AI Native ML Tool

Most AI coding tools today are Copilots. They are autocomplete on steroids. They assume you know the architecture, you know the constraints, and you just need help typing the syntax.

But for Machine Learning, syntax is rarely the bottleneck. Architecture is.

Gaslamp is built on a different philosophy: The Principal AI Collaborator. It assumes that the hardest part of ML isn't writing pytorch.layout, but deciding what to build in the first place.

The Gap: Why "Chat" Isn't Enough

When you ask a standard LLM to "build a fraud detector," it immediately spits out code. It hallucinates a dataset, picks a random model (usually a Transformer), and gives you a script that runs but solves nothing.

It skipped the most important part: The Interview.

What is the cost of a false positive?
Is inference latency a constraint?
Do we have labeled data?

Gaslamp's 4-Phase Lifecycle

We built Gaslamp to mimic the workflow of a Senior Staff Engineer. It refuses to write code until it understands the problem.

1. The Collaborative Interview

Before a single line of Python is written, Gaslamp enters "Human Mode". It interrogates you. It forces you to define success metrics. It creates a Project Brief.

2. Strategic Data Ops

Code is easy; data is hard. Gaslamp's "Asset Hunter" skill scouts for permissible datasets or synthesizes training data using "Style Transfer" techniques to bootstrap your model.

3. Intent-Aware Modeling

Gaslamp chooses the tool for the job. If you need <10ms latency, it won't suggest Llama-3. It will scaffold an XGBoost pipeline. It captures this Design Rationale in a Living Model Card.

4. Vibe-Based Evaluation

Accuracy metrics (F1 Score) are for machines. "Vibes" are for humans. Gaslamp auto-deploys a functional web demo so you can feel how the model performs on real inputs, shifting the focus from "Test Accuracy" to "Product Utility".

The Future is Agentic

We believe the future isn't just about faster typing. It's about Agents that can hold the entire context of a project—from the business "why" to the deployment "how"—in their active memory.

Gaslamp is that memory.