Nipun Goyal

Engineering
May 22, 2025

Nipun Goyal

May 22, 2025
Engineering

Fixing SDK Adoption with AI

Let’s begin with a simple truth: Most SDKs today are excellent at getting discovered but fail to get adopted.

And this isn’t because of the lack of features. SDKs today are packed with powerful modules, developer guides, and sample apps. But in 2025, developers are no longer choosing based on what an SDK offers but based on how quickly it delivers outcomes.

The unfortunate reality is for many SDKs, the path from installation to “live in production” is still riddled with friction. There are too many steps, too many assumptions, and too many unspoken dependencies.

In a world where developers are integrating half a dozen vendors into a single sprint, every SDK is fighting not just for mindshare but for velocity.

And velocity is where adoption breaks.

The Great Developer Bottleneck

Integration used to be a one-time event. You installed an SDK, followed the docs, spent a couple of days wiring it up, and that was that.

But modern apps don’t work like that anymore.

They’re architected across platforms (web, mobile, cross-platform), across teams (backend, frontend, infra), and across paradigms (monolith vs microservices, REST vs GraphQL, native UI vs React Native). The same SDK might look wildly different depending on where and how it’s being used.

This makes “drop-in integration” a myth. And developers know it.

Even the most thoroughly documented SDKs often fail to answer a far more contextual How does this fit into my app?” And that’s where the friction starts.

Why SDK Adoption Fails?

Let’s talk about the real reasons adoption breaks down. Not from a surface-level view, but from inside the trenches of engineering teams.

1. Docs aren’t enough because they aren’t aware.

Documentation, no matter how well-written, is static. It assumes a generic setup, a clean slate, and a perfect architecture. But most developers are integrating SDKs into imperfect codebases with undocumented business logic.

So while the SDK shows how to use startFeedSession(), the developer is trying to figure out where it should even go.

2. Integration is rarely one step

Installing an SDK kicks off a cascade:

  • Environment setup
  • API key management
  • Route + state mapping
  • UI scaffolding
  • Platform-specific edge cases
  • Analytics instrumentation

Even a “lightweight” SDK can take hours to stabilize. Every integration becomes a mini-project with unclear scope and uncertain ROI.

3. Most SDKs don’t understand the host app.

They don’t know:

  • the folder structure,
  • what state management library is in use,
  • and if dark mode is enabled, if the user roles are tiered, or if analytics are self-hosted.

So what they offer is boilerplate and developers are left bridging the gap manually.

And This is Exactly What AI Can Fix

What if SDKs didn’t just ship documentation but shipped understanding?

The rise of AI-native development environments (s never existed before: To move from static modules to interactive scaffolds.

Imagine this workflow:

Prompt: “Add a chat module that only premium users can access, with support for images and offline fallback.”

Behind the scenes:

  • The AI agent scans your app’s structure, detects user roles from the auth context, and finds the best placement for the new screen.
  • It chooses the right SDK preset, configures upload handling, and integrates it with your app’s existing Redux store.
  • You preview it live, right in your IDE, with all your app’s styles, hooks, and guards in place.

Closing Thought

SDK adoption is no longer a documentation problem, it’s an orchestration problem that has been traditionally dependent entirely on developer bandwidth.

But as more teams shift to AU-native workflows, that dependency starts to dissolve. Now, the SDKs that succeed won’t just offer more features. They’ll offer less friction, faster time to live, and the ability for any stakeholders (and not just devs) to deploy them.

At LikeMinds, we’re building toward this future with Feature Prompting: a system where anyone (including engineers or product managers) can describe what they want in simple English, and our AI agent integrates the right SDK, configures it, and shows a live preview and generate a PR.

If you're curious about what that looks like, we’d love to show you.

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Deploy customised features on top of chat and feed in 15 minutes using LikeMinds SDK.

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Fixing SDK Adoption with AI

Nipun Goyal
/
May 22, 2025
/

Let’s begin with a simple truth: Most SDKs today are excellent at getting discovered but fail to get adopted.

And this isn’t because of the lack of features. SDKs today are packed with powerful modules, developer guides, and sample apps. But in 2025, developers are no longer choosing based on what an SDK offers but based on how quickly it delivers outcomes.

The unfortunate reality is for many SDKs, the path from installation to “live in production” is still riddled with friction. There are too many steps, too many assumptions, and too many unspoken dependencies.

In a world where developers are integrating half a dozen vendors into a single sprint, every SDK is fighting not just for mindshare but for velocity.

And velocity is where adoption breaks.

The Great Developer Bottleneck

Integration used to be a one-time event. You installed an SDK, followed the docs, spent a couple of days wiring it up, and that was that.

But modern apps don’t work like that anymore.

They’re architected across platforms (web, mobile, cross-platform), across teams (backend, frontend, infra), and across paradigms (monolith vs microservices, REST vs GraphQL, native UI vs React Native). The same SDK might look wildly different depending on where and how it’s being used.

This makes “drop-in integration” a myth. And developers know it.

Even the most thoroughly documented SDKs often fail to answer a far more contextual How does this fit into my app?” And that’s where the friction starts.

Why SDK Adoption Fails?

Let’s talk about the real reasons adoption breaks down. Not from a surface-level view, but from inside the trenches of engineering teams.

1. Docs aren’t enough because they aren’t aware.

Documentation, no matter how well-written, is static. It assumes a generic setup, a clean slate, and a perfect architecture. But most developers are integrating SDKs into imperfect codebases with undocumented business logic.

So while the SDK shows how to use startFeedSession(), the developer is trying to figure out where it should even go.

2. Integration is rarely one step

Installing an SDK kicks off a cascade:

  • Environment setup
  • API key management
  • Route + state mapping
  • UI scaffolding
  • Platform-specific edge cases
  • Analytics instrumentation

Even a “lightweight” SDK can take hours to stabilize. Every integration becomes a mini-project with unclear scope and uncertain ROI.

3. Most SDKs don’t understand the host app.

They don’t know:

  • the folder structure,
  • what state management library is in use,
  • and if dark mode is enabled, if the user roles are tiered, or if analytics are self-hosted.

So what they offer is boilerplate and developers are left bridging the gap manually.

And This is Exactly What AI Can Fix

What if SDKs didn’t just ship documentation but shipped understanding?

The rise of AI-native development environments (s never existed before: To move from static modules to interactive scaffolds.

Imagine this workflow:

Prompt: “Add a chat module that only premium users can access, with support for images and offline fallback.”

Behind the scenes:

  • The AI agent scans your app’s structure, detects user roles from the auth context, and finds the best placement for the new screen.
  • It chooses the right SDK preset, configures upload handling, and integrates it with your app’s existing Redux store.
  • You preview it live, right in your IDE, with all your app’s styles, hooks, and guards in place.

Closing Thought

SDK adoption is no longer a documentation problem, it’s an orchestration problem that has been traditionally dependent entirely on developer bandwidth.

But as more teams shift to AU-native workflows, that dependency starts to dissolve. Now, the SDKs that succeed won’t just offer more features. They’ll offer less friction, faster time to live, and the ability for any stakeholders (and not just devs) to deploy them.

At LikeMinds, we’re building toward this future with Feature Prompting: a system where anyone (including engineers or product managers) can describe what they want in simple English, and our AI agent integrates the right SDK, configures it, and shows a live preview and generate a PR.

If you're curious about what that looks like, we’d love to show you.

Supercharge your retention with in-app social features

Deploy customised features on top of chat and feed in 15 minutes using LikeMinds SDK.

Let's start!