Welcome to the first edition of The AI Native Engineer by Zencoder, this newsletter will take approximately 5 mins to read.

If you only have one, here are the 5 most important things:

• The EU will propose a unified startup regime.
  
  
• n8n bags $180M Series C.
  
  
• OpenAI launched Apps in ChatGPT (again?), opening up new opportunities to build apps that live natively inside the chat interface.
  
  
• Build AI Custom Agents directly in your IDE - Quick Guide 
• Zen Podcast - AI First Dev Stack; Git, IDE and Agents - RSVP

Learn more below.

The Week ChatGPT Became an App Store

OpenAI dropped some major news this week in what feels like a major shift for its flagship ChatGPT platform: a new way for developers to build apps that run directly inside ChatGPT.

At DevDay 2025, OpenAI unveiled its plans for apps inside ChatGPT with the launch of the Apps SDK, giving developers full-stack access to build fully interactive applications within ChatGPT itself. Developers can use the Apps SDK to go beyond custom GPTs and create rich, functional software experiences that live natively inside the chat interface.

What Apps SDK Is

The new Apps SDK turns ChatGPT into a hosting platform for third-party tools. Developers can now build apps that render visuals, handle user input, and connect to external APIs — all from within an AI-powered chat window.

Imagine asking ChatGPT to “book me a flight to Lisbon” and instantly seeing an Expedia interface appear in the conversation — no browser tab, no redirection. Or instructing ChatGPT to “make a playlist for rainy mornings,” and the AI puts together a new Spotify playlist within your chat that you can start listening to instantly.

In the words of OpenAI CEO Sam Altman, this launch “...will enable a new generation of apps that are interactive, adaptive, and personalized, that you can chat with.”

Why It Matters

• ChatGPT just went from a conversational AI tool to an app-hosting platform. With the Apps SDK, ChatGPT becomes a host for an entire ecosystem of AI-native apps, each able to deliver full experiences through conversation.
• App distribution is being reinvented. Instead of hunting for apps in an app store, users discover them in context. You ask a question, and ChatGPT recommends the right tool. App discovery becomes intent-driven, not search-driven.
• Developers have new opportunities to monetize. Contextual placement inside the chat creates direct pathways for engagement, in-chat transactions, and subscription models. Developers who design apps that integrate seamlessly into conversations could unlock new revenue streams.
• It redefines user interfaces and experiences. Apps don’t compete for homescreen real estate anymore — they compete for conversational relevance. ChatGPT is turning into a unified workspace where interaction replaces navigation.
• 

The Bigger Trend

This is the beginning of AI-native third-party platforms, resulting in a more cohesive digital ecosystem where conversation is the interface and intent is the input. 

We’ve seen similar trends in the past: operating systems birthed app stores and web browsers delivered extensions. Now, AI assistants are giving rise to conversational apps that live inside the chat itself, turning dialogue into a workspace and intent into action.

This hints at a future where you no longer have to “open the right app,” but rather talk with AI about what you want to do and the right app will simply appear in front of you, within a single conversational interface.

For startups, this opens up new opportunities to build apps for ChatGPT that bypass crowded app marketplaces. If you can build tools for niche workflows and intents, which slot seamlessly into the user’s conversational context, you will be well-positioned to capitalize on this new ecosystem.

Actionable steps you can try this week:

• Run a Repo Info Agent: Context management agent that generates and maintains comprehensive project snapshots for enhanced coding agent performance - docs
• Build a Custom Agent: Pick a boring but high-frequency task (e.g., update license headers across files). Build an agent to execute it in a sandbox branch and measure time saved. - Steps on how can build a custom agent and share the expertise within your org
• Embed guardrails: Add unit checks and CI validations to any agent workflow. Agents accelerate work — but the human-in-the-loop prevents regressions.

 At pilot scale, teams using Zencoder agents reduced time spent on repetitive PR tasks by ~30% in our internal tests — mainly by automating trivial fixes and surfacing high-value reviewer comments.

Zencoder AI Agents makes building agents easier; helps make them safe and reliable at scale. If your team is evaluating agents, start with a single narrow workflow, monitor results, and iterate.

 News

• Anthropic to open its first India office in 2026.
  
  
• The EU will propose a unified startup regime in 2026 to replace 27 national systems and ease cross-border growth for scaleups.
  
  
• Sora hit 1M downloads faster than ChatGPT.
  
  
• Google launches its AI vibe-coding app Opal.
  
  

 Fundraising

• n8n bags $180M Series C, bringing its valuation to $2.5B.
  
  
• David AI secures $50M Series B to further develop its AI audio/data tech.
  
  
• Base Power raises $1B to deploy home batteries everywhere.
  
  

Tech Fact / History Byte

🧮 The First Commit: How Collaboration Went from Email to Branch

Before Git and the Pull Request (which, surprisingly, is not a Git feature but a feature of platforms like GitHub built on top of Git), there was just... email.

The story of collaborative code starts with the early development of the Linux kernel. When Linus Torvalds needed to manage contributions from thousands of developers, he relied on a massive, complex system of patches sent over email. This process, while decentralized, was slow, cumbersome, and incredibly difficult to audit. Each patch had to be manually reviewed, applied, and tracked.

The proprietary version control system BitKeeper solved this problem for the Linux community for a few years, but when its free license was revoked in 2005, Linus was forced to create something new: Git.

Linus’s core design goal was not convenience, but speed and data integrity. He wanted a system where the "commit" was a cryptographic snapshot of the entire repository history, making it impossible to change old versions without notice. Git fundamentally changed the nature of development, formalizing the idea of a branch as a safe space for experimentation.

The Pull Request, invented by platforms like GitHub, leveraged this concept. It’s essentially a friendly ticket: "Please, pull my branch into yours." It transformed the manual, messy email-based patch exchange into a structured, collaborative review platform. This single UI innovation is what unlocked modern velocity.

Reflection: The PR moved us from a solo-developer world to a highly-collaborative one. With AI agents now able to review code, triage issues, and even write the first draft of fixes, what do you think will be today’s "Pull Request moment" for AI-native code collaboration?

Zen Podcast 

AI-First Dev Stack: Git, IDE & Agents

Join another session of Zen Podcast where our host Shantanu discusses with Abylaikhan Turlassov  who first hand helped build coding agents inside VsCode and JetBrains

What will be discussed? 

→ Why agents are now essential to the developer stack

→ The turning point that made autonomous coding real

→ How your workflow changes with Git, IDE, and Agents

October 15, 2025 - RSVP