How Do You Use TypeScript Advanced Types Without Hindering Performance

I once inherited a TypeScript codebase where the type files had more lines of code than the actual implementation. Three hundred interface definitions scattered across forty files, most of them copy-pasted variations of the same five shapes.

The team had convinced themselves that "more types = more safety," so they'd typed everything. Multiple times. Differently.

The result? Code reviews took hours because half the discussion was "wait, which UserProfile type are we using here?" Refactoring was terrifying because changing one interface meant hunting down its seventeen cousins to update them too.

This is what happens when you confuse type volume with type safety.

TypeScript's type system rewards precision, not quantity. When you copy-paste interfaces across files and nest generics three levels deep without understanding why, you create fragility disguised as safety.

TypeScript ships three advanced features that eliminate this chaos: generics create reusable type logic, conditional types branch at compile time, and mapped types automate structural transformations.

This guide shows you how to architect type systems that scale without sprawling into maintenance hell. You'll learn when to reach for generics versus when you're over-engineering.

1. Generics: when duplication becomes a code smell

The first time I saw the value of generics was also the first time I saw someone completely misuse them. We had an API client with twenty different fetch methods, each with its own hand-written response interface.

Someone got excited about "DRY principles" and created a generic so abstract that it took three type parameters, and nobody could figure out how to use it. We went from obvious duplication to incomprehensible abstraction overnight.

Here's the pattern you've probably seen: three nearly identical interfaces scattered across feature directories, each slowly diverging from the API contract they're supposed to model. You update the backend to add a field, update one interface, and months later discover that your analytics dashboard is still using the old shape because it had its own copy of the type.

A generic introduces a type variable (<T>) to functions, classes, or type aliases, letting you reuse one definition across multiple shapes without sacrificing type safety. The compiler enforces contracts while your JavaScript stays clean — though this compile-time erasure can confuse developers who forget the types vanish at runtime.

The three mistakes most devs make

I've code-reviewed hundreds of PRs with generic types, and the same three mistakes appear constantly:

Defaulting to any when stuck: This is the nuclear option that destroys your type safety. I've seen developers write function process<T = any>(data: T) because they couldn't figure out the right constraint. Now your generic is just an elaborate way to opt out of type checking. If you find yourself reaching for any of them, that's your signal to step back and rethink the abstraction.

Creating unreadable signatures with nested parameters: You know the pattern: function transform<T, U, V, W>(...). I once reviewed a function with seven type parameters. Seven. Nobody could remember what each one meant, so the function just sat there unused while people wrote duplicate implementations they could actually understand. Clear naming matters — <TItem, TResult> is infinitely better than <T, U>.

Using unbounded type variables that accept garbage: When you write <T> with no constraints, TypeScript assumes T could be anything, which means you can't safely do anything with it. You'll end up checking if (value && typeof value === 'object') at runtime anyway, which defeats the purpose. Constrain your generics with <T extends SomeType> to make the compiler actually help you.

A pattern that works

Here's a utility I've written at most codebases I've worked on, slightly adapted each time, but fundamentally the same:

type Response<T = unknown> = {
  data: T;
  success: boolean;
  error?: string;
};

With this single Response<T> wrapper, every fetch helper, Redux slice, or service layer returns a consistent shape. Declare Response<User> or Response<string[]> at the call site, and this one abstraction eliminates hundreds of redundant interface definitions.

Code reviews get faster because everyone recognizes the pattern. Refactoring gets safer because there's only one definition to change.

The = unknown default is important. Without it, you'd have to write Response<unknown> every time, which is noise. With it, you can use bare Response for fire-and-forget calls where you don't care about the data type, and the compiler still catches mistakes like trying to access response.data.someField on an untyped response.

When to use generics (and when not to)

I have a simple rule: reach for a generic when you have the same shape in three places.

One instance? Just write the type.

Two instances? Maybe they'll diverge, perhaps they won't — wait and see.

Three instances? You have a pattern, abstract it.

The exception is when those three instances are fundamentally different concepts that happen to look similar at the moment. I once abstracted three "user" types into a generic type, only to discover that one was for authentication (required password fields), one for display (required avatar URLs), and one for analytics (required behavioral data).

They started identical but evolved differently, and my clever generic became a straitjacket. I spent two days ripping it out.

Generics are for structural duplication, not semantic duplication. If the types look the same but mean different things, keep them separate. Your future self will thank you when requirements inevitably diverge.

2. Advanced generic patterns that don't make you want to quit

After you've written your first few generics, you hit a wall. Not every type substitution is clean. You try to pass Dog where Animal is expected, and sometimes it works, sometimes it doesn't. You try to create a generic factory function, and suddenly, TypeScript is yelling about incompatible signatures.

Welcome to variance, the concept that nobody explains well and everyone struggles with.

I once spent an entire week debugging a type error that boiled down to variance. We had a function that accepted an Array<Animal>, and I tried to pass it an Array<Dog>. Seems obvious, right? Dogs are animals.

But TypeScript rejected it, and I couldn't figure out why until I realized that arrays should, in theory, be invariant for type safety. Still, TypeScript treats them as covariant for pragmatic reasons. This trade-off can lead to runtime errors but makes the language more usable. For truly safe covariance, use readonly arrays.

Reading the TypeScript handbook helped with the mechanics, but production codebases reveal the real friction: overloaded functions with ambiguous resolution and generic factories that leak type information.

Variance: the concept that breaks everything

Here's the mental model that finally made variance click for me: parameters are contravariant (accepting supertypes - broader, more general types), while return types are covariant (returning subtypes - narrower, more specific types).

When you return a Dog, callers expecting an Animal are happy — they got everything they asked for plus extra fields. Covariant. When you accept an Animal parameter, callers can safely pass a Dog because you promised to handle any animal. Contravariant.

But when you mix these positions (like in an array that both reads and writes), things get complicated. Arrays are mutable, so TypeScript has to be conservative. If it lets you treat Array<Dog> as Array<Animal>, you could push a Cat into the array, and suddenly your supposedly dog-only array has a cat in it. Type safety violated.

Three patterns that work

Order overload signatures from most-specific to least-specific: TypeScript matches the first compatible signature, so placing broad declarations first causes type mismatches. I learned this the hard way when a function with three overloads kept resolving to the wrong one.

The fix was stupid simple, just reorder them, but it took me an hour to figure out:

function parse(value: string): string;
function parse(value: number): number;
function parse(value: boolean): boolean;
function parse(value: any): any {
  return value;  // Implementation
}

Write your overloads in descending specificity, then implement once with the broadest signature.

Use unknown with type guards instead of any: When parameter shapes are uncertain, unknown forces you to validate the value before using it. I've code-reviewed PRs where someone used any "temporarily," and it was still there years later, spreading through the codebase like a virus.

With unknown, you have to be explicit:

function process(value: unknown) {
  if (typeof value === 'string') {
    return value.toUpperCase();  // Safe, TypeScript knows it's a string
  }
  throw new Error('Expected string');
}

Build higher-order helpers for repeated patterns: Instead of copy-pasting fetch logic everywhere, create a generic factory:

function createFetcher<T>() {
  return async (url: string): Promise<T> => {
    const response = await fetch(url);
    return response.json();
  };
}

const fetchUser = createFetcher<User>();
const fetchPosts = createFetcher<Post[]>();

This pattern eliminated about 200 lines of duplicate code from a project I worked on. Each endpoint got type safety without any per-endpoint type definitions.

When clever becomes too clever

Here's a utility I wrote that I later regretted:

function mapValues<T extends Record<string, any>>(
  obj: T,
  fn: <K extends keyof T>(v: T[K]) => any
): Record<keyof T, any> {
  const result = {} as Record<keyof T, any>;
  for (const key in obj) {
    if (obj.hasOwnProperty(key)) {
      result[key as keyof T] = fn(obj[key]);
    }
  }
  return result;
}

It works. It's type-safe. And nobody on my team could understand it. The function signature was so complex that people would rather use a for loop than figure out how to call this utility. I thought I was being clever by making it fully generic. I was actually making it unusable.

The lesson: if your generic requires more than two type parameters, you're probably over-engineering. If teammates have to read it three times to understand it, you've failed. Document variance expectations in JSDoc comments and write examples in the docstring. Better yet, simplify the signature until documentation becomes obvious

3. Conditional types: compile-time branching that makes sense

The moment conditional types clicked for me was during a refactor from hell. We had component props that could be string | number | null, and every component had to check all three cases with runtime guards.

The code was a mess of if (typeof props.value === 'string') scattered everywhere. I knew there had to be a better way.

Conditional types let you branch at compile time, expressing the rule once while the compiler enforces every edge case. The syntax mirrors a ternary: A extends B ? X : Y. TypeScript asks at compile time: "Is A assignable to B?" and evaluates to X or Y accordingly.

Simple in theory. Absolutely mind-bending in practice until you internalize how distribution works.

Here's what the TypeScript handbook calls "distributive conditional types": when you apply a conditional to a union, it distributes across each member. So Foo | Bar becomes Cond<Foo> | Cond<Bar>.

This behavior is incredibly powerful for building utility types, but it's also the source of most conditional type confusion I see in code reviews.

The three ways this goes wrong

Unexpected union expansion that obscures intent: I once wrote a conditional type that I thought would return a simple true | false. Instead, TypeScript expanded it into a union of eight different types because I'd inadvertently distributed over a union of unions. Debugging this required opening the TypeScript playground and manually expanding each step until I found where the explosion happened.

Unreadable nested ternaries: Someone on my team once wrote a conditional type that spanned eleven lines of nested ternary operators. It technically worked, but nobody (including the author three months later) could explain what it did. We refactored it into three named helpers, and suddenly it made sense.

Type instantiation depth errors with minimal debugging context: You'll be cruising along, writing increasingly clever conditional types, and suddenly hit "Type instantiation is excessively deep and possibly infinite." The error provides no information about where the problem is. You just have to start commenting out pieces until the error goes away, then figure out which combination caused the recursion.

Two habits that prevent the chaos

First, wrap checked types in square brackets ([T] extends [U] ? X : Y) to turn off distribution when you're testing the union as a whole rather than distributing over its members. 

This one trick has saved me hours of debugging weird union expansions:

type IsNever<T> = [T] extends [never] ? true : false;

// Without brackets, IsNever<never> would distribute and return never
// With brackets, it correctly returns true

Second, extract complex logic into named helpers. Don't try to be clever by cramming everything into one line. I used to write massive one-liner conditional types because I thought it was elegant.

Now I break them into pieces with descriptive names, and my code reviews go way faster because reviewers actually understand what's happening.

Built-in utilities you should be using

Conditional types already power many built-ins that you should reach for before writing your own.

Exclude<T, U> removes every branch assignable to U:

type HTTP = "GET" | "POST" | "DELETE";
type Safe = Exclude<HTTP, "DELETE">; // "GET" | "POST"

I've used this pattern a lot to create subsets of string literal unions, usually for restricting which operations are allowed in specific contexts. It's way clearer than maintaining separate type definitions that drift out of sync.

The real test of whether you understand conditional types is whether you can build utilities like DeepPartial<T> or Asyncify<T> from scratch.

Not because you'll necessarily use them (the community has already built most of what you need), but because designing them forces you to think through how conditional types interact with recursion, distribution, and type inference.

That mental model pays off when you're debugging bizarre type errors at midnight.

4. The infer keyword: automatic type extraction

I used to copy function signatures across multiple files just to extract their return type or first parameter. Manual tracking created drift. I'd update one signature and forget to update its extracted types in three other places. Code reviews would catch maybe half of these mistakes. The rest became production bugs.

The infer keyword hands that work to the compiler. Inside a conditional type, infer captures part of a matched pattern and exposes it for reuse.

The syntax is straightforward: T extends (...args: any[]) => infer R ? R : never, but it unlocks significant automation. This is how standard library utilities like ReturnType<T> and Parameters<T> actually work under the hood.

When I finally got it

The first time I really understood infer was when I needed to extract the payload type from Redux actions. We had hundreds of action creators, each returning a different payload shape. Writing types manually was tedious and error-prone.

With infer, I automated the whole thing:

type ExtractPayload<T> = T extends (...args: any[]) => { payload: infer P } 
  ? P 
  : never;

// Usage
const createUser = (name: string) => ({ type: 'CREATE_USER', payload: { name } });
type UserPayload = ExtractPayload<typeof createUser>; // { name: string }

One utility type replaced dozens of hand-written interfaces. When we changed an action creator's signature, the payload type updated automatically. Code reviews stopped getting sidetracked by "Did you remember to update the type in constants.ts?"

Two problems that surface at scale

Nested inference chains become unreadable: I once tried to extract the third parameter's second property's type from a callback function. The type definition was 15 lines long and used three nested inference statements. It worked, but nobody could maintain it. I broke it into four named helpers (ExtractThirdParam<T>, ExtractSecondProperty<T>, etc.) and suddenly it was obvious what each step did.

Recursive references trigger depth errors: Circular type dependencies may hit "type instantiation is excessively deep" errors faster than you'd expect.

I learned to add explicit recursion limits:

type DeepReadonly<T, Depth extends number = 5> = 
  Depth extends 0 
    ? T 
    : T extends object
      ? { readonly [K in keyof T]: DeepReadonly<T[K], Prev<Depth>> }
      : T;

Wrapper types that limit recursion depth keep compilation fast while preserving functionality. The tradeoff is that deeply nested objects might not get the complete treatment, but in practice, five levels of nesting cover 99% of real-world use cases.

Naming matters more than you think

Clear naming prevents confusion when infer placeholders start multiplying. Prefix inferred variables with descriptive nouns (TFirstArg, TReturn, TPayload), so teammates immediately recognize their purpose without reading the entire conditional type:

type FirstArg<T> = 
  T extends (a: infer TFirstArg, ...rest: any[]) => any 
    ? TFirstArg 
    : never;

// Usage example
type ClickHandler = (event: MouseEvent, element: HTMLElement) => void;
type EventType = FirstArg<ClickHandler>; // MouseEvent

I used to use single letters (A, B, R) because I thought it was concise. Then I'd come back to the code a month later and have no idea what each one meant. Descriptive names take five extra seconds to write and save hours during debugging.

When infer eliminates boilerplate

When you rely on infer properly, boilerplate just disappears. I worked on a project with 50+ API endpoints, each with its own request and response types. Someone had meticulously hand-written type definitions for all of them.

Then, we realized we could extract everything from the fetch functions:

type ApiResponse<T> = T extends (...args: any[]) => Promise<infer R> ? R : never;

const fetchUser = async (id: string): Promise<User> => { /* ... */ };
type UserResponse = ApiResponse<typeof fetchUser>; // User

One generic helper replaced hundreds of interface definitions. When we changed the shape of an API response, the types were updated automatically. This is the killer app for infer: automating away the mechanical work of keeping types in sync with implementations.

The pattern I follow now: if I find myself writing the same type transformation more than twice, I check whether infer can automate it. Usually, it can, and the five minutes spent writing the utility type saves hours of manual synchronization down the road.

5. Mapped types: automated transformations that scale

A team I've once worked with maintains what I call the "interface graveyard", a folder full of near-identical types that someone copied and pasted months ago and nobody dares delete. One readonly version for Redux, another optional variant for PATCH endpoints, a third for test mocks, and probably two or three more that nobody remembers why they exist.

I once counted 23 variations of the same User interface across a single codebase. Twenty-three. Each one is slightly different, all claiming to represent the same domain concept. When we changed the underlying User model, we had to hunt down and update all 23, inevitably missing a few and creating subtle bugs that took weeks to discover.

Mapped types generate these variants automatically, rather than relying on copy-paste engineering. They iterate over each key of an existing type ({ [K in keyof T]: ... }) and rebuild a new shape according to your rules.

Built-in utilities like Partial<T> and Required<T> are thin wrappers around this syntax; you're already using mapped types without realizing it.

Three challenges that bite everyone

Modifiers vanish during iteration: I learned this the hard way when I wrote a mapped type to transform our data transfer objects, and suddenly all the readonly modifiers I'd carefully added disappeared. Turns out you have to preserve or remove modifiers explicitly, as the iteration doesn't maintain them automatically.

Key remapping with template literals: Understanding how to transform property names requires familiarity with the as clause, which wasn't evident from the documentation. I had to dig through the TypeScript release notes to figure out how to suffix property names with "Event" or prefix them with "async_".

Preserving type constraints on nested generics: Complex objects with nested generics need careful handling to maintain integrity. I once tried to map over an object with a generic property, and TypeScript lost the constraint information, allowing invalid assignments. The fix required explicit constraint preservation using T[K] extends SomeConstraint.

A pattern that generates event handlers

Here's something I've used at two companies for type-safe event systems:

type Events<T> = {
  [K in keyof T & string as `${K}Event`]: () => void
};

// Usage
interface User { name: string; age: number; }
type UserEvents = Events<User>; // { nameEvent: () => void; ageEvent: () => void; }

With this one mapped type, we generated type-safe event handlers for every property of every domain object. Changes to the domain model automatically updated event types. Zero manual synchronization required.

The & string part is critical — it narrows keyof T to only string keys, which is necessary for template literal types. Without it, TypeScript complains that it can't template-literal a symbol or number key. This tripped me up the first time because the error message was cryptic.

Stripping modifiers explicitly

You can strip modifiers explicitly with -? or -readonly inside the brackets:

type Mutable<T> = { -readonly [K in keyof T]-?: T[K] };

// Usage
interface ReadonlyUser {
  readonly name: string;
  readonly age?: number;
}

type MutableUser = Mutable<ReadonlyUser>;
// { name: string; age: number } - both modifiers removed

I used this pattern when we needed to create test fixtures from production types. Our production types were readonly for safety, but the test setup needed to mutate values. One Mutable<T> utility gave us writable versions without maintaining separate test interfaces.

When mapped types prevent drift

The killer app for mapped types is enforcing identical DTO contracts across microservices. We had four services that all needed to understand the same User shape, but with different subsets of fields.

Instead of maintaining four interfaces that slowly drifted:

type FullUser = { id: string; name: string; email: string; password: string; };

type PublicUser = Pick<FullUser, 'id' | 'name'>;
type AuthUser = Omit<FullUser, 'password'>;
type CreateUser = Omit<FullUser, 'id'>;

One source of truth, three derived types, zero drift. When we added a field to FullUser, we had to decide whether it should appear in the public view, the auth context, or the creation payload.

Code reviews caught mistakes immediately because the relationships were explicit in the types.

6. Combining conditional and mapped types

The pattern that really blew my mind was realizing you could combine mapped types with conditional logic to create transformations that would have required hundreds of lines of manual type definitions.

I hit this when we decided to make our entire API async. Every synchronous function needed to return a Promise instead. Going through 200+ functions to manually wrap return types in Promise<...> sounded like a nightmare.

Then someone showed me the Asyncify<T> pattern:

type Asyncify<T> = {
  [K in keyof T]: T[K] extends (...args: any[]) => infer R
    ? (...args: Parameters<T[K]>) => Promise<R>
    : T[K];
};

interface API {
  getData(): number;
  processItem(item: string): boolean;
}

type AsyncAPI = Asyncify<API>;
// { getData(): Promise<number>; processItem(item: string): Promise<boolean>; }

The [K in keyof T] loop visits every property. The extends check filters only functions—everything else passes through unchanged. For functions, we extract the return type with infer R, preserve the parameters with Parameters<T[K]>, and wrap the return in Promise<R>.

Clean, surgical, and it saved us probably 40 hours of manual refactoring.

When this pattern backfires

I got so excited about this pattern that I started using it everywhere. Auto-memoize selectors? Mapped type with conditional. Add tracing metadata? Another mapped conditional. Generate mock factories? You guessed it.

Then compile times started creeping up. What used to be a 5-second build became 25 seconds. The tsc --extendedDiagnostics output showed that most of the time was spent in type instantiation for these clever mapped conditionals.

Two problems surface with complex combinations:

Compiler performance degrades with deep conditional trees. Each nested conditional multiplies the work the compiler has to do. I had one mapped type with three levels of nested conditionals that was causing a 10-second slowdown by itself.

Breaking it into named helpers that composed together dropped it to under a second:

// Slow - everything nested
type Complex<T> = {
  [K in keyof T]: T[K] extends Function
    ? T[K] extends (...args: infer A) => infer R
      ? R extends Promise<any>
        ? T[K]
        : (...args: A) => Promise<R>
      : never
    : T[K]
};

// Fast - composed helpers
type AsyncifyFunction<T> = 
  T extends (...args: infer A) => infer R
    ? R extends Promise<any>
      ? T
      : (...args: A) => Promise<R>
    : T;

type Asyncify<T> = {
  [K in keyof T]: T[K] extends Function ? AsyncifyFunction<T[K]> : T[K]
};

Debugging nested types becomes cryptic. The TypeScript playground's "quick info" feature helps inspect intermediate results, but complex combinations still require methodical deconstruction. I've spent hours breaking apart a single type error because the error message pointed to a mapped conditional that was itself calling another mapped conditional.

The pattern I follow now: monitor compile times with tsc --extendedDiagnostics and set a budget. If type-checking takes more than 20% of your total build time, you've over-engineered your types. Refactor toward simplicity.

A production pattern that works

Here's a pattern I used to wrap Redux action creators with promise-based middleware:

type Promisify<T> = {
  [K in keyof T]: T[K] extends (...args: infer A) => infer R
    ? (...args: A) => Promise<R>
    : T[K];
};

const syncActions = {
  increment: (n: number) => ({ type: 'INC', payload: n }),
  decrement: (n: number) => ({ type: 'DEC', payload: n }),
};

type AsyncActions = Promisify<typeof syncActions>;
// Every action creator now returns Promise<{type, payload}>

This lets us introduce async middleware without touching hundreds of action creator files. The types updated automatically, and TypeScript caught any code that wasn't handling the new Promise return values.

Saved probably a week of manual refactoring and prevented countless runtime errors.

7. Choosing the Right Tool (Without Over-Engineering)

The hardest part of advanced types is knowing when to use them and when just to write a simple interface. I've seen teams turn their type layer into its own mini-framework, complete with utility types for utility types.

At some point, you cross the line from "helpful abstraction" to "unmaintainable complexity."

Here are the three questions I ask before reaching for advanced types:

Am I reusing logic across arbitrary shapes? If so, a generic is usually sufficient — no conditional gymnastics required. I recently reviewed a PR in which someone created a conditional type to handle two different shapes. We replaced it with a simple generic that accepted both, and the code got simpler and faster to compile.

Am I transforming every property of an object? That's squarely a mapped type's job, whether you're making properties optional, readonly, or renaming them systematically. Don't reach for a conditional unless you're actually branching based on the property type.

Does the shape branch depending on the input? Only then do you need conditional types. Add infer only when you're pattern-matching parts of another type. I've seen people use infer where a simple generic would have worked, just because they'd recently learned about infer and wanted to use it.

Warning signs you've gone too far

Even well-aimed choices can backfire. Watch for these signals:

Prolonged compile times: When tsc takes noticeably longer, your type graph is too clever. The "type instantiation is excessively deep" error explicitly warns of this. I set a hard rule: if type-checking takes more than 10 seconds on a modern machine, something needs to be simplified.

Run tsc --extendedDiagnostics regularly to identify slow files: Sort by type-checking time, find the worst offenders, and refactor them. Usually, it's one or two overly complex mapped conditionals that account for 80% of the slowdown.

Confused teammates during code review: If reviewers struggle to understand your type logic, the complexity has exceeded its value. I once wrote what I thought was an elegant utility type for transforming API responses. Three reviewers asked for clarification. That's when I knew I'd failed because good code explains itself.

The fix: document intentions in JSDoc comments with examples, or, better yet, simplify the code until it's self-documenting. If you need a paragraph of comments to explain a type utility, you've over-engineered it.

Overlapping functionality between utilities. When two advanced type patterns solve the same problem, you have duplication in your type layer. Extract a utility type or simplify the code. I once found three different utilities that all did the same thing (make properties optional), because different teams had written them independently without checking what already existed.

The right level of abstraction

The right advanced type emerges from your actual constraints, not theoretical elegance. Start with the simplest solution that passes your team's code review, then refactor toward abstraction only when duplication creates measurable friction.

I follow this progression:

1. First instance: Write a concrete type. Don't abstract yet.
2. Second instance: Copy-paste if the shapes are truly identical; otherwise, write separately.
3. Third instance: Now you have a pattern. Abstract it with a generic or mapped type.
4. Code review: If teammates understand it immediately, you're good. If they ask questions, simplify.

Most production codebases need fewer than five custom utility types. The rest should lean on TypeScript's built-in helpers—Partial, Pick, Omit, Record, ReturnType, etc. These are well-tested, well-documented, and everyone on your team already knows them.

When you do write custom utilities, put them in a shared types/ directory with clear documentation and usage examples. Make them discoverable during code review by linking to them in your style guide.

The worst thing is when someone writes a utility that would have saved them time, but didn't know it existed, so they solve the problem in a worse way.

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