n\nSo mobile support became part of the direction, especially for phone scan exports and quick review workflows.
\n\n\n\n\nIf you work with any of these areas, I would really value your feedback:
\n\nThe most useful feedback would be:
\n\nLive demo: https://lidar.aurtech.mx/
\nGitHub: https://github.com/Aurtechmx/openlidarviewer/
If the project is useful, a GitHub star helps. But real feedback from people who work with point-cloud data helps even more.
\n\n\n\n\nThe browser is becoming a serious runtime for technical software.
\n\nNot for everything.
\n\nNot for every workflow.
\n\nBut for the first step — opening, inspecting, measuring, and understanding spatial data — it is starting to make a lot of sense.
\n\nThat is the direction OpenLiDARViewer is exploring.
","score":3},{"source":"https://dev.to/feed/tag/node","sourceHost":"dev.to","title":"A Domain-Driven Notification Microservice — Patterns From Production","link":"https://dev.to/hammadxcm/a-domain-driven-notification-microservice-patterns-from-production-a05","pubDate":"Fri, 22 May 2026 21:06:57 +0000","description":"Notifications start small. \"Send the user an email when their order ships.\" A function. A library. Done.
\n\nA year later, you have email, SMS, push, in-app, Slack, and Microsoft Teams. You have user preferences per channel. You have quiet hours, batching, throttling, and a \"do not disturb\" mode. You have unsubscribe links and bounce handling. You have analytics on open rates and template-level metrics. You have multi-language templates and timezone-aware scheduling.
\n\nWhat started as a function is now a system. If you keep it as a sprawling collection of sendEmail and sendSlack helpers across your codebase, that system will eat your engineering team alive.
This is the shape of the notification microservice I built (and have rebuilt twice). The pattern isn't novel — it's domain-driven design applied to notifications — but the specifics matter.
\n\nA notification has three distinct concerns:
\n\nMost codebases collapse all three into one function:
\n
async function sendOrderShippedEmail(orderId: string, userId: string) {\n const user = await getUser(userId)\n const order = await getOrder(orderId)\n const html = renderTemplate('order-shipped', { user, order })\n await sendgrid.send({ to: user.email, subject: 'Your order shipped', html })\n}\nThis function knows about the domain event, the message type, and the channel. Three concerns. One function. Each one will change for different reasons, and changes will ripple across all the call sites.
\n\nThe DDD-shaped version separates them.
\n\nThe business code emits an event. It doesn't know or care how the notification gets delivered.
\n
// In your order service\nawait eventBus.publish({\n type: 'order.shipped',\n occurredAt: new Date().toISOString(),\n data: {\n orderId: order.id,\n userId: order.userId,\n trackingNumber: order.trackingNumber,\n },\n})\nThis is a fire-and-record operation. The order service is done. It moves on. The event lands in your event bus (BullMQ, Kafka, NATS, whatever).
\n\nThe notification service consumes events. Its job is to translate \"order.shipped\" into \"a transactional email to the user with this template.\"
\n\nThe user's preferences live in a separate domain. They might be stored in the notification service's database or in a profile service — doesn't matter, as long as they're queryable:
\n
type UserNotificationPreferences = {\n userId: string\n channels: {\n email: { enabled: boolean; address: string }\n sms: { enabled: boolean; number?: string }\n push: { enabled: boolean; deviceTokens: string[] }\n slack: { enabled: boolean; userId?: string }\n }\n perEventType: Record<string, {\n channels: ('email' | 'sms' | 'push' | 'slack')[]\n enabled: boolean\n }>\n quietHours: { start: string; end: string; tz: string } | null\n}\nWhen the notification service receives order.shipped, it looks up the user's preferences. The user has email enabled, SMS enabled, push enabled — but for this event type (order.shipped), they've only chosen email. So the service sends one email.
This decoupling is crucial. The business code emits one event. The notification service decides what to do with it based on user preferences. The user can change their preferences without anyone touching the business code.
\n\nThe middle layer is a dispatcher that:
\n\ntype Channel = 'email' | 'sms' | 'push' | 'slack'\n\nclass NotificationDispatcher {\n async handle(event: DomainEvent) {\n const userId = (event.data as any).userId\n if (!userId) return\n\n const prefs = await this.prefsRepo.findByUserId(userId)\n if (!prefs) return\n\n const eventPrefs = prefs.perEventType[event.type]\n if (!eventPrefs?.enabled) return\n\n const now = new Date()\n const channels = this.filterByQuietHours(eventPrefs.channels, prefs, now)\n\n for (const channel of channels) {\n await this.deliveryQueue.add('deliver', {\n eventType: event.type,\n userId,\n channel,\n eventData: event.data,\n scheduledAt: now.toISOString(),\n })\n }\n }\n\n private filterByQuietHours(\n requested: Channel[],\n prefs: UserNotificationPreferences,\n now: Date\n ): Channel[] {\n if (!prefs.quietHours) return requested\n const isQuiet = isWithinQuietHours(now, prefs.quietHours)\n if (!isQuiet) return requested\n // During quiet hours, only allow non-disruptive channels (e.g., email/in-app)\n return requested.filter(c => c === 'email' || c === 'in-app')\n }\n}\nThe dispatcher is pure routing logic. It doesn't render templates. It doesn't call any provider. It just figures out which channels to deliver on and queues delivery jobs.
\n\nEach channel is a separate worker that consumes jobs from the delivery queue and dispatches to its specific channel.
\n
class EmailDeliveryWorker {\n async handle(job: DeliveryJob) {\n if (job.channel !== 'email') return\n\n const user = await this.userRepo.findById(job.userId)\n const prefs = await this.prefsRepo.findByUserId(job.userId)\n if (!user || !prefs?.channels.email.enabled) return\n\n const template = await this.templateRepo.find(job.eventType, 'email')\n const rendered = await this.renderer.render(template, {\n user,\n ...job.eventData,\n })\n\n await this.emailProvider.send({\n to: prefs.channels.email.address,\n from: rendered.from,\n subject: rendered.subject,\n html: rendered.html,\n text: rendered.text,\n headers: {\n 'X-Event-Type': job.eventType,\n 'X-User-Id': job.userId,\n 'List-Unsubscribe': `<${this.unsubscribeUrl(job.userId, job.eventType)}>`,\n },\n })\n\n await this.deliveryLog.record({\n userId: job.userId,\n channel: 'email',\n eventType: job.eventType,\n sentAt: new Date().toISOString(),\n provider: this.emailProvider.name,\n })\n }\n}\nThe email worker knows about:
\n\nIt doesn't know about Slack, SMS, push, or any business logic. If I want to add a new channel, I add a new worker. The dispatcher already knows how to route to it (the channel is just a string in the job).
\n\nA template store is its own small domain. Each template has:
\n\norder.shipped).email, sms).en, de, fr).type Template = {\n id: string\n eventType: string\n channel: Channel\n language: string\n version: number\n subjectTemplate?: string // for email\n bodyTemplate: string\n format: 'html' | 'markdown' | 'plain'\n createdAt: string\n active: boolean\n}\nThe renderer takes a template and a context object and produces a rendered message. Use a templating library that has a sandboxed mode (Handlebars's strict mode, for example) — you do not want template authors writing arbitrary JS that gets executed during rendering.
\n
class Renderer {\n async render(template: Template, context: Record<string, any>): Promise<Rendered> {\n const compiled = Handlebars.compile(template.bodyTemplate, { strict: true })\n const body = compiled(context)\n const subject = template.subjectTemplate\n ? Handlebars.compile(template.subjectTemplate, { strict: true })(context)\n : undefined\n return { body, subject, format: template.format }\n }\n}\nTemplates being stored in a database (not in code) means non-engineers can edit them. We had marketing folks editing email copy via an admin panel without ever touching a deploy.
\n\nEvery notification should be unsubscribable. The dispatcher checks enabled flags before queuing, but the user needs a way to flip those flags. Two patterns:
The unsubscribe link encodes the user ID, the event type, and a signed token. Clicking it flips the preference:
\n
function unsubscribeUrl(userId: string, eventType: string): string {\n const token = sign({ userId, eventType, action: 'unsubscribe' })\n return `https://app.example.com/api/notify/unsubscribe?token=${token}`\n}\nThe endpoint verifies the token, updates the preference, and shows a \"you're unsubscribed\" page. The same endpoint can power the List-Unsubscribe header for email clients that support one-click unsubscribe.
Each delivery generates two records:
\n\nBoth feed into the same table, keyed by a message ID. The observability story collapses into \"show me everything that happened for user Y this week,\" which is what support teams ask for.
\n\nTwo problems show up at scale:
\n\nThe dispatcher is where this logic lives. Before queuing a delivery, check a sliding-window counter (Redis-backed):
\n
async handle(event: DomainEvent) {\n // ...existing routing logic...\n\n for (const channel of channels) {\n const window = `notify:${userId}:${channel}:${eventType}`\n const count = await this.redis.incr(window)\n if (count === 1) await this.redis.expire(window, 60) // 1-minute window\n\n if (count > MAX_PER_MINUTE) {\n // Throttled. Queue for batching instead.\n await this.batchQueue.add('batch', { userId, channel, eventType, eventData: event.data })\n } else {\n await this.deliveryQueue.add('deliver', { /* ... */ })\n }\n }\n}\nThe batch worker runs periodically (every 5 minutes or whatever the digest schedule is), collects everything in the batch queue for a user, renders a \"digest\" template, and sends one combined notification.
\n\nDon't put rendering in the dispatcher. Keep the dispatcher routing-only. The dispatcher decides which channels to use; the workers decide how to render and send. Mixing them couples your routing logic to your template engine in ways that hurt later.
\n\nUse job retries with caution. If the email provider returns a 503, retry. If it returns a 400 with \"invalid recipient,\" do not retry — that's a permanent failure. Different error codes have different retry semantics; encode this in the worker.
\n\nTrack template performance. Open rate per template per language is a real signal. Templates that score below 10% open rate are usually broken (bad subject line, bad timing, irrelevant content). Surface this in your admin UI.
\n\nMake the dead-letter queue visible. Failed deliveries should go somewhere humans can see them. We had three months of bounces piling up before anyone noticed; turned out a customer had typo'd their email and was getting nothing. A weekly dashboard of \"delivery failures by user\" caught it.
\n\nA notification microservice is one of the highest-leverage extractions you can do. The business code becomes simple (emit events). User preferences become a first-class concept. Templates become editable without deploys. New channels become new workers, not new branches in shared functions.
\n\nThe pattern is more code than sendgrid.send(...). It's also the difference between \"we have a notification feature\" and \"we have a notification platform.\" If you're shipping more than two notification types and you're tired of touching the same five functions every time product adds a new channel, this extraction pays for itself within a quarter.
\n\n\n\"One recurring issue in enterprise Angular apps: forms that start simple… then become entire application platforms.\"
\n
I've seen it across multiple production systems.
\n\nA product configuration screen ships with 40 fields. Requirements evolve. Validations multiply. Dynamic sections get added. Conditional logic compounds.
\n\nTwelve months later: 1,200+ controls. One FormGroup. Zero architectural boundaries.
And the team wonders why scrolling feels sluggish.
\n\nThis is not a Reactive Forms limitation. It's what happens when form architecture doesn't keep pace with form complexity.
\n\nIn this post, I'll break down:
\n\nMost Angular tutorials cover Reactive Forms at a comfortable scale. A login form. A registration screen. A checkout flow. At that scale, everything the framework provides is sufficient.
\n\nThe problems begin when forms are asked to do more than they were initially designed for.
\n\nIn enterprise applications, forms frequently evolve into workflow engines:
\n\nThe form didn't become complex overnight. It became complex incrementally — one field, one validator, one subscription at a time. And without intentional architectural boundaries, that incremental complexity accumulates into a system that is difficult to reason about, slow to render, and expensive to maintain.
\n\nThe key insight: Large forms are not primarily UI problems. They are state-management and rendering-architecture problems that happen to manifest as UI degradation.
\n\nUnderstanding this distinction changes how you approach the solution.
\n\n\n\n\nAngular's change detection is the first place large forms reveal their architectural debt.
\n\nIn Angular's default change detection strategy, a value change in any part of the component tree can trigger checks across the entire component tree. For a form with 1,000+ controls, this creates a predictable cascade:
\n\nFormControl emits a value change eventThis is not a bug in Angular. It's the default behaviour of zone.js-based change detection operating on a component tree without explicit boundaries.
\n\nThe profiler makes this visible. Open Angular DevTools on a large, unoptimised form, type a single character, and observe the flame chart. You'll see change detection running across components that have no logical relationship to the field you just edited.
\n\nThe issue scales non-linearly with form size. A form with 100 controls might have acceptable performance in Default change detection mode. At 500 controls, the detection overhead becomes noticeable. At 1,000+, it affects the perceived responsiveness of the form in ways that users notice and report.
\n
// The problem: one FormGroup, no detection boundaries\n@Component({\n selector: 'app-large-form',\n // Default change detection — entire subtree checked on every change\n template: `\n <form [formGroup]=\"rootForm\">\n <!-- 1,200 controls in one flat tree -->\n <input formControlName=\"field_1\" />\n <input formControlName=\"field_2\" />\n <!-- ... 1,198 more controls -->\n </form>\n `\n})\nexport class LargeFormComponent {\n rootForm = this.fb.group({\n field_1: [''],\n field_2: [''],\n // ... 1,198 more controls\n });\n}\nEvery change to field_1 triggers evaluation of expressions bound to field_1198. That is the rendering overhead problem.
Validation is the second compounding bottleneck.
\n\nAt the scale of individual forms, synchronous validators are fast and inconsequential. At the scale of hundreds of controls with cross-field dependencies, they become a measurable cost.
\n\nAngular's Reactive Forms run synchronous validators on every valueChanges emission. Every keystroke in every field dispatches a validation pass. For a root FormGroup with 1,000+ controls and a set of cross-field validators, this means:
startDate and endDate, quantity and minimumOrderValue, and regionCode and availableRegions — all fireAsync validators compound this further. If multiple fields trigger HTTP validation requests, and those requests aren't properly debounced and scoped, a form can generate significant network traffic from normal user interaction.
\n
// The problem: cross-field validators wired to root FormGroup\nconst rootForm = this.fb.group(\n {\n startDate: ['', Validators.required],\n endDate: ['', Validators.required],\n region: ['', Validators.required],\n quantity: [0, Validators.min(1)],\n // ... 996 more controls\n },\n {\n // This validator fires on EVERY change to ANY control in the root group\n validators: [\n dateRangeValidator,\n regionAvailabilityValidator,\n minimumOrderValidator\n ]\n }\n);\nWhen dateRangeValidator, regionAvailabilityValidator, and minimumOrderValidator are all wired to the root FormGroup, they execute on every change to every one of the 1,000 controls — including controls that have no logical relationship to the validation rules.
Subscription management is the third bottleneck — and the most likely to manifest as a production issue rather than a development-time observation.
\n\nReactive Forms expose valueChanges and statusChanges observables on every FormControl, FormGroup, and FormArray. These are powerful tools. They're also easy to accumulate carelessly.
In a large form component that has grown over time, it's common to find:
\n
ngOnInit() {\n // Subscription 1: react to section A changes\n this.rootForm.get('sectionA').valueChanges.subscribe(val => {\n this.updateSectionBDefaults(val);\n });\n\n // Subscription 2: sync UI state\n this.rootForm.statusChanges.subscribe(status => {\n this.isFormValid = status === 'VALID';\n });\n\n // Subscription 3: autosave\n this.rootForm.valueChanges.pipe(\n debounceTime(2000)\n ).subscribe(val => {\n this.autosaveService.save(val);\n });\n\n // ... 6 more subscriptions added by different developers over 12 months\n}\nIf these subscriptions are not explicitly destroyed when the component is destroyed — and in practice, many aren't — they create retained references that prevent garbage collection. In a single-page application where users navigate in and out of the form view, each navigation creates a new subscription set without cleaning up the previous one.
\n\nThe result: memory usage that grows monotonically with user navigation, and event handlers firing on components that no longer exist in the DOM.
\n\n\n\n\nThe solution to all three bottlenecks is the same architectural decision: treat large forms as modular systems, not monolithic components.
\n\nEach logical section of the form becomes a bounded module with:
\n\nFormGroup and validation scopeOnPush change detectionThis is not over-engineering. It is the minimum architecture that allows large forms to remain maintainable as they grow.
\n\nHere is the enterprise form structure we'll build toward:
\n
RootFormComponent (OnPush, orchestration only)\n├── PersonalInfoSection (OnPush, isolated FormGroup, scoped subscriptions)\n├── ConfigurationSection (OnPush, isolated FormGroup, scoped subscriptions)\n├── LineItemsSection (OnPush, FormArray, virtual scrolling)\n│ ├── LineItemRow × N (OnPush, minimal FormGroup per row)\n└── ReviewSection (OnPush, read-only derived state)\nLet's build each piece.
\n\n\n\n\nThe first and most impactful change is to replace one large flat FormGroup with a hierarchy of bounded sub-groups, each owned by its own component.
// enterprise-form.component.ts\nimport { Component, ChangeDetectionStrategy, inject } from '@angular/core';\nimport { FormBuilder, ReactiveFormsModule } from '@angular/forms';\n\n@Component({\n selector: 'app-enterprise-form',\n standalone: true,\n changeDetection: ChangeDetectionStrategy.OnPush,\n imports: [ReactiveFormsModule],\n template: `\n <form [formGroup]=\"rootForm\" (ngSubmit)=\"onSubmit()\">\n <app-personal-info-section\n [formGroup]=\"personalInfoGroup\">\n </app-personal-info-section>\n\n <app-configuration-section\n [formGroup]=\"configurationGroup\">\n </app-configuration-section>\n\n <app-line-items-section\n [formArray]=\"lineItemsArray\">\n </app-line-items-section>\n </form>\n `\n})\nexport class EnterpriseFormComponent {\n private fb = inject(FormBuilder);\n\n rootForm = this.fb.group({\n personalInfo: this.buildPersonalInfoGroup(),\n configuration: this.buildConfigurationGroup(),\n lineItems: this.fb.array([]),\n });\n\n get personalInfoGroup() {\n return this.rootForm.get('personalInfo') as FormGroup;\n }\n\n get configurationGroup() {\n return this.rootForm.get('configuration') as FormGroup;\n }\n\n get lineItemsArray() {\n return this.rootForm.get('lineItems') as FormArray;\n }\n\n private buildPersonalInfoGroup(): FormGroup {\n return this.fb.group({\n firstName: ['', [Validators.required, Validators.minLength(2)]],\n lastName: ['', [Validators.required, Validators.minLength(2)]],\n email: ['', [Validators.required, Validators.email]],\n phoneNumber: ['', Validators.pattern(/^\\+?[\\d\\s\\-()]{10,}$/)],\n });\n }\n\n private buildConfigurationGroup(): FormGroup {\n return this.fb.group({\n region: ['', Validators.required],\n currency: ['USD', Validators.required],\n planTier: ['standard', Validators.required],\n maxUsers: [10, [Validators.required, Validators.min(1)]],\n });\n }\n\n onSubmit() {\n if (this.rootForm.valid) {\n // Handle submission\n }\n }\n}\n// personal-info-section.component.ts\nimport {\n Component, Input, ChangeDetectionStrategy, OnInit, OnDestroy, inject\n} from '@angular/core';\nimport { FormGroup, ReactiveFormsModule } from '@angular/forms';\nimport { Subject } from 'rxjs';\nimport { takeUntil, debounceTime } from 'rxjs/operators';\n\n@Component({\n selector: 'app-personal-info-section',\n standalone: true,\n changeDetection: ChangeDetectionStrategy.OnPush,\n imports: [ReactiveFormsModule],\n template: `\n <section [formGroup]=\"formGroup\">\n <h3>Personal Information</h3>\n\n <div class=\"field-row\">\n <label for=\"firstName\">First Name</label>\n <input id=\"firstName\" formControlName=\"firstName\" />\n @if (formGroup.get('firstName')?.invalid && formGroup.get('firstName')?.touched) {\n <span class=\"error\">First name is required</span>\n }\n </div>\n\n <div class=\"field-row\">\n <label for=\"email\">Email Address</label>\n <input id=\"email\" type=\"email\" formControlName=\"email\" />\n </div>\n </section>\n `\n})\nexport class PersonalInfoSectionComponent implements OnInit, OnDestroy {\n @Input({ required: true }) formGroup!: FormGroup;\n\n private destroy$ = new Subject<void>();\n\n ngOnInit() {\n // Subscriptions are scoped to THIS section's lifecycle\n // Not to the root form's lifecycle\n this.formGroup.get('email')?.valueChanges.pipe(\n debounceTime(400),\n takeUntil(this.destroy$)\n ).subscribe(email => {\n // Handle email-specific side effects in isolation\n });\n }\n\n ngOnDestroy() {\n this.destroy$.next();\n this.destroy$.complete();\n }\n}\nWhat this achieves:
\n\nPersonalInfoSectionComponent. A value change in the configuration section does not trigger checks in this component.FormGroup.OnPush reduces the cost of change detection cycles. @defer reduces the cost of the initial render by mounting sections only when needed.
Angular 17 introduced @defer as a first-class template syntax for deferred loading. For large forms, it provides two key benefits:
FormControl instances are not included in the initial change detection scope<!-- enterprise-form.template.html -->\n\n<!-- Section 1: Always rendered (above the fold) -->\n<app-personal-info-section\n [formGroup]=\"personalInfoGroup\">\n</app-personal-info-section>\n\n<!-- Section 2: Rendered when it enters the viewport -->\n@defer (on viewport) {\n <app-configuration-section\n [formGroup]=\"configurationGroup\">\n </app-configuration-section>\n} @placeholder {\n <app-section-skeleton label=\"Configuration\" fieldCount=\"6\">\n </app-section-skeleton>\n}\n\n<!-- Section 3: Line items — heavy section, deferred -->\n@defer (on interaction(lineItemsTrigger)) {\n <app-line-items-section\n [formArray]=\"lineItemsArray\">\n </app-line-items-section>\n} @loading (minimum 200ms) {\n <div class=\"loading-indicator\">Loading line items...</div>\n} @placeholder {\n <button #lineItemsTrigger type=\"button\" class=\"load-section-btn\">\n Load Line Items ({{ lineItemsArray.length }} items)\n </button>\n}\n// section-skeleton.component.ts\n@Component({\n selector: 'app-section-skeleton',\n standalone: true,\n changeDetection: ChangeDetectionStrategy.OnPush,\n template: `\n <div class=\"skeleton-section\">\n <div class=\"skeleton-title\"></div>\n @for (i of fields; track i) {\n <div class=\"skeleton-field\">\n <div class=\"skeleton-label\"></div>\n <div class=\"skeleton-input\"></div>\n </div>\n }\n </div>\n `\n})\nexport class SectionSkeletonComponent {\n @Input() fieldCount = 4;\n get fields() { return Array(this.fieldCount).fill(0); }\n}\nWhat this achieves:
\n\n@placeholder state provides visual continuity without empty spaceAngular 16 introduced takeUntilDestroyed() — a cleaner alternative to the Subject/takeUntil pattern for subscription cleanup.
// configuration-section.component.ts\nimport {\n Component, Input, ChangeDetectionStrategy, OnInit,\n inject, DestroyRef\n} from '@angular/core';\nimport { takeUntilDestroyed } from '@angular/core/rxjs-interop';\nimport { FormGroup } from '@angular/forms';\nimport { debounceTime, distinctUntilChanged } from 'rxjs/operators';\n\n@Component({\n selector: 'app-configuration-section',\n standalone: true,\n changeDetection: ChangeDetectionStrategy.OnPush,\n})\nexport class ConfigurationSectionComponent implements OnInit {\n @Input({ required: true }) formGroup!: FormGroup;\n\n private destroyRef = inject(DestroyRef);\n\n ngOnInit() {\n // Automatically unsubscribes when component is destroyed\n // No manual ngOnDestroy required\n this.formGroup.get('planTier')?.valueChanges.pipe(\n debounceTime(300),\n distinctUntilChanged(),\n takeUntilDestroyed(this.destroyRef)\n ).subscribe(tier => {\n this.adjustMaxUsersForTier(tier);\n });\n\n this.formGroup.get('region')?.valueChanges.pipe(\n debounceTime(200),\n distinctUntilChanged(),\n takeUntilDestroyed(this.destroyRef)\n ).subscribe(region => {\n this.updateCurrencyForRegion(region);\n });\n }\n\n private adjustMaxUsersForTier(tier: string) {\n const maxUsersControl = this.formGroup.get('maxUsers');\n const limits: Record<string, number> = {\n starter: 5,\n standard: 50,\n enterprise: 500\n };\n if (limits[tier]) {\n maxUsersControl?.setValue(limits[tier], { emitEvent: false });\n }\n }\n\n private updateCurrencyForRegion(region: string) {\n const currencyMap: Record<string, string> = {\n 'EU': 'EUR',\n 'UK': 'GBP',\n 'US': 'USD',\n };\n const currency = currencyMap[region] ?? 'USD';\n this.formGroup.get('currency')?.setValue(currency, { emitEvent: false });\n }\n}\nNotice { emitEvent: false } in the setValue calls above. When you programmatically update a control value in response to another control's change, omitting this option creates a feedback loop:
region → subscription fires → currency is updatedcurrency.valueChanges emits → any subscriber to currency fires{ emitEvent: false } breaks this cycle. It updates the control value without emitting a valueChanges event — which is the correct behaviour for programmatic, reactive updates.
Cross-field validators should be scoped to the smallest FormGroup that contains all the fields they need to read. They should never be placed on a parent group to validate children they don't need.
\n\n\nA validator belongs on the lowest
\nFormGroupthat contains all of its required controls.
\n
// isolated-validators.ts\n\n/**\n * Validates that endDate is not before startDate.\n * Scoped to a FormGroup containing only startDate and endDate.\n */\nexport function dateRangeValidator(\n startKey = 'startDate',\n endKey = 'endDate'\n): ValidatorFn {\n return (group: AbstractControl): ValidationErrors | null => {\n const start = group.get(startKey)?.value;\n const end = group.get(endKey)?.value;\n\n if (!start || !end) return null;\n\n return new Date(start) > new Date(end)\n ? { dateRange: { start, end, message: 'End date must be after start date' } }\n : null;\n };\n}\n\n/**\n * Validates that quantity does not exceed available inventory.\n * Scoped to a FormGroup containing quantity and productId.\n * Async — hits inventory API only for that sub-group.\n */\nexport function inventoryAvailabilityValidator(\n inventoryService: InventoryService\n): AsyncValidatorFn {\n return (group: AbstractControl): Observable<ValidationErrors | null> => {\n const productId = group.get('productId')?.value;\n const quantity = group.get('quantity')?.value;\n\n if (!productId || !quantity) return of(null);\n\n return inventoryService.checkAvailability(productId, quantity).pipe(\n debounceTime(400),\n map(available => available ? null : { insufficientInventory: { productId, quantity } }),\n catchError(() => of(null))\n );\n };\n}\n// line-item-row.component.ts — validator on sub-group, not root\nprivate buildLineItemGroup(): FormGroup {\n return this.fb.group(\n {\n productId: ['', Validators.required],\n quantity: [1, [Validators.required, Validators.min(1)]],\n startDate: ['', Validators.required],\n endDate: ['', Validators.required],\n unitPrice: [0, [Validators.required, Validators.min(0)]],\n },\n {\n validators: [dateRangeValidator('startDate', 'endDate')],\n asyncValidators: [inventoryAvailabilityValidator(this.inventoryService)],\n updateOn: 'blur' // Reduces async validator frequency significantly\n }\n );\n}\nupdateOn: 'blur' on the group level is another important lever. For groups with async validators, changing the update strategy from change (default) to blur reduces API calls from \"one per keystroke\" to \"one per field exit.\"
When a form contains a repeating list of rows — line items, user entries, product configurations — the CDK VirtualScrollViewport provides consistent rendering performance regardless of list length.
ng add @angular/cdk\n// line-items-section.component.ts\nimport {\n Component, Input, ChangeDetectionStrategy, inject\n} from '@angular/core';\nimport { FormArray, FormBuilder, ReactiveFormsModule } from '@angular/forms';\nimport { ScrollingModule } from '@angular/cdk/scrolling';\n\n@Component({\n selector: 'app-line-items-section',\n standalone: true,\n changeDetection: ChangeDetectionStrategy.OnPush,\n imports: [ReactiveFormsModule, ScrollingModule],\n template: `\n <div class=\"line-items-header\">\n <h3>Line Items ({{ formArray.length }})</h3>\n <button type=\"button\" (click)=\"addLineItem()\">Add Item</button>\n </div>\n\n <!--\n itemSize: estimated height of each row in px\n height must be set explicitly on the viewport\n -->\n <cdk-virtual-scroll-viewport\n itemSize=\"64\"\n style=\"height: 480px; overflow-y: auto;\"\n class=\"line-items-viewport\">\n\n <div\n *cdkVirtualFor=\"let ctrl of lineItemControls; trackBy: trackByIndex\"\n class=\"line-item-row\">\n <app-line-item-row\n [formGroup]=\"asFormGroup(ctrl)\"\n (remove)=\"removeLineItem($index)\">\n </app-line-item-row>\n </div>\n\n </cdk-virtual-scroll-viewport>\n\n <div class=\"line-items-footer\">\n <span>Total: {{ lineItemTotal | currency }}</span>\n </div>\n `\n})\nexport class LineItemsSectionComponent {\n @Input({ required: true }) formArray!: FormArray;\n\n private fb = inject(FormBuilder);\n\n get lineItemControls() {\n return this.formArray.controls;\n }\n\n get lineItemTotal(): number {\n return this.formArray.value.reduce(\n (sum: number, item: any) => sum + ((item.quantity ?? 0) * (item.unitPrice ?? 0)),\n 0\n );\n }\n\n addLineItem() {\n this.formArray.push(\n this.fb.group({\n productId: ['', Validators.required],\n quantity: [1, [Validators.required, Validators.min(1)]],\n unitPrice: [0, Validators.min(0)],\n startDate: [''],\n endDate: [''],\n })\n );\n }\n\n removeLineItem(index: number) {\n this.formArray.removeAt(index);\n }\n\n trackByIndex(index: number) {\n return index;\n }\n\n asFormGroup(ctrl: AbstractControl): FormGroup {\n return ctrl as FormGroup;\n }\n}\nThe virtual scroll viewport renders only the rows currently visible in the scrollable area — typically 10–15 rows at a time — regardless of how many rows exist in the FormArray. As the user scrolls, DOM nodes are recycled and reused with new data.
The practical effect: a FormArray with 2,000 line items renders with the same DOM complexity as one with 20 visible rows.
Angular 16+ introduced Signals, and Angular 17+ provides stable toSignal and toObservable bridges for reactive interop. For form-heavy components, the toSignal bridge provides a clean way to derive computed state from form values without additional subscriptions.
\n
// form-signals.component.ts\nimport {\n Component, ChangeDetectionStrategy, inject, computed\n} from '@angular/core';\nimport { FormBuilder, ReactiveFormsModule } from '@angular/forms';\nimport { toSignal } from '@angular/core/rxjs-interop';\nimport { debounceTime } from 'rxjs/operators';\n\n@Component({\n selector: 'app-order-form',\n standalone: true,\n changeDetection: ChangeDetectionStrategy.OnPush,\n imports: [ReactiveFormsModule],\n template: `\n <form [formGroup]=\"orderForm\">\n <!-- form fields -->\n </form>\n\n <!-- Computed state derived from signals — no subscription needed -->\n <div class=\"order-summary\">\n <p>Subtotal: {{ subtotal() | currency }}</p>\n <p>Tax ({{ taxRate() }}%): {{ taxAmount() | currency }}</p>\n <p>Total: {{ orderTotal() | currency }}</p>\n </div>\n\n @if (isOrderValid()) {\n <button type=\"submit\">Place Order</button>\n }\n `\n})\nexport class OrderFormComponent {\n private fb = inject(FormBuilder);\n\n orderForm = this.fb.group({\n lineItems: this.fb.array([]),\n taxRate: [0.1],\n discount: [0],\n });\n\n // Bridge form value changes into the signal graph\n // debounceTime reduces the frequency of signal emissions\n private formValue = toSignal(\n this.orderForm.valueChanges.pipe(debounceTime(100)),\n { initialValue: this.orderForm.value }\n );\n\n private formStatus = toSignal(\n this.orderForm.statusChanges,\n { initialValue: this.orderForm.status }\n );\n\n // Derived state computed from signals — no subscriptions, no ngOnDestroy\n subtotal = computed(() => {\n return this.formValue()?.lineItems?.reduce(\n (sum: number, item: any) => sum + ((item.quantity ?? 0) * (item.unitPrice ?? 0)),\n 0\n ) ?? 0;\n });\n\n taxRate = computed(() =>\n ((this.formValue()?.taxRate ?? 0) * 100).toFixed(0)\n );\n\n taxAmount = computed(() =>\n this.subtotal() * (this.formValue()?.taxRate ?? 0)\n );\n\n orderTotal = computed(() =>\n this.subtotal() + this.taxAmount() - (this.formValue()?.discount ?? 0)\n );\n\n isOrderValid = computed(() =>\n this.formStatus() === 'VALID'\n );\n}\nWhat this achieves:
\n\ncomputed signals are lazy — they only recalculate when their dependencies changengOnDestroy needed for the derived state — signals are garbage collected with their owning componentorderTotal depends on// ❌ Anti-pattern: everything in one place\n\n@Component({\n // No OnPush — default change detection\n template: `<form [formGroup]=\"rootForm\">...</form>`\n})\nexport class MonolithicFormComponent implements OnInit {\n rootForm = this.fb.group({\n // 1,200 controls in one flat tree\n firstName: ['', Validators.required],\n // ... 1,199 more\n }, {\n // Cross-field validators on the root group\n validators: [dateRangeValidator, regionValidator, inventoryValidator]\n });\n\n ngOnInit() {\n // Subscriptions on the root form — never explicitly destroyed\n this.rootForm.valueChanges.subscribe(v => this.autosave(v));\n this.rootForm.get('region').valueChanges.subscribe(r => this.updateCurrency(r));\n // ... 8 more subscriptions added over 12 months\n }\n\n // No ngOnDestroy — subscriptions are never cleaned up\n}\nProblems:
\n\n// ✅ Scalable pattern: bounded modules\n\n@Component({\n changeDetection: ChangeDetectionStrategy.OnPush, // OnPush at root\n template: `\n <form [formGroup]=\"rootForm\">\n <!-- Section 1: always visible -->\n <app-personal-info-section [formGroup]=\"personalInfoGroup\" />\n\n <!-- Section 2: deferred until viewport -->\n @defer (on viewport) {\n <app-configuration-section [formGroup]=\"configurationGroup\" />\n } @placeholder {\n <app-section-skeleton />\n }\n\n <!-- Section 3: line items with virtual scroll -->\n @defer (on interaction(trigger)) {\n <app-line-items-section [formArray]=\"lineItemsArray\" />\n } @placeholder {\n <button #trigger>Load Line Items</button>\n }\n </form>\n `\n})\nexport class ModularFormComponent {\n rootForm = this.fb.group({\n personalInfo: this.buildPersonalInfoGroup(), // Bounded group\n configuration: this.buildConfigurationGroup(), // Bounded group\n lineItems: this.fb.array([]), // FormArray, virtually scrolled\n });\n}\n\n// Each section: OnPush, scoped subscriptions, isolated validators\n@Component({ changeDetection: ChangeDetectionStrategy.OnPush })\nexport class ConfigurationSectionComponent implements OnInit {\n @Input({ required: true }) formGroup!: FormGroup;\n private destroyRef = inject(DestroyRef);\n\n ngOnInit() {\n this.formGroup.get('planTier')?.valueChanges.pipe(\n debounceTime(300),\n takeUntilDestroyed(this.destroyRef) // Auto-cleanup\n ).subscribe(tier => this.adjustMaxUsers(tier));\n }\n}\nWhat changed:
\n\nThe patterns in this post are not Angular-specific optimisations in the narrow sense. They are the application of standard software engineering principles — bounded contexts, separation of concerns, explicit ownership — to the domain of reactive forms.
\n\nA FormGroup without explicit boundaries is a module without explicit dependencies. A validator on a root group is a global function with implicit inputs. A subscription without explicit cleanup is a resource without an owner.
The performance improvements that result from applying these patterns are real and measurable. But the more durable benefit is architectural: forms that are segmented, isolated, and scoped are easier to reason about, easier to test, easier to maintain, and easier to hand off to another developer.
\n\n\n\n\n\"Large forms should behave like modular systems — not giant components.\"
\n
The forms in your enterprise applications will grow. The requirements will change. The team will turn over. The architecture you establish in week one determines whether those changes are routine or painful.
\n\n\n\n\nOn rendering:
\n\nOnPush change detection to every form section componentOn validation:
\n\nFormGroup that contains all required controlsupdateOn: 'blur' for groups with async validators to reduce API call frequencyOn subscriptions:
\n\ntakeUntilDestroyed(this.destroyRef) for automatic cleanup (Angular 16+){ emitEvent: false } when programmatically updating controls in response to other controlsOn rendering performance:
\n\n@defer (on viewport) to mount sections only when they enter the viewportCdkVirtualScrollViewport for repeating row lists with more than ~50 rowsOn state management:
\n\ntoSignal to bridge form observables into the signal graph for derived statecomputed signals over subscribe for derived values — they're lazy and self-cleaningOn team scalability:
\n\nEnterprise Angular forms become difficult to manage not because Reactive Forms is insufficient, but because the architectural patterns that work at small scale don't hold at large scale.
\n\nThe shift is conceptual: stop thinking about a large form as a FormGroup with many controls, and start thinking about it as a system of bounded modules that each own their rendering, validation, and state responsibilities.
The Angular tooling to support this architecture is all present and stable: OnPush, @defer, FormArray, CdkVirtualScrollViewport, takeUntilDestroyed, toSignal, and standalone components. The decisions about how to apply them are yours.
Have you hit performance issues with large Angular forms in production? What patterns worked for your team? Drop a comment below — I read every one.
\n\n\n\n\n📌 More From Me
\nI share daily insights on web development, architecture, and frontend ecosystems.
\nFollow me here on Dev.to, and connect on LinkedIn for professional discussions.
🌐 Connect With Me
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🔗 LinkedIn — Professional discussions, architecture breakdowns, and engineering insights.
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Tags: #angular #webdev #typescript #frontend
Security monitoring tools store your real session tokens.
\n\nEvery JWT. Every credential. For every user, across every application they protect — sitting in a third-party database. If that vendor is breached, every application they monitor is compromised. This is not a theoretical risk. It is the architecture of almost every API security platform on the market today.
\n\nWe spent 18 months building a different approach. DevFortress launches today.
\n\n\n\n\nWhen you integrate a traditional API security monitor, your SDK sends real session tokens to the monitoring platform for analysis. The platform logs them, correlates them, runs ML models on them. The platform needs the real token to do its job.
\n\nThis means the security tool designed to protect you is also the largest single point of credential exposure in your infrastructure.
\n\nWhen the DevFortress SDK intercepts a session token, it generates a completely random alias — no mathematical relationship to the real token — and sends only that alias to the platform. The real token never leaves your application boundary.
\n\nIf DevFortress is breached tomorrow: attackers get a database of random strings that authenticate nothing, anywhere.
\n\nTraditional security tools detect a threat and send you an alert. The attacker's session stays active while you wake up, read the alert, triage it, and decide what to do. Average human response time: hours. Time an attacker needs to exfiltrate data: seconds.
\n\nDevFortress closes the loop automatically. When a threat is detected, the platform fires a signed HMAC-SHA256 webhook to your application. Your application revokes the session, blocks the IP, and confirms back to the platform. The entire cycle completes in under 2 seconds. No human in the loop.
\n\nAI agents introduced a new attack surface. A LangChain or AutoGen agent running in production holds real API keys with broad scope. One successful prompt injection and an attacker has those credentials.
\n\nDevFortress for agents:
\n\nInstall the SDK (free):
\n
npm install devfortress-sdk@4.8.0\nRead the full architecture writeup:
\nhttps://www.devfortress.net/blog/devfortress-launch
Get the Master Edition textbook (full architecture deep-dive + interactive demos):
\nhttps://devfortress.gumroad.com/l/master-edition
Subscribe to the weekly security journal:
\nhttps://devfortress.substack.com
Happy to go deep on any of the inventions in the comments — especially the credential isolation mechanism, the closed-loop webhook architecture, or the AI agent scope enforcement.
","score":3},{"source":"https://github.blog/feed","sourceHost":"github.blog","title":"GitHub recognized as a Leader in the Gartner® Magic Quadrant™ for Enterprise AI Coding Agents for the third year in a row","link":"https://github.blog/ai-and-ml/github-copilot/github-recognized-as-a-leader-in-the-gartner-magic-quadrant-for-enterprise-ai-coding-agents-for-the-third-year-in-a-row/","pubDate":"Fri, 22 May 2026 16:10:21 +0000","description":"We are committed to empowering every developer by building an open, secure, and AI-powered platform that defines the future of software development.
\nThe post GitHub recognized as a Leader in the Gartner® Magic Quadrant™ for Enterprise AI Coding Agents for the third year in a row appeared first on The GitHub Blog.
","score":4},{"source":"https://medium.com/feed/tag/typescript","sourceHost":"medium.com","title":"Advanced TypeScript Patterns Every Senior Developer Uses in 2026","link":"https://medium.com/@tarxemo/advanced-typescript-patterns-every-senior-developer-uses-in-2026-68c5a4d51943?source=rss------typescript-5","pubDate":"Fri, 22 May 2026 15:31:20 GMT","description":"","score":3},{"source":"https://www.smashingmagazine.com/feed","sourceHost":"smashingmagazine.com","title":"Four Levels Of Customer Understanding","link":"https://smashingmagazine.com/2026/05/four-levels-customer-understanding/","pubDate":"Fri, 22 May 2026 13:00:00 GMT","description":"What people say, feel, think, and do are often very different things. To understand the underlying reasons for user behavior, it helps to look beyond the surface and explore hidden motivations, root causes, and the different layers of reality that shape how people act. Brought to you by Measuring UX Impact, **friendly video course on UX** and design patterns by Vitaly.","score":2},{"source":"https://dev.to/feed/tag/node","sourceHost":"dev.to","title":"Bundle size creep goes unnoticed until PR review — so I built `buildwatch`","link":"https://dev.to/mumicrotools/bundle-size-creep-goes-unnoticed-until-pr-review-so-i-built-buildwatch-52im","pubDate":"Fri, 22 May 2026 12:45:45 +0000","description":"During development, bundle size creep goes unnoticed — there is no fast way to see that a code change just added 50 KB to your dist folder until it surfaces in a PR review or a production performance regression.
\n\nIf you've hit this before, you know how it goes — you end up finding out during a PR review, or worse, after a production deploy.
\n\nbuildwatch\nWatch your build output directory and report file size changes on every rebuild
\n\nIt's zero-dependency Node.js, so you can run it immediately without installing anything:
\n
npx buildwatch ./dist\nOutput:
\n
buildwatch v1.0.0\nWatching /projects/myapp/dist\nInitial state: 3 files, 1.0 MB total\n\n[10:23:41] 2 files changed\n\n main.js 136.4 KB +12.1 KB (+9.7%)\n main.css 8.3 KB +0.1 KB (+1.2%)\n vendor.js 892.1 KB no change\n\n Total: 1,036.8 KB +12.2 KB (+1.2%)\nPure Node.js using fs.watch() for directory monitoring and fs.stat() for file sizes, with 300ms debouncing to handle rapid build events, printing ANSI-colored per-file and total size deltas.
\n\nFound repeated complaints in r/webdev and r/node about not noticing bundle size regressions until PR review or production. Existing tools like bundlesize and size-limit require CI config changes or build plugin integration. No popular zero-dep tool gives you instant terminal feedback during local development — a file watcher that just runs alongside your dev server fills this gap cleanly.
\n\nnpx buildwatch --help\nPart of µ micro — one new developer tool, shipped every day. All tools are zero-dependency Node.js and run instantly with npx.
Every developer building with Large Language Models eventually hits the same painful reality: the API bill always catches up to you. Between massive system instructions, multi-turn chat histories, and heavy Retrieval-Augmented Generation (RAG) contexts, prompt sizes explode fast. And since LLM providers charge you per token for every single request, you are constantly paying a premium for linguistic filler words (the, is, and, available) that the AI models don't even need to understand your intent.
\n\nI wanted a way to automatically strip out prompt waste and cut my API costs without rewriting my entire application logic.
\n\nSo, I built and shipped llm-cost-optimizer-node—a zero-config, drop-in client wrapper that intercepts outgoing messages, optimizes them in the cloud, and pipes them seamlessly to your LLM provider.
\n\nThe entire philosophy of this tool is zero structural friction. Instead of forcing you to manually pass every string through an optimization utility before a fetch request, it acts as a local proxy wrapper around your initialized client instance.
\n\nIntercept: The wrapper captures the outgoing payload right as chat.completions.create is fired.
Optimize: It securely runs the text blocks through an engine to handle minification, stop-word stripping, or stemming.
Log & Pipe: It prints the exact token savings straight to your development terminal and forwards the lean prompt to the LLM.
Integrating it takes exactly three lines of code. You wrap your native client instance once, and leave the rest of your codebase completely untouched.
\n
const { OpenAI } = require('openai');\nconst { wrapClient } = require('llm-cost-optimizer-node');\n\n// 1. Initialize and wrap your standard client instance\nconst openai = wrapClient(new OpenAI({ apiKey: process.env.OPENAI_API_KEY }), {\n rapidApiKey: process.env.RAPID_API_KEY,\n strategy: [\"minify\", \"strip_stopwords\"] \n});\n\n// 2. Run your existing production code exactly as before!\nconst response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: \"You are a warehouse assistant.\" },\n { role: \"user\", content: \"The ergonomic office chair is highly accessible and available in warehouse-4 right now.\" }\n ]\n});\nThe moment that request executes, your console streams live telemetry showing you exactly how much money and context window you just saved:
\n
--- [Optimizer Proxy] Intercepting Outgoing Messages... ---\n🟢 [Metrics] Msg 0 | Slashed: 35 -> 28 tokens (20.00% Saved)\nWhen building developer infrastructure, application uptime is non-negotiable. I didn't want a network hiccup or an expired API key to crash a production system.
\n\nTo solve this, the SDK is built with a strict fail-safe guardrail loop:
\n
try {\n const compressed = await callOptimizationEngine(text);\n return compressed;\n} catch (error) {\n console.warn(`⚠️ [Optimizer Proxy Warning] Compression failed: ${error.message}`);\n return originalText; // Transparent fallback fallback execution\n}\nIf your network goes down or the gateway API hits a rate limit, the client wrapper instantly catches the exception, prints a subtle warning to your server logs, and safely drops back to forwarding your original untouched prompt to your LLM provider. Your application production uptime remains completely bulletproof.
\n\nThe package is fully open-source and live on the global npm registry right now.
\n\nI'm currently working on adding specialized optimization profiles for heavy RAG workflows and complex Agent state loops.
\n\nI'd love to hear your thoughts! What optimization strategies are you using to keep your production LLM bills under control? Drop a comment below!
","score":5},{"source":"https://medium.com/feed/tag/typescript","sourceHost":"medium.com","title":"Richfolio, three months in: AI architecture in production","link":"https://furic.medium.com/richfolio-three-months-in-ai-architecture-in-production-bcdadf200851?source=rss------typescript-5","pubDate":"Fri, 22 May 2026 10:20:58 GMT","description":"![](\"https://cdn-images-1.medium.com/max/1693/1*b6A_j2U4ZDmP7_DglaJQOQ.png\")
From v1.0 to v1.6 — what I rebuilt, what I borrowed, and what’s running in production for $0/month
Check out these 10 open source tools that help game developers create art, animation, levels, audio, dialogue, debug UIs, and engine-ready assets.
\nThe post Beyond the engine: 10 open source projects shaping how games actually get made appeared first on The GitHub Blog.
","score":4},{"source":"https://dev.to/feed/tag/vue","sourceHost":"dev.to","title":"How to Make Large Time-Series Charts Smooth in Vue.js + ApexCharts (and fix Zoom & Scroll behavior issues)","link":"https://dev.to/aaronestrada/how-to-make-large-time-series-charts-smooth-in-vuejs-apexcharts-and-fix-zoom-scroll-behavior-3k6n","pubDate":"Thu, 21 May 2026 17:14:49 +0000","description":"Over the last few weeks, I've been working on a front-end application project built with Vue.js and ApexCharts that displays time-series data about different sensors installed in different rooms and buildings.
\n\nApexCharts is an excellent library for creating interactive charts, and integrating it in Vue.js is really a piece of cake. However, when it comes to displaying a time-series chart with thousands of points, the performance can suffer, sometimes causing the page to freeze during the rendering or when the user zooms or navigates through the data.
\n\nTo solve this issue, it is recommended to downsample the data to reduce the number of displayed points, especially when the selected time range is large enough that highly granular data is unnecessary. If the user later needs to inspect detailed data, the application can switch back to raw data after zooming in.
\n\nThe criteria for switching between downsampled and raw data depends on the use case. It can be based on the zoom level, the selected date range, or the number of points to display.
\n\nSwitching between raw and downsampled data also requires filtering the points to display according to the selected time range. And this is where the problem appears: once you filter data, the zooming and scrolling events of the chart stop working properly when using the mouse scroll. In the era of AI and agent-based coding, solving the issue might seem trivial. But after several attempts and different interactions, I could not find a complete working example that fully
\nsolved the problem.
Here, I would like to propose a solution that includes the use of downsampled data when the number of displayed points is high enough to avoid performance issues with a line chart and switches to raw data otherwise. The solution is based on the zoomed, scrolled and beforeResetZoom chart events while still relying on the reactive state of the Vue.js component.
To make the solution work correctly, it is also necessary to include a hidden dataset containing the minimum and maximum dates of the complete time series, both with null values. This hidden dataset allows the chart to zoom out completely and pan across the entire x-axis using both the chart controls and the mouse wheel.
\n\nWithout this additional dataset, these interactions only work correctly when using the built-in chart buttons.
\nIn a real application, the data usually comes from an API or another external source. After loading the data, each dataset can be downsampled and stored in the component’s reactive state.
\n\nAlternatively, downsampling could also be performed in the backend before sending the data to the frontend, depending on how much control you have over the application architecture.
\n\nFor this example, I generate three random temperature datasets with 10,000 points each and downsample them using the Largest-Triangle Three-Buckets (LTTB) algorithm provided by the downsample package, reducing each dataset to 700 points.
\n
import {LTTB} from 'downsample/methods/LTTB';\n\nlet northStationData = generateTemperaturePoints({\n startDate: new Date('2026-01-01T00:00:00'),\n count: 10000,\n intervalMs: 60 * 1000,\n baseTemp: 15,\n dailyAmplitude: 6.5,\n noise: 1.1,\n warmingTrendPerHour: 0.0021\n});\n\nlet northStationLTTB = LTTB(northStationData, 700);\n\nthis.data = {\n northStation: {\n raw: northStationData,\n downsample: northStationLTTB\n }\n}\nWe also need to store the minimum and maximum dates across all datasets, since they will later be used to create the hidden dataset.
\n
const allX = [northStationData, cityCenterData, southStationData].flatMap(s => s.map(p => p.x));\n\nlet minimumDate = Math.min(...allX);\nlet maximumDate = Math.max(...allX);\n\nthis.ranges = {\n minimumDate: minimumDate,\n maximumDate: maximumDate,\n};\nOnce the minimum and maximum dates are stored in the reactive state, we can create a computed property that returns a dataset containing only those boundary dates.
\n
plotMinMaxDataset()\n{\n return {\n name: 'rangeMinMaxInvisible',\n data: [\n {x: new Date(this.ranges.minimumDate).getTime(), y: null},\n {x: new Date(this.ranges.maximumDate).getTime(), y: null},\n ],\n showInLegend: false,\n };\n}\nWhen displaying the chart for the first time, we can create another computed property that returns the downsampled datasets together with the hidden boundary dataset.
\n
plotSeries()\n{\n let seriesData = [];\n Object.keys(this.data).forEach(key => {\n seriesData.push({\n name: `${this.data[key].label} (downsampled)`,\n data: toRaw(this.data[key].downsample),\n })\n });\n\n /**\n * Add the minimum and maximum points available in the datasets\n * to avoid zooming and scrolling to lose the minimum and maximum ranges.\n */\n seriesData.push(toRaw(this.plotMinMaxDataset));\n return seriesData;\n}\nThis method is called whenever the user zooms or scrolls through the chart. It filters the data according to the selected range and switches between raw and downsampled datasets based on a defined criterion. At the end, it also includes the dataset with the minimum and maximum dates to avoid zooming and scrolling to lose the original boundaries.
\n\nIn this example, the criterion is the number of points available in the filtered raw dataset.
\n
plotRangeOnChange(chartContext, minDate, maxDate)\n{\n let dataUpdate = [];\n\n Object.keys(this.data).forEach(key => {\n let dataTypeName = 'raw'\n let dataRaw = pointFilter(this.data[key].raw, minDate, maxDate);\n\n /**\n * Based on the number of points in the raw dataset after filtering,\n * decide whether to keep raw or downsampled datasets.\n *\n * The criteria in this case is to verify the number of points, but it could\n * also be the range of dates.\n */\n if (dataRaw.length > 1000) {\n dataRaw = pointFilter(this.data[key].downsample, minDate, maxDate);\n dataTypeName = 'downsampled';\n }\n\n dataUpdate.push({\n name: `${this.data[key].label} (${dataTypeName})`,\n data: toRaw(dataRaw),\n });\n });\n\n /**\n * Add the minimum and maximum points available in the datasets\n * to avoid zooming and scrolling to lose the minimum and maximum ranges.\n */\n dataUpdate.push(toRaw(this.plotMinMaxDataset));\n\n chartContext.updateOptions({\n series: toRaw(dataUpdate),\n xaxis: {\n min: minDate,\n max: maxDate,\n }\n });\n}\nWe need to configure the zoomed and scrolled events to trigger the switch between raw and downsampled data using the method defined above. We also configure the beforeResetZoom event to restore the chart to the full available date range.
These events provide:
\n\nAt this stage, we also hide the auxiliary hidden dataset from the legend and remove its marker by filtering the series name in the legend.formatter of the plot options and setting its marker size to 0.
\n
let markersSize = [];\nObject.keys(this.data).forEach(key => {\n markersSize.push(8);\n});\n\nmarkersSize.push(0);\n\nlet plotOptions = {\n chart: {\n id: 'vuechart-example',\n events: {\n /**\n * Update data based on the selected range in the x-axis when events occur\n */\n zoomed: (chartContext, {xaxis}) => this.plotRangeOnChange(chartContext, xaxis.min, xaxis.max),\n scrolled: (chartContext, {xaxis}) => this.plotRangeOnChange(chartContext, xaxis.min, xaxis.max),\n beforeResetZoom: (chartContext) => this.plotRangeOnChange(chartContext, null, null),\n },\n },\n legend: {\n formatter: function (seriesName) {\n /**\n * Hide the marker containing the minimum and maximum values in the x-axis range\n */\n return seriesName === 'rangeMinMaxInvisible' ? null : seriesName;\n },\n markers: {\n size: markersSize\n },\n },\n /* ... */\n}\nIn this article, I showed how to use downsampling data in a Vue.js + ApexCharts application to reduce the number of points to display and improve the user experience, while still allowing to zoom in, zoom out and scroll over the chart without encountering problems with the mouse wheel.
\n\nThe complete code of this example is available in the GitHub repository vue-apexcharts-downsample-zoom.
\n\nYou can also try the live demo here.
\n\nLeave a comment if you have any questions or suggestions to improve, I'll be happy to interact with you!
\n\nLet's make the world a better place together, one snippet at a time!
","score":3},{"source":"https://github.blog/feed","sourceHost":"github.blog","title":"Building GitHub’s next chapter in accessibility","link":"https://github.blog/open-source/building-githubs-next-chapter-in-accessibility/","pubDate":"Thu, 21 May 2026 16:00:00 +0000","description":"Explore our update on GitHub’s accessibility strategy, and learn how you can join us in building a culture of accessibility.
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","score":4},{"source":"https://stackoverflow.com/feeds/tag/node.js","sourceHost":"stackoverflow.com","title":"Next.js development stage BFF pattern DX log optimization","link":"https://stackoverflow.com/questions/79944721/next-js-development-stage-bff-pattern-dx-log-optimization","pubDate":"2026-05-21T14:23:40Z","description":"During development, if BFF is enabled and data returned by the backend gets truncated by BFF, are there any good approaches for DX optimization? (Exclude the following solution: directly returning logs from BFF in the test environment, as it still delivers poor readability and subpar effects.)
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","score":4},{"source":"https://javascriptweekly.com/rss","sourceHost":"javascriptweekly.com","title":"Dr. Axel's blog is gone (for now)","link":"https://javascriptweekly.com/issues/786","pubDate":"Tue, 19 May 2026 00:00:00 +0000","description":"| \n \n \n
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Kick off work in VS Code or the CLI, finish it from your phone. Remote control for GitHub Copilot sessions is now generally available on github.com and GitHub Mobile.
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","score":4},{"source":"https://github.blog/feed","sourceHost":"github.blog","title":"Raising the bar: Quality, shared responsibility, and the future of GitHub’s bug bounty program","link":"https://github.blog/security/raising-the-bar-quality-shared-responsibility-and-the-future-of-githubs-bug-bounty-program/","pubDate":"Fri, 15 May 2026 14:00:00 +0000","description":"We're updating our bug bounty program standards to prioritize quality submissions, clarify shared responsibility boundaries, and evolve how we reward low-risk findings.
\nThe post Raising the bar: Quality, shared responsibility, and the future of GitHub’s bug bounty program appeared first on The GitHub Blog.
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