One of the issues I am facing currently, we have a website with over 10,000 pages and since we built it with CSR (Client Side Rendering) and CSG (Client Side Generation) it is relatively very expensive with the CDN cost. Purely did this because of the SEO and caching issue.

Do you have a solution for this or is this still how everyone doing this bearing the cost of scale as we are?
I find this an expensive deployment for us. I appreciate your thoughts.

I’ve been exploring what happens when a React component library drops floating cards, soft shadows, and 60fps spring physics in favor of production mechanics from sequential print layout and traditional cel animation.

The result is FRAME/12 (GitHub (MIT) · Live Motion Studio), a zero-dependency copy-in React + TypeScript registry (shadcn-compatible) and AI agent skill.

Here are the four most interesting React & DOM problems we had to solve while building it:

1. Quantizing an easing curve "on twos" (useOnTwos)

In hand-drawn animation, most movement is shot "on twos"—each drawing is held for two frames at 24fps (83.33ms per pose, 12 poses per second). Simply applying CSS steps(4) with linear timing feels robotic because every jump covers the exact same distance.

Instead, useOnTwos samples a cubic ease-out spacing chart inside requestAnimationFrame, bucketed into 83.33ms (1000 / 12) intervals. You get a wide initial jump and tight settling poses, while still holding each discrete pose cleanly:

export const FRAME_MS = 1000 / 12; // 83.333ms ("on twos")

const easeOut = (t: number) => 1 - Math.pow(1 - t, 3);

// Inside requestAnimationFrame loop:

const elapsed = now - t0;

const k = Math.min(frames, Math.floor(elapsed / FRAME_MS) + 1);

setProgress(easeOut(k / frames));

We also strict-split what gets stepped:

• Discrete state changes (SegmentedControl cell jumps, Metric count-ups, ToneAreaChart clip-path wipes, Badge 2-frame SVG impact bursts) run on twos (12fps).
• Direct manipulation (hover states, scrolling, typing, chart pointer/keyboard crosshairs) stays at 0ms immediate latency.
• Initial mount (useChanged): A custom hook tracks whether a key has changed after first mount so entrance animations never fire on initial page load.
• Accessibility: prefers-reduced-motion: reduce automatically switches all hooks and CSS classes to 'cut' (0ms hard cuts).

2. Non-warping diagonal panel frames with ResizeObserver

In PanelPair, two vertically paired KPI panels are separated by a 12px gutter with a 16px diagonal slant (slant-bottom over slant-top). If you use CSS clip-path: polygon(...), CSS borders get clipped off along the diagonal edge; if you scale a fixed-viewBox <svg> with preserveAspectRatio="none", the 2.5px ink stroke stretches unevenly.

Our Panel component attaches a ResizeObserver to measure the panel's exact width × height in pixels and computes mitered <polygon> points inset by half the stroke width (1.25px), keeping the 2.5px ink border uniform at any aspect ratio.

3. Zero-dependency SVG screentone charts with built-in keyboard & table fallbacks

Instead of pulling in recharts or d3 and rendering translucent gradient fills, ToneAreaChart renders a <pattern id="..." patternTransform="rotate(45)"> of halftone dots (var(--f-tone)), an inked 2.5px polyline, a marker-yellow peak dot, and a speech-balloon SVG annotation (Balloon) that flips direction near the right edge of the viewBox.

Every chart is focusable (tabIndex={0}), supports ArrowLeft / ArrowRight / Home / End / Escape to inspect data points via an aria-live readout, and includes a collapsible <details className="tbl"> semantic HTML <table>.

4. Linting UI structure so AI coding agents don't drift

When AI agents generate React UI, they quickly drift toward arbitrary Tailwind gaps (gap-4, gap-6), hex literals, or unstyled primitives. Because FRAME/12's rules are structural, we wrote scripts/check-frame12.mjs (plus skills/frame12/SKILL.md and an ESLint flat-config plugin) to enforce:

1. color-literal: Zero hex/RGB/HSL literals outside frame12.css
2. illegal-gutter: Only 12px (pair), 20px (scene), and 32px (change) gutters allowed
3. marker-misuse: Marker yellow (--f-marker, #ffd60a) only allowed as a background paired with --f-marker-ink (#121212) text
4. cut-order: Every <Panel cut="C-01"> must increment sequentially (C-01, C-02, C-03…) in DOM and reading order
5. raw-shadcn-import: Forbids raw u/radix-ui/* or external chart/animation libraries where a u/frame12/registry component or adapter exists

You can install the full kit or individual components via the shadcn CLI:

npx shadcn@latest add https://frame12.dev/r/frame12.json

• Repo (MIT): https://github.com/lbrodie11/frame12
• Interactive Motion Studio (12fps vs 60fps vs 0ms): https://frame12.dev/motion
• Component Gallery & Style Sheet: https://frame12.dev/components

Would love feedback from folks here on the hook ergonomics and the static UI grammar linter!

Hi everyone,

As a side project to explore React 19 and client-side processing workflows, I put together a small developer utility suite calledFixCode.

Technical Details:

• Frontend: React 19, Vite, Tailwind CSS
• State & Security: All transformations (JWT decoding, chmod calculations, Docker YAML generation) run entirely on the client side using pure JavaScript algorithms. No data is sent to external servers.
• Routing & SEO: Dynamic metadata handled via react-helmet-async for indexation across single-page routes.

Just wanted to share the approach for fast, client-side utility tools with zero server overhead

Hey everyone!

A friend and I are planning a React learning session this weekend. Instead of just going through documentation and building another basic project, we want to explore some of the interesting problems React developers encounter in real-world applications.

I'd love to hear from experienced React developers, senior frontend engineers, and anyone who has worked on performance-critical React applications.

I'm particularly interested in problems that aren't immediately obvious and require some actual investigation to solve.

For example:

- Unnecessary re-renders: How do you identify which components are re-rendering unnecessarily, and how do you determine the actual root cause?

- State management issues: Have you encountered performance problems caused by poorly placed state, Context API, or excessive state updates? How did you restructure things?

- Expensive rendering: What happens when a component renders a large list, processes complex data, or performs expensive calculations? How do you identify whether the bottleneck is React, JavaScript, or the browser?

- Memory leaks and stale closures: What are some subtle bugs involving "useEffect", event listeners, subscriptions, or closures that you've encountered in production?

- Memoization gone wrong: Have you ever made an application slower by overusing "useMemo", "useCallback", or "React.memo"? How did you figure that out?

- Network and rendering bottlenecks: How do you investigate slow API-driven interfaces, waterfalls, unnecessary requests, or components that feel sluggish despite fast API responses?

- Large-scale component architecture: Have you encountered performance problems that required changing component boundaries or the way an application was structured rather than simply optimizing individual components?

I'm not just looking for a list of best practices. I'd really appreciate hearing about the actual debugging process.

For instance:

1. What symptoms or user reports made you suspect a performance problem?

2. Which tools did you use to investigate it?

3. How did you identify the actual root cause?

4. What changes did you make to fix it?

5. How did you measure the improvement and verify that the fix worked?

If you have a small code example, a production incident, a particularly tricky bug, or even a mistake you made while trying to optimize something, I'd love to hear about it.

Also, if there are any React performance problems that you think every developer should learn to diagnose early in their career, please share those.

We're hoping to use your experiences as a starting point for practical experiments rather than just memorizing optimization techniques.

Thanks!

You know the pattern: createContext<User | null>(null), then a useAuth() hook that throws "useAuth must be used within AuthProvider", and every consumer either trusts that or null-checks. A missing provider is a runtime error you find when that screen renders, or when a test does.

I wanted it to be a lint error instead, so I built an oxlint plugin that checks it across the whole program:

``` const AuthContext = createContext<User | null>(null);

function Children() { const user = use(AuthContext); return <p>{user?.name}</p>; }

root.render( <AuthContext value={user}> <Children /> {/* ok */} </AuthContext>, );

root.render(<Children />); // <sup>^</sup> react-context(require-provider): <Children> needs AuthContext // (use(AuthContext) at Children.tsx:4), but no <AuthContext> provider is above it. ```

Children needs no annotations. Requirements are inferred from use, useContext, <Ctx.Consumer> and static contextType. They're passed up through components, custom hooks, HOCs, memo/forwardRef/lazy, children and render props, and React Router outlets, until a provider meets them or a root gets them. Roots are createRoot().render, testing-library's render, Storybook stories, and framework pages you list in a setting. Wrapper components like <AuthProvider>{children}</AuthProvider> count as providers because they render children inside one.

There's also a tiny optional runtime: createRequiredContext<User>() gives you a context where use() is typed User instead of User | null, because the lint rule now guarantees the provider. It returns a plain React context, so nothing else changes.

Why not types? That was the first thing I tried. JSX erases element types: <Children /> and <span /> are both just JSX.Element, so a requirement can't travel from a child up to its provider through the type system. Forking @types/react doesn't help, and TypeScript 7.1's new APIs didn't open a way either. The only version that type-checked used generator components and h() calls instead of JSX, which nobody wants.

As for the speed, it's a whole program so it costs more than a normal lint rule. On excalidraw (709 files), oxlint alone takes 0.07 s, oxlint with the rule 0.8 s, and the standalone CLI 0.5 s. In the editor (oxlint's language server), a keystroke costs about 50 ms because only the edited file is re-parsed. The README suggests a separate oxlint config so that your everyday oxlint stays as fast as before, while the editor uses the config with the rule and CI runs react-context-check as its own step. Not on oxlint? The CLI runs the same check without it.

As for limitations, it's syntactic and not type-aware. For code it can't see (node_modules, dynamic tags, useContext(cond ? A : B)) it makes certain assumptions. A provider hidden in such code shows up as a false positive, which you silence with @context-provided. A null-default context that your code actually treats as optional needs a // @context-optional comment. On excalidraw, that one comment was the difference between 337 reports and zero.

It's not on npm yet:

npm i -D github:TmLev/react-context-typesafe oxlint

Repo: https://github.com/TmLev/react-context-typesafe

Reports of wrong diagnostics are the most useful feedback right now. A few lines of code that reproduce one are enough.

Hey r/reactjs,

I recently open-sourced OrderCraft, a local-first multi-chart technical analysis terminal built with React 19, TypeScript, Vite, and lightweight-charts (backed by a local Go WebSocket server).

Interesting frontend challenges we solved:

• Custom HTML5 Canvas 2D Drawing Engine (ISeriesPrimitive): lightweight-charts renders candlesticks quickly on Canvas, but doesn't ship with interactive drawing tools. We built a custom Canvas 2D primitive engine from scratch in TypeScript supporting interactive Trendlines, Fibonacci Retracements, Parallel Channels, Rectangles, and Long/Short Position calculators with drag handles and OHLC magnet snapping.
• High-Frequency WebSocket State Without Re-Render Storms: Streaming live ticks across 4 synchronized split-screen chart panes (1x1, 2V, 2H, 3, 4 layouts) with synced crosshairs, symbols, and timeframes without triggering full React component tree re-renders on every tick.
• Single-Binary Embedding + PWA Standalone Mode: The Vite production bundle is embedded into a single ~14MB Go executable and includes a Web App Manifest so launching with --app-mode opens a native frameless desktop window.

GitHub Repo: https://github.com/ordercraft-hq/terminal

Would love any feedback from fellow React/TypeScript devs on the frontend architecture and Canvas primitive implementation!

Hey everyone!

I've been learning React for the past 2–3 months, and I've recently built my second project, GameVault, using my own knowledge and what I've learned so far.

GameVault is a gaming e-commerce website where users can explore games and browse a gaming store.

Project Links:

• Live Demo: https://gamevault-rhlydav216581.vercel.app/
• GitHub Repository: https://github.com/rhlydv216581/Gamevault

I'm still learning and improving my development skills, so I'd really appreciate honest feedback from experienced developers and the community.

I'd love to know:

1. What do you think about the overall UI/UX?
2. Is my React code structure and component organization good?
3. What should I improve in terms of performance and best practices?
4. Does this project demonstrate enough skills for a React/frontend developer internship?

I'm open to constructive criticism and suggestions. I want to learn from my mistakes and become a better developer.

Thanks for taking the time to check out my project!

I'm building Breatic, a React/TypeScript creative workspace. Its shared projects combine document briefs, a React Flow canvas and an agent that can propose edits. I wanted to share the implementation of the proposal UI and get feedback from people building collaborative editors.

Repository: https://github.com/orime-org/breatic

The document proposal card receives a structured proposal. For rewrites, it parses the proposed body using the document schema and computes a word-level diff against the proposal's original text. The person reviewing it sees the additions and removals instead of just an instruction to replace the paragraph.

The card provides separate actions to locate the target, add an eligible proposal as a comment, and apply a rewrite or insertion. It re-reads displayed state when the document version or card state changes.

The component is here: https://github.com/orime-org/breatic/blob/8eaa0da9888cc6a1a8967b98faaa3e8b80f4e91d/packages/web/src/pages/project/chat/DocumentProposalCard.tsx

Collaboration uses Yjs and self-hosted Hocuspocus. The important distinction for me is that synchronizing a document and deciding to accept an agent's edit are different operations. A convergent shared document doesn't automatically mean an old suggestion is still appropriate.

This isn't a claim that every stale-proposal or concurrent-edit edge case has been solved. I'd welcome feedback on the UI boundary: how do you make it clear which document state a proposal refers to while collaborators keep working?

The repo includes usage and development guides. Its custom source-available license permits internal/private deployment and restricts public-facing platform hosting; storage and model services need separate configuration.

What criteria do you consider when choosing between CSR, SSR, or a hybrid approach (using RSC or getServerSideProps for the initial render, then calling APIs directly from the client to update the UI for subsequent interactions)?

Hey everyone,

I'm working on a large project using React with server-side rendering through Laravel. My frontend has 60+ pages, and every route has its own dedicated page component.

I'm currently using React Router with lazy-loaded routes (lazy() / Suspense), conditional routes, and route groups. However, I'm noticing that as I navigate through the application and use more pages, it gradually becomes slower and eventually starts feeling laggy.

I expected lazy loading to prevent unnecessary components from loading until they're needed, but it seems like more and more things are being loaded over time.

I'm wondering:

What's the best way to structure React Router in a large Laravel SSR project?

Is having 60+ individual page components and routes a problem?

Should I organize routes into separate modules or route groups?

Is it normal for lazy-loaded components to remain in memory after navigation?

How should conditional routes and nested layouts be handled efficiently?

What could cause performance degradation after navigating through multiple pages?

I'd really appreciate hearing how you structure React Router in large production applications, especially with Laravel SSR.

Thanks!

Hey everyone,

I've been preparing for frontend interviews and couldn't find a quiz tool I liked, so I built one: devsets.com

What's inside:

• 2,700+ questions across 12 topics (JavaScript, TypeScript, React, Next.js, Angular, Vue, Svelte, HTML & CSS, state management, testing, patterns, system design)
• Junior / Middle / Senior levels, and every answer comes with an explanation and links to the docs
• Interview mode with a timer per question
• Stack quizzes (e.g. "React Developer") that mix several topics like a real interview
• Spaced repetition: questions you got wrong come back after 1, 3 and 7 days
• 18 JavaScript coding tasks (debounce, deepClone, Promise.all...) with tests that run in the browser

It's free, ad-free and works without an account (sign in only if you want to sync progress). Available in English, Ukrainian and Polish.

I'd really appreciate feedback: wrong answers, confusing questions, missing topics, anything. Every question has a "report a problem" button too.

A few months ago I wrote about the React Compiler, and my advice for existing code was to leave useMemo and useCallback alone at first, then clean them up slowly, with tests, once the compiler has been running for a while.

I'm curious how that went for people who actually did it.

The public numbers I found are from InfoQ's write-up: Sanity saw 20-30% less render time and latency on their Studio packages (1,231 of 1,411 components compiled on the first pass), and Wakelet's INP went from 275ms to 240ms.

If you're running it in production, did you rip out the manual memoization? Did anything break?

My article, if you want the details on the compiled output: https://neciudan.dev/react-compiler-explained

hey guys,

I built this website over the last month to learn more about making website and handling real time data, it uses React, Next.js and Supabase

You can try it out from here: https://project-s-nine-kappa.vercel.app

(Please just click the "Explore Demo" button on the login screen so you don't have to create an account!)

Source code is here: https://github.com/zzz-nv/s-social

Would love to hear any feedback!

Hi everyone!

I'm currently implementing Google Places Autocomplete using the new Autocomplete Data API with React and @vis.gl/react-google-maps.

The implementation works, but I'm concerned about Google Maps API pricing, especially during development. I managed to hit my API usage limit within just two days, and I'm trying to understand whether my implementation is making unnecessary requests or whether this is expected behavior.

I'd appreciate some clarification from anyone who has experience with the Google Places API.

1. My current implementation
   I'm using a 500ms debounce to avoid sending a request on every keystroke.

SearchBox component

```
import { MagnifyingGlassIcon, MapPinIcon } from '@phosphor-icons/react'
import { useState } from 'react'
import { useDebounce } from '@/hooks/useDebounce'
import { useAnimatedPlaceholder } from '@/features/search/hooks/useAnimatedPlaceholder'
import { useMapsLibrary } from '@vis.gl/react-google-maps'
import { useAutocompleteSuggestions } from '@/hooks/useAutoCompleteSuggestions'
import { useNavigate } from 'react-router-dom'

import styles from './styles.module.css'

const SearchBox = () => {
const [searchQuery, setSearchQuery] = useState('')
const debouncedSearchQuery = useDebounce(searchQuery, 500)
const inputRef = useAnimatedPlaceholder()

return (
<>
<div className={styles.searchBox}>
<MagnifyingGlassIcon size={24} />
<input placeholder="Search for " ref={inputRef} value={searchQuery} onChange={e => setSearchQuery(e.currentTarget.value)} />
</div>
<SearchSuggestions debouncedSearchQuery={debouncedSearchQuery} />
</>

)
}

export default SearchBox

type SearchSuggestions = {
debouncedSearchQuery: string
}

function SearchSuggestions({ debouncedSearchQuery }: SearchSuggestions) {
const navigate = useNavigate()
const places = useMapsLibrary('places');
const { suggestions, resetSession } = useAutocompleteSuggestions(debouncedSearchQuery, {
includedPrimaryTypes: ["locality"]
})

const handleSuggestionClick = async (suggestion: google.maps.places.AutocompleteSuggestion) => {
if (!places || !suggestion.placePrediction) return

const place = suggestion.placePrediction.toPlace()
await place.fetchFields({
fields: [
"addressComponents"
]
})
// calling fetchFields invalidates the session-token, so we now have to call
// resetSession() so a new one gets created for further search
resetSession()

const city = place.addressComponents && place.addressComponents[0].longText
const placeId = suggestion.placePrediction.placeId
if (city && placeId) return navigate(`${city}/${placeId}`)
}

return !!suggestions.length && (
<div className={styles.optionBox}>
{
suggestions.map(suggestion => <button key={suggestion.placePrediction?.placeId} className={styles.option} onClick={() => handleSuggestionClick(suggestion)}>
<div className={styles.iconWrapper}>
<MapPinIcon weight="fill" size={20} />
</div>
<span className={styles.suggestionText}>
{suggestion.placePrediction?.text.text}
</span>
</button>
)
}
</div >
)
}
```
useAutocompleteSuggestions hook
```import { useEffect, useRef, useState } from 'react';
import { useMapsLibrary } from '@vis.gl/react-google-maps';

export type UseAutocompleteSuggestionsReturn = {
suggestions: google.maps.places.AutocompleteSuggestion[];
isLoading: boolean;
resetSession: () => void;
};

/**
* A reusable hook that retrieves autocomplete suggestions from the Google Places API.
* The data is loaded from the new Autocomplete Data API.
* (https://developers.google.com/maps/documentation/javascript/place-autocomplete-data)
*
* @param inputString The input string for which to fetch autocomplete suggestions.
* @param requestOptions Additional options for the autocomplete request
* (See {@link https://developers.google.com/maps/documentation/javascript/reference/autocomplete-data#AutocompleteRequest}).
*
* @returns An object containing the suggestions, the current loading-status,
* and a function to reset the session.
*
* @example
* ```jsx
* const MyComponent = () => {
* const [input, setInput] = useState('');
* const { suggestions, isLoading, resetSession } = useAutocompleteSuggestions(input, {
* includedPrimaryTypes: ['restaurant']
* });
*
* return (
* <>
* <input value={input} onChange={(e) => setInput(e.target.value)} />
* <ul>
* {suggestions.map(({placePrediction}) => (
* <li key={placePrediction.placeId}>{placePrediction.text.text}</li>
* ))}
* </ul>
* </>
* );
* }
* ```
*/
export function useAutocompleteSuggestions(
inputString: string,
requestOptions: Partial<google.maps.places.AutocompleteRequest> = {}
): UseAutocompleteSuggestionsReturn {
const placesLib = useMapsLibrary('places');

// stores the current sessionToken
const sessionTokenRef =
useRef<google.maps.places.AutocompleteSessionToken | null>(null);

// the suggestions based on the specified input
const [suggestions, setSuggestions] = useState<
google.maps.places.AutocompleteSuggestion[]
>([]);

// indicates if there is currently an incomplete request to the places API
const [isLoading, setIsLoading] = useState(false);

// once the PlacesLibrary is loaded and whenever the input changes, a query
// is sent to the Autocomplete Data API.
useEffect(() => {
if (!placesLib) return;

const { AutocompleteSessionToken, AutocompleteSuggestion } = placesLib;

// Create a new session if one doesn't already exist. This has to be reset
// after `fetchFields` for one of the returned places is called by calling
// the `resetSession` function returned from this hook.
if (!sessionTokenRef.current) {
sessionTokenRef.current = new AutocompleteSessionToken();
}

const request: google.maps.places.AutocompleteRequest = {
...requestOptions,
input: inputString,
sessionToken: sessionTokenRef.current
};

if (inputString === '') {
if (suggestions.length > 0) setSuggestions([]);
return;
}

setIsLoading(true);
AutocompleteSuggestion.fetchAutocompleteSuggestions(request).then(res => {
setSuggestions(res.suggestions);
setIsLoading(false);
});
}, [placesLib, inputString]);

return {
suggestions,
isLoading,
resetSession: () => {
sessionTokenRef.current = null;
setSuggestions([]);
}
};
}
```
2. My main questions about pricing
1. How exactly do session tokens work?
Google documentation mentions session-based pricing, and I'm having trouble understanding what this means in practice.

Does one session token group multiple autocomplete requests into a single billing session?
Are autocomplete requests within the same session free, discounted, or individually billed?
Does the session only become complete when the user selects a prediction and requests Place Details?
What happens if the user types something but never selects a suggestion?
2. Does debouncing actually reduce API costs?
For example, suppose a user types TOKYO.

With a 500ms debounce, will Google receive:

One request after the user finishes typing?
Multiple requests if the user pauses between keystrokes for more than 500ms?
If the user types slowly, could this result in five separate autocomplete requests within the same session?

1. What does Google mean by unlimited autocomplete requests?
   I've seen references to unlimited requests in the Google Maps pricing documentation.

Does this mean unlimited requests within a session, or does it mean something else?

I'm particularly confused about how this relates to actual billing.

1. Is my implementation correct?
   I'd also appreciate feedback on whether my current implementation follows Google's recommended session-token lifecycle.

Some things I'm unsure about:

Am I creating and reusing session tokens correctly?
Is resetting the token after fetchFields() the correct approach?
Should I create a new token when the user clears the search input?
Should I reset the session when the user abandons a search without selecting anything?
Is there a better way to prevent unnecessary API requests?
4. Is Google Places Autocomplete expensive?
I'm building an application that will use city autocomplete as an important feature.

I'm concerned about the cost when the application has hundreds or thousands of users searching for cities.

During development alone, I seem to have hit my API usage limit within two days.

Is this normal, or does it indicate that something is wrong with my implementation?

Would you recommend any alternatives or optimization strategies for reducing Google Places API costs?

1. My goal
   I want to implement Google Places Autocomplete properly while keeping API costs predictable.

I'm not looking for ways to bypass Google's billing. I just want to understand the pricing model, session tokens, and best practices

Any explanations, implementation suggestions, or real-world experiences would be greatly appreciated!

Thanks in advance!