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I kept hitting the same wall at work every time we needed to ship an AI feature. What looked like a week of work turned into picking a model, setting up a vector DB, managing embeddings, wiring up chat history, handling retries — none of it was the actual feature.
So I built Modular. You register a function that returns your app's data, then call ai.run() for one-shot features or ai.chat() for stateful conversation. Everything else — context management, embeddings, session history, model routing, retries — is handled.
MCP-native from day one. Works with Claude, GPT-4o, and Gemini.
Still early — collecting feedback before building the full SDK. Would love to hear if others have hit this same wall, or if you think I'm solving the wrong problem.
Show HN: Modular – drop AI features into your app with two function calls
A solution to the "same wall" developers hit when shipping AI features, handling context management, embeddings, session history, model routing, and retries with minimal code.
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AI Executive Synthesis
A solution to the "same wall" developers hit when shipping AI features, handling context management, embeddings, session history, model routing, and retries with minimal code.
Modular directly addresses a significant developer pain point: the complexity and boilerplate associated with integrating AI capabilities into applications. By abstracting common infrastructure components like vector databases, embedding management, chat history, and model routing, it drastically reduces the time-to-market for AI features. This platform enables developers to focus on core application logic rather than AI plumbing, accelerating innovation and reducing development costs. The support for multiple leading LLMs (Claude, GPT-4o, Gemini) ensures flexibility and future-proofing. This represents a critical trend in the AI ecosystem: the emergence of developer platforms that democratize AI integration, making advanced capabilities accessible to a broader range of applications and enterprises.
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Deep-Dive FAQs
What is Modular – drop AI features into your app with two function calls?
Modular – drop AI features into your app with two function calls is analyzed by our AI as: A solution to the "same wall" developers hit when shipping AI features, handling context management, embeddings, session history, model routing, and retries with minimal code.. It focuses on Modular directly addresses a significant developer pain point: the complexity and boilerplate associated with integrating AI capabilities into appl...
Where did Modular – drop AI features into your app with two function calls originate?
Data for Modular – drop AI features into your app with two function calls was aggregated directly from the Hacker News community ecosystem, representing raw developer and early-adopter sentiment.
When was Modular – drop AI features into your app with two function calls publicly launched?
The initial public indexing or launch date for Modular – drop AI features into your app with two function calls within our tracked developer communities was recorded on April 20, 2026.
How popular is Modular – drop AI features into your app with two function calls?
Modular – drop AI features into your app with two function calls has achieved measurable traction, logging over 5 traction score and facilitating 1 recorded discussions or engagements.
Which technical categories define Modular – drop AI features into your app with two function calls?
Based on metadata extraction, Modular – drop AI features into your app with two function calls is categorized under topics such as: AI features, vector DB, managing embeddings, chat history.
What are some commercial alternatives to Modular – drop AI features into your app with two function calls?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as AppDeploy, which offers overlapping value propositions.
Are there open-source alternatives related to Modular – drop AI features into your app with two function calls?
Yes, the GitHub ecosystem contains correlated projects. For example, a repository named fikrikarim/parlor shares highly similar architectural descriptions and topics.
How does the creator describe Modular – drop AI features into your app with two function calls?
The original author or development team describes the product as follows: "I kept hitting the same wall at work every time we needed to ship an AI feature. What looked like a week of work turned into picking a model, setting up a vector DB, managing embeddings, wiring up ..."
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Discovery Source
Hacker News Aggregated via automated community intelligence tracking.
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No direct open-source NPM package mentions detected in the product documentation.
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