Gemini vs AntiGravity

Recently, in our low code for AI course - I encountered a situation that is getting increasingly common i.e. the lines between ‘low code’ and ‘full code’ are blurring - and specifically, people developing low code systems may need the ability to understand code

I have tried to articulate this in terms of Gemini vs Antigravity - but you could also think of similar with lovable and cursor

Finally, I am a relative beginner to both Gemini and Antigravity - so my understanding may have limits

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What kind of applications are developed with Gemini 3.0 vs antigravity in other words, given the choice of these two approaches - which would you choose and when?

Gemini 3.0 and Google Antigravity are not the same approach because they operate at different levels of the stack.

Gemini 3.0 leverages the foundation model - and Antigravity is based on a software building approach.

You don’t really choose one instead of the other — you choose which layer you want to work at.

Related questions:

1. Which approach works best for spec driven development?

2. Which approach works best for forward deployed engineers

Here is a more detailed analysis

Typically, Gemini 3.0 is used to develop AI capabilities inside an application.

These are apps where the core value is the model’s intelligence:

• Multimodal assistants (text + image + video reasoning)

• Image understanding / vision apps

• Document understanding and summarisation

• Chatbots with deep reasoning

• Search / Q&A over large corpora

• Agentic systems that reason, plan, and call tools

• AI copilots embedded into products

The application exists because the model is powerful.

You spend time on:

• Prompting

• Context management

• Tool calling

• Safety and grounding

• Latency and cost
  

For example, “An app that analyses images of equipment and explains faults.” is a Gemini-first problem because its primarily dependent on the model’s capabilities.

In contrast, Antigravity takes a software engineering approach where the value comes from structure, workflow, and intent, not raw intelligence.

Typical Antigravity-centric applications are:

• Internal tools

• Dashboards

• CRUD apps

• Workflow systems

• Simple SaaS products

• Prototypes and MVPs

• Business logic apps

• Data viewers and utilities

Here, the app exists because someone needs a tool, not because AI is impressive.

You spend time on:

• Writing clear specs

• Defining flows and rules

• Iterating on requirements

• Shipping something usable quickly

For example, “An internal tool to track requests and show status.” is an Antigravity-first problem.

“Let me use a powerful model and then build an app around it.”

You start with:

• Capabilities of the model

• What it can reason about

• What modalities it supports

“Let me describe the app I want, and let the system generate it.”

You start with:

• User intent

• Business logic

• App structure

• UX flow
  

Thus, Gemini 3.0 is an AI model with a primary focus is Intelligence. You think about Prompts, reasoning, tools. Best forAI-native products

Antigravity is an App generation system - primary focus is Structure & execution

You think about Specs, flows, constraints.Best for Software products. Output is Full codebases

Choose Gemini 3.0 when:

• The hard part is understanding content

• You need multimodal reasoning

• The app’s value collapses without AI

• You are building an AI product, not just an app

• You are comfortable engineering prompts and flows

Example: AI tutor, document analyst, visual inspector, research assistant

Choose Antigravity when:

• The hard part is building the software

• The logic is clear and rule-based

• You want a real app quickly

• You care about structure, not novelty

• You are a beginner or prototyping fast

Example: Admin tools, dashboards, trackers, simple SaaS, MVPs

In practice, the strongest teams use both. Antigravity Generates the app, handles UI, routing, storage, workflows. Gemini 3.0 Is called inside the app handles reasoning, vision, language

If you want to use the spec driven development approach, what should be your choice between Gemini 3 and Antigravity ?

If you want to practice Spec-Driven Development (SDD), your primary choice should be Antigravity. Gemini 3 is not a replacement for SDD — it is a component you may embed inside an SDD system.

SDD optimises for this loop:

Intent → Executable Spec → Generated System → Evaluation → Spec Update

Here,

• The spec is the source of truth

• The system is generated from the spec

• Changes happen by editing the spec, not the code

• Structure, workflows, and interfaces are first-class

This is not primarily an AI reasoning problem. It is a software construction discipline.

In this case, Gemini 3 is:

• A foundation model

• Optimised for reasoning, language, vision, multimodal understanding

• Invoked via prompts and APIs

• Stateless unless you impose structure

Gemini 3 can: help write and interpret specs. Gemini 3 does not enforce the project structure and the ‘spec to code’ approach. It cannot implement reproducible builds and versioned specs as artefacts (as is needed by SDD). Therefore: Gemini 3 cannot be your SDD engine.

In contrast, Antigravity is:

• A spec-first system

• Built around translating intent → structured artefacts

• Designed to generate real projects from written specs

• Oriented around iteration via spec changes

Antigravity embodies SDD because:

• The spec is explicit

• The build process is repeatable

• The output is a full codebase

• Iteration happens by modifying the spec

Therefore: Antigravity is an SDD tool by design.

What is the best approach for the Forward Deployed Engineer FDE?

An FDE sits between customers and engineering.

The job of the SDE is to:

• Translate messy, real-world problems into working software

• Build something usable fast, on-site or close to the customer

• Iterate in days, not months

• Leave behind a system that can be owned by a product or engineering team

This role is about execution under ambiguity, not model research.

What matters most for an FDE

1. Speed to a working system

2. Ability to encode customer intent

3. Iteration without breaking everything

4. Clear handover artefacts

5. Pragmatic use of AI

Thus, Anigravity is aligned with FDE work because

Antigravity lets you:

• Turn customer conversations into specs

• Generate real, deployable apps quickly

• Iterate by editing the spec live with the customer

• Produce handover-ready artefacts (code, structure, README)

As an FDE, you are judged on:

• “Did it work?”

• “Did the customer use it?”

• “Can someone else maintain it?”

Antigravity directly supports all three.

This thought process, although a bit contrived, is especially relevant because today, the boundaries are getting blurred. Specifically, with many non developers aspiring to be developers - and tools getting easier - the choice and tradeoff of tools is even more relevant.